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Search Results (197)

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12 pages, 3863 KiB  
Article
Induction Motors Under Voltage Unbalance Combined with Voltage Subharmonics
by Piotr Gnaciński, Marcin Pepliński, Adam Muc and Damian Hallmann
Energies 2024, 17(24), 6324; https://doi.org/10.3390/en17246324 - 15 Dec 2024
Viewed by 599
Abstract
In power systems, various power quality disturbances are present, including voltage deviation, voltage unbalance, and voltage waveform distortions. Voltage waveform distortions are usually identified with harmonics, but in some systems, subharmonics (subsynchronous interharmonics) and interharmonics may also occur—that is, components of frequency less [...] Read more.
In power systems, various power quality disturbances are present, including voltage deviation, voltage unbalance, and voltage waveform distortions. Voltage waveform distortions are usually identified with harmonics, but in some systems, subharmonics (subsynchronous interharmonics) and interharmonics may also occur—that is, components of frequency less than the fundamental frequency, or not an integer multiple of it. This study examines torque pulsations of an induction motor under voltage subharmonics combined with voltage unbalance. The motor and the driven DC generator vibrations were analysed under the power quality disturbances. Investigations were carried out using finite element and empirical methods. Experimental tests were performed for the maximal levels of the power quality disturbances specified or proposed in the relevant standards. For the investigated motor, under voltage subharmonics or voltage unbalance occurring as a single power quality disturbance, the vibration level was within the prescribed limit. However, under unbalance combined with subharmonics, the level could be accepted for only a limited time. Consequently, the permissible level of voltage subharmonics in non-generation installations should be interconnected with the voltage unbalance in the power system. Full article
(This article belongs to the Special Issue Electric Machinery and Transformers III)
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<p>Applied mesh.</p>
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<p>The investigated drivetrain—the generator (1), the coupling shield (2), the motor (3), the frame (4), the concrete pedestal (5). Blue arrows approximate where the sensor is mounted.</p>
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<p>Simplified diagram of the measurement setup.</p>
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<p>Electromagnetic torque waveform for motor1, 2% voltage unbalance (VUF = 2%) with a subharmonic value <span class="html-italic">u<sub>sh</sub></span> = 0.3% and frequency <span class="html-italic">f<sub>sh</sub></span> = 20 Hz.</p>
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<p>Spectrum of the electromagnetic torque waveform shown in <a href="#energies-17-06324-f004" class="html-fig">Figure 4</a>.</p>
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<p>ATP (as a percentage of the rated torque) versus the subharmonic frequency for motor1: subharmonics occurring as an SPQD (a) and combined with VUF = 2% (b).</p>
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<p>Vibration velocity versus the subharmonic frequency for motor2 and <span class="html-italic">u<sub>sh</sub></span> = 0.3%.</p>
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<p>Vibration velocity versus the subharmonic frequency for the DC generator coupled with motor2, and voltage subharmonics of <span class="html-italic">u<sub>sh</sub></span> = 0.3%, occurring as an SPQD.</p>
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<p>Vibration velocity versus the subharmonic frequency for the DC generator coupled with motor2, and voltage subharmonics of the value <span class="html-italic">u<sub>sh</sub></span> = 0.3% combined with VUF = 2%.</p>
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<p>Vibration velocity versus the value of voltage subharmonics for the DC generator for <span class="html-italic">f<sub>sh</sub></span> = 25 Hz combined with VUF = 2%.</p>
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31 pages, 10502 KiB  
Article
Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
by Joaquín E. Caicedo, Olga Zyabkina, Edwin Rivas and Jan Meyer
Appl. Sci. 2024, 14(23), 11031; https://doi.org/10.3390/app142311031 - 27 Nov 2024
Viewed by 406
Abstract
Voltage notches are steady-state sub-cycle waveform distortions caused by the normal operation of line-commutated power converters, significantly impacting power quality in industrial low-voltage (LV) networks. Despite their common occurrence, research on this phenomenon is still incipient, and realistic simulation platforms are lacking. This [...] Read more.
Voltage notches are steady-state sub-cycle waveform distortions caused by the normal operation of line-commutated power converters, significantly impacting power quality in industrial low-voltage (LV) networks. Despite their common occurrence, research on this phenomenon is still incipient, and realistic simulation platforms are lacking. This paper introduces a detailed MATLAB (R2024a)/Simulink-based simulation platform that models a benchmark low-voltage industrial installation, including a six-pulse controlled rectifier, linear loads, and a capacitor bank for power factor correction. Systematic simulations are performed with the platform to examine the sensitivity of notch characteristics to key parameters within plausible ranges, such as short-circuit power at the point of common coupling, commutation reactance, firing angle, snubber circuits, and rated power of the rectifier. In addition, parameters such as the rated power of linear loads and the compensation power of the capacitor bank are examined. Other influencing parameters including background voltage unbalance and distortion are also modeled and considered. A comparative analysis with field measurements from German industrial LV networks validates the plausibility and suitability of the simulations. Building upon this platform, a Monte Carlo simulation approach is adopted to generate extensive datasets of realistic voltage notch waveforms by randomly varying these key parameters. A case study conducted under conditions typical of German LV networks demonstrates the applicability of the simulations. To support further research, the simulation platform and exemplary synthetic waveforms are provided alongside the paper, serving as a valuable tool for testing and designing strategies for analysis, detection, and monitoring of voltage notches. Full article
(This article belongs to the Special Issue Analysis, Modelling and Simulation in Electrical Power Systems)
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<p>Characteristics of voltage notches. (<b>a</b>) Depth, width, and area of a classical notch (adapted from ref. [<a href="#B5-applsci-14-11031" class="html-bibr">5</a>]). (<b>b</b>) Notch with commutation oscillations due to a capacitor bank with no detuning. (<b>c</b>) Notch with commutation oscillations due to snubber capacitance.</p>
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<p>Waveforms of a six-pulse controlled rectifier under different commutation conditions. The graphs display (from top to bottom): three-phase line currents, phase-to-neutral voltages, phase-to-phase voltages, and DC output voltage. (<b>a</b>) Ideal conditions: Commutation occurs smoothly without voltage notches. (<b>b</b>) Non-ideal conditions: Commutation leads to voltage notches in the waveforms.</p>
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<p>Benchmark LV installation implemented in Simulink for voltage notch simulation.</p>
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<p>General model of a six-pulse controlled rectifier.</p>
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<p>Overvoltage ratio <span class="html-italic">U<sub>rm</sub></span>/<span class="html-italic">U<sub>r</sub></span> as a function of the normalized snubber resistor <span class="html-italic">R<sub>sn</sub></span>/<span class="html-italic">R<sub>base</sub></span> for RC-snubbers. Curves for thyristors used in LV applications (adapted from ref. [<a href="#B21-applsci-14-11031" class="html-bibr">21</a>]).</p>
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<p><span class="html-italic">I<sub>rm</sub></span> versus <span class="html-italic">−di</span>/<span class="html-italic">dt</span> for thyristors in LV applications (adapted from ref. [<a href="#B23-applsci-14-11031" class="html-bibr">23</a>]).</p>
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<p>Conceptual diagram illustrating the interrelationships among key parameters.</p>
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<p>Impact of (<b>a</b>) <span class="html-italic">S<sub>sc</sub></span>, (<b>b</b>) <span class="html-italic">S<sub>r</sub></span>, (<b>c</b>) <span class="html-italic">X<sub>com</sub></span>, and (<b>d</b>) <span class="html-italic">α</span> on voltage notch waveforms (<b>top</b>) and input line currents (<b>bottom</b>).</p>
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<p>Impact of (<b>a</b>) <span class="html-italic">S<sub>sc</sub></span>, (<b>b</b>) <span class="html-italic">S<sub>r</sub></span>, (<b>c</b>) <span class="html-italic">X<sub>com</sub></span>, and (<b>d</b>) <span class="html-italic">α</span> on voltage notch waveforms (<b>top</b>) and input line currents (<b>bottom</b>).</p>
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<p>Impact of (<b>a</b>) RC-snubber and (<b>b</b>) <span class="html-italic">R<sub>sn</sub></span> on voltage notch waveforms (<b>top</b>) and input line currents (<b>bottom</b>).</p>
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<p>Impact of <span class="html-italic">S<sub>ll</sub></span> = <span class="html-italic">P<sub>ll</sub></span> + <span class="html-italic">jQ<sub>ll</sub></span>, with a fixed <span class="html-italic">PF<sub>ll</sub></span> = 0.9, on voltage notch waveforms (<b>top</b>), input line currents of the rectifier (<b>middle</b>), and total line currents of the LV installation (<b>bottom</b>).</p>
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<p>Impact of the capacitor bank <span class="html-italic">Q<sub>cb</sub></span> on voltage notch waveforms (<b>top</b>), input line currents of the rectifier (<b>middle</b>), and total line currents of the LV installation (<b>bottom</b>). (<b>a</b>) Non-detuned capacitor bank and (<b>b</b>) detuned capacitor bank.</p>
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<p>Impact of α, with linear loads enabled, while maintaining a target <span class="html-italic">PF</span> at the PCC by adjusting <span class="html-italic">Q<sub>cb</sub></span> on voltage notch waveforms (<b>top</b>), input line currents of the rectifier (<b>middle</b>), and total line currents (<b>bottom</b>). (<b>a</b>) Non-detuned capacitor bank and (<b>b</b>) detuned capacitor bank.</p>
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<p>Impact of background unbalance on voltage notch waveforms (<b>top</b>), input line currents of the rectifier (<b>middle</b>), and total line currents of the LV installation (<b>bottom</b>). (<b>a</b>) Non-detuned capacitor bank and (<b>b</b>) detuned capacitor bank.</p>
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<p>Impact of background distortion on voltage notch waveforms (<b>top</b>), input line currents of the rectifier (<b>middle</b>), and total line currents of the LV installation (<b>bottom</b>). (<b>a</b>) Non-detuned capacitor bank and (<b>b</b>) detuned capacitor bank.</p>
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<p>Case study from field measurements.</p>
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<p>Example of three-phase voltage (<b>top</b>) and current (<b>bottom</b>) waveforms from a field measurement record (<b>left</b>) and a simulation (<b>right</b>).</p>
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<p>Overlaid voltage notch (<b>top</b>) and current (<b>bottom</b>) waveforms from the example of the field measurement record and simulation in <a href="#applsci-14-11031-f016" class="html-fig">Figure 16</a> for visual comparison. Phase <span class="html-italic">a</span> (<b>left</b>), phase <span class="html-italic">b</span> (<b>middle</b>), and phase <span class="html-italic">c</span> (<b>right</b>).</p>
