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Electronics
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Power System Fault Detection and Location Based on Machine Learning
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Power System Fault Detection and Location Based on Machine Learning

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 8342

Special Issue Editors

Prof. Dr. Azeddine Kaddouri

E-Mail Website
Guest Editor
Department of Electrical Engineering, University of Moncton, Moncton, NB E1A 3E9, Canada
Interests: power systems; machine learning; deep learning; fault detection and location; systems identification; nonlinear control
Dr. Nouha Bouchiba

E-Mail Website
Guest Editor
Faculty of Engineering, University of Moncton, Moncton, NB E1A 3E9, Canada
Interests: power systems; machine learning; deep learning; fault detection and location; artificial intelligence

Special Issue Information

Dear Colleagues,

The predictive abilities of machine learning approaches have led them to be widely used and increasingly applied in many fields. Since electrical energy is a vital component, the electricity service quality has become an important issue for researchers and electricity producer. Many efforts are currently being made to improve the electrical networks’ performance and protection. Machine learning techniques can be used to either predict failures or to detect and locate these after their occurrence.

In this Special Issue, entitled “Power system fault detection and location based on Machine Learning”, we invite authors to submit original research and review articles related to the abovementioned topics. The main objective of this Special Issue is to highlight recent advancements and improvements in the field of power system protection based on machine learning. Topics of interest for this Special Issue include, but are not limited to, the following:

  • Deep learning;
  • Machine learning;
  • Fault diagnosis;
  • Anomaly detection;
  • Predictive maintenance;
  • Fault detection;
  • Computer vision;
  • Machine monitoring.

Prof. Dr. Azeddine Kaddouri
Dr. Nouha Bouchiba
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. Electronics 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 2400 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

  • artificial intelligence
  • machine learning
  • fault detection and location
  • electrical power system protection

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

19 pages, 6086 KiB  
Article
Variational Mode Decomposition-Based Processing for Detection of Short-Circuited Turns in Transformers Using Vibration Signals and Machine Learning
by David Camarena-Martinez, Jose R. Huerta-Rosales, Juan P. Amezquita-Sanchez, David Granados-Lieberman, Juan C. Olivares-Galvan and Martin Valtierra-Rodriguez
Electronics 2024, 13(7), 1215; https://doi.org/10.3390/electronics13071215 - 26 Mar 2024
Cited by 3 | Viewed by 1260
Abstract
Transformers are key elements in electrical systems. Although they are robust machines, different faults can appear due to their inherent operating conditions, e.g., the presence of different electrical and mechanical stresses. Among the different elements that compound a transformer, the winding is one [...] Read more.
Transformers are key elements in electrical systems. Although they are robust machines, different faults can appear due to their inherent operating conditions, e.g., the presence of different electrical and mechanical stresses. Among the different elements that compound a transformer, the winding is one of the most vulnerable parts, where the damage of turn-to-turn short circuits is one of the most studied faults since low-level damage (i.e., a low number of short-circuited turns—SCTs) can lead to the overall fault of the transformer; therefore, early fault detection has become a fundamental task. In this regard, this paper presents a machine learning-based method to diagnose SCTs in the transformer windings by using their vibrational response. In general, the vibration signals are firstly decomposed by means of the variational mode decomposition method, where a comparison with the empirical mode decomposition (EMD) method and the ensemble empirical mode decomposition (EEMD) method is also carried out. Then, entropy, energy, and kurtosis indices are obtained from each decomposition as fault indicators, where both the combination of features and the dimensionality reduction by using the principal component analysis (PCA) method are analyzed for the global effectiveness improvement and the computational burden reduction. Finally, a pattern recognition algorithm based on artificial neural networks (ANNs) is used for automatic fault detection. The obtained results show 100% effectiveness in detecting seven fault conditions, i.e., 0 (healthy), 5, 10, 15, 20, 25, and 30 SCTs. Full article
(This article belongs to the Special Issue Power System Fault Detection and Location Based on Machine Learning)
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16 pages, 3561 KiB  
Article
An Efficient Noise Reduction Method for Power Transformer