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Sensors and Fault Diagnostics in Power System
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Sensors and Fault Diagnostics in Power System

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 17794

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Michał Kunicki

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758 Opole, Poland
Interests: measurements and metrology; technical diagnostics; high-voltage engineering; partial discharges
Special Issues, Collections and Topics in MDPI journals
Dr. Jan Fulneček

E-Mail Website
Guest Editor
Department of Electrical Power Engineering, VSB Technical University of Ostrava, 17 listopadu 15/2172, Potuba, 708 00 Ostrava, Czech Republic
Interests: electric power engineering; high voltage engineering; partial discharges; fault diagnostics; overhead lines; condition-based maintenance
Prof. Dr. Pawel Rozga

E-Mail Website
Guest Editor
Institute of Electrical Power Engineering, Lodz University of Technology, Stefanowskiego 20, 90-537 Lodz, Poland
Interests: high-voltage engineering; pre-breakdown and breakdown phenomena in dielectric liquids; alternative dielectric liquids for electrical purposes; statistics in electrical engineering; partial discharges; insulation of power transformers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The adequate technical condition assessment of key apparatus is a crucial assumption in the provision of reliable and continuous electric power delivery to customers. To meet this requirement, any fault in the power system must be detected and diagnosed as early as possible, with particular emphasis on the precision of the diagnostic process. Various on-line and off-line diagnostic methods are widely applied to the early detection of any system malfunctions. Furthermore, a number of different sensors may also be applied to capture selected physical quantities that may be used to indicate the type of potential faults. A specific fault diagnostic process is typically performed by experts in the field; however, artificial intelligence (AI)-based systems are increasingly being proposed to support the decision-making process related to this task. The essential step of the fault diagnostic process is signal analysis, supported by feautures including (but not limited to) signal processing, feature extraction, modelling and prediction methods.

With this in mind, we are launching a Special Issue entitled Sensors and Fault Diagnostics in Power System. We invite researchers to contribute high-quality original research or technical papers, reviews, and case studies to this Special Issue. Practical papers in which either examples of good present practice are described and disseminated, or new proposals of improvements and applications of innovative solutions regarding sensor design, evaluation and applications, signal processing and measurements, prediction, or modelling and classification regarding fault diagnostics in power engineering are particularly sought. The proposed papers may concern both individual elements of the power system (transformers, cables, generators, AC/DC converters, etc.), as well as comprehensive system approaches. Theoretical papers of high technical merit relying on mathematical arguments and computation are also welcomed, but authors are asked to highlight and justify their potential industrial applications.

Dr. Michał Kunicki
Dr. Jan Fulneček
Prof. Dr. Pawel Rozga
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fault diagnostics
  • condition-based maintenance
  • predictive maintenance
  • decision support system
  • high voltage
  • cable
  • transformer
  • overhead line
  • insulation
  • dielectrics
  • substation
  • transmission
  • distribution
  • generation
  • reliability

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Published Papers (11 papers)

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Editorial

Jump to: Research

4 pages, 146 KiB  
Editorial
Sensors and Fault Diagnostics in Power System
by Michał Kunicki, Jan Fulneček and Pawel Rozga
Sensors 2024, 24(18), 5999; https://doi.org/10.3390/s24185999 - 16 Sep 2024
Viewed by 957
Abstract
The adequate technical condition assessment of key apparatuses is a crucial assumption in the delivery of reliable and continuous electric power to customers [...] Full article
(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)