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<p>Histograms of features extracted from field measurements.</p>
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<p>Matrix of scatter plots of features from field measurements and simulations.</p>
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<p>Correlation matrices of features extracted from field measurements (<b>left</b>) and simulations (<b>middle</b>) and normalized absolute error matrix (<b>right</b>).</p>
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<p>Flow diagram of Monte Carlo simulation-based data generation.</p>
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<p>Examples of voltage notch (<b>top</b>) and current waveforms drawn by the LV installation (<b>bottom</b>) obtained from the Monte Carlo simulation for the case study. (<b>a</b>) Scenario 1, (<b>b</b>) Scenario 2, (<b>c</b>) Scenario 3 with non-detuned capacitor bank, (<b>d</b>) Scenario 3 with detuned capacitor bank, (<b>e</b>) Scenario 4 with non-detuned capacitor bank, and (<b>f</b>) Scenario 4 with detuned capacitor bank.</p>
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25 pages, 1607 KiB  
Review
Optimizing Power Flow and Stability in Hybrid AC/DC Microgrids: AC, DC, and Combined Analysis
by Ghanshyam Meena, Veerpratap Meena, Akhilesh Mathur, Vinay Pratap Singh, Ahmad Taher Azar and Ibrahim A. Hameed
Math. Comput. Appl. 2024, 29(6), 108; https://doi.org/10.3390/mca29060108 - 24 Nov 2024
Viewed by 537
Abstract
A microgrid (MG) is a unique area of a power distribution network that combines distributed generators (conventional as well as renewable power sources) and energy storage systems. Due to the integration of renewable generation sources, microgrids have become more unpredictable. MGs can operate [...] Read more.
A microgrid (MG) is a unique area of a power distribution network that combines distributed generators (conventional as well as renewable power sources) and energy storage systems. Due to the integration of renewable generation sources, microgrids have become more unpredictable. MGs can operate in two different modes, namely, grid-connected and islanded modes. MGs face various challenges of voltage variations, frequency deviations, harmonics, unbalances, etc., due to the uncertain behavior of renewable sources. To study the impact of these issues, it is necessary to analyze the behavior of the MG system under normal and abnormal operating conditions. Two different tools are used for the analysis of microgrids under normal and abnormal conditions, namely, power flow and short-circuit analysis, respectively. Power flow analysis is used to determine the voltages, currents, and real and reactive power flow in the MG system under normal operating conditions. Short-circuit analysis is carried out to analyze the behavior of MGs under faulty conditions. In this paper, a review of power flow and short-circuit analysis algorithms for MG systems under two different modes of operation, grid-connected and islanded, is presented. This paper also presents a comparison of various power flow as well as short-circuit analysis techniques for MGs in tabular form. The modeling of different components of MGs is also discussed in this paper. Full article
(This article belongs to the Special Issue Applied Optimization in Automatic Control and Systems Engineering)
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<p>Configurations and classification of MGs.</p>
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<p>Architecture of DC MG.</p>
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<p>Architecture of AC MG.</p>
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<p>Architecture of AC/DC hybrid MG.</p>
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<p>Droop control characteristics.</p>
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<p>(<b>a</b>) Positive-sequence, (<b>b</b>) negative-sequence, and (<b>c</b>) zero-sequence components of IBDG.</p>
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<p>Summarization of power flow techniques.</p>
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<p>Flowchart for power flow analysis of hybrid AC/DC MG.</p>
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<p>Flowchart for short-circuit analysis of AC MG.</p>
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25 pages, 7500 KiB  
Article
An ANN-Based Method for On-Load Tap Changer Control in LV Networks with a Large Share of Photovoltaics—Comparative Analysis
by Klara Janiga, Piotr Miller, Robert Małkowski and Michał Izdebski
Energies 2024, 17(22), 5749; https://doi.org/10.3390/en17225749 - 17 Nov 2024
Viewed by 750
Abstract
The paper proposes a new local method of controlling the on-load tap changer (OLTC) of a transformer to mitigate negative voltage phenomena in low-voltage (LV) networks with a high penetration of photovoltaic (PV) installations. The essence of the method is the use of [...] Read more.
The paper proposes a new local method of controlling the on-load tap changer (OLTC) of a transformer to mitigate negative voltage phenomena in low-voltage (LV) networks with a high penetration of photovoltaic (PV) installations. The essence of the method is the use of the load compensation (LC) function with settings determined via artificial neural network (ANN) algorithms. The proposed method was compared with other selected local methods recommended in European regulations, in particular with those currently required by Polish distribution system operators (DSOs). Comparative studies were performed using the model of the 116-bus IEEE test network, taking into account the unbalance in the network and the voltage variation on the medium voltage (MV) side. Full article
(This article belongs to the Collection Artificial Intelligence and Smart Energy)
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<p>Diagram of the 116-bus IEEE test network (nodes for which voltage waveforms will be presented are numbered in frames).</p>
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<p>Diagram of the DSL dynamic model of the two-stage overvoltage protection of the PV system inverter.</p>
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<p>Tests of the first stage overvoltage protection model (V&gt;).</p>
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<p><span class="html-italic">Q</span>(<span class="html-italic">V</span>) characteristics modeled according to (3).</p>
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<p>Implementation of QDSL model for <span class="html-italic">Q</span>(<span class="html-italic">V</span>) inverter mode.</p>
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<p>DSL dynamic model diagram for inverter <span class="html-italic">Q</span>(<span class="html-italic">V</span>) mode.</p>
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<p><span class="html-italic">P</span>(<span class="html-italic">V</span>) characteristics modeled according to (4).</p>
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<p>The algorithm used to obtain the data needed to train the neural network.</p>
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<p>Data from the training set of the designed ANN.</p>
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<p>OLTC load compensation setting determined via the ANN for the test network.</p>
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<p>Simulation results for case 1, Scenario III: (<b>a</b>) voltage waveforms in selected nodes (phase values); (<b>b</b>) voltage waveform in the last node of the network (114); (<b>c</b>) generated active power and reactive power of the PV installation in node 114.</p>
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<p>Simulation results for modified case 1 (with active overvoltage protections), Scenario III: (<b>a</b>) voltage waveforms in selected nodes (phase values); (<b>b</b>) voltage waveform in the last node of the network (114); (<b>c</b>) generated active power and reactive power of the PV installation in node 114.</p>
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<p>Simulation results for case 2, Scenario III: (<b>a</b>) voltage waveforms in selected nodes (phase values); (<b>b</b>) voltage waveform in the last node of the network (114); (<b>c</b>) generated active power and consumed reactive power of the PV installation in node 114.</p>
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<p>Simulation results for case 3, Scenario III: (<b>a</b>) voltage waveforms in selected nodes (phase values); (<b>b</b>) voltage waveform in the last node of the network (114); (<b>c</b>) <span class="html-italic">Q</span>/<span class="html-italic">P</span>(<span class="html-italic">V</span>) characteristics obtained from simulation results; (<b>d</b>) generated active power and consumed reactive power of the PV installation in node 114.</p>
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<p>Simulation results for case 4, Scenario III: (<b>a</b>) voltage waveforms in selected nodes (phase values); (<b>b</b>) voltage waveform in the last node of the network (114); (<b>c</b>) waveform of the generated active power (maximum and actual—after activating the <span class="html-italic">P</span>(<span class="html-italic">V</span>) mode) and the reactive power consumed by the PV installation in node 114.</p>
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<p>Simulation results for case 5, Scenario III: (<b>a</b>) voltage waveforms in selected nodes (phase values); (<b>b</b>) voltage waveform in the last node of the network (114); (<b>c</b>) OLTC position; (<b>d</b>) generated active power and consumed reactive power of the PV installation in node 114.</p>
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<p>Summary of simulation results for all cases—Scenario I: (<b>a</b>) exceeding the upper threshold of 1.1 <span class="html-italic">V</span><sub>n</sub> (number of nodes with exceedances and total exceedance time related to the total simulation time); (<b>b</b>) exceeding the lower threshold of 0.95 <span class="html-italic">V</span><sub>n</sub> (number of nodes with exceedances and total exceedance time related to the total simulation time); (<b>c</b>) voltage range recorded during simulation in all nodes; (<b>d</b>) total reactive energy flow; (<b>e</b>) energy losses in the network.</p>
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<p>Summary of simulation results for all cases—Scenario II: (<b>a</b>) exceeding the upper threshold of 1.1 <span class="html-italic">V</span><sub>n</sub> (number of nodes with exceedances and, total exceedance time related to the total simulation time); (<b>b</b>) exceeding the lower threshold of 0.95 <span class="html-italic">V</span><sub>n</sub> (number of nodes with exceedances and total exceedance time related to the total simulation time); (<b>c</b>) voltage range recorded during simulation in all nodes; (<b>d</b>) total reactive energy flow; (<b>e</b>) energy losses in the network.</p>
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<p>Summary of simulation results for all cases—Scenario III: (<b>a</b>) exceeding the upper threshold of 1.1 <span class="html-italic">V</span><sub>n</sub> (number of nodes with exceedances and total exceedance time related to the total simulation time); (<b>b</b>) exceeding the lower threshold of 0.95 <span class="html-italic">V</span><sub>n</sub> (number of nodes with exceedances and total exceedance time related to the total simulation time); (<b>c</b>) voltage range recorded during simulation in all nodes; (<b>d</b>) total reactive energy flow; (<b>e</b>) energy losses in the network.</p>
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29 pages, 12522 KiB  
Article
Motor Fault Diagnosis and Detection with Convolutional Autoencoder (CAE) Based on Analysis of Electrical Energy Data
by YuRim Choi and Inwhee Joe
Electronics 2024, 13(19), 3946; https://doi.org/10.3390/electronics13193946 - 7 Oct 2024
Viewed by 1304
Abstract
This study develops a Convolutional Autoencoder (CAE) and deep neural network (DNN)-based model optimized for real-time signal processing and high accuracy in motor fault diagnosis. This model learns complex patterns from voltage and current data and precisely analyzes them in combination with DNN [...] Read more.
