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26 pages, 5763 KiB  
Article
Incremental Pyraformer–Deep Canonical Correlation Analysis: A Novel Framework for Effective Fault Detection in Dynamic Nonlinear Processes
by Yucheng Ding, Yingfeng Zhang, Jianfeng Huang and Shitong Peng
Algorithms 2025, 18(3), 130; https://doi.org/10.3390/a18030130 - 25 Feb 2025
Viewed by 180
Abstract
Smart manufacturing systems aim to enhance the efficiency, adaptability, and reliability of industrial operations through advanced data-driven approaches. Achieving these objectives hinges on accurate fault detection and timely maintenance, especially in highly dynamic industrial environments. However, capturing nonlinear and temporal dependencies in dynamic [...] Read more.
Smart manufacturing systems aim to enhance the efficiency, adaptability, and reliability of industrial operations through advanced data-driven approaches. Achieving these objectives hinges on accurate fault detection and timely maintenance, especially in highly dynamic industrial environments. However, capturing nonlinear and temporal dependencies in dynamic nonlinear industrial processes poses significant challenges for traditional data-driven fault detection methods. To address these limitations, this study presents an Incremental Pyraformer–Deep Canonical Correlation Analysis (DCCA) framework that integrates the Pyramidal Attention Mechanism of the Pyraformer with the Broad Learning System for incremental learning in a DCCA basis. The Pyraformer model effectively captures multi-scale temporal features, while the BLS-based incremental learning mechanism adapts to evolving data without full retraining. The proposed framework enhances both spatial and temporal representation, enabling robust fault detection in high-dimensional and continuously changing industrial environments. Experimental validation on the Tennessee Eastman (TE) process, Continuous Stirred-Tank Reactor (CSTR) system, and injection molding process demonstrated superior detection performance. In the TE scenario, our framework achieved a 100% Fault Detection Rate with a 4.35% False Alarm Rate, surpassing DCCA variants. Similarly, in the CSTR case, the approach reached a perfect 100% Fault Detection Rate (FDR) and 3.48% False Alarm Rate (FAR), while in the injection molding process, it delivered a 97.02% FDR with 0% FAR. The findings underline the framework’s effectiveness in handling complex and dynamic data streams, thereby providing a powerful approach for real-time monitoring and proactive maintenance. Full article
(This article belongs to the Special Issue Optimization Methods for Advanced Manufacturing)
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<p>Outline of the present study.</p>
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<p>A schematic of Deep CCA.</p>
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<p>Framework of the proposed Incremental Pyraformer–DCCA Model.</p>
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<p>Pyraformer Module. N* denotes the number of repeated layers in the module.</p>
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<p>Pyramid Attention Module. Blue dashed lines: indicate self-attention within each node. Green dashed lines: represent connections between nodes at different levels (inter-scale connectivity). Orange dashed lines: show connections between nodes within the same level (intra-scale connectivity). Red dotted line: marks the longest information propagation path between any two nodes.</p>
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<p>Coarse-Scale Construction Module.</p>
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<p>Incremental Pyraformer–DCCA process monitoring framework. Different colors in the figure represent distinct process input variables.</p>
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<p>Flowchart of the TE process [<a href="#B19-algorithms-18-00130" class="html-bibr">19</a>]. The numbered labels represent different process measurement and control variables, categorized as follows: yellow for level, green for pressure, blue for flow rate, red for temperature, purple for composition, and gray for other parameters. Black solid squares indicate process measurement points for data collection, while gray solid squares indicate manipulated variables used for process optimization.</p>
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<p>Monitoring results for Fault IDV (6) in the TE process. (<b>a</b>) Linear CCA. (<b>b</b>) LSTM–DCCA. (<b>c</b>) Fedformer–DCCA. (<b>d</b>) Crossformer–DCCA. (<b>e</b>) Autoformer–DCCA. (<b>f</b>) GRU–DCCA. (<b>g</b>) Incremental Pyraformer–DCCA.</p>
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<p>Flowchart of the CSTR process [<a href="#B32-algorithms-18-00130" class="html-bibr">32</a>].</p>
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<p>Monitoring results for Fault 4 in the CSTR process. (<b>a</b>) Linear CCA. (<b>b</b>) LSTM–DCCA. (<b>c</b>) Fedformer–DCCA. (<b>d</b>) Crossformer–DCCA. (<b>e</b>) Autoformer–DCCA. (<b>f</b>) GRU–DCCA. (<b>g</b>) Incremental Pyraformer–DCCA.</p>
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<p>Monitoring results for Fault 1 in the mold injection process. (<b>a</b>) Linear CCA. (<b>b</b>) LSTM–DCCA. (<b>c</b>) Fedformer–DCCA. (<b>d</b>) Crossformer–DCCA. (<b>e</b>) Autoformer–DCCA. (<b>f</b>) GRU–DCCA. (<b>g</b>) Incremental Pyraformer–DCCA.</p>
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31 pages, 1630 KiB  
Article
A Model Transformation Method Based on Simulink/Stateflow for Validation of UML Statechart Diagrams
by Runfang Wu, Ye Du and Meihong Li
Mathematics 2025, 13(5), 724; https://doi.org/10.3390/math13050724 - 24 Feb 2025
Viewed by 127
Abstract
A model transformation method based on state refinement and semantic mapping is proposed to address the challenges of high modeling complexity and resource consumption in symbolic validation of industrial software requirements. First, a rule-based semantic mapping system is constructed through the explicit definition [...] Read more.
A model transformation method based on state refinement and semantic mapping is proposed to address the challenges of high modeling complexity and resource consumption in symbolic validation of industrial software requirements. First, a rule-based semantic mapping system is constructed through the explicit definition of element correspondence between statechart components and verification models, coupled with a composite state-level refinement strategy to structurally optimize model hierarchy. Second, an automated transformation algorithm is developed to bridge graphical modeling tools with formal verification environments, supported by quantitative evaluation metrics for mapping validity. To demonstrate its practical applicability, the methodology is systematically applied to railway infrastructure safety—specifically the railroad turnout control system—as a critical case study. The experimental implementation converts operational statecharts of turnout control logic into optimized NuSMV models. Not only did the models remain intact, but the state space was also effectively reduced through the optimization of the hierarchical structure. In the validation phase, the converted model is tested for robustness using the fault injection method, and boundary condition anomalies that are not explicitly stated in the requirement specification are successfully detected. The experimental results show that the validation model generated by this method has improved validation efficiency in the NuSMV tool, which is significantly better than the traditional conversion method. Full article
(This article belongs to the Special Issue Formal Methods in Computer Science: Theory and Applications)
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<p>The overall process of model-driven architecture.</p>
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<p>Framework diagram of model detection knowledge logic.</p>
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<p>Schematic diagram of real-time system formal verification.</p>
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<p>Decomposing a language into a syntax tree (AST).</p>
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<p>Process of model transform from Stateflow to SMV.</p>
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<p>Performing lexical and grammatical analysis on information through ANTLR.</p>
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<p>Converting based on a complete NuSMV specification template.</p>
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<p>State diagram of railway section route algorithm.</p>
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<p>Example of partial element transformation in the model.</p>
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<p>State diagram of railway turnout state transition.</p>
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<p>State diagram of railway turnout state transition.</p>
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<p>State Diagram of Railway Turnout State Transition. The red information flow shows thecomplete link from the initial state to the successful route determination.</p>
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<p>State diagram of railway turnout state transition. The red information flow shows thecomplete link from the initial state to the routeNotFound determination.</p>
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<p>State diagram of railway turnout state transition.</p>
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16 pages, 6369 KiB  
Article
Imaging of Groundwater Salinity and Seawater Intrusion in Subiya Peninsula, Northern Kuwait, Using Transient Electromagnetics
by Firyal Bou-Rabee, Pritam Yogeshwar, Sven Burberg, Bülent Tezkan, Michael Duane and Ismael M. Ibraheem
Water 2025, 17(5), 652; https://doi.org/10.3390/w17050652 - 24 Feb 2025
Viewed by 209
Abstract
This study investigates the presence and spatial extent of saline water and seawater intrusion in the Subiya Peninsula, Kuwait, a region designated for the establishment of the new Silk City. We collected transient electromagnetic (TEM) data at 63 stations using a coincident loop [...] Read more.
