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Advanced Fault Monitoring for Smart Power Systems
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Advanced Fault Monitoring for Smart Power Systems

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

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 4251

Special Issue Editor

Dr. Moussa Kafal

E-Mail Website
Guest Editor
1. Institut Polytechnique des Sciences Avancées, 94200 Ivry-sur-Seine, France
2. Nexans France, S.A., 62225 Calais, France
Interests: power systems; fault detection; power networks; smart grids

Special Issue Information

Dear Colleagues,

As the integration of renewable energy sources and advanced technologies continues to transform power systems, ensuring their reliability and resilience becomes increasingly crucial. By combining the capabilities of smart grids and fault analytics, researchers are pioneering novel techniques, accurate diagnosis, and effective mitigation strategies. Moreover, the integration of artificial intelligence and IoT devices is revolutionizing fault monitoring practices in advanced power systems, enabling proactive maintenance and minimizing downtime.

This Special Issue, “Advanced Fault Monitoring for Smart Power Systems”, focuses on innovative technologies aimed at enhancing power network reliability. A wide range of topics on fault mitigation and detection, including data-driven fault detection algorithms, machine learning approaches, sensor technologies, and real-time monitoring systems, are covered in this Special Issue for the readers.

Dr. Moussa Kafal
Guest Editor

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

  • power system resilience

  • fault detection and mitigation
  • cybersecurity and smart grids
  • AI and machine learning for power systems
  • IoT and sensor technologies for power networks
  • big data analytics
  • real-time monitoring
  • proactive and predictive maintenance
  • blockchain for power systems
  • renewable energy integration

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Published Papers (4 papers)