This study develops a Convolutional Autoencoder (CAE) and deep neural network (DNN)-based model optimized for real-time signal processing and high accuracy in motor fault diagnosis. This model learns complex patterns from voltage and current data and precisely analyzes them in combination with DNN through latent space representation. Traditional diagnostic methods relied on vibration and current sensors, empirical knowledge, or harmonic and threshold-based monitoring, but they had limitations in recognizing complex patterns and providing accurate diagnoses. Our model significantly enhances the accuracy of power data analysis and fault diagnosis by mapping each phase (R, S, and T) of the electrical system to the red, green, and blue (RGB) channels of image processing and applying various signal processing techniques. Optimized for real-time data streaming, this model demonstrated high practicality and effectiveness in an actual industrial environment, achieving 99.9% accuracy, 99.8% recall, and 99.9% precision. Specifically, it was able to more accurately diagnose motor efficiency and fault risks by utilizing power system analysis indicators such as phase voltage, total harmonic distortion (THD), and voltage unbalance. This integrated approach significantly enhances the real-time applicability of electric motor fault diagnosis and is expected to provide a crucial foundation for various industrial applications in the future. Full article
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<p>Data processing and analysis flow for electric motor fault diagnosis; (<b>a</b>) data collection, (<b>b</b>) real-time data processing and analysis flow through power system analyzer, (<b>c</b>) sliding window technique for time-series segmentation of electrical signals, and (<b>d</b>) CAE + DNN model inference.</p>
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<p>Signal analysis methods for motor drives.</p>
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<p>Visualization of the electric motor fault diagnosis process using CNN autoencoder and DNN: (<b>a</b>) data collection; (<b>b</b>) real-time data processing and analysis flow through power system analyzer; (<b>c</b>) CNN data structure of power system analysis data; (<b>d</b>) sliding window technique for time-series segmentation of electrical signals; (<b>e</b>) CAE structure for raw data and power system analysis data; (<b>f</b>) DNN structure combining CAE features.</p>
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<p>Experimental equipment configuration diagram.</p>
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<p>Data collection process.</p>
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<p>Real-time data processing and analysis using the power system analyzer.</p>
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<p>Signal processing architecture of power system analysis data.</p>
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<p>Sliding window technique for time-series segmentation of electrical signals.</p>
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<p>Sliding window of entire data: raw data and power system analysis data. (<b>a</b>) The raw current data visually represents the raw data of the R, S, and T three-phase currents, which are the real-time measurements of the motor’s condition and form the basis for fault analysis. (<b>b</b>) The power system analysis data includes various power quality and analysis data extracted from the power system, such as current and voltage imbalance and harmonic analysis, which are used in conjunction with the current data to detect abnormal signs. (<b>c</b>) The raw current data are segmented into fixed frames using the sliding window technique, allowing for the precise analysis of anomalies. The frame size is set to 512 samples, with each frame processed with an overlap of 64 samples. (<b>d</b>) The power system analysis data are also synchronized with the raw current data and processed using the sliding window technique.</p>
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<p>Combined CAE and DNN structure for electrical signal analysis.</p>
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<p>CAE structure of raw data.</p>
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<p>CAE structure of power system analysis data.</p>
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<p>DNN structure combined with CAE features.</p>
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<p>Visualization of latent space using t-SNE and PCA: (<b>a</b>) the t-SNE of encoded features: the visualization of the latent space features extracted by the CAE model; (<b>b</b>) the PCA of encoded features: the projection of the feature vectors learned by the CAE model onto a 2D space using Principal Component Analysis (PCA).</p>
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<p>Training and validation using CAE with power system analysis data: (<b>a</b>) epoch-wise changes in Mean Squared Error (MSE) and Structural Similarity Index (SSIM); (<b>b</b>) graph of Peak Signal–Noise Ratio (PSNR) changes for normal and abnormal samples.</p>
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<p>Training and validation results in the DNN combined with raw data CAE and power system analysis data CAE: (<b>a</b>) the graph of MSE loss changes in the training and validation data of the combined DNN; (<b>b</b>) the graph of accuracy changes in the training and validation data.</p>
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<p>(<b>a</b>) Changes in the F1 scores during the training and validation phases; (<b>b</b>) the graph of ROC-AUC performance across the training and validation epochs of the model.</p>
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<p>Analysis and operation equipment using CNN autoencoder and DNN combination for power system analysis: (<b>a</b>) voltage and current analysis system (power system analysis); (<b>b</b>) 110-ton mechanical press; (<b>c</b>) 80-ton mechanical press; (<b>d</b>) motor and real-time data monitoring software.</p>
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25 pages, 14943 KiB  
Article
Robust Control Scheme for Optimal Power Sharing and Selective Harmonic Compensation in Islanded Microgrids
by Ali Gaeed Seger Al-salloomee, Enrique Romero-Cadaval and Carlos Roncero-Clemente
Electronics 2024, 13(18), 3719; https://doi.org/10.3390/electronics13183719 - 19 Sep 2024
Viewed by 1014
Abstract
In power systems, nonlinear loads cause harmonic distortion, adversely affecting sensitive equipment such as induction motors, power electronics, and variable-speed drives. This paper presents a novel control strategy that integrates with existing hierarchical control systems to mitigate voltage imbalances and harmonic disturbances in [...] Read more.