This study investigates the presence and spatial extent of saline water and seawater intrusion in the Subiya Peninsula, Kuwait, a region designated for the establishment of the new Silk City. We collected transient electromagnetic (TEM) data at 63 stations using a coincident loop setup on a regional, as well as local, scale. The data were analyzed through conventional 1D inversion techniques, including Occam and Levenberg–Marquardt methods, to create detailed resistivity models of the subsurface. Our findings indicate significant variations in groundwater salinity, with increased salinity towards the coast and partly decreasing resistivity with depth, suggesting a transition from brackish to saline water. In the northern region, close to the Abdali farms and Al-Raudhatain freshwater fields, groundwater remains fresher at greater depths, while in the south, saline conditions are encountered, occurring at shallower depths. Local scale analysis reveals potential saltwater intrusion pathways and highlighted geological features such as faults. A thorough understanding of the hydrogeological conditions is crucial, as saltwater injection for oil recovery is common in Kuwait, and may correlate with present-day seismic activity. These insights are critical for the sustainable planning and development of Silk City, emphasizing the necessity for further geophysical studies and borehole data to ensure construction safety and sustainable water supply management. This research provides a foundational understanding of the hydrogeological conditions essential for the successful implementation of the Silk City project and for groundwater management in northern Kuwait. Full article
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<p>(<b>a</b>) A surface geological map of Kuwait illustrating its primary structural features, alongside the distribution and ages of outcrops and sedimentary cover [<a href="#B26-water-17-00652" class="html-bibr">26</a>]. The focus area of this study, Subiya, is outlined by a black square with coordinates at the lower-left corner at 29°30′50.22″ N, 47°35′43.32″ E, and at the upper-right corner at 30°02′44.28″ N, 48°09′37.26″ E. (<b>b</b>) A stratigraphic column of the major geological formations, from the Miocene to Holocene, in the Kuwait Group [<a href="#B27-water-17-00652" class="html-bibr">27</a>]. The white numbers on the geologic map indicate outcrops: (1) Jal Az-Zor Escarpment, (2) Subiyah, (3) Ahmadi Quarry, (4) Khairan Ridges, and (5) Enjefa Beach.</p>
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<p>The TEM survey design in Subiya. (<b>a</b>) A TDS map, with values in g/L (after [<a href="#B38-water-17-00652" class="html-bibr">38</a>]). The location of the survey area is marked by a black square. The regional scale profile, with ~70 km length, is indicated by a black line, and is subdivided into three zones: A-A′ (Z1) in the north, A′-B′ (Z2) in the central part, and B′-B (Z3) in the south. (<b>b</b>) Soundings along the regional scale profile are marked as red dots. The background image shows the freshwater fields in blue and the Sabriya oil field in brown (after [<a href="#B38-water-17-00652" class="html-bibr">38</a>]). (<b>c</b>) Local scale soundings covering the tidal flats in the south of the Subiya Peninsula. The inferred faults are shown as black dashed lines, and the red GF as a red dashed outline.</p>
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<p>Exemplary TEM data for soundings A-01, A-08, A-13, and B-01. The locations are shown in <a href="#water-17-00652-f002" class="html-fig">Figure 2</a>. (<b>a</b>) Observed induced voltage decay curves. (<b>b</b>) Late-time apparent resistivity curves.</p>
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<p>TEM data plotted for one time channel, t = 1 × 10<sup>−3</sup> s, as late-time apparent resistivity for the regional scale transect. The transect is divided into AA′, A′B′, and B′B.</p>
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<p>One-dimensional models, together with data and model response, for soundings A-01 (<b>a</b>,<b>c</b>) and B-01 (<b>b</b>,<b>d</b>). The data fit chi is displayed in the respective legend. The LM models are marked in red, and the equivalent models (EQUI) in gray. Occam R1/R2 models are marked in green and blue. The DOI, derived based on the approach suggested by Spies [<a href="#B45-water-17-00652" class="html-bibr">45</a>], is indicated by black lines.</p>
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<p>A scatter plot of the modeled subsurface resistivity, depicted on (<b>a</b>) the satellite image, together with station/zone labels (top row), (<b>b</b>) the TDS map from [<a href="#B38-water-17-00652" class="html-bibr">38</a>], with values in g/L (middle row), and (<b>c</b>) the topography map from [<a href="#B46-water-17-00652" class="html-bibr">46</a>], with labeled altitude (bottom row). The elevations for the model scatter plots are −10 m, −30 m, and −60 m. The maps are derived from the Occam R1 results.</p>
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<p>The stitched and interpolated 1D resistivity section derived from the Occam R1 results. For improved visualization, the models are linearly interpolated along the profile. The black lines indicate the transitions between the different layers in the DF. The formations are labeled. The vertical gray/white lines separate the zones Z1, Z2, and Z3 (cf. <a href="#water-17-00652-f006" class="html-fig">Figure 6</a>). Note that Z3 is oriented NE–SW. The DOI is marked as the black shaded area.</p>
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<p>(<b>a</b>) A color-coded map of apparent resistivity late-time data for t = 1 ms. (<b>b</b>) An image of the outcrop of the red GF. (<b>c</b>) A stitched and interpolated 1D resistivity section derived from the Occam R1 results for the local scale transect (B-B’) at the Subiya coast. The inferred formations are labeled. Note that Z3 is oriented from SW to NE.</p>
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<p>(<b>a</b>) One-dimensional models for sounding A-01, (<b>b</b>) the derived fluid resistivity <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ρ</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> [Ωm], and (<b>c</b>) the TDS value [g/L] of the fluid. The values of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ρ</mi> </mrow> <mrow> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math> and TDS are shown for formation factors F = 3.1, 3.5, and 21. The yellow area indicates partially saturated soil; the blue area indicates fully saturated soil. Note that the DOI is at roughly −80 m elevation, and, therefore, is not plotted (see the DOI in <a href="#water-17-00652-f005" class="html-fig">Figure 5</a>a).</p>
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13 pages, 9723 KiB  
Article
Demagnetization Fault Diagnosis for PMSM Drive System with Dual Extended Kalman Filter
by Jiahan Wang, Chen Li and Zhanqing Zhou
World Electr. Veh. J. 2025, 16(2), 112; https://doi.org/10.3390/wevj16020112 - 18 Feb 2025
Viewed by 234
Abstract
Aiming at the irreversible demagnetization of permanent magnet synchronous motors (PMSMs) under extreme working conditions, a fault diagnosis method for permanent magnet demagnetization based on multi-parameter estimation is proposed in this paper. This scheme aims to provide technical support for enhancing the safety [...] Read more.