Research

Jump to: Editorial

13 pages, 724 KiB  
Article
Method of Equivalent Error as a Criterion of the Assessment of the Algorithms Used for Estimation of Synchrophasor Parameters Taken from the Power System
by Malgorzata Binek and Pawel Rozga
Sensors 2024, 24(14), 4619; https://doi.org/10.3390/s24144619 - 17 Jul 2024
Cited by 1 | Viewed by 704
Abstract
The development of digital techniques in control engineering leads to the creation of innovative algorithms for measuring specific parameters. In the field of electric power engineering these parameters may be amplitude, phase and frequency of voltage or current occurring in the analyzed electric [...] Read more.
The development of digital techniques in control engineering leads to the creation of innovative algorithms for measuring specific parameters. In the field of electric power engineering these parameters may be amplitude, phase and frequency of voltage or current occurring in the analyzed electric grid. Thus, the algorithms mentioned, applied in relation to the quoted parameters, may provide precise and reliable measurement results in the electric grid as well as ensure better grid monitoring and security. Signal analysis regarding its identification due to the type of interference is very difficult because the multitude of information obtained is very large. In order to indicate the best method for determining errors in measuring synchronous parameters of the measured current or voltage waveforms, the authors propose in this paper a new form of one error for all testing functions, which is called an equivalent error. This error is determined for each error’s value defined in the applicable standards for each of selected 15 methods. The use of the equivalent error algorithm is very helpful in identifying a group of methods whose operation is satisfactory in terms of measurement accuracy for various types of disturbances (both in the steady state and in the dynamic state) that may occur in the power grid. The results are analyzed for phasor measurement unit (PMU) devices of class P (protection) and M (measurement). Full article
(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)
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<p>The TVE graphs for subsequent functions.</p> Full article ">
20 pages, 9808 KiB  
Article
Measurement of Transient Overvoltages by Capacitive Electric Field Sensors
by Felipe L. Probst, Michael Beltle and Stefan Tenbohlen
Sensors 2024, 24(5), 1357; https://doi.org/10.3390/s24051357 - 20 Feb 2024
Cited by 1 | Viewed by 1576
Abstract
The accurate measurement and the investigation of electromagnetic transients are becoming more important, especially with the increasing integration of renewable energy sources into the power grid. These sources introduce new transient phenomena due to the extensive use of power electronics. To achieve this, [...] Read more.
The accurate measurement and the investigation of electromagnetic transients are becoming more important, especially with the increasing integration of renewable energy sources into the power grid. These sources introduce new transient phenomena due to the extensive use of power electronics. To achieve this, the measurement devices must have a broadband response capable of measuring fast transients. This paper presents a capacitive electric field sensor-based measurement system to measure transient overvoltages in high-voltage substations. The concept and design of the measurement system are first presented. Then, the design and concept are validated using tests performed in a high-voltage laboratory. Afterwards, two different calibration techniques are discussed: the simplified method (SM) and the coupling capacitance compensation (CCC) method. Finally, three recorded transients are evaluated using the calibration methods. The investigation revealed that the SM tends to overestimate the maximum overvoltage, highlighting the CCC method as a more suitable approach for calibrating transient overvoltage measurements. This measurement system has been validated using various measurements and can be an efficient and flexible solution for the long-term monitoring of transient overvoltages in high-voltage substations. Full article
(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)
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Figure 1