In power systems, nonlinear loads cause harmonic distortion, adversely affecting sensitive equipment such as induction motors, power electronics, and variable-speed drives. This paper presents a novel control strategy that integrates with existing hierarchical control systems to mitigate voltage imbalances and harmonic disturbances in AC-islanded microgrids. The proposed method utilizes selective harmonic order filtering through multiple second-order generalized integrators (MSOGI) to extract negative, positive, and harmonic order components. The distributed generation (DG) unit control mechanism is designed to immediately correct voltage imbalances and harmonic disruptions, distributing the compensatory load evenly to rectify real and reactive power imbalances and harmonic disturbances. The microgrid’s control architecture primarily includes droop controllers for real and reactive power of positive sequences, voltage and current regulation inner control loops, an additional loop for correcting imbalances and harmonics, and secondary controllers to maintain voltage magnitude and frequency at nominal levels, ensuring high-quality voltage at the point of common coupling (PCC). The effectiveness of this approach is demonstrated through simulation results on the MATLAB/Simulink platform, proving its ability to effectively mitigate voltage imbalances and harmonic issues with the total harmonic of voltage reduced to approximately THDv = 0.5% and voltage unbalance factor (VUF) within approximately 0.1%. Full article
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<p>Typical structure of MG with multiple parallel-connected DG units.</p>
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<p>MSOGI structure for fundamental and harmonic extraction.</p>
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<p>Block diagram of the proposed control scheme for the islanded microgrid.</p>
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<p>Primary and secondary control actions.</p>
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<p>SOGI bandpass filter for sequence power extraction.</p>
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<p>Power and control stages diagram.</p>
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<p>Root locus of the transfer function of the inverter.</p>
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<p>Nyquist diagram of the inverter.</p>
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<p>Two microgrid systems under simulation study.</p>
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<p>Power sharing performance in microgrids under unbalanced and nonlinear load.</p>
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<p>The proposed compensation method will be performed at 2.5 s in an electrical network with an unbalanced and nonlinear load. (<b>a</b>) Voltage output of inverter 1, (<b>b</b>) zoomed section before and after activating the proposed control loop.</p>
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<p>Performance of the proposed compensation method at 2.5 s in an electrical network with unbalanced load and nonlinear load (<b>a</b>) The voltage output of inverter 2, (<b>b</b>) zoomed section before and after activating the proposed control loop.</p>
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<p>Negative sequence reactive powers.</p>
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<p>Negative sequence voltage of inverter 1.</p>
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<p>Negative sequence voltage of inverter 2.</p>
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<p>5th Harmonic voltage (<b>a</b>,<b>b</b>) inverter 1, (<b>c</b>,<b>d</b>) inverter 2.</p>
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<p>5th Harmonic voltage (<b>a</b>,<b>b</b>) inverter 1, (<b>c</b>,<b>d</b>) inverter 2.</p>
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<p>5th Harmonic voltage (<b>a</b>,<b>b</b>) inverter 1, (<b>c</b>,<b>d</b>) inverter 2.</p>
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<p>7th harmonic voltage (<b>a</b>,<b>b</b>) inverter 1, (<b>c</b>,<b>d</b>) inverter 2.</p>
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<p>7th harmonic voltage (<b>a</b>,<b>b</b>) inverter 1, (<b>c</b>,<b>d</b>) inverter 2.</p>
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<p>7th harmonic voltage (<b>a</b>,<b>b</b>) inverter 1, (<b>c</b>,<b>d</b>) inverter 2.</p>
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<p>11th harmonic voltage (<b>a</b>,<b>b</b>) inverter 1, (<b>c</b>,<b>d</b>) inverter 2.</p>
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<p>11th harmonic voltage (<b>a</b>,<b>b</b>) inverter 1, (<b>c</b>,<b>d</b>) inverter 2.</p>
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<p>11th harmonic voltage (<b>a</b>,<b>b</b>) inverter 1, (<b>c</b>,<b>d</b>) inverter 2.</p>
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<p>13rd harmonic voltage (<b>a</b>,<b>b</b>) inverter 1, (<b>c</b>,<b>d</b>) inverter 2.</p>
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<p>13rd harmonic voltage (<b>a</b>,<b>b</b>) inverter 1, (<b>c</b>,<b>d</b>) inverter 2.</p>
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<p>13rd harmonic voltage (<b>a</b>,<b>b</b>) inverter 1, (<b>c</b>,<b>d</b>) inverter 2.</p>
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18 pages, 9611 KiB  
Article
An Improved Collaborative Control Scheme to Resist Grid Voltage Unbalance for BDFG-Based Wind Turbine
by Defu Cai, Rusi Chen, Sheng Hu, Guanqun Sun, Erxi Wang and Jinrui Tang
Electronics 2024, 13(17), 3582; https://doi.org/10.3390/electronics13173582 - 9 Sep 2024
Viewed by 847
Abstract
This article presents an improved collaborative control to resist grid voltage unbalance for brushless doubly fed generator (BDFG)-based wind turbine (BDFGWT). The mathematical model of grid-connected BDFG including machine side converter (MSC) and grid side converter (GSC) in the αβ reference frame during [...] Read more.
This article presents an improved collaborative control to resist grid voltage unbalance for brushless doubly fed generator (BDFG)-based wind turbine (BDFGWT). The mathematical model of grid-connected BDFG including machine side converter (MSC) and grid side converter (GSC) in the αβ reference frame during unbalanced grid voltage condition is established. On this base, the improved collaborative control between MSC and GSC is presented. Under the control, the control objective of the whole BDFGWT system, including canceling the pulsations of electromagnetic torque and the unbalance of BDFGWT’s total currents, pulsations of BDFGWT’s total powers are capable of being realized; therefore, the control capability of BDFGWT to resist unbalanced grid voltage is greatly improved. Moreover, improved single-loop current controllers adopting PR regulators are proposed for both MSC and GSC where the sequence extractions for both MSC and GSC currents are not needed any more, and hence the proposed control is much simpler. In addition, the transient characteristics are also improved. Moreover, in order to achieve the decoupling control of current and average power, current controller also adopts the feedforward control approach. Case studies for a two MW BDFGWT system are implemented, and the results verify that the presented control is capable of effectively improving the control capability for BDFGWT to resist grid voltage unbalance and exhibit good stable and dynamic control performances. Full article
(This article belongs to the Special Issue Advances in Renewable Energy and Electricity Generation)
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<p>Topology of grid-connected BDFGWT.</p>
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<p>Spatial relations between reference frames of (<span class="html-italic">α</span><sub>p</sub>β<sub>p</sub>), (α<sub>r</sub> β<sub>r</sub>), (α<sub>c</sub>β<sub>c</sub>), (<span class="html-italic">dq</span>)<sup>+</sup> and (<span class="html-italic">dq</span>)<sup>−.</sup></p>
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<p>Power characteristic versus rotating speed under different wind speeds.</p>
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<p>Proposed MSC(CW) PR current controller.</p>
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<p>Bode plots for MSC current closed-loop transfer function.</p>
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<p>Bode plots of MSC(CW) current opened-loop transfer function.</p>
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<p>Proposed PR controller for GSC current.</p>
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<p>Bode plots of closed-loop transfer function for GSC current.</p>
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<p>Bode plots of GSC current opened-loop transfer function.</p>
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<p>Proposed overall collaborative control scheme for BDFGWT.</p>
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<p>Fast decomposition algorithm and enhanced PLL for BDFGWT.</p>
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<p>Waveforms of unbalanced grid voltage: (<b>a</b>) 8.5% unbalance (p.u.); (<b>b</b>) 9.5% unbalance (p.u.).</p>
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<p>Waveforms of wind speed and BDFG’s rotor speed. (<b>a</b>) Wind speed (m/s). (<b>b</b>) Rotor speed (p.u.). (<b>A</b>) Invariable wind speed and rotor speed. (<b>B</b>) Variable wind speed and rotor speed.</p>
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<p>Waveforms for BDFGWT system with traditional vector control and proposed control under 8.5% stable network unbalance (ω<sub>r</sub> = 0.8 p.u.). (<b>a</b>) Total currents (p.u.). (<b>b</b>) Total active power (p.u.). (<b>c</b>) Active powers of PW and GSC (p.u.). (<b>d</b>) Total reactive power (p.u.). (<b>e</b>) Reactive powers of PW and GSC (p.u.). (<b>f</b>) Electromagnetic torque (p.u.). (<b>g</b>) GSC current under dq and <span class="html-italic">α<sub>p</sub>β<sub>p</sub></span> reference frames (p.u.). (<b>h</b>) CW current (p.u.). (<b>i</b>) CW currents in dq and <span class="html-italic">α<sub>p</sub>β<sub>p</sub></span> reference frames (p.u.). (<b>A</b>) Traditional vector control where grid unbalance is not considered. (<b>B</b>) Proposed control.</p>
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<p>Waveforms under the change in rotor rotary speed during 9.5% grid voltage unbalance. (<b>a</b>) Total currents into network (p.u.). (<b>b</b>) MSC currents (p.u.). (<b>c</b>) GSC currents (p.u.). (<b>d</b>) Total active power (p.u.). (<b>e</b>) Active powers of PW and GSC (p.u.). (<b>f</b>) Total reactive power (p.u.). (<b>g</b>) Reactive powers of PW and GSC (p.u.). (<b>h</b>) Generator torque (p.u.). (<b>A</b>) Traditional vector control where grid unbalance is not considered. (<b>B</b>) Proposed control.</p>
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<p>Waveforms under the change in rotor rotary speed during 9.5% grid voltage unbalance. (<b>a</b>) Total currents into network (p.u.). (<b>b</b>) MSC currents (p.u.). (<b>c</b>) GSC currents (p.u.). (<b>d</b>) Total active power (p.u.). (<b>e</b>) Active powers of PW and GSC (p.u.). (<b>f</b>) Total reactive power (p.u.). (<b>g</b>) Reactive powers of PW and GSC (p.u.). (<b>h</b>) Generator torque (p.u.). (<b>A</b>) Traditional vector control where grid unbalance is not considered. (<b>B</b>) Proposed control.</p>
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13 pages, 6260 KiB  
Article
Assessing the Static Security of the Italian Grid by Means of the N-1 Three-Phase Contingency Analysis
by Giovanni Gardan, Luca Rusalen and Roberto Benato
Energies 2024, 17(17), 4429; https://doi.org/10.3390/en17174429 - 4 Sep 2024
Viewed by 531
Abstract
The ongoing replacement of synchronous machine generators (SMs) with converter-interface generators (CIGs) is raising the voltage unbalance of power systems, affecting power quality and grid stability. This paper focuses on a key power quality index for power systems, i.e., the voltage unbalance factor. [...] Read more.