Aiming at the irreversible demagnetization of permanent magnet synchronous motors (PMSMs) under extreme working conditions, a fault diagnosis method for permanent magnet demagnetization based on multi-parameter estimation is proposed in this paper. This scheme aims to provide technical support for enhancing the safety and reliability of permanent magnet motor drive systems. In the proposed scheme, multiple operating states of the motor are acquired by injecting sinusoidal current signals into the d-axis, ensuring that the parameter estimation equation satisfies the full rank condition. Furthermore, the accurate dq-axis inductance parameters are obtained based on a recursive least square method. Subsequently, a dual extended Kalman filter is employed to acquire real-time permanent magnet flux linkage data of PMSMs, and the estimation data between the two algorithms are transferred to each other to eliminate the bias of permanent magnet flux estimation caused by a parameter mismatch. Finally, accurate evaluation of the remanence level of the rotor permanent magnet and demagnetization fault diagnosis can be achieved based on the obtained permanent magnet flux linkage parameters. The experimental results show that the relative estimation errors of the dq-axis inductance and permanent magnet flux linkage are within 5%, which can realize the effective diagnosis of demagnetization fault and high-precision condition monitoring of a permanent magnet health. Full article
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<p>Block diagram of the demagnetization fault diagnosis based on DEKF.</p>
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<p>Control block diagram of the proposed demagnetization fault diagnosis method.</p>
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<p>Experimental platform.</p>
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<p>Experimental waveforms of the stator current and its spectrum. (<b>a</b>) M1. (<b>b</b>) M2. (<b>c</b>) M3.</p>
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<p>Experimental results of the proposed diagnosis method in M1.</p>
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<p>Experimental results of the proposed diagnosis method in M2.</p>
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<p>Experimental results of the proposed diagnosis method in M3.</p>
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<p>Experimental results of the proposed diagnosis method at 1.2 N·m (<b>a</b>) M1 (<b>b</b>) M2, (<b>c</b>) M3.</p>
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27 pages, 4804 KiB  
Article
A Comparison of Reliability and Resource Utilization of Radiation Fault Tolerance Mechanisms in Spaceborne Electronic Systems
by Changhyeon Kim, Dongmin Lee and Jongwhoa Na
Aerospace 2025, 12(2), 152; https://doi.org/10.3390/aerospace12020152 - 17 Feb 2025
Viewed by 265
Abstract
The advent of the New Space Era has significantly accelerated the development of space equipment systems using commercial off-the-shelf components. Field Programmable Gate Arrays are increasingly favored for their ability to be easily modified, which substantially reduces both development time and costs. However, [...] Read more.
The advent of the New Space Era has significantly accelerated the development of space equipment systems using commercial off-the-shelf components. Field Programmable Gate Arrays are increasingly favored for their ability to be easily modified, which substantially reduces both development time and costs. However, their high susceptibility to space radiation poses a considerable risk of mission failure, potentially compromising system reliability in harsh space environments. To mitigate this vulnerability, the implementation of fault-tolerant mechanisms is essential. In this study, we applied eight distinct fault-tolerant mechanisms to various circuits and conducted a comparative analysis between two different categories: hardware redundancy and informational redundancy. This comparison was based on consistent criteria, specifically the Architectural Vulnerability Factor and resource consumption. Utilizing statistical fault injection tests and specialized software, we quantitatively measured structural vulnerability, power consumption, delay, and area. The results revealed that while the Hamming Code achieved the lowest structural vulnerability, it resulted in approximately fourfold increases in resource consumption. Conversely, Triple Modular Redundancy provided high reliability with relatively minimal resource usage. This research elucidates the trade-offs between reliability and resource overhead among different fault-tolerant mechanisms, highlighting the critical importance of selecting appropriate mechanisms based on system requirements to optimize the balance between reliability and resource utilization. Our analysis offers new insights essential for optimizing fault-tolerant mechanisms in space applications. Future work should explore more complex circuit architectures and diverse fault models to refine the selection criteria for fault-tolerant mechanisms tailored to real-world space missions. Full article
(This article belongs to the Special Issue On-Board Systems Design for Aerospace Vehicles (2nd Edition))
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<p>Number of launched LEO satellites (Data from the UCS Satellite Database [<a href="#B16-aerospace-12-00152" class="html-bibr">16</a>]).</p>
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<p>Single event upset occurrence process [<a href="#B22-aerospace-12-00152" class="html-bibr">22</a>].</p>
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<p>Classification of fault-tolerant mechanisms for electronic equipment [<a href="#B21-aerospace-12-00152" class="html-bibr">21</a>,<a href="#B24-aerospace-12-00152" class="html-bibr">24</a>,<a href="#B25-aerospace-12-00152" class="html-bibr">25</a>].</p>
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<p>Block diagram: TMR.</p>
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<p>Block diagram: DMR+.</p>
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<p>Block diagram: Hamming Code.</p>
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<p>Statistical fault injection test flowchart.</p>
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<p>AES-128 operation flow.</p>
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<p>Target circuit with FTM applied.</p>
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<p>Target circuit with TMR applied.</p>
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<p>Target circuit with DMR+ applied.</p>
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<p>Target circuit with Hamming Code applied.</p>
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<p><math display="inline"><semantics> <mrow> <mn>1</mn> <mo>−</mo> <msub> <mrow> <mi>A</mi> <mi>V</mi> <mi>F</mi> </mrow> <mrow> <mi>a</mi> <mi>v</mi> <mi>g</mi> </mrow> </msub> </mrow> </semantics></math> per target circuits (%).</p>
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<p>Power-delay-area product by target circuit.</p>
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22 pages, 9608 KiB  
Article
Research and Application of Geomechanics Using 3D Model of Deep Shale Gas in Luzhou Block, Sichuan Basin, Southwest China
by Ye Chen, Wenzhe Li, Xudong Wang, Yuan Wang, Li Fu, Pengcheng Wu and Zhiqiang Wang
Geosciences 2025, 15(2), 65; https://doi.org/10.3390/geosciences15020065 - 13 Feb 2025
Viewed by 308
Abstract
The deep shale gas resources of the Sichuan Basin are abundant and constitute an important component of China’s natural gas production. Complicated by fault zones and other geostructures, the in situ stress state of the deep shale gas reservoirs in the Luzhou block [...] Read more.
The deep shale gas resources of the Sichuan Basin are abundant and constitute an important component of China’s natural gas production. Complicated by fault zones and other geostructures, the in situ stress state of the deep shale gas reservoirs in the Luzhou block remains poorly understood. This study integrated multiple datasets, including acoustic logging, diagnostic fracture injection testing (DFIT), imaging logging, and laboratory stress measurements, for calibration and constraint. A high-precision geomechanical model of the Luzhou block was constructed using the finite element method. This model characterizes the geomechanical properties of the reservoir and explores its applications in optimizing shale gas horizontal well placement, drilling processes, and fracture design. The study findings indicate that the Longmaxi Formation reservoir demonstrates abnormally high pore pressure, with gradients ranging from 16.7 to 21.7 kPa/m. The predominant stress regime is strike-slip, with an overburden stress gradient of 25.5 kPa/m and a minimum horizontal principal stress gradient ranging from 18.8 to 24.5 kPa/m. Based on a three-dimensional geomechanical model, a quantitative delineation of areas conducive to density reduction and pressure control drilling was conducted, and field experiments were implemented in well Y65-X. Utilizing an optimized drilling fluid density of 1.85 g/cm3, the deviated horizontal section was completed in a single trip, resulting in a 67% reduction in the drilling cycle compared to adjacent wells. Similarly, the Y2-X well demonstrated a test daily output of 506,900 cubic meters following an optimization of segmentation clustering and fracturing parameters. Studies indicate that 3D geomechanical modeling, informed by multi-source data constraints, can markedly enhance model precision, and such geomechanical models and their results can effectively augment drilling operational efficiency, elevate single-well production, and are advantageous for development. Full article
(This article belongs to the Section Geomechanics)
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<p>Deep shale gas development area in southwestern Sichuan Basin [<a href="#B1-geosciences-15-00065" class="html-bibr">1</a>]: (<b>a</b>) location of well area, (<b>b</b>) structure and burial depth of the well area, and (<b>c</b>) lithological column.</p>
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<p>Porosity model of Y block with DFIT, FMI, and rock mechanics testing wells marked.</p>
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<p>Three-dimensional geomechanics modeling process.</p>
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<p>Compressional velocity–density relationship of Longmaxi reservoir in Luzhou block.</p>
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<p>FMI logs on a well and estimation of maximum horizontal stress: (<b>a</b>) wellbore breakouts on FMI logs on a well and (<b>b</b>) stress polygon.</p>
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<p>Three-dimensional geological model of Y block: (<b>a</b>) 3D geological model and (<b>b</b>) grid division.</p>
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<p>One-dimensional rock mechanical parameters of Y-1 well in block Y. Red dots indicate calibration data. Track 1 = measured depth, 2 = horizon, 3 = static Poisson’s ratio, 4 = static Young’s modulus, 5 = unconfined compressive strength.</p>
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<p>(<b>a</b>) Young’s modulus and (<b>b</b>) Poisson’s ratio of the Long111 sub-layer of the shale gas reservoir in block Y.</p>
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<p>Three-dimensional pore pressure and in situ stress check of shale gas wells in block Y.</p>
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<p>Pore pressure of Long111 sub-layer of shale gas reservoir in block Y.</p>
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<p>In situ field and horizontal stress difference of the Long111 sub-layer of shale gas reservoir in block Y. (<b>a</b>) Minimum horizontal principal stress; (<b>b</b>) maximum horizontal principal stress; and (<b>c</b>) horizontal stress difference.</p>
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<p>AΦ of the Long111 sub-layer of shale gas reservoir in block Y and its volume-weighted histograms.</p>
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<p>Volume-weighted histograms of horizontal stress difference. (<b>a</b>) Long111 sub-layer; (<b>b</b>) Long112 sub-layer.</p>
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<p>Safe window of mud for design well Y-5.</p>
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<p>Drilling quality of the H40 platform in block Y.</p>
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<p>Geomechanical parameters and segmented clustering scheme for Y2-X well.</p>
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15 pages, 10497 KiB  
Article
Application of the Fault Injection Method for the Verification of the Behavior of Multiple Unmanned Aircraft Systems Flying in Formation
by Iván Felipe Rodríguez, Ana María Ambrosio, Danny Stevens Traslaviña, Jaime Enrique Orduy and Pedro Fernando Melo
Drones 2025, 9(2), 133; https://doi.org/10.3390/drones9020133 - 12 Feb 2025
Viewed by 345
Abstract
This research aims to present an analysis of the behavior of multiple Remotely Piloted Aircraft Systems (multi-RPAS) flying in formation, a key aspect of advanced aerial mobility in the aerospace industry. This involves the positioning and relative distance in three dimensions (3D) of [...] Read more.