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Research

17 pages, 2134 KiB  
Article
A New Approach to Electrical Fault Detection in Urban Structures Using Dynamic Programming and Optimized Support Vector Machines
by Reynaldo Villarreal, Sindy Chamorro-Solano, Yolanda Vega-Sampayo, Carlos Alejandro Espejo, Steffen Cantillo, Luis Gaviria, Jheifer Paez, Carlos Ochoa, Silvia Moreno, Claudet Polo, Roberto Pestana-Nobles and Camilo Montoya
Sensors 2025, 25(7), 2215; https://doi.org/10.3390/s25072215 - 1 Apr 2025
Viewed by 346
Abstract
Electrical power systems are crucial, yet vulnerable, due to their complex and interconnected nature, necessitating effective fault detection and diagnostics to ensure stability and prevent disruptions. Advances in artificial intelligence (AI) and the Internet of Things (IoT) have transformed the ability to identify [...] Read more.
Electrical power systems are crucial, yet vulnerable, due to their complex and interconnected nature, necessitating effective fault detection and diagnostics to ensure stability and prevent disruptions. Advances in artificial intelligence (AI) and the Internet of Things (IoT) have transformed the ability to identify and resolve electrical system problems efficiently. Electrical systems operate at various scales, ranging from individual households to large-scale regional grids. In this study, we focus on medium-scale urban infrastructures. These environments present unique electrical challenges, such as phase imbalances and transient voltage fluctuations, which require robust fault detection mechanisms. This work investigates the use of AI with dynamic programming and a support vector machine (SVM) to improve fault detection. The data collected from voltage measurements in urban office buildings with smart meters over a period of six weeks was used to develop an AI model, demonstrating its applicability to similar urban infrastructures. This model achieved high accuracy in detecting system failures, identifying them with a performance greater than 99%, highlighting the potential of smart sensing technologies combined with AI to improve urban infrastructure management. The integration of smart sensors and advanced data analytics significantly increases the reliability and efficiency of energy systems, promoting sustainable and resilient urban environments. Full article
(This article belongs to the Special Issue Advanced Fault Monitoring for Smart Power Systems)
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18 pages, 1308 KiB  
Article
Kolmogorov–Arnold Network in the Fault Diagnosis of Oil-Immersed Power Transformers
by Thales W. Cabral, Felippe V. Gomes, Eduardo R. de Lima, José C. S. S. Filho and Luís G. P. Meloni
Sensors 2024, 24(23), 7585; https://doi.org/10.3390/s24237585 - 27 Nov 2024
Cited by 1 | Viewed by 1056
Abstract
Instabilities in energy supply caused by equipment failures, particularly in power transformers, can significantly impact efficiency and lead to shutdowns, which can affect the population. To address this, researchers have developed fault diagnosis strategies for oil-immersed power transformers using dissolved gas analysis (DGA) [...] Read more.
Instabilities in energy supply caused by equipment failures, particularly in power transformers, can significantly impact efficiency and lead to shutdowns, which can affect the population. To address this, researchers have developed fault diagnosis strategies for oil-immersed power transformers using dissolved gas analysis (DGA) to enhance reliability and environmental responsibility. However, the fault diagnosis of oil-immersed power transformers has not been exhaustively investigated. There are gaps related to real scenarios with imbalanced datasets, such as the reliability and robustness of fault diagnosis modules. Strategies with more robust models increase the overall performance of the entire system. To address this issue, we propose a novel approach based on Kolmogorov–Arnold Network (KAN) for the fault diagnosis of power transformers. Our work is the first to employ a dedicated KAN in an imbalanced data real-world scenario, named KANDiag, while also applying the synthetic minority based on probabilistic distribution (SyMProD) technique for balancing the data in the fault diagnosis. Our findings reveal that this pioneering employment of KANDiag achieved the minimal value of Hamming loss—0.0323—which minimized the classification error, guaranteeing enhanced reliability for the whole system. This ground-breaking implementation of KANDiag achieved the highest value of weighted average F1-Score—96.8455%—ensuring the solidity of the approach in the real imbalanced data scenario. In addition, KANDiag gave the highest value for accuracy—96.7728%—demonstrating the robustness of the entire system. Some key outcomes revealed gains of 68.61 percentage points for KANDiag in the fault diagnosis. These advancements emphasize the efficiency and robustness of the proposed system. Full article
(This article belongs to the Special Issue Advanced Fault Monitoring for Smart Power Systems)
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15 pages, 8890 KiB  
Article
Research on Lightweight Method of Insulator Target Detection Based on Improved SSD
by Bing Zeng, Yu Zhou, Dilin He, Zhihao Zhou, Shitao Hao, Kexin Yi, Zhilong Li, Wenhua Zhang and Yunmin Xie
Sensors 2024, 24(18), 5910; https://doi.org/10.3390/s24185910 - 12 Sep 2024
Cited by 1 | Viewed by 972
Abstract
Aiming at the problems of a large volume, slow processing speed, and difficult deployment in the edge terminal, this paper proposes a lightweight insulator detection algorithm based on an improved SSD. Firstly, the original feature extraction network VGG-16 is replaced by a lightweight [...] Read more.
Aiming at the problems of a large volume, slow processing speed, and difficult deployment in the edge terminal, this paper proposes a lightweight insulator detection algorithm based on an improved SSD. Firstly, the original feature extraction network VGG-16 is replaced by a lightweight Ghost Module network to initially achieve the lightweight model. A Feature Pyramid structure and Feature Pyramid Network (FPN+PAN) are integrated into the Neck part and a Simplified Spatial Pyramid Pooling Fast (SimSPPF) module is introduced to realize the integration of local features and global features. Secondly, multiple Spatial and Channel Squeeze-and-Excitation (scSE) attention mechanisms are introduced in the Neck part to make the model pay more attention to the channels containing important feature information. The original six detection heads are reduced to four to improve the inference speed of the network. In order to improve the recognition performance of occluded and overlapping targets, DIoU-NMS was used to replace the original non-maximum suppression (NMS). Furthermore, the channel pruning strategy is used to reduce the unimportant weight matrix of the model, and the knowledge distillation strategy is used to fine-adjust the network model after pruning, so as to ensure the detection accuracy. The experimental results show that the parameter number of the proposed model is reduced from 26.15 M to 0.61 M, the computational load is reduced from 118.95 G to 1.49 G, and the mAP is increased from 96.8% to 98%. Compared with other models, the proposed model not only guarantees the detection accuracy of the algorithm, but also greatly reduces the model volume, which provides support for the realization of visible light insulator target detection based on edge intelligence. Full article
(This article belongs to the Special Issue Advanced Fault Monitoring for Smart Power Systems)
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18 pages, 7824 KiB  
Article
Contrastive-Active Transfer Learning-Based Real-Time Adaptive Assessment Method for Power System Transient Stability
by Jinman Zhao, Xiaoqing Han, Chengmin Wang, Jing Yang and Gengwu Zhang
Sensors 2024, 24(15), 5052; https://doi.org/10.3390/s24155052 - 4 Aug 2024
Cited by 1 | Viewed by 1462
Abstract
The transient stability assessment based on machine learning faces challenges such as sample data imbalance and poor generalization. To address these problems, this paper proposes an intelligent enhancement method for real-time adaptive assessment of transient stability. In the offline phase, a convolutional neural [...] Read more.
The transient stability assessment based on machine learning faces challenges such as sample data imbalance and poor generalization. To address these problems, this paper proposes an intelligent enhancement method for real-time adaptive assessment of transient stability. In the offline phase, a convolutional neural network (CNN) is used as the base classifier. A model training method based on contrastive learning is introduced, aiming to increase the spatial distance between positive and negative samples in the mapping space. This approach effectively improves the accuracy of the model in recognizing unbalanced samples. In the online phase, when real data with different distribution characteristics from the offline data are encountered, an active transfer strategy is employed to update the model. New system samples are obtained through instance transfer from the original system, and an active sampling strategy considering uncertainty is designed to continuously select high-value samples from the new system for labeling. The model parameters are then updated by fine-tuning. This approach drastically reduces the cost of updating while improving the model’s adaptability. Experiments on the IEEE39-node system verify the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Advanced Fault Monitoring for Smart Power Systems)
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