The ongoing replacement of synchronous machine generators (SMs) with converter-interface generators (CIGs) is raising the voltage unbalance of power systems, affecting power quality and grid stability. This paper focuses on a key power quality index for power systems, i.e., the voltage unbalance factor. The purpose of this work is twofold. First, it presents the generalization of a three-phase power flow algorithm developed by University of Padova, named PFPD_3P, to assess the voltage unbalance factors of power systems supplied by CIGs. In particular, it is demonstrated that CIGs can be modelled as three-phase PV/PQ constraints embedding their positive-, negative- and zero-sequence admittances. Then, the concept of three-phase contingency analysis is introduced. Indeed, for static security evaluation, the classical single-phase contingency analysis may no longer be sufficient, as it lacks power quality computations, e.g., voltage/current unbalance factors. Numerical simulations evaluating the unbalance factors due to different generation mix scenarios and contingencies are tested on the Italian extra-high-voltage/high-voltage (EHV/HV) grid. The choice of this network relies on its representativeness, as CIGs are the majority of new installations in the Italian generation mix. Full article
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<p>CIG treatment by means of PV and PQ constraints.</p>
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<p>Treatment of inverter without negative-sequence-correcting current injection, as composition of sequence-frame-of-reference networks.</p>
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<p>The Italian EHV/HV transmission network. Different colors represent the different voltage levels: 380 kV (red), 220 kV (green), 150/132 kV (blue), 66 kV (orange).</p>
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<p>The towers adopted to model the Italian network transmission lines. The dimensions are represented in meters.</p>
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<p>Inverter-based RES active power increasing (blue and green columns) and in-service SM differences in the considered scenarios.</p>
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<p>Voltage unbalance factor curve in the transmission sections (380/220 kV) in the analyzed scenarios.</p>
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<p>Voltage unbalance factor curves in the sub-transmission sections (150/132 kV) in the analyzed scenarios.</p>
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<p>Average voltage unbalance factors in the different operational areas in the four analyzed scenarios.</p>
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<p>Voltage unbalance factor variations (Δ<span class="html-italic">UF</span>) due to the outage of the most-loaded line in the Italian transmission network.</p>
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<p>Voltage unbalance factor variations (Δ<span class="html-italic">UF</span>) due to the outage of the generator with the highest scheduled power in the Italian transmission network.</p>
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<p>Voltage unbalance factor variations (Δ<span class="html-italic">UF</span>) due to the outage of the highest load in the Italian transmission network.</p>
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25 pages, 32228 KiB  
Article
A Virtual Synchronous Generator-Based Control Strategy and Pre-Synchronization Method for a Four-Leg Inverter under Unbalanced Loads
by Zhenshan Huang, Zhijie Liu, Gang Shen, Kejun Li, Yuanzong Song and Baihe Su
Symmetry 2024, 16(9), 1116; https://doi.org/10.3390/sym16091116 - 28 Aug 2024
Viewed by 1201
Abstract
Virtual synchronous generator (VSG) control has positive effects on the stability of microgrids. In practical power systems, both single-phase loads and three-phase unbalanced loads are present. The four-leg inverter is an alternative solution for the power supply of unbalanced loads and grid connections. [...] Read more.
Virtual synchronous generator (VSG) control has positive effects on the stability of microgrids. In practical power systems, both single-phase loads and three-phase unbalanced loads are present. The four-leg inverter is an alternative solution for the power supply of unbalanced loads and grid connections. The traditional VSG control strategy still faces challenges when using a four-leg inverter to provide a symmetrical voltage and stable frequency in the load power supply and pre-synchronization. This paper proposes a VSG-based control strategy along with a pre-synchronization method for four-leg inverters. An improved VSG control strategy is put forward for four-leg inverters. The improved virtual impedance control and power calculation methods are integrated into the control loop to suppress the voltage asymmetry and frequency ripples. Building on the improved VSG control strategy, a pre-synchronization control approach is proposed to minimize the amplitude and phase angle discrepancies between the inverter output voltage and the power grid voltage. In addition, an optimized design method for control parameters is presented to improve VSG dynamic performance. A hardware prototype of the four-leg inverter is built; the results show that the voltage unbalance ratio can be reduced by 89%, and the response time can be shortened by 50%. Full article
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<p>Diagram of unbalanced load power supply system.</p>
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<p>Topology of the three phase four-leg converter.</p>
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<p>Average model of the four-leg inverter.</p>
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<p>Diagram of the existing VSG control strategy for a four-leg inverter.</p>
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<p>Problems of the existing control strategy under load variation.</p>
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<p>Equivalent sequential network model of VSG with an unbalanced load.</p>
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<p>Improved VSG control block diagram.</p>
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<p>Diagram of a four-leg inverter transitioning from off-grid to grid-connected operation.</p>
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<p>Improved VSG pre-synchronization control block diagram.</p>
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<p>Schematic diagram of a PLL.</p>
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<p>Pole map of the current control loop with varying <span class="html-italic">k</span><sub>c</sub>.</p>
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<p>Pole map of the voltage control loop with varying <span class="html-italic">k</span><sub>r</sub>.</p>
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<p>Simulation model of the main circuit and proposed control strategy.</p>
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<p>Simulation results with an unbalanced load. (<b>a</b>) Traditional VSG for a conventional converter [<a href="#B40-symmetry-16-01116" class="html-bibr">40</a>]; (<b>b</b>) traditional VSG [<a href="#B38-symmetry-16-01116" class="html-bibr">38</a>] for a four-leg converter; (<b>c</b>) improved VSG for a four-leg converter.</p>
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<p>Simulation results of voltages of the power grid and four-leg converter under pre-synchronization control.</p>
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<p>Photograph of a down-scaled prototype of the four-leg inverter.</p>
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<p>Single-line diagram of the experimental setup.</p>
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<p>Steady-state waveforms with an unbalanced load. (<b>a</b>) Voltages of a traditional VSG for a conventional inverter; (<b>b</b>) currents of a traditional VSG for a conventional inverter; (<b>c</b>) voltages of a traditional VSG for a four-leg inverter; (<b>d</b>) currents of a traditional VSG for a four-leg inverter; (<b>e</b>) voltages of an improved VSG for a four-leg inverter; (<b>f</b>) currents of an improved VSG for a four-leg inverter.</p>
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<p>Waveforms of frequency with an unbalanced load.</p>
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<p>Waveforms under load being turned on.</p>
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<p>Waveforms of voltages of the power grid and four-leg converter.</p>
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<p>Waveforms of the pre-synchronization process.</p>
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<p>Waveforms under the system turned on with different control parameters. (<b>a</b>) Parameters I; (<b>b</b>) parameters II; (<b>c</b>) proposed optimized parameters.</p>
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<p>Active power waveforms under a system turned on with different control parameters.</p>
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36 pages, 15797 KiB  
Article
Multi-Timescale Voltage Regulation for Distribution Network with High Photovoltaic Penetration via Coordinated Control of Multiple Devices
by Qingyuan Yan, Xunxun Chen, Ling Xing, Xinyu Guo and Chenchen Zhu
Energies 2024, 17(15), 3830; https://doi.org/10.3390/en17153830 - 2 Aug 2024
Viewed by 834
Abstract
The high penetration of distributed photovoltaics (PV) in distribution networks (DNs) results in voltage violations, imbalances, and flickers, leading to significant disruptions in DN stability. To address this issue, this paper proposes a multi-timescale voltage regulation approach that involves the coordinated control of [...] Read more.