This research aims to present an analysis of the behavior of multiple Remotely Piloted Aircraft Systems (multi-RPAS) flying in formation, a key aspect of advanced aerial mobility in the aerospace industry. This involves the positioning and relative distance in three dimensions (3D) of two RPAS, taking into account their operational requirements and limitations, recognizing the operating states, and addressing potential situations encountered during formation flight. For this study, the “Conformance and Fault Injection—CoFI” methodology is employed. This methodology guides the user towards a comprehensive understanding of the system and enables the creation of a set of finite state machines representing the system’s behavior under study. Consequently, models and requirements for the behavior of multi-RPAS flying in formation are presented. By applying the CoFI methodology to inject faults into the operation and predict behavior in anomalous situations, both normal and abnormal behavior models, as well as the flight behavior requirements of the multi-RPAS formation, are outlined. This analysis is expected to facilitate the identification of formation flight behavior in multi-RPAS, thereby reducing associated operational risks. Full article
(This article belongs to the Special Issue Flight Control and Collision Avoidance of UAVs)
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<p>V Model [<a href="#B23-drones-09-00133" class="html-bibr">23</a>].</p>
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<p>Concept of Operations for Multi-RPAS. Modified from Si et al. in 2015 [<a href="#B28-drones-09-00133" class="html-bibr">28</a>].</p>
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<p>SUT of multi-RPAS flying in formation.</p>
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<p>State machine model for the nominal and specific operation of multi-RPAS flying in formation. (<b>a</b>) State machine model for nominal operation of multi-RPAS flying in formation; (<b>b</b>) State machine model for specific operation of multi-RPAS flying in formation.</p>
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<p>State machine model of stealth routes or untimely entries of multi-RPAS flying in formation.</p>
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<p>State machine model for fault tolerance of multi-RPAS flying in formation. Note: The blue color indicates the establishment of a safe mode for different scenarios.</p>
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<p>State machine model analysis of nominal operation of multi-RPAS flying in formation.</p>
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23 pages, 8051 KiB  
Article
Mechanism of Casing Deformation of Shale Gas Platform Wells in Luzhou Block Before Fracturing and Countermeasures for Prevention and Control
by Xiaojun Zhang, Jun Li, Yuxuan Zhao, Wei Cao, Wenbo Zhang, Zongyu Lu and Gonghui Liu
Processes 2025, 13(2), 479; https://doi.org/10.3390/pr13020479 - 10 Feb 2025
Viewed by 451
Abstract
The deep shale gas resources in the Luzhou area of the southern Sichuan Basin are abundant and have been identified as a key replacement field for natural gas development following the medium-to-shallow shale gas fields in Changning and Weiyuan. However, the frequent occurrence [...] Read more.
The deep shale gas resources in the Luzhou area of the southern Sichuan Basin are abundant and have been identified as a key replacement field for natural gas development following the medium-to-shallow shale gas fields in Changning and Weiyuan. However, the frequent occurrence of “pre-deformation without fracturing” in horizontal wells has significantly restricted large-scale production. In this study, the Lu203 and Yang101 well areas were analyzed to investigate the characteristics of casing deformation and the correlation with faults and natural fractures (fracture systems). A numerical model of multi-stage fracturing for platform wells was established based on microseismic event data, and the effects of fracturing on the stress and casing stress of adjacent wells were simulated and analyzed. The results indicate that the development of fracture systems is the primary cause of the “pre-deformation without fracturing” phenomenon. The propagation of fracturing fluid through fractures significantly increases the stress and loading around adjacent wells, causing casing stress to exceed its yield strength. To mitigate this issue, a method involving the injection of approximately 10 MPa of internal casing pressure into unfractured wells was proposed, effectively reducing the risk of casing deformation and failure. This provides technical support for the efficient development of deep shale gas. Full article
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<p>Statistical results of casing deformation wells in the Luzhou block.</p>
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<p>The correlation between casing deformation points and natural fractures in the Luzhou block. (<b>a</b>) Relationship between casing deformation points and natural fracture distances. (<b>b</b>) Relationship between casing deformation points and natural fracture angles.</p>
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<p>Relationship between fracture distribution and casing deformation point of platforms H1–H4 in the Lu203 well area.</p>
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<p>Schematic of casing deformation before fracturing in platform wells.</p>
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<p>Distribution characteristics of microseismic signals from 1 to 10 fracturing of Well X-6 in the Lu203 well area.</p>
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<p>Simplified processing map of microseismic event points for the X Platform.</p>
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<p>Finite element numerical modeling of platform well fracturing. (<b>a</b>) Finite element model. (<b>b</b>) Localized view of casing-cement formation assemblage. (<b>c</b>) Model cross-section. (<b>d</b>) Fracturing region.</p>
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<p>Finite element numerical modeling of platform well fracturing. (<b>a</b>) Finite element model. (<b>b</b>) Localized view of casing-cement formation assemblage. (<b>c</b>) Model cross-section. (<b>d</b>) Fracturing region.</p>
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<p>Characterization of pore pressure variations around unfractured wells. (<b>a</b>) Well X-4. (<b>b</b>) Well X-5.</p>
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<p>Characterization of maximum horizontal principal stress variations around unfractured wells. (<b>a</b>) Well X-4. (<b>b</b>) Well X-5.</p>
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<p>Characterization of casing stress changes in Well X-6. (<b>a</b>) Before fracturing. (<b>b</b>) After fracturing. (<b>c</b>) Casing stress changes at different fracturing stages.</p>
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<p>Characteristics of casing stress changes in Well X-4 at different fracturing stages.</p>
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<p>Characteristics of casing stress change in Well X-5 at different fracturing stages.</p>
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<p>Characteristics of casing circumferential stress distribution at different locations in Well X-5.</p>
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<p>Deformation displacement of Well X-5 casing in the direction of the maximum principal stress at different fracturing stages.</p>
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<p>Interpreted results of multi-arm well log measurement at 4591.7–4593.28 m in Well X-5.</p>
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<p>Prevention and control schematic.</p>
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<p>Characterization of casing stress changes in unfractured wells under pre-injection of internal pressure of 20 MPa.</p>
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<p>Characteristics of casing stress changes in unfractured wells under different internal pressure conditions.</p>
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11 pages, 3534 KiB  
Article
Arc Fault Location for Photovoltaic Distribution Cables Based on Time Reversal
by Jingang Su, Xingwang Huang, Peng Zhang, Xianhai Pang, Yuwei Liang, Longxiang Zhang, Yanfei Bai and Yan Li
Symmetry 2025, 17(2), 240; https://doi.org/10.3390/sym17020240 - 6 Feb 2025
Viewed by 448
Abstract
The direct current (DC) cable serves as the link for energy output in photovoltaic (PV) systems. Its degradation can cause arcs, which easily lead to fire accidents. Locating arc faults, however, is challenging. To cope with it, this paper proposes an arc location [...] Read more.