Figure 1
<p>Schematic diagram of the transient overvoltage measurement system.</p> Full article ">Figure 2
<p>Three-dimensional model of the measurement setup for calculation of stray capacitance <span class="html-italic">C</span><sub>1</sub>.</p> Full article ">Figure 3
<p>External components of the CDS. (<b>a</b>) Coupling plane; (<b>b</b>) metal housing with connections.</p> Full article ">Figure 4
<p>Electronic circuit of the CDS. (<b>a</b>) Top view; (<b>b</b>) parallel capacitors of <span class="html-italic">C</span><sub>2</sub>.</p> Full article ">Figure 5
<p>The control cabinet with the power quality monitor.</p> Full article ">Figure 6
<p>Measurement of the accuracy of the CDSs. (<b>a</b>) Schematic diagram; (<b>b</b>) test setup.</p> Full article ">Figure 7
<p>Measurement results. (<b>a</b>) Voltage ratio; (<b>b</b>) stray capacitance <span class="html-italic">C</span><sub>1</sub>.</p> Full article ">Figure 8
<p>Lightning impulse test. (<b>a</b>) Schematic diagram; (<b>b</b>) test setup.</p> Full article ">Figure 9
<p>Lightning impulse response of device MD1.</p> Full article ">Figure 10
<p>Bandwidth measurement of device MD1. (<b>a</b>) Test setup; (<b>b</b>) measurement result.</p> Full article ">Figure 11
<p>Measurement system installed in the substation. (<b>a</b>) CDS on the base of a disconnector; (<b>b</b>) Control cabinet on the concrete base of a circuit breaker.</p> Full article ">Figure 12
<p>Coupling capacitances between a CDS and adjacent phases.</p> Full article ">Figure 13
<p>Comparison between the reference and measured voltages.</p> Full article ">Figure 14
<p>Equivalent circuit representing the measurement device installed in phase A.</p> Full article ">Figure 15
<p>Transient overvoltage reconstruction with different calibration parameters. (<b>a</b>) Response of phase C; (<b>b</b>) zoomed waveforms on the region of maximum overvoltage.</p> Full article ">Figure 16
<p>Normalized ratios according to different values of stray capacitances.</p> Full article ">Figure 17
<p>Transient overvoltage reconstruction—line energization. (<b>a</b>) Response of phase C; (<b>b</b>) zoomed waveforms on the region of maximum overvoltage.</p> Full article ">Figure 18
<p>Transient overvoltage reconstruction—line energization. (<b>a</b>) Response of phase C; (<b>b</b>) zoomed waveforms on the region of maximum negative overvoltage.</p> Full article ">Figure 19
<p>Transient overvoltage reconstruction—line de-energization.</p> Full article ">Figure 20
<p>Maximum voltage measured during the energization of the transmission line.</p> Full article ">Figure 21
<p>Transient signal with the maximum measured overvoltage.</p> Full article ">
24 pages, 6594 KiB  
Article
Voiceprint Fault Diagnosis of Converter Transformer under Load Influence Based on Multi-Strategy Improved Mel-Frequency Spectrum Coefficient and Temporal Convolutional Network
by Hui Li, Qi Yao and Xin Li
Sensors 2024, 24(3), 757; https://doi.org/10.3390/s24030757 - 24 Jan 2024
Cited by 2 | Viewed by 1451
Abstract
In order to address the challenges of low recognition accuracy and the difficulty in effective diagnosis in traditional converter transformer voiceprint fault diagnosis, a novel method is proposed in this article. This approach takes account of the impact of load factors, utilizes a [...] Read more.
In order to address the challenges of low recognition accuracy and the difficulty in effective diagnosis in traditional converter transformer voiceprint fault diagnosis, a novel method is proposed in this article. This approach takes account of the impact of load factors, utilizes a multi-strategy improved Mel-Frequency Spectrum Coefficient (MFCC) for voiceprint signal feature extraction, and combines it with a temporal convolutional network for fault diagnosis. Firstly, it improves the hunter–prey optimizer (HPO) as a parameter optimization algorithm and adopts IHPO combined with variational mode decomposition (VMD) to achieve denoising of voiceprint signals. Secondly, the preprocessed voiceprint signal is combined with Mel filters through the Stockwell transform. To adapt to the stationary characteristics of the voiceprint signal, the processed features undergo further mid-temporal processing, ultimately resulting in the implementation of a multi-strategy improved MFCC for voiceprint signal feature extraction. Simultaneously, load signal segmentation is introduced for the diagnostic intervals, forming a joint feature vector. Finally, by using the Mish activation function to improve the temporal convolutional network, the IHPO-ITCN is proposed to adaptively optimize the size of convolutional kernels and the number of hidden layers and construct a transformer fault diagnosis model. By constructing multiple sets of comparison tests through specific examples and comparing them with the traditional voiceprint diagnostic model, our results show that the model proposed in this paper has a fault recognition accuracy as high as 99%. The recognition accuracy was significantly improved and the training speed also shows superior performance, which can be effectively used in the field of multiple fault diagnosis of converter transformers. Full article
(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)
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Figure 1
<p>Voiceprint data acquisition system.</p> Full article ">Figure 2
<p>On-site acquisition.</p> Full article ">Figure 3
<p>(<b>a</b>) Time domain characteristics; (<b>b</b>) frequency domain characteristics.</p> Full article ">Figure 4
<p>Characteristics of the main frequency of the voiceprint signal of the converter transformer with the variation in current.</p> Full article ">Figure 5
<p>(<b>a</b>) SPM chaotic mapping value distribution; (<b>b</b>) circle chaotic mapping value distribution.</p> Full article ">Figure 5 Cont.
<p>(<b>a</b>) SPM chaotic mapping value distribution; (<b>b</b>) circle chaotic mapping value distribution.</p> Full article ">Figure 6
<p>(<b>a</b>) Comparison of the optimization performance of the measurement function (14); (<b>b</b>) comparison of the optimization performance of the measurement function (15).</p> Full article ">Figure 7
<p>Multi-strategy improvement of MFCC flowchart.</p> Full article ">Figure 8
<p>Activation function 0-mean comparison.</p> Full article ">Figure 9
<p>Improve temporal convolutional neural network architecture.</p> Full article ">Figure 10
<p>Multi-strategy improved MFCC-IHPO-VMD-ITCN fault diagnosis modeling.</p> Full article ">Figure 11
<p>Comparison of fitness function values of different optimization algorithms.</p> Full article ">Figure 12
<p>(<b>a</b>–<b>d</b>) Component IMF1–IMF16 after IHPO-VMD decomposition.</p> Full article ">Figure 13
<p>(<b>a</b>) Empirically selected VMD decomposition results; (<b>b</b>) IHPO-VMD decomposition results.</p> Full article ">Figure 14
<p>Correlation coefficients of components.</p> Full article ">Figure 15
<p>(<b>a</b>) Traditional MFCC features; (<b>b</b>) multi-strategy improved MFCC features.</p> Full article ">Figure 16
<p>(<b>a</b>–<b>c</b>) are the typical defective spectral characteristics of the converter transformer.</p> Full article ">Figure 17
<p>Change in fitness function.</p> Full article ">Figure 18
<p>(<b>a</b>) Model identification results; (<b>b</b>) model testing set prediction results.</p> Full article ">Figure 19
<p>(<b>a</b>) 1 IHPO-ITCN based on joint voiceprint–electric feature vectors, (<b>a</b>) 2 IHPO-TCN based on joint voiceprint–electric feature vectors, (<b>a</b>) 3 IHPO-ITCN based on voiceprint feature vectors; (<b>b</b>) prediction results of the voiceprint feature model testing set.</p> Full article ">
15 pages, 2082 KiB  
Article
Exploitation Perspective Index as a Support of the Management of the Transformer Fleet
by Michał Kunicki, Sebastian Borucki and Jan Fulneček
Sensors 2023, 23(21), 8681; https://doi.org/10.3390/s23218681 - 24 Oct 2023
Cited by 1 | Viewed by 987
Abstract
This paper presents an alternative approach to the Transformer Assessment Index (TAI) by proposing a relatively simple rating method called the Exploitation Perspective Index (EPI). The method provides two numerical indicators: the first reflects the overall technical condition of the particular unit, and [...] Read more.
This paper presents an alternative approach to the Transformer Assessment Index (TAI) by proposing a relatively simple rating method called the Exploitation Perspective Index (EPI). The method provides two numerical indicators: the first reflects the overall technical condition of the particular unit, and the second shows the condition of the unit in the context of the entire fleet. The objective of the EPI method is to support the decision-making process regarding the technical condition assessment of each of the transformers in the target population, considering not only technical but also economic aspects of transformer maintenance. Application of the method is described step by step, including input data, parametrization of the weights, and interpretation of the output results it provides. The proposed method is evaluated by two representative use cases and compared with two other methods. As a result, EPI confirms its applicability, and it has already been successfully implemented by the electric power industry. EPI can be potentially freely adopted for any transformer fleet, as well as for the specific situation of the utility, by adjusting the relevant parameters. Full article
(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)
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Figure 1