The high penetration of distributed photovoltaics (PV) in distribution networks (DNs) results in voltage violations, imbalances, and flickers, leading to significant disruptions in DN stability. To address this issue, this paper proposes a multi-timescale voltage regulation approach that involves the coordinated control of a step voltage regulator (SVR), switched capacitor (SC), battery energy storage system (BESS), and electric vehicle (EV) across different timescales. During the day-ahead stage, the proposed method utilizes artificial hummingbird algorithm optimization-based least squares support vector machine (AHA-LSSVM) forecasting to predict the PV output, enabling the formulation of a day-ahead schedule for SVR and SC adjustments to maintain the voltage and voltage unbalance factor (VUF) within the limits. In the intra-day stage, a novel floating voltage threshold band (FVTB) control strategy is introduced to refine the day-ahead schedule, enhancing the voltage quality while reducing the erratic operation of SVR and SC under dead band control. For real-time operation, the African vulture optimization algorithm (AVOA) is employed to optimize the BESS output for precise voltage regulation. Additionally, a novel smoothing fluctuation threshold band (SFTB) control strategy and an initiate charging and discharging strategy (ICD) for the BESS are proposed to effectively smooth voltage fluctuations and expand the BESS capacity. To enhance user-side participation and optimize the BESS capacity curtailment, some BESSs are replaced by EVs for voltage regulation. Finally, a simulation conducted on a modified IEEE 33 system validates the efficacy of the proposed voltage regulation strategy. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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<p>Flowchart of AHA-LSSVM in PV forecasting.</p>
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<p>Flowchart of DVVR.</p>
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<p>Flowchart of FTVB.</p>
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<p>Flowchart of FVTB control via SVR and SC cooperation.</p>
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<p>Flowchart of real-time control.</p>
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<p>Flowchart of AVOA for BESS output.</p>
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<p>Flowchart of suppressed voltage fluctuation.</p>
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<p>State of BESS.</p>
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<p>The principle of mode 1.</p>
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<p>The principle of mode 2.</p>
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<p>The principle of mode 3.</p>
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<p>Framework of multi-timescale voltage coordinated control.</p>
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<p>Modified IEEE 33 system.</p>
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<p>The power of charging EVs on the station.</p>
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<p>Comparison of PV forecasting.</p>
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<p>Voltage and VUF profiles in Case 1-1. (<b>a</b>) Voltage profile of node 18; (<b>b</b>) voltage profile of node 33; (<b>c</b>) VUF profile of node 33.</p>
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<p>Voltage and VUF profiles in Case 1-2. (<b>a</b>) Voltage profile of node 18; (<b>b</b>) voltage profile of node 33; (<b>c</b>) VUF profile of node 33.</p>
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<p>Operation of SVR and SC in Case 1-2. (<b>a</b>) SVR operation; (<b>b</b>) SC2 operation.</p>
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<p>Voltage and VUF profiles in Case 2-1. (<b>a</b>) Voltage profile of node 18; (<b>b</b>) voltage profile of node 33; (<b>c</b>) VUF profile of node 33.</p>
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<p>Voltage and VUF profiles in Case 2-2. (<b>a</b>) Voltage profile of node 18; (<b>b</b>) voltage profile of node 33; (<b>c</b>) VUF profile of node 33.</p>
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<p>Operation of SVR and SC in Case 2-2. (<b>a</b>) SC1 operation; (<b>b</b>) SC2 operation; (<b>c</b>) SVR operation.</p>
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<p>Voltage and VUF profiles in Case 2-3. (<b>a</b>) Voltage profile of node 18; (<b>b</b>) voltage profile of node 33; (<b>c</b>) VUF profile of node 33.</p>
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<p>Operation of SVR and SC in Case 2-3. (<b>a</b>) SC1 operation; (<b>b</b>) SC2 operation; (<b>c</b>) SVR operation.</p>
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<p>Voltage and VUF profiles in Case 3-1. (<b>a</b>) Voltage profile of node 18; (<b>b</b>) voltage profile of node 33; (<b>c</b>) VUF profile of node 33.</p>
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<p>Voltage and VUF profiles in Case 3-2. (<b>a</b>) Voltage profile of node 18; (<b>b</b>) voltage profile of node 33; (<b>c</b>) VUF profile of node 33.</p>
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<p>BESS output and SOC profile in Case 3-2. (<b>a</b>) BESS output; (<b>b</b>) SOC profile.</p>
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<p>Voltage and VUF profiles in Case 3-3. (<b>a</b>) Voltage profile of node 18; (<b>b</b>) voltage profile of node 33; (<b>c</b>) VUF profile of node 33.</p>
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<p>BESS output and SOC profile in Case 3-3. (<b>a</b>) BESS output; (<b>b</b>) SOC profile.</p>
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<p>Voltage and VUF profiles in Case 3-4. (<b>a</b>) Voltage profile of node 18; (<b>b</b>) voltage profile of node 33; (<b>c</b>) VUF profile of node 33.</p>
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<p>Voltage profiles of node 18 in Case 3-4. (<b>a</b>) Voltage profile in phase ab of node 18; (<b>b</b>) voltage profile in phase bc of node 18; (<b>c</b>) voltage profile in phase ca of node 18.</p>
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<p>BESS output and SOC profile in Case 3-4. (<b>a</b>) BESS output; (<b>b</b>) SOC profile.</p>
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<p>Voltage and VUF profiles in Case 3-5. (<b>a</b>) Voltage profile of node 18; (<b>b</b>) voltage profile of node 33; (<b>c</b>) VUF profile of node 33.</p>
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<p>BESS output and SOC profile in Case 3-5. (<b>a</b>) BESS output; (<b>b</b>) SOC profile.</p>
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<p>Voltage and VUF profiles in Case 3-6. (<b>a</b>) Voltage profile of node 18; (<b>b</b>) voltage profile of node 33; (<b>c</b>) VUF profile of node 33.</p>
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<p>BESS output, SOC profile, and EVs charging/discharging power in Case 3-6. (<b>a</b>) BESS output; (<b>b</b>) SOC profile; (<b>c</b>) EVs charging/discharging power.</p>
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36 pages, 28072 KiB  
Article
Four-Wire Three-Level NPC Shunt Active Power Filter Using Model Predictive Control Based on the Grid-Tied PV System for Power Quality Enhancement
by Zoubida Amrani, Abdelkader Beladel, Abdellah Kouzou, Jose Rodriguez and Mohamed Abdelrahem
Energies 2024, 17(15), 3822; https://doi.org/10.3390/en17153822 - 2 Aug 2024
Viewed by 1041
Abstract
The primary objective of this paper focuses on developing a control approach to improve the operational performance of a three-level neutral point clamped (3LNPC) shunt active power filter (SAPF) within a grid-tied PV system configuration. Indeed, this developed control approach, based on the [...] Read more.
The primary objective of this paper focuses on developing a control approach to improve the operational performance of a three-level neutral point clamped (3LNPC) shunt active power filter (SAPF) within a grid-tied PV system configuration. Indeed, this developed control approach, based on the used 3LNPC-SAPF topology, aims to ensure the seamless integration of a photovoltaic system into the three-phase four-wire grid while effectively mitigating grid harmonics, grid current unbalance, ensuring grid unit power factor by compensating the load reactive power, and allowing power sharing with the grid in case of an excess of generated power from the PV system, leading to overall high power quality at the grid side. This developed approach is based initially on the application of the four-wire instantaneous p-q theory for the identification of the reference currents that have to be injected by the 3LNPC-SAPF in the grid point of common coupling (PCC). Whereas, the 3LNPC is controlled based on using the finite control set model predictive control (FCS-MPC), which can be accomplished by determining the convenient set of switch states leading to the voltage vector, which is the most suitable to ensure the minimization of the selected cost function. Furthermore, the used topology requires a constant DC-link voltage and balanced split-capacitor voltages at the input side of the 3LNPN. Hence, the cost function is adjusted by the addition of another term with a selected weighting factor related to these voltages to ensure their precise control following the required reference values. However, due to the random changes in solar irradiance and, furthermore, to ensure efficient operation of the proposed topology, the PV system is connected to the 3LNPN-SAPF via a DC/DC boost converter to ensure the stability of the 3LNPN input voltage within the reference value, which is achieved in this paper based on the use of the maximum power point tracking (MPPT) technique. For the validation of the proposed control technique and the functionality of the used topology, a set of simulations has been presented and investigated in this paper following different irradiance profile scenarios such as a constant irradiance profile and a variables irradiance profile where the main aim is to prove the effectiveness and flexibility of the proposed approach under variable irradiance conditions. The obtained results based on the simulations carried out in this study demonstrate that the proposed control approach with the used topology under different loads such as linear, non-linear, and unbalanced can effectively reduce the harmonics, eliminating the unbalance in the currents and compensating for the reactive component contained in the grid side. The obtained results prove also that the proposed control ensures a consistent flow of power based on the sharing principle between the grid and the PV system as well as enabling the efficient satisfaction of the load demand. It can be said that the proposal presented in this paper has been proven to have many dominant features such as the ability to accurately estimate the power sharing between the grid and the PV system for ensuring the harmonics elimination, the reactive power compensation, and the elimination of the neutral current based on the zero-sequence component compensation, even under variable irradiance conditions. This feature makes the used topology and the developed control a valuable tool for power quality improvement and grid stability enhancement with low cost and under clean energy. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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<p>Proposed configuration of the grid connected to two-stage photovoltaic systems using an active power filter (APF) with a control strategy that is based on a three-level NPC inverter.</p>
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<p>Single diode model of the PV module.</p>
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<p>PV array at 25 °C and specified irradiances (250, 500 and 1000 w/m<sup>2</sup>), (<b>a</b>) the out put current versu the out put voltage, (<b>b</b>) the output power versus the output voltage.</p>
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<p>Boost topology.</p>
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<p>Flowchart of the P and O algorithm.</p>
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<p>Three-level NPC multilevel converter power circuit.</p>
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<p>An analogous circuit consisting of an APF that is linked in parallel.</p>
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<p>The illustration of reference current calculation.</p>
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<p>Diagram and fundamental concepts of model predictive control.</p>
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<p>The flowchart demonstrates the implementation of the suggested FSC-MPC.</p>
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<p>Irradiance profiles. (<b>a</b>) profile of constant irradiance, (<b>b</b>) profile of variable irradiance.</p>
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<p>Three-phase load currents.</p>
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<p>Load current and voltage.</p>
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<p>Grid current and voltage.</p>
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<p>Active and reactive power of load, APF, and grid.</p>
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<p>Neutral current.</p>
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<p>DC-link voltage.</p>
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<p>Grid voltage and current.</p>
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<p>Three-phase grid currents.</p>
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<p>Neutral current.</p>
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<p>Active and reactive power of load, APF, and grid.</p>
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<p>DC-link voltage.</p>
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<p>Three-phase load currents.</p>
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<p>Three-phase grid currents.</p>
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<p>Load voltage and current.</p>
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<p>Grid voltage and current.</p>
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<p>Active and reactive power of load, APF, and grid.</p>
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<p>DC link voltage.</p>
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<p>Three-phase grid currents.</p>
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<p>Grid voltage and current.</p>
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<p>Active and reactive power of load, APF, and grid.</p>
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<p>DC link voltage.</p>
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<p>Three-phase load currents.</p>
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<p>Three-phase grid currents.</p>
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<p>Active and reactive power of load, APF, and grid.</p>
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<p>Grid voltage and current.</p>
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<p>Neutral current.</p>
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<p>DC link voltage.</p>
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<p>Three-phase grid currents.</p>
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<p>Grid voltage and current.</p>
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<p>Active and reactive power of load, APF, and grid.</p>
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<p>DC link voltage.</p>
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<p>Neutral current.</p>
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<p>Power sharing impacts on quality of grid current.</p>
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17 pages, 3082 KiB  
Article
Study of an Electric Vehicle Charging Strategy Considering Split-Phase Voltage Quality
by Fulu Yan, Mian Hua, Feng Zhao and Xuan Liang
World Electr. Veh. J. 2024, 15(7), 315; https://doi.org/10.3390/wevj15070315 - 18 Jul 2024
Viewed by 786
Abstract
Slow-charging electric vehicle (EV) loads are single-phase loads in the power distribution network (PDN). The random access of these EVs to the network brings to the forefront the split-phase voltage quality issues. Therefore, a two-layer EV charging strategy considering split-phase voltage quality is [...] Read more.