The direct current (DC) cable serves as the link for energy output in photovoltaic (PV) systems. Its degradation can cause arcs, which easily lead to fire accidents. Locating arc faults, however, is challenging. To cope with it, this paper proposes an arc location method based on time reversal. The method has been tried to locate system fault. However, its application in the arc fault location of photovoltaic systems is seldom discussed and needs further research. For this purpose, the voltage waveforms of an arc fault collected at one of the cable ends is reversed. This transformation derives a symmetrical arc fault signal. Afterwards, the reversed signal is injected back into the cable to trace the fault location, which is a symmetrical process of the arc fault signal travelling from its origin to the detection point. Utilizing the energy-focusing characteristics of time reversal, the position with the highest energy in the derived waveform corresponds to the actual fault location. To verify the proposed method, a DC arc fault test is performed to obtain the wave characteristics. The Paukert arc model is chosen based on the tested result. A PV system containing a DC cable with an arc fault is simulated with Simulink with the affecting factors, i.e., grounded resistance, cable length, fault location and sampling frequency. The simulated results demonstrate that the localization error is within 5% in the worst case. Full article
(This article belongs to the Special Issue Fault Diagnosis and Electronic Engineering in Symmetry)
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<p>Test setup for arc generation and measurement.</p>
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<p>Comparison of voltage measured waveforms and simulated waveforms. (<b>a</b>) Measured arc voltage signal. (<b>b</b>) Simulated arc voltage signal.</p>
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<p>Equivalent circuit of cable distribution parameters.</p>
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<p>Equivalent network for cable faults in photovoltaic systems.</p>
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<p>Arc faults in photovoltaic systems.</p>
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<p>Fault point selection flowchart.</p>
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<p>Normalized voltage data waveform before and after time inversion: (<b>a</b>) normalized voltage waveform before reversal; (<b>b</b>) normalized voltage waveform after reversal.</p>
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<p>The calculated current energy value at the expected fault points.</p>
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<p>Fault location accuracy with sampling frequency.</p>
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19 pages, 7641 KiB  
Article
Control Strategy for Asymmetric Faults on the Low-Frequency Side of a Sparse Modular Multilevel Converter
by Yuwei Sun, Shengce Wang, Chao Fu, Zelin Zhang, Guoliang Zhao, Yunfei Xu, Bao Liu and Chen Jia
Electronics 2025, 14(3), 426; https://doi.org/10.3390/electronics14030426 - 22 Jan 2025
Viewed by 452
Abstract
Sparse modular multilevel converters (SMMCs) are a new type of lightweight high-voltage large-power AC/AC converter that significantly reduces the number of components compared to modular multilevel matrix converters (M3Cs). This study proposes a fault ride through a control strategy for SMMC to address [...] Read more.
Sparse modular multilevel converters (SMMCs) are a new type of lightweight high-voltage large-power AC/AC converter that significantly reduces the number of components compared to modular multilevel matrix converters (M3Cs). This study proposes a fault ride through a control strategy for SMMC to address the issues of arm energy imbalances and valve-side overvoltage, which occur during asymmetric faults on the low-frequency side. First, we establish models of the energy deviation of the arms under asymmetric short-circuit faults on the low-frequency side of SMMC. We also study the influence mechanism of the control strategies on the arm energy imbalance during faults. On this basis, an arm energy balancing strategy based on zero-sequence voltage injections combined with AC voltage control is proposed; this can achieve arm energy balance and suppress the negative sequence current and overvoltage of the SMMC. Finally, we construct a simulation model of an offshore wind power low-frequency transmission system based on the SMMC. The simulation results show that the proposed energy balance strategy can realize the stable operation of the low-frequency transmission system (LFTS) under asymmetric faults on the low-frequency side, that the maximum capacitor voltage deviation during the fault does not exceed 10% and that capacitor voltage returns to normal 0.25 s after the fault occurs. Full article
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<p>Topology of the SMMC. (<b>a</b>) Single-phase SMMC topology; (<b>b</b>) Three-phase SMMC topology.</p>
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<p>Internal power distribution of the single-phase SMMC.</p>
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<p>Offshore low-frequency transmission system based on an SMMC.</p>
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<p>Control strategy of the SMMC under normal operational conditions.</p>
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<p>Energy deviation of the arm with a third harmonic injection under a single phase-to-ground fault. (<b>a</b>) Relationship between the third harmonic injection amplitude and energy deviation of the half-bridge arm; (<b>b</b>) relationship between the third harmonic injection amplitude and energy deviation of the full-bridge arm.</p>
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<p>Energy deviation of an arm with a zero-sequence injection under a single phase-to-ground fault. (<b>a</b>) Energy deviation of HBAs; (<b>b</b>) energy deviation of FBAs.</p>
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<p>Energy deviation of the arm during a single-phase fault within one cycle with a zero-sequence injection. (<b>a</b>) HB arm; (<b>b</b>) FB arm.</p>
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<p>Energy deviation of the arm with a zero-sequence injection under a phase-to-phase fault. (<b>a</b>) Energy deviation of HBAs; (<b>b</b>) energy deviation of FBAs.</p>
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<p>Control strategy of the equivalent converter of the windfarm.</p>
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<p>Low-frequency-side voltage vector relationship.</p>
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<p>Low-frequency-side overall control strategy of SMMC.</p>
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<p>Control diagram of switch of unfolder and arm modulation of SMMC.</p>
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<p>The waveforms of (<b>a</b>) <span class="html-italic">u</span><sub>gu</sub>, <span class="html-italic">u</span><sub>sa</sub>; (<b>b</b>) <span class="html-italic">u</span><sub>Hu</sub>; (<b>c</b>) <span class="html-italic">u</span><sub>ha</sub>; (<b>d</b>) <span class="html-italic">u</span><sub>Fa</sub>; (<b>e</b>) <span class="html-italic">u</span><sub>Fka1,4</sub>, <span class="html-italic">u</span><sub>Fka2,3</sub>; (<b>f</b>) <span class="html-italic">u</span><sub>Hka1,4</sub>, <span class="html-italic">u</span><sub>Hka2,3</sub>.</p>
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<p>The waveforms of (<b>a</b>) <span class="html-italic">u</span><sub>gu</sub>, <span class="html-italic">u</span><sub>sa</sub>; (<b>b</b>) <span class="html-italic">u</span><sub>Hu</sub>; (<b>c</b>) <span class="html-italic">u</span><sub>ha</sub>; (<b>d</b>) <span class="html-italic">u</span><sub>Fa</sub>; (<b>e</b>) <span class="html-italic">u</span><sub>Fka1,4</sub>, <span class="html-italic">u</span><sub>Fka2,3</sub>; (<b>f</b>) <span class="html-italic">u</span><sub>Hka1,4</sub>, <span class="html-italic">u</span><sub>Hka2,3</sub>.</p>