Figure 1
<p>General flowchart of the EPI method.</p> Full article ">Figure 2
<p>Flowchart of initialization of parameters for EPI calculation of the specific transformer.</p> Full article ">Figure 3
<p>Rated power (<b>a</b>) and age structure (<b>b</b>) of the representative transformer population.</p> Full article ">Figure 4
<p>Histogram of technical condition assessment results in the analyzed population using different methods: (<b>a</b>) EPI, (<b>b</b>) HI1, and (<b>c</b>) HI2.</p> Full article ">Figure 5
<p>Percentile plot of EPI results in the analyzed population.</p> Full article ">
17 pages, 708 KiB  
Article
Smart Grid Outlier Detection Based on the Minorization–Maximization Algorithm
by Lina Qiao, Wengen Gao, Yunfei Li, Xinxin Guo, Pengfei Hu and Feng Hua
Sensors 2023, 23(19), 8053; https://doi.org/10.3390/s23198053 - 24 Sep 2023
Cited by 1 | Viewed by 1363
Abstract
Outliers can be generated in the power system due to aging system equipment, faulty sensors, incorrect line connections, etc. The existence of these outliers will pose a threat to the safe operation of the power system, reduce the quality of the data, affect [...] Read more.
Outliers can be generated in the power system due to aging system equipment, faulty sensors, incorrect line connections, etc. The existence of these outliers will pose a threat to the safe operation of the power system, reduce the quality of the data, affect the completeness and accuracy of the data, and thus affect the monitoring analysis and control of the power system. Therefore, timely identification and treatment of outliers are essential to ensure stable and reliable operation of the power system. In this paper, we consider the problem of detecting and localizing outliers in power systems. The paper proposes a Minorization–Maximization (MM) algorithm for outlier detection and localization and an estimation of unknown parameters of the Gaussian mixture model (GMM). To verify the performance of the method, we conduct simulation experiments by simulating different test scenarios in the IEEE 14-bus system. Numerical examples show that in the presence of outliers, the MM algorithm can detect outliers better than the traditional algorithm and can accurately locate outliers with a probability of more than 95%. Therefore, the algorithm provides an effective method for the handling of outliers in the power system, which helps to improve the monitoring analyzing and controlling ability of the power system and to ensure the stable and reliable operation of the power system. Full article
(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)
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Figure 1