Slow-charging electric vehicle (EV) loads are single-phase loads in the power distribution network (PDN). The random access of these EVs to the network brings to the forefront the split-phase voltage quality issues. Therefore, a two-layer EV charging strategy considering split-phase voltage quality is proposed in this paper. Issues with voltage unbalance (VU), split-phase voltage deviation (VD), and split-phase voltage harmonics (VHs) are included in the optimization objective model. An upgraded version of the multi-objective non-dominated sorting genetic algorithm (NSGA-II) is used in the inner layer of the model and to pass the generated EV phase selection scheme to the outer layer. The outer layer consists of a split-phase harmonic current algorithm based on the forward–backward generation method, and feeds the voltage quality calculation results to the inner layer. After several iterations, the optimal EV phase selection scheme can be obtained when the inner layer algorithm satisfies the convergence condition. The results gained for the example indicate that the suggested EV charging approach can effectively handle the PDN’s split-phase voltage quality. Furthermore, it enhances the energy efficiency of PDN operations and promotes further energy consumption. Full article
(This article belongs to the Special Issue Data Exchange between Vehicle and Power System for Optimal Charging)
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<p>Electric vehicle phase selection flowchart.</p>
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<p>Schematic diagram of EVs’ access to the grid.</p>
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<p>Bilayer algorithmic flow.</p>
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<p>IEEE-33 node schematic.</p>
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<p>EV load and wind power output.</p>
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<p>Three-phase base load diagrams for PDN nodes and access points. (<b>a</b>) Three-phase base load for IEEE-33 systems; (<b>b</b>) three-phase base load at nodes 17 and 31.</p>
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<p>Load distribution of three-phase EVs at access points before and after split-phase access. (<b>a</b>) Node 17; (<b>b</b>) node 31.</p>
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<p>Overall three-phase unbalance before and after split-phase access for EVs. (<b>a</b>) Average access; (<b>b</b>) Disordered access; (<b>c</b>) Split-phase access; (<b>d</b>) Voltage unbalance at the access point.</p>
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<p>Voltage deviation before and after split-phase access at the access points. (<b>a</b>) Maximum voltage deviation at node 17; (<b>b</b>) three-phase voltage deviation at node 17; (<b>c</b>) maximum voltage deviation at node 31; (<b>d</b>) three-phase voltage deviation at node 31.</p>
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<p>Three-phase voltage harmonics at the access point before and after split-phase access. (<b>a</b>) Node 17; (<b>b</b>) Node 31.</p>
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<p>Voltage quality at node 17 before and after split-phase access with different numbers of EVs. (<b>a</b>) Maximum three-phase voltage unbalance; (<b>b</b>) maximum split-phase voltage deviation; (<b>c</b>) Maximum split-phase voltage harmonics.</p>
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42 pages, 6360 KiB  
Review
Shunt Active Power Filters in Three-Phase, Three-Wire Systems: A Topical Review
by Mihaela Popescu, Alexandru Bitoleanu, Constantin Vlad Suru, Mihaita Linca and Laurentiu Alboteanu
Energies 2024, 17(12), 2867; https://doi.org/10.3390/en17122867 - 11 Jun 2024
Cited by 5 | Viewed by 1800
Abstract
The increasingly extensive use of non-linear loads, mostly including static power converters, in large industry, commercial, and domestic applications, as well as the spread of renewable energy sources in distribution-generated units, make the use of the most efficient power quality improvement systems a [...] Read more.
The increasingly extensive use of non-linear loads, mostly including static power converters, in large industry, commercial, and domestic applications, as well as the spread of renewable energy sources in distribution-generated units, make the use of the most efficient power quality improvement systems a current concern. The use of active power filters proved to be the most advanced solution with the best compensation performance for harmonics, reactive power, and load unbalance. Thus, issues related to improving the power quality through active power filters are very topical and addressed by many researchers. This paper presents a topical review on the shunt active power filters in three-phase, three-wire systems. The power circuit and configurations of shunt active filtering systems are considered, including the multilevel topologies and use of advanced power semiconductor devices with lower switching losses and higher switching frequencies. Several compensation strategies, reference current generation methods, current control techniques, and DC-voltage control are pointed out and discussed. The direct power control method is also discussed. New advanced control methods that have better performance than conventional ones and gained attention in the recent literature are highlighted. The current state of renewable energy sources integration with shunt active power filters is analyzed. Concerns regarding the optimum placement and sizing of the active power filters in a given power network to reduce the investment costs are also presented. Trends and future developments are discussed at the end of this paper. For a rigorous substantiation, more than 250 publications on this topic, most of them very recent, constitute the basis of bibliographic references and can assist readers who are interested to explore the subject in greater detail. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Types of static converters used in the structure of SAPF: (<b>a</b>) voltage source inverter and (<b>b</b>) current source inverter.</p>
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<p>Topology of SAPF with two parallel interleaved inverters sharing the same DC capacitor.</p>
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<p>The three topologies of the most used three-phase, three-wire, three-level SAPFs: (<b>a</b>) NPC, (<b>b</b>) FC, and (<b>c</b>) CHB.</p>
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<p>Objectives of active power filtering under non-sinusoidal voltage conditions.</p>
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<p>General scheme of a two-level SAPF system.</p>
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<p>Structure of the control system in the case of direct current control.</p>
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<p>Reference current generation methods for direct current control.</p>
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<p>Calculation of reference currents by the <span class="html-italic">p-q method</span> for total compensation.</p>
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<p>Calculation of reference currents by the SRF method.</p>
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<p>Calculation of reference currents by the id-iq method.</p>
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<p>Calculation of reference currents by the CPT-based method for total compensation.</p>
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<p>Calculation of reference currents by FBD-based method.</p>
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<p>Calculation of reference currents by GIRP-based method.</p>
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<p>Calculation of reference currents by the CPC-based method.</p>
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<p>Structure of the control system in the case of indirect current control, performed only based on the output of the DC-voltage controller.</p>
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<p>Structure of the control system in the case of indirect current control using a reference component resulting from the measured load current and the supply voltage.</p>
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<p>Fixed band current hysteresis control loop structure.</p>
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<p>Adaptive band current hysteresis control loop structure in case of indirect current control.</p>
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<p>The eight voltage vectors and the prescribed reference vector specific to SVPWM.</p>
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<p>Block diagram of a fuzzy controller for the DC voltage.</p>
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<p>Block diagram of the direct power control.</p>
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<p>Twelve sectors in the stationary reference frame for voltage phasor location.</p>
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<p>Block diagram of a common PV-based SAPF.</p>
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<p>Block diagram of a wind energy-based SAPF.</p>
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<p>Block diagram of a fuel-cell-based SAPF.</p>
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<p>General structure of the hybrid RES-integrated SAPF.</p>
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31 pages, 12281 KiB  
Article
Voltage and Current Unbalance Reduction in Power Networks with Distributed Generation and Electric Vehicles
by Krzysztof Dobrzynski and Stanislaw Czapp
Energies 2024, 17(11), 2780; https://doi.org/10.3390/en17112780 - 6 Jun 2024
Viewed by 811
Abstract
The current development of prosumer microsources and the expected spread of electric vehicles may cause the appearance of significant current and voltage unbalance in low-voltage (LV) networks. This unbalance, which is an unfavorable phenomenon, may occur when using single-phase photovoltaic (PV) microsources and [...] Read more.