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<p>Simulation waveforms of the SMMC with the proposed control under a single-phase ground fault. (<b>a</b>) Average voltage of all sub-module capacitors; (<b>b</b>) low-frequency cable-side AC voltage; (<b>c</b>) low-frequency cable-side AC current.</p>
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<p>Average input power on the low-frequency side of SMMC under a single-phase-to-ground fault. (<b>a</b>) Without the zero-sequence voltage injection; (<b>b</b>) with the zero-sequence voltage injection.</p>
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<p>Half-bridge and full-bridge SM capacitor voltage of the SMMC under a single-phase to-ground fault. (<b>a</b>) Average capacitor voltage of all SMs in the HBA in the three phases; (<b>b</b>) average capacitor voltage of the HBA in each phase (without a zero-sequence voltage injection); (<b>c</b>) average capacitor voltage of the HBA in each phase (with zero-sequence voltage injection); (<b>d</b>) average capacitor voltage of all SMs in the FBA of the three phases; (<b>e</b>) average capacitor voltage of FBAs in each phase (without zero-sequence voltage injection); (<b>f</b>) average capacitor voltage of FBA in each phase (with zero-sequence voltage injection).</p>
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<p>Simulation waveforms of SMMC with the proposed control under a phase-to-phase fault. (<b>a</b>) Average capacitor voltage of all SMs; (<b>b</b>) AC voltage on the cable side of the low-frequency side; (<b>c</b>) AC current on the cable side of the low-frequency side.</p>
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<p>Average input power on the low-frequency side of SMMC under a phase-to-phase fault. (<b>a</b>) Without the zero-sequence voltage injection; (<b>b</b>) with the zero-sequence voltage injection.</p>
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<p>Half-bridge and full-bridge submodule capacitor voltage of the SMMC under a phase-to-phase fault. (<b>a</b>) Average capacitor voltage of all SMs in the HBA of the three phases; (<b>b</b>) average capacitor voltage of HBA in each phase (without zero-sequence voltage injection); (<b>c</b>) average capacitor voltage of HBA in each phase (with zero-sequence voltage injection); (<b>d</b>) average capacitor voltage of all SMs in the FBA of the three phases; (<b>e</b>) average capacitor voltage of FBAs in each phase (without zero-sequence voltage injection); (<b>f</b>) average capacitor voltage of FBA in each phase (with zero-sequence voltage injection).</p>
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24 pages, 2667 KiB  
Review
A Review of Adaptive Control Methods for Grid-Connected PV Inverters in Complex Distribution Systems
by Tiantian Cao, Zhengyang Ye, Qiong Wu, Xiaorong Wan, Jiangyun Wang and Dayi Li
Energies 2025, 18(3), 473; https://doi.org/10.3390/en18030473 - 21 Jan 2025
Viewed by 619
Abstract
With the growth of energy demand and the aggravation of environmental problems, solar photovoltaic (PV) power generation has become a research hotspot. As the key interface between new energy generation and power grids, a PV grid-connected inverter ensures that the power generated by [...] Read more.
With the growth of energy demand and the aggravation of environmental problems, solar photovoltaic (PV) power generation has become a research hotspot. As the key interface between new energy generation and power grids, a PV grid-connected inverter ensures that the power generated by new energy can be injected into the power grid in a stable and safe way, and its power grid adaptability has also received more and more close attention in the field of new energy research. This research focuses on the discussion of PV grid-connected inverters under the complex distribution network environment, introduces in detail the domestic and international standards and requirements on grid-connected inverter grid adaptability, and then analyzes in depth the impacts of the access point voltage changes, access point frequency changes, and access point harmonic changes on the inverters. In order to enhance the adaptability of grid-connected inverters under these abnormal conditions, this research systematically summarizes and concludes a series of inverter adaptive control strategies, which provide literature guidance to effectively reduce the probability of power system faults and improve the reliability of the power system. Finally, the future development direction of PV inverter technology is outlooked, pointing out that, with the increase in the proportion of PV power generation in the power system, PV inverters need to evolve gradually from adapting to the grid to supporting the grid and promote the transformation of PV power generation from the auxiliary power source to the main power source through the integration of PV and energy storage. Full article
(This article belongs to the Special Issue Progress and Challenges in Grid-Connected Inverters and Converters)
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<p>Global PV installed capacity from 2016 to 2023.</p>
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<p>Typical control structure diagram of grid-connected inverter.</p>
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<p>Influencing factors of grid adaptability of grid-connected inverters. The different colors in the figure represent the three phases, specifically Phase A, Phase B, and Phase C.</p>
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<p>IGBT junction temperature diagram: (<b>a</b>) the normal working current is 300 A; (<b>b</b>) the step current is 300 A.</p>
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<p>System structure block diagram of synchronous reference frame PLL.</p>
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<p>Control block diagram of DDSRF-PLL.</p>
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<p>Control block diagram of the DSOGI-PLL: (<b>a</b>) the implementation of the SOGI-PLL; and (<b>b</b>) the implementation of the SOGI stage.</p>
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<p>Bode diagrams for G<sub>SOGI1</sub> (s) and G<sub>SOGI2</sub> (s): (<b>a</b>) Bode plot of G<sub>SOGI1</sub> (s) for different values of K<sub>SOGI</sub>; and (<b>b</b>) Bode plot of G<sub>SOGI2</sub> (s) for different values of K<sub>SOGI</sub>.</p>
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<p>Interaction between inverters and power grid.</p>
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<p>Bode Plot: (<b>a</b>) the Bode plot of the coefficient from the inverter output voltage to the common bus voltage; and (<b>b</b>) the Bode plot of the coefficient from the grid voltage to the common bus voltage.</p>
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<p>The distribution of damping resistance on the LCL filter.</p>
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<p>Virtual resistance control algorithm equivalent structure diagram: (<b>a</b>) series resistance of the grid-side inductor; (<b>b</b>) parallel resistance of the grid-side inductor; (<b>c</b>) series resistance of the capacitor branch; and (<b>d</b>) parallel resistance of the capacitor branch.</p>
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<p>Grid-connected inverter control strategies: (<b>a</b>) virtual resistance control method; and (<b>b</b>) resonance peak suppression method of the composite notch controller.</p>
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<p>The Bode plot of the notch filter.</p>
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16 pages, 28400 KiB  
Article
Compliance Control of a Cable-Driven Space Manipulator Based on Force–Position Hybrid Drive Mode
by Runhui Xiang, Hejie Xu, Xinliang Li, Xiaojun Zhu, Deshan Meng and Wenfu Xu
Aerospace 2025, 12(1), 69; https://doi.org/10.3390/aerospace12010069 - 19 Jan 2025
Viewed by 981
Abstract
Multi-cable cooperative control is essential for cable-driven space manipulators to achieve in-orbit services such as fault spacecraft maintenance, fuel injection, on-orbit assembly, and orbital garbage removal. To prevent the cables from becoming slack or excessively tight, the force in each cable must be [...] Read more.