Figure 1
<p>IEEE 14-bus system.</p> Full article ">Figure 2
<p>The distribution of measurements errors.</p> Full article ">Figure 3
<p>The distribution of measurement errors in the presence of outliers.</p> Full article ">Figure 4
<p>The distribution of measurements errors with MM algorithm.</p> Full article ">Figure 5
<p>The error of the parameter varies with the number of iterations.</p> Full article ">Figure 6
<p>The error of parameter <math display="inline"><semantics> <msub> <mi>π</mi> <mi>k</mi> </msub> </semantics></math> varies with the number of buses with outliers.</p> Full article ">Figure 7
<p>The error of parameter <math display="inline"><semantics> <msub> <mi>μ</mi> <mi>k</mi> </msub> </semantics></math> varies with the number of buses with outliers.</p> Full article ">Figure 8
<p>The error of parameter <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>k</mi> </msub> </semantics></math> varies with the number of buses with outliers.</p> Full article ">Figure 9
<p>Data distribution before and after outliers in IEEE 14-bus system.</p> Full article ">Figure 10
<p>Outlier detection results.</p> Full article ">Figure 11
<p>Comparison of detection performance at different outlier strengths.</p> Full article ">Figure 12
<p>False alarm rate of MM algorithm for different outlier strengths.</p> Full article ">
12 pages, 1877 KiB  
Article
Simplifying Rogowski Coil Modeling: Simulation and Experimental Verification
by Alessandro Mingotti, Christian Betti, Roberto Tinarelli and Lorenzo Peretto
Sensors 2023, 23(19), 8032; https://doi.org/10.3390/s23198032 - 22 Sep 2023
Cited by 3 | Viewed by 1894
Abstract
The integration of renewable energy sources, electric vehicles, and other electrical assets has introduced complexities in monitoring and controlling power networks. Consequently, numerous grid nodes have been equipped with sensors and complex measurement systems to enhance network observability. Additionally, real-time power network simulators [...] Read more.
The integration of renewable energy sources, electric vehicles, and other electrical assets has introduced complexities in monitoring and controlling power networks. Consequently, numerous grid nodes have been equipped with sensors and complex measurement systems to enhance network observability. Additionally, real-time power network simulators have become crucial tools for predicting and estimating the behavior of electrical quantities at different network components, such as nodes, branches, and assets. In this paper, a new user-friendly model for Rogowski coils is presented and validated. The model’s simplicity stems from utilizing information solely from the Rogowski coil datasheet. By establishing the input/output relationship, the output of the Rogowski coil is obtained. The effectiveness and accuracy of the proposed model are tested using both simulations and commercially available Rogowski coils. The results confirm that the model is simple, accurate, and easily implementable in various simulation environments for a wide range of applications and purposes. Full article
(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)
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Figure 1
<p>Picture of the Rogowski coil and its components.</p> Full article ">Figure 2
<p>Flowchart of the modeling procedure.</p> Full article ">Figure 3
<p>Cross-section geometries.</p> Full article ">Figure 4
<p>Schematic of the equivalent circuit of the RC.</p> Full article ">Figure 5
<p><math display="inline"><semantics> <mi>ε</mi> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>∆</mo> <mi>φ</mi> </mrow> </semantics></math> for each test signal described in <a href="#sensors-23-08032-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 6
<p>Probability density function obtained for <math display="inline"><semantics> <mi>C</mi> </semantics></math> after 100,000 Monte Carlo trials.</p> Full article ">Figure 7
<p>The measurement setup used for the experimental measurements on the commercial RCs.</p> Full article ">
17 pages, 2673 KiB  
Article
Decentralized Sensor Fault-Tolerant Control of DC Microgrids Using the Attracting Ellipsoid Method
by Hisham M. Soliman, Ehab H. E. Bayoumi, Farag A. El-Sheikhi and Michele De Santis
Sensors 2023, 23(16), 7160; https://doi.org/10.3390/s23167160 - 14 Aug 2023
Cited by 5 | Viewed by 1144
Abstract
System stability deterioration in microgrids commonly occurs due to unpredictable faults and equipment malfunctions. Recently, robust control techniques have been used in microgrid systems to address these difficulties. In this paper, for DC-islanded microgrids that have sensors faults, a new passive fault-tolerant control [...] Read more.
System stability deterioration in microgrids commonly occurs due to unpredictable faults and equipment malfunctions. Recently, robust control techniques have been used in microgrid systems to address these difficulties. In this paper, for DC-islanded microgrids that have sensors faults, a new passive fault-tolerant control strategy is developed. The suggested approach can be used to maintain system stability in the presence of flaws, such as faulty actuators and sensors, as well as component failures. The suggested control is effective when the fault is never recognized (or when the fault is not being precisely known, and some ambiguity in the fault may be interpreted as uncertainty in the system’s dynamics following the fault). The design is built around a derived sufficient condition in the context of linear matrix inequalities (LMIs) and the attractive ellipsoid technique. The ellipsoidal stabilization idea is to bring the state trajectories into a small region including the origin (an ellipsoid with minimum volume) and the trajectories will not leave the ellipsoid for the future time. Finally, computational studies on a DC microgrid system are carried out to assess the effectiveness of the proposed fault-tolerant control approach. When compared with previous studies, the simulation results demonstrate that the proposed control technique can significantly enhance the reliability and efficiency of DC microgrid systems. Full article
(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)
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Figure 1

Figure 1
<p>The electrical structure of DG-i.</p> Full article ">Figure 2
<p>A DC microgrid with six islanded DGs.</p> Full article ">Figure 3
<p>The DGs voltage during sensor effectiveness in DG1 is degraded from 100% to 80%; (<b>a</b>,<b>c</b>) the conventional PI-tuned; (<b>b</b>,<b>d</b>) the proposed control.</p> Full article ">Figure 4
<p>The DGs voltage during sensor effectiveness in DG<sub>5</sub> is degraded from 100% to 90%; (<b>a</b>,<b>c</b>) the conventional PI-tuned; (<b>b</b>,<b>d</b>) the proposed control.</p> Full article ">Figure 5
<p>The DGs voltage during successive sensor’s effectiveness degrades in DG<sub>2</sub> (80%) followed by DG<sub>4</sub> (90%); (<b>a</b>,<b>c</b>) the conventional PI-tuned; (<b>b</b>,<b>d</b>) the proposed control.</p> Full article ">Figure 6
<p>The DGs voltage during concurrent sensor’s effectiveness degrades in DG<sub>2</sub> (80%) and in DG<sub>4</sub> (90%) concurrently; (<b>a</b>,<b>c</b>) the conventional PI-tuned; (<b>b</b>,<b>d</b>) the proposed control.</p> Full article ">Figure 7
<p>The DGs voltage during concurrent sensor’s effectiveness degrades in DG<sub>2</sub> (60%) followed by DG<sub>4</sub> (50%) outside the design range using the proposed control; (<b>a</b>) DG<sub>1</sub>, DG<sub>2</sub>, and DG<sub>3</sub>; (<b>b</b>) DG<sub>4</sub>, DG<sub>5</sub>, and DG<sub>6</sub>.</p> Full article ">
20 pages, 6159 KiB  
Article
Condition Assessment of Natural Ester–Mineral Oil Mixture Due to Transformer Retrofilling via Sensing Dielectric Properties
by Hesham S. Karaman, Diaa-Eldin A. Mansour, Matti Lehtonen and Mohamed M. F. Darwish
Sensors 2023, 23(14), 6440; https://doi.org/10.3390/s23146440 - 16 Jul 2023
Cited by 12 | Viewed by 2892
Abstract
Mineral oil (MO) is the most popular insulating liquid that is used as an insulating and cooling medium in electrical power transformers. Indeed, for green energy and environmental protection requirements, many researchers introduced other oil types to study the various characteristics of alternative [...] Read more.
Mineral oil (MO) is the most popular insulating liquid that is used as an insulating and cooling medium in electrical power transformers. Indeed, for green energy and environmental protection requirements, many researchers introduced other oil types to study the various characteristics of alternative insulating oils using advanced diagnostic tools. In this regard, natural ester oil (NEO) can be considered an attractive substitute for MO. Although NEO has a high viscosity and high dielectric loss, it presents fire safety and environmental advantages over mineral oil. Therefore, the retrofilling of aged MO with fresh NEO is highly recommended for power transformers from an environmental viewpoint. In this study, two accelerated aging processes were applied to MO for 6 and 12 days to simulate MO in service for 6 and 12 years. Moreover, these aged oils were mixed with 80% and 90% fresh NEO. The dielectric strength, relative permittivity, and dissipation factor were sensed using a LCR meter and oil tester devices for all prepared samples to support the condition assessment performance of the oil mixtures. In addition, the electric field distribution was analyzed for a power transformer using the oil mixtures. Furthermore, the dynamic viscosity was measured for all insulating oil samples at different temperatures. From the obtained results, the sample obtained by mixing 90% natural ester oil with 10% mineral oil aged for 6 days is considered superior and achieves an improvement in dielectric strength and relative permittivity by approximately 43% and 48%, respectively, compared to fresh mineral oil. However, the dissipation factor was increased by approximately 20% but was at an acceptable limit. On the other hand, for the same oil sample, due to the higher molecular weight of the NEO, the viscosities of all mixtures were at a higher level than the mineral oil. Full article
(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)
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<p>Schematic diagram to illustrate the preparation process of the oil samples.</p> Full article ">Figure 2
<p>AC breakdown tester for liquid insulations.</p> Full article ">Figure 3
<p>LCR meter and the test cell used for the dielectric measurements.</p> Full article ">Figure 4
<p>Cumulative probability against breakdown voltage for fresh mineral oil (MO) and fresh natural ester oil (NEO).</p> Full article ">Figure 5
<p>Cumulative probability against breakdown voltage for several aged oil samples.</p> Full article ">Figure 6
<p>Relative permittivity versus frequency for fresh mineral oil (MO) and fresh natural ester oil (NEO).</p> Full article ">Figure 7
<p>Dissipation factor versus frequency for fresh mineral oil (MO) and fresh natural ester oil (NEO).</p> Full article ">Figure 8
<p>Relative permittivity versus frequency for several aged oil samples.</p> Full article ">Figure 9
<p>Dissipation factor versus frequency for several aged oil samples.</p> Full article ">Figure 10
<p>The variation in fresh insulating oil viscosity with temperature increment.</p> Full article ">Figure 11
<p>The variation in viscosity with temperature increment for all insulating aged oil samples.</p> Full article ">Figure 12
<p>Two-dimensional geometry of the studied model (all dimensions in mm).</p> Full article ">Figure 13
<p>View of geometry creation of the model on COMSOL Multiphysics with millimeter dimensions for points (a, b, c) and line sections (AA’ and BB’).</p> Full article ">Figure 14
<p>Model after the creation of the meshes for the finite element method solution (finer meshes style).</p> Full article ">Figure 15
<p>AC potential distribution inside all transformer parts based on FEM.</p> Full article ">Figure 16
<p>Electric field distribution inside the modeled transformer based on FEM.</p> Full article ">Figure 17
<p>Electric field inside the modeled transformer along (<b>a</b>) line AA’ and (<b>b</b>) line BB’.</p> Full article ">Figure 18
<p>Electric field inside the modeled transformer at points a, b, and c.</p> Full article ">
24 pages, 9756 KiB  
Article
Realization of a Test Tool for Diagnosis of Contact Resistance and Measurement of Selected Types of Conductive Materials
by Petr Kacor, Petr Bernat, Tomas Mlcak and Leopold Hrabovsky
Sensors 2023, 23(13), 5867; https://doi.org/10.3390/s23135867 - 24 Jun 2023
Cited by 5 | Viewed by 1922
Abstract
Contact connections in electrical machines and apparatus are important elements in the whole power supply network and a high level of reliability is expected there. Contact resistance is a fundamental criterion in the design of an electrical contact or contact system. The contact [...] Read more.
Contact connections in electrical machines and apparatus are important elements in the whole power supply network and a high level of reliability is expected there. Contact resistance is a fundamental criterion in the design of an electrical contact or contact system. The contact resistance should be as low as possible to minimize losses due to the current passage and the related heating of the contact connection. The value of the contact resistance depends on the material used, the value of the applied force, the type of contact, and, last but not least, the quality of the surface and chemical layers. In this paper, an initial diagnosis of the contact material is performed based on the determination of the sample’s specific resistivity by the four-wire method and the evaluation of the measurement uncertainty. The work is followed by the design of a testing device that uses crossed bars to measure the change in contact resistance as a function of the magnitude of the applied force. An analysis of the sample mounting method is performed here using FEM simulations of the current field and shows the interaction between the holder and the sample in terms of current line transfer. The proposed system is then used for experimental measurements of the material-dependent coefficient KC for verification of existing or newly developed materials in electrical engineering, where the values of the KC coefficient are not known. Finally, the paper also deals with the measurement of fritting voltage for individual contact pairs having surface quality corresponding to brushing. Full article
(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)
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<p>Examples of power apparatus contact systems: (<b>a</b>) Knife contact of fuse and fuse disconnector; (<b>b</b>) Low-voltage circuit breaker; (<b>c</b>) AC power motor contactor; (<b>d</b>) Old-style AC power motor contactor; (<b>e</b>) DC contactor without extinguishing chamber; (<b>f</b>) AC power switch.</p> Full article ">Figure 2
<p>Basic design layout of testing tool with initial CAD design (The red arrow shows the direction of the applied force F).</p> Full article ">Figure 3
<p>Detailed view of crossed bars and location of force gauge on testing tool (CAD design).</p> Full article ">Figure 4
<p>Assembled measuring system and detail of crossed bars in holders: (<b>a</b>) Overall view; (<b>b</b>) Front view; (<b>c</b>) Detail of crossed bars and sample holders.</p> Full article ">Figure 5
<p>FEM simulation of current line deformation due to weak holder surface connection.</p> Full article ">Figure 6
<p>Degree of deformation of current lines with increasing distance from the holder.</p> Full article ">Figure 7
<p>Selected simulations of current path entering the sample: (<b>a</b>) At the longitudinal edge of the holder only; (<b>b</b>) Across the entire contact area of the sample and the holder; (<b>c</b>) Across the entire contact area of the sample and the two-sided power supply of the holder.</p> Full article ">Figure 8
<p>Schematic diagram of the circuit for measuring the resistivity and practical realization.</p> Full article ">Figure 9
<p>Example of the surfaces of samples: (<b>a</b>) Copper; (<b>b</b>) Aluminum; (<b>c</b>) Brass; (<b>d</b>) Steel.</p> Full article ">Figure 10
<p>Time dependence of current, voltage and resistance during measurement, <span class="html-italic">F</span> = 5 N.</p> Full article ">Figure 11
<p>Experimental evaluation of the <span class="html-italic">K<sub>Cu</sub></span> coefficient and exponent <span class="html-italic">n</span> for copper bars (The blue cross represents the average value of the measured points).</p> Full article ">Figure 12
<p>Experimental evaluation of the <span class="html-italic">K<sub>Al</sub></span> coefficient and exponent <span class="html-italic">n</span> for aluminum bars (The blue cross represents the average value of the measured points).</p> Full article ">Figure 13
<p>Experimental evaluation of the <span class="html-italic">K<sub>CuZn</sub></span> coefficient and exponent <span class="html-italic">n</span> for brass bars (The blue cross represents the average value of the measured points).</p> Full article ">Figure 14
<p>Experimental evaluation of the <span class="html-italic">K<sub>Steel</sub></span> coefficient and exponent <span class="html-italic">n</span> for steel bars (The blue cross represents the average value of the measured points).</p> Full article ">Figure 15
<p>Current and voltage waveform with <span class="html-italic">V-I</span> characteristic for copper bars.</p> Full article ">Figure 16
<p>Current and voltage waveform with <span class="html-italic">V-I</span> characteristic for aluminum bars.</p> Full article ">Figure 17
<p>Current and voltage waveform with <span class="html-italic">V-I</span> characteristic for brass bars.</p> Full article ">Figure 18