The current development of prosumer microsources and the expected spread of electric vehicles may cause the appearance of significant current and voltage unbalance in low-voltage (LV) networks. This unbalance, which is an unfavorable phenomenon, may occur when using single-phase photovoltaic (PV) microsources and single-phase home chargers for electric vehicles. This paper presents a proposal for the symmetrization of the LV network using devices for the reconfiguration of phases in the power supply. Both the different locations of these devices and the different objective functions for device implementation are analyzed. The research was carried out on an example LV network, taking into account several variants of the development of PV microsources and home chargers for electric vehicles. The analysis indicates that the appropriate location of phase reconfiguration devices and the use of an appropriate objective function leads to a significant reduction in unfavorable unbalancing in the LV network. Full article
(This article belongs to the Section E: Electric Vehicles)
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<p>Structure of the real network. The analyzed part—low-voltage (LV) network.</p>
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<p>Sample measurement results of one consumer current (line conductors L1, L2, and L3) in the analyzed LV network: (<b>a</b>) Example 1; (<b>b</b>) Example 2.</p>
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<p>Assumed model of power generation by a single-phase photovoltaic (PV) source (<b>a</b>) and the charging of electric vehicles (EVs) (<b>b</b>).</p>
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<p>Even distribution of prosumers with a PV source and an electric vehicle charger as well as the deployment of phase reconfiguration devices in main lines.</p>
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<p>Concentrated distribution of prosumers (red circles indicate places of their concentration) with a PV source and an electric vehicle charger, as well as the deployment of phase reconfiguration devices in main lines.</p>
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<p>Possible scenarios of the implementation of the objective functions. <span class="html-italic">I</span><sub>N</sub>—neutral conductor current.</p>
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<p>Phase reconfiguration direction. The arrows show the direction of phase reconfiguration.</p>
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<p>Voltage unbalance factor without (<b>a</b>) and with (<b>b</b>) phase reconfiguration. Objective function FICR-LVc. Variant W1.</p>
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<p>Voltage unbalance factor without (<b>a</b>) and with (<b>b</b>) phase reconfiguration. Objective function FICR-LVc. Variant W1.</p>
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<p>Currents in the neutral conductor of the main power lines without (<b>a</b>) and with (<b>b</b>) phase reconfiguration. Objective function FICR-LVc. Variant W1.</p>
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<p>Currents in the neutral conductor of the main power lines without (<b>a</b>) and with (<b>b</b>) phase reconfiguration. Objective function FICR-LVc. Variant W1.</p>
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<p>Active power losses in the analyzed LV network. Objective function FICR-LVc. Variant W1. Δ<span class="html-italic">P</span><sub>losses</sub> (right vertical axis in %)—the difference in power losses “with reconfiguration” compared to “without reconfiguration”.</p>
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<p>Voltage unbalance factor for the direction of phase reconfiguration from the end of main lines to the MV/LV substation (<b>a</b>) and from the MV/LV substation to the end of main lines (<b>b</b>). Objective function FICR-LVc. Variant W1.</p>
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<p>Currents in the neutral conductor of the main power lines for the direction of phase reconfiguration from the end of the main lines to the MV/LV substation (<b>a</b>) and from the MV/LV substation to the end of the main lines (<b>b</b>). Objective function FICR-LVc. Variant W1.</p>
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<p>Active power losses in the analyzed LV network for the direction of phase reconfiguration from the end of the main lines to the MV/LV substation (<b>a</b>) and from the MV/LV substation to the end of the main lines (<b>b</b>). Objective function FICR-LVc. Variant W1. Δ<span class="html-italic">P</span><sub>losses</sub> (right vertical axis in %)—the difference in power losses “with reconfiguration” compared to “without reconfiguration”.</p>
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<p>Voltage unbalance factor without (<b>a</b>) and with (<b>b</b>) phase reconfiguration. Objective function FICR-LVc. Variant W2.</p>
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<p>Currents in the neutral conductor of the main power lines without (<b>a</b>) and with (<b>b</b>) phase reconfiguration. Objective function FICR-LVc. Variant W2.</p>
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<p>Active power losses in the analyzed LV network. Objective function FICR-LVc. Variant W2. Δ<span class="html-italic">P</span><sub>losses</sub> (right vertical axis in %)—the difference in power losses “with reconfiguration” compared to “without reconfiguration”.</p>
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<p>Voltage unbalance factor without (<b>a</b>) and with (<b>b</b>) phase reconfiguration. Objective function FICR-LIc. Variant W2.</p>
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<p>Currents in the neutral conductor of the main power lines without (<b>a</b>) and with (<b>b</b>) phase reconfiguration. Objective function FICR-LIc. Variant W2.</p>
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<p>Active power losses in the analyzed LV network. Objective function FICR-LIc. Variant W2. Δ<span class="html-italic">P</span><sub>losses</sub> (right vertical axis in %)—the difference in power losses “with reconfiguration” compared to “without reconfiguration”.</p>
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<p>Voltage unbalance factor for variant W1 (<b>a</b>) and variant W2 (<b>b</b>). Objective function FICR-MIs.</p>
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<p>Voltage unbalance factor for variant W1 (<b>a</b>) and variant W2 (<b>b</b>). Objective function FICR-MIs.</p>
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<p>Currents in the neutral conductor of the main power lines for variant W1 (<b>a</b>) and for variant W2 (<b>b</b>). Objective function FICR-MIs.</p>
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<p>Currents in the neutral conductor of the main power lines for variant W1 (<b>a</b>) and for variant W2 (<b>b</b>). Objective function FICR-MIs.</p>
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<p>Active power losses in the analyzed LV network for variant W1 (<b>a</b>) and variant W2 (<b>b</b>). Objective function FICR-MIs. Δ<span class="html-italic">P</span><sub>losses</sub> (right vertical axis in %)—the difference in power losses “with reconfiguration” compared to “without reconfiguration”.</p>
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<p>Active power losses in the analyzed LV network for variant W1 (<b>a</b>) and variant W2 (<b>b</b>). Objective function FICR-MIs. Δ<span class="html-italic">P</span><sub>losses</sub> (right vertical axis in %)—the difference in power losses “with reconfiguration” compared to “without reconfiguration”.</p>
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<p>Voltage unbalance factor for variant W1 (<b>a</b>) and variant W2 (<b>b</b>). Objective function DMCR-MIs.</p>
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<p>Currents in the neutral conductor of the main power lines for variant W1 (<b>a</b>) and for variant W2 (<b>b</b>). Objective function of DMCR-MIs.</p>
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<p>Currents in the neutral conductor of the main power lines (detailed data for sequence no. 26) for variant W1 (<b>a</b>) and for variant W2 (<b>b</b>). Objective function of DMCR-MIs.</p>
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<p>Active power losses in the analyzed LV network for variant W1 (<b>a</b>) and variant W2 (<b>b</b>). Objective function of DMCR-MIs. Δ<span class="html-italic">P</span><sub>losses</sub> (right vertical axis in %)—the difference in power losses “with reconfiguration” compared to “without reconfiguration”.</p>
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30 pages, 3696 KiB  
Article
A Zonal Approach for Wide-Area Temporary Voltage Quality Assessment in a Smart Grid
by Miodrag Forcan, Aleksandar Simović, Srđan Jokić and Jovana Forcan
Energies 2024, 17(11), 2475; https://doi.org/10.3390/en17112475 - 22 May 2024
Cited by 1 | Viewed by 878
Abstract
Wide-area voltage quality assessment represents one of the mandatory objectives for distribution system operators in the development of advanced distribution management systems supporting smart grid requirements. This paper introduces a zonal approach for wide-area temporary voltage quality evaluation in a distribution network. The [...] Read more.
Wide-area voltage quality assessment represents one of the mandatory objectives for distribution system operators in the development of advanced distribution management systems supporting smart grid requirements. This paper introduces a zonal approach for wide-area temporary voltage quality evaluation in a distribution network. The concept of temporary voltage quality evaluation and assessment is recommended to incentivize active/online management of voltage quality issues. A decision support system based on simple deterministic rules is proposed for rating the voltage quality zones in a distribution network and making recommendations to the distribution system operator. Voltage RMS level, unbalance, and total harmonic distortion are considered voltage quality indices representing the inputs in the decision support system. Residential, commercial, and industrial load types are considered when setting the thresholds for voltage quality indices. The proposed zonal approach for the division of distribution networks in voltage quality zones is applied to the example of a typical European-type distribution network. The operation of a decision support system is tested using the developed distribution smart grid model. The following simulation case studies are conducted: loads with low power factors, manual voltage regulation at MV/LV transformers, unbalanced loads, integration of solar power plant, and nonlinear loads. The obtained simulation results reveal the benefits of the proposed voltage quality assessment approach. Cybersecurity challenges that may impact the proposed approach are addressed, including security vulnerabilities, data privacy, and resilience to cyber threats. Full article
(This article belongs to the Section A1: Smart Grids and Microgrids)
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<p>Conceptualized DSO PQ online monitoring center collecting and analyzing data from IEDs, PQ monitors, and SMs.</p>
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<p>Temporary VQ 10 min time snapshots and ratings.</p>
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<p>DN division into VQ zones according to the proposed zonal approach.</p>
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<p>DN model—base configuration with marked VQ-feeder zones.</p>
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<p>Voltage RMS level profiles of different feeders—DN base case.</p>
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<p>Voltage RMS level profiles of different feeders—loads with low power factor case.</p>
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<p>Voltage RMS level profiles of different feeders—manual voltage regulation case.</p>
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<p>Voltage unbalance profiles of different feeders—unbalanced loads case.</p>
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<p>Voltage RMS level profile of Sub22—feeder 9—DN base case and SPP integration cases.</p>
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<p>Voltage THD profile of Sub22—feeder 9—DN base case and SPP integration cases.</p>
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<p>Voltage THD profiles of different feeders—nonlinear loads case.</p>
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