Multi-cable cooperative control is essential for cable-driven space manipulators to achieve in-orbit services such as fault spacecraft maintenance, fuel injection, on-orbit assembly, and orbital garbage removal. To prevent the cables from becoming slack or excessively tight, the force in each cable must be distributed appropriately. The force distribution among different cables requires real-time adjustments; otherwise, the system may become unstable. This paper proposes a compliance control method based on the force–position hybrid drive mode to address the challenges of multi-cable cooperative control. Firstly, the mapping relationship between the cable space and the joint space of the cable-driven space manipulator is established. Then, the force mapping relationship for this structure is derived. The control scheme categorizes the cables into two types: active-side cables and antagonistic-side cables. Position control and force control are implemented separately, significantly reducing the computational requirements and enhancing the overall performance of the control system. Finally, the feasibility of the proposed algorithm is demonstrated through simulations and compared with the PID control method. When tracking the same trajectory, the proposed method reduces the tracking error by 49.14% and the maximum force by 58.58% compared to the PID control method, effectively addressing the problem of force distribution in multi-rope coordinated control. Full article
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<p>Space manipulator system and equivalent simplified model.</p>
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<p>Multiple single-joint module structure.</p>
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<p>Multiple single-joint module simplified diagram.</p>
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<p>Schematic diagram of control system.</p>
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<p>Main structure of the manipulator’s Simscape model.</p>
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<p>Simulink model.</p>
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<p>(<b>a</b>) Tracking trajectory curve. (<b>b</b>) Tracking error.</p>
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<p>(<b>a</b>) Cable length variation. (<b>b</b>) Cable tension variation.</p>
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<p>(<b>a</b>) Joint angular velocity. (<b>b</b>) Joint torque.</p>
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<p>Joint angle tracking error comparison.</p>
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<p>Cable force comparison.</p>
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<p>(<b>a</b>) Drag torque. (<b>b</b>) Actual torque.</p>
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<p>(<b>a</b>) Drag trajectory. (<b>b</b>) Tracking error.</p>
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<p>(<b>a</b>) Cable length variation. (<b>b</b>) Cable force.</p>
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<p>Prototype of joint module.</p>
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<p>Joint angle, joint angular velocity, and joint angular acceleration.</p>
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<p>(<b>a</b>) Estimated joint torque. (<b>b</b>) Cable force.</p>
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<p>(<b>a</b>) Motor position. (<b>b</b>) Motor current.</p>
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22 pages, 5098 KiB  
Article
Optimization of Development Strategies and Injection-Production Parameters in a Fractured-Vuggy Carbonate Reservoir by Considering the Effect of Karst Patterns: Taking C Oilfield in the Tarim Basin as an Example
by Mengqin Li, Qi Wang, Chao Yao, Fangfang Chen, Qinghong Wang and Jing Zhang
Energies 2025, 18(2), 319; https://doi.org/10.3390/en18020319 - 13 Jan 2025
Viewed by 461
Abstract
The spatial structural characteristics of fractured-vuggy units vary greatly in different karst patterns, which significantly influence the study of remaining oil distribution patterns in ultra-deep fractured-vuggy reservoirs and the determination of the most efficient development strategies. However, few numerical simulation studies have focused [...] Read more.
The spatial structural characteristics of fractured-vuggy units vary greatly in different karst patterns, which significantly influence the study of remaining oil distribution patterns in ultra-deep fractured-vuggy reservoirs and the determination of the most efficient development strategies. However, few numerical simulation studies have focused on improving water and gas injection in fractured-vuggy reservoirs by considering the effect of karst patterns. By taking a typical fractured-vuggy reservoir in C oilfield in Tarim Basin, China as an example, the development dynamic characteristics of eight typical fractured-vuggy units in three different karst patterns are analyzed, and based on the newly proposed numerical simulation method of fluid vertical equilibrium, the residual oil reservoir distribution in different karst pattern fractured-vuggy units are studied, and the effects of fracture-vuggy karst patterns on the development characteristics, on the remaining oil morphology pattern, on the development strategies, and on the injection-production parameters are explored. This study shows that for different karst patterns fractured-vuggy units, the complexity of spatial structure, reserve scale, and oil-water relationship aggravates the heterogeneity of reservoirs and results in substantial differences in the development of dynamic patterns. In the northern facing karst fractured-vuggy units, there are two main types of remaining oil: well-spacing type and local-blocking type, and the reasonable development strategies are affected by reservoir morphology and the connectivity of structure patterns. Attic-type remaining oil mainly occurs in platform margin overlay and fault-controlled karst fractured-vuggy units. In the southern fault-controlled karst area, the remaining oil is mostly found along the upper part, and periodic gas injection or N2 huff-n-puff is recommended with priority for potential tapping. The fractured-vuggy karst patterns show a significant influence on the optimal level of injection-production parameters for improving the development of gas injection development strategies. The ideas of improving water injection and gas injection for fracture-vuggy reservoirs proposed in this paper also provide a good reference to further improve water control and increase oil production in other similar carbonate reservoirs. Full article
(This article belongs to the Section H: Geo-Energy)
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<p>Planar distribution karst zone of Lianglitage Formation in C oilfield.</p>
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<p>Vertical distribution of typical fractured-vuggy units under different karst patterns: (<b>a</b>) bedding karst; (<b>b</b>) platform margin overlay and fault-controlled karst; (<b>c</b>) fault-controlled karst.</p>
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<p>Dynamic curves of C3-O producer in bedding karst fractured-vuggy unit.</p>
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<p>Dynamic curve of C5-O producer in platform margin overlay-controlled fractured-vuggy unit.</p>
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<p>Dynamic curve of C7-O producer in fault-controlled fractured-vuggy unit.</p>
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<p>Fluid distribution of C3 fractured-vuggy unit after production history matching.</p>
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<p>Incremental oil recovery of C3 fractured-vuggy unit by gas or water injection.</p>
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<p>Fluid distribution evolution during WAG injection in C3 fractured-vuggy unit.</p>
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<p>Dynamic responses of C3 fractured-vuggy unit via gas or water injection.</p>
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<p>Fluid distribution of C5 fractured-vuggy unit after production history matching.</p>
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<p>Incremental oil recovery of C5 fractured-vuggy unit by gas or water injection.</p>
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<p>Fluid distribution evolution during N<sub>2</sub> huff-n-puff in C5 fractured-vuggy unit.</p>
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<p>Dynamic responses of C5 fractured-vuggy unit by gas or water injection.</p>
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<p>Fluid distribution of C7 fractured-vuggy unit after production history matching.</p>
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<p>Incremental oil recovery of C7 fractured-vuggy unit by gas or water injection.</p>
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<p>Fluid distribution evolution during periodic gas injection in C7 fractured-vuggy unit.</p>
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<p>Dynamic responses of C7 fractured-vuggy unit by gas or water injection.</p>
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<p>Influence of different injection-production parameters on incremental oil recovery of WAG injection in C3 fractured-vuggy unit.</p>
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<p>Influence of different injection-production parameters on incremental oil recovery by N<sub>2</sub> huff-n-puff in C5 fractured-vuggy unit.</p>
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<p>Influence of different injection-production parameters on incremental oil recovery of periodic gas injection in C7 fractured-vuggy unit.</p>
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25 pages, 7806 KiB  
Article
A Simple Single-Ended Post-Fault Location Technique for DC Lines Based on Controlled Re-Energizations
by Kumar Mahtani, José M. Guerrero, Julien Decroix and Carlos A. Platero
Electronics 2025, 14(2), 275; https://doi.org/10.3390/electronics14020275 - 11 Jan 2025
Viewed by 540
Abstract
Fault location in medium-voltage direct current (MVDC) systems is an essential yet underexplored area compared to high-voltage (HVDC) and low-voltage (LVDC) systems. MVDC systems, characterized by intermediate line lengths and fault resistances, as well as rapid fault clearance requirements, demand specialized solutions. This [...] Read more.