<p>Current and voltage waveform with <span class="html-italic">V-I</span> characteristic for steel bars.</p> Full article ">
18 pages, 7306 KiB  
Article
An Improved Magnetic Field Method to Locate the Grounding Conductor
by Fan Yang, Songlin Liu, Yijun Lai, Jiayuan Hu and Shaohua Wang
Sensors 2023, 23(8), 3879; https://doi.org/10.3390/s23083879 - 11 Apr 2023
Cited by 2 | Viewed by 1649
Abstract
The location of the grounding grid conductors is critical for performing corrosion diagnosis and maintenance work. An improved magnetic field differential method to locate the unknown grounding grid based on truncation errors and the round-off errors analysis is presented in this paper. It [...] Read more.
The location of the grounding grid conductors is critical for performing corrosion diagnosis and maintenance work. An improved magnetic field differential method to locate the unknown grounding grid based on truncation errors and the round-off errors analysis is presented in this paper. It was proven that a different order of the magnetic field derivative can be used to determine the position of the grounding conductor according to the peak value of the derivative. Due to the accumulative error of higher differentiation, the truncation error and rounding error were used to analyze to accumulative error and to determine the optimal step size to measure and calculate the higher differentiation. The possible range and probability distribution of the two kinds of errors at each order are described, and the index of peak position error was derived, which can be used to locate the grounding conductor in the power substation. Full article
(This article belongs to the Special Issue Sensors and Fault Diagnostics in Power System)
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<p>Single conductor current-carrying model.</p> Full article ">Figure 2
<p>Curves of three shape functions.</p> Full article ">Figure 3
<p>Simple current-carrying grid model.</p> Full article ">Figure 4
<p>The distribution of the magnetic flux density mode at the plane <span class="html-italic">h</span> = 0.5 m.</p> Full article ">Figure 5
<p>The curve of magnetic flux density differential at the survey line <span class="html-italic">x</span> = 6 m: (<b>a</b>) the absolute value curve of magnetic flux density; (<b>b</b>) the second-order differential absolute value curve of the magnetic flux density; (<b>c</b>) the fourth-order differential absolute value curve of the magnetic flux density.</p> Full article ">Figure 6
<p>When <span class="html-italic">n</span> = 1~4, the distribution of <math display="inline"><semantics> <msubsup> <mi>p</mi> <mi>r</mi> <mrow> <mo>(</mo> <mi>n</mi> <mo>)</mo> </mrow> </msubsup> </semantics></math> on <span class="html-italic">E<sub>r</sub></span>.</p> Full article ">Figure 7
<p>The expectation and variance of the main peak position error at the three conductors (<span class="html-italic">d</span> = 0.05 m): (<b>a</b>) Expectation <span class="html-italic">M</span>(<span class="html-italic">d</span>) of the main peak position error at the second-order differential; (<b>b</b>) Variance <span class="html-italic">D</span>(<span class="html-italic">d</span>) of the main peak position error at the second-order differential; (<b>c</b>) Expectation <span class="html-italic">M</span>(<span class="html-italic">d</span>) of the main peak position error at the fourth-order differential; (<b>d</b>) Variance <span class="html-italic">D</span>(<span class="html-italic">d</span>) of the main peak position error at the fourth-order differential.</p> Full article ">Figure 8
<p>The expectation and variance of the main peak position error at the three conductors (<span class="html-italic">d</span> = 0.01~1 m): (<b>a</b>) Expectation <span class="html-italic">M</span>(<span class="html-italic">d</span>) of the main peak position error at the second-order differential; (<b>b</b>) Variance <span class="html-italic">D</span>(<span class="html-italic">d</span>) of the main peak position error at the second-order differential; (<b>c</b>) Expectation <span class="html-italic">M</span>(<span class="html-italic">d</span>) of the main peak position error at the fourth-order differential; (<b>d</b>) Variance <span class="html-italic">D</span>(<span class="html-italic">d</span>) of the main peak position error at the fourth-order differential.</p> Full article ">Figure 9
<p>The 500 kV substation field experiments: (<b>a</b>) schematic diagram of conductor positioning experiment; (<b>b</b>) field experiment photo.</p> Full article ">Figure 10
<p>Schematic diagram of survey lines and topology.</p> Full article ">Figure 11
<p>Normalized magnetic flux density absolute value measurement results when the measurement step size <span class="html-italic">d</span> = 0.02 m: (<b>a</b>) magnetic flux density; (<b>b</b>) second-order differential of the magnetic flux density; (<b>c</b>) fourth-order differential of the magnetic flux density.</p> Full article ">Figure 11 Cont.
<p>Normalized magnetic flux density absolute value measurement results when the measurement step size <span class="html-italic">d</span> = 0.02 m: (<b>a</b>) magnetic flux density; (<b>b</b>) second-order differential of the magnetic flux density; (<b>c</b>) fourth-order differential of the magnetic flux density.</p> Full article ">Figure 12
<p>Normalized magnetic flux density absolute value measurement results when the measurement step size <span class="html-italic">d</span> = 0.05 m: (<b>a</b>) magnetic flux density; (<b>b</b>) second-order differential of the magnetic flux density; (<b>c</b>) fourth-order differential of the magnetic flux density.</p> Full article ">
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