Fault location in medium-voltage direct current (MVDC) systems is an essential yet underexplored area compared to high-voltage (HVDC) and low-voltage (LVDC) systems. MVDC systems, characterized by intermediate line lengths and fault resistances, as well as rapid fault clearance requirements, demand specialized solutions. This paper proposes a novel single-ended, offline fault location method based on controlled re-energizations after fault clearance. This approach employs a switched grounding resistor and a bypass connection through the current-limiting inductor to extract fault parameters from the discharge curves of two re-energization cycles. By analyzing the time constants derived from these curves, the method estimates fault location and resistance with high accuracy. The proposed method eliminates the need for additional active injection sources and circuit breaker modifications, ensuring seamless integration into existing MVDC infrastructure. Furthermore, the method avoids inter-terminal communication delays and sampling delays before fault clearance. Validation through electromagnetic transient simulations demonstrates fault location errors below 5% for fault resistances up to 50 Ω. Results show that the method performs better for faults farther from the active terminal, with the higher errors seen for short distances and elevated resistances. The proposed technique offers a robust and practical solution for post-fault location in DC lines. Full article
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Figure 1
<p>Multi-terminal fully selective DC network based on bipolar links.</p>
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<p>An electrical diagram of the proposed configuration.</p>
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<p>Re-energization switching sequence: (<b>a</b>) first energization, charging; (<b>b</b>) first energization, discharging; (<b>c</b>) second energization, charging; (<b>d</b>) second energization, discharging.</p>
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<p>Equivalent circuit during discharging of first energization (P-G fault).</p>
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<p>Equivalent circuit during discharging of second energization (P-G fault).</p>
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<p>Equivalent circuit during discharging of first energization (P-P fault).</p>
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<p>Equivalent circuit during discharging of second energization (P-P fault).</p>
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<p>Proposed operation flowchart.</p>
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<p>Fault characterization process (obtention of <span class="html-italic">x</span> and <span class="html-italic">R<sub>f</sub></span>).</p>
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<p>A representation of the EMTDC simulation model.</p>
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<p>Current evolution from before the fault until the end of the fault characterization (P-G fault at location <span class="html-italic">x</span> = 50% with fault resistance <span class="html-italic">R<sub>f</sub></span> = 1 Ω).</p>
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<p>The current evolution throughout the fault characterization process (P-G fault at location <span class="html-italic">x</span> = 50% with fault resistance <span class="html-italic">R<sub>f</sub></span> = 1 Ω).</p>
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<p>Current evolution throughout the circuit discharging cycles for P-G faults. (<b>a</b>) <span class="html-italic">x</span> = 1%, first re-energization; (<b>b</b>) <span class="html-italic">x</span> = 1%, second re-energization; (<b>c</b>) <span class="html-italic">x</span> = 25%, first re-energization; (<b>d</b>) <span class="html-italic">x</span> = 25%, second re-energization; (<b>e</b>) <span class="html-italic">x</span> = 50%, first re-energization; (<b>f</b>) <span class="html-italic">x</span> = 50%, second re-energization; (<b>g</b>) <span class="html-italic">x</span> = 75%, first re-energization; (<b>h</b>) <span class="html-italic">x</span> = 75%, second re-energization; (<b>i</b>) <span class="html-italic">x</span> = 100%, first re-energization; (<b>j</b>) <span class="html-italic">x</span> = 100%, second re-energization.</p>
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<p>Current evolution throughout the circuit discharging cycles for P-G faults. (<b>a</b>) <span class="html-italic">x</span> = 1%, first re-energization; (<b>b</b>) <span class="html-italic">x</span> = 1%, second re-energization; (<b>c</b>) <span class="html-italic">x</span> = 25%, first re-energization; (<b>d</b>) <span class="html-italic">x</span> = 25%, second re-energization; (<b>e</b>) <span class="html-italic">x</span> = 50%, first re-energization; (<b>f</b>) <span class="html-italic">x</span> = 50%, second re-energization; (<b>g</b>) <span class="html-italic">x</span> = 75%, first re-energization; (<b>h</b>) <span class="html-italic">x</span> = 75%, second re-energization; (<b>i</b>) <span class="html-italic">x</span> = 100%, first re-energization; (<b>j</b>) <span class="html-italic">x</span> = 100%, second re-energization.</p>
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<p>Current evolution throughout the circuit discharging cycles, for P-P faults. (<b>a</b>) <span class="html-italic">x</span> = 1%, first re-energization; (<b>b</b>) <span class="html-italic">x</span> = 1%, second re-energization; (<b>c</b>) <span class="html-italic">x</span> = 25%, first re-energization; (<b>d</b>) <span class="html-italic">x</span> = 25%, second re-energization; (<b>e</b>) <span class="html-italic">x</span> = 50%, first re-energization; (<b>f</b>) <span class="html-italic">x</span> = 50%, second re-energization; (<b>g</b>) <span class="html-italic">x</span> = 75%, first re-energization; (<b>h</b>) <span class="html-italic">x</span> = 75%, second re-energization; (<b>i</b>) <span class="html-italic">x</span> = 100%, first re-energization; (<b>j</b>) <span class="html-italic">x</span> = 100%, second re-energization.</p>
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<p>Current evolution throughout the circuit discharging cycles, for P-P faults. (<b>a</b>) <span class="html-italic">x</span> = 1%, first re-energization; (<b>b</b>) <span class="html-italic">x</span> = 1%, second re-energization; (<b>c</b>) <span class="html-italic">x</span> = 25%, first re-energization; (<b>d</b>) <span class="html-italic">x</span> = 25%, second re-energization; (<b>e</b>) <span class="html-italic">x</span> = 50%, first re-energization; (<b>f</b>) <span class="html-italic">x</span> = 50%, second re-energization; (<b>g</b>) <span class="html-italic">x</span> = 75%, first re-energization; (<b>h</b>) <span class="html-italic">x</span> = 75%, second re-energization; (<b>i</b>) <span class="html-italic">x</span> = 100%, first re-energization; (<b>j</b>) <span class="html-italic">x</span> = 100%, second re-energization.</p>
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<p>Time constants as function of the fault location (<span class="html-italic">x</span>) and fault resistance (<span class="html-italic">R<sub>f</sub></span>). (<b>a</b>) P-G faults, first re-energization (<span class="html-italic">τ</span><sub>1</sub>); (<b>b</b>) P-G faults, second re-energization (<span class="html-italic">τ</span><sub>2</sub>); (<b>c</b>) P-P faults, first re-energization (<span class="html-italic">τ</span><sub>1</sub>); (<b>d</b>) P-P faults, second re-energization (<span class="html-italic">τ</span><sub>2</sub>).</p>
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<p>Errors as a function of the fault location (<span class="html-italic">x</span>) and fault resistance (<span class="html-italic">R<sub>f</sub></span>). (<b>a</b>) P-G faults; (<b>b</b>) P-G faults.</p>
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12 pages, 6006 KiB  
Article
Relay Protection Device Reliability Assessment Through Radiation, Fault Injection and Fault Tree Analysis
by Hualiang Zhou, Hao Yu, Zhiyang Zou, Zhantao Su, Zheng Xu, Weitao Yang and Chaohui He
Micromachines 2025, 16(1), 69; https://doi.org/10.3390/mi16010069 - 8 Jan 2025
Viewed by 595
Abstract
Relay protection devices must operate continuously throughout the year without anomalies. With the integration of advanced technology and process chips in secondary equipment, new risks need to be addressed to ensure the reliability of these relay protection devices. One such risk is the [...] Read more.
Relay protection devices must operate continuously throughout the year without anomalies. With the integration of advanced technology and process chips in secondary equipment, new risks need to be addressed to ensure the reliability of these relay protection devices. One such risk is the impact of α-particles inducing single event effects (SEEs) on the secondary equipment. To date, there has been limited assessment of the effects of α-particles on relay protection devices from a system perspective. This study evaluates the impact of SEE on relay protection devices through a Monte Carlo simulation, which is verified by α-particle radiation, fault injection, and fault tree analysis. It discusses the influence of SEEs with and without hardening measures in place. Additionally, this study examines the soft error probability when the target processor runs both general workloads and specific application workloads. The current research proposes a low-cost and effective reliability assessment method for secondary equipment considering single event effects. The findings provide new insights for the enhancement of future electric power grid systems. Full article
(This article belongs to the Special Issue The 15th Anniversary of Micromachines)
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Figure 1

Figure 1
<p>The α-particles impinging from different deflection angles: (<b>a</b>) from 0°; (<b>b</b>) from 15°; (<b>c</b>) from 30°; (<b>d</b>) from 45°; (<b>e</b>) from 60°; (<b>f</b>) from 75°.</p>
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<p>The distribution of different MCUs at the same incidence of particle striking, in where vertical is the bit offset and horizontal is the word offset: (<b>a</b>) 10-bit upset, (<b>b</b>) 11-bit upset.</p>
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<p>Different deflection angles of impinging α-particles were set in the fault injection testing; four abnormal results were detected: (<b>a</b>) from 30°; (<b>b</b>) from 60°; (<b>c</b>) from 90°.</p>
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<p>The number of abnormal results caused by DCUs and MCUs varied under different conditions.</p>
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<p>Different modules share the same program space in the device. (<b>Top</b>) is the photo of the device, and the (<b>bottom</b>) is the specific workloads.</p>
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<p>The fault tree of a secondary equipment system comprising two main components, including a management CPU core and a real-time CPU core.</p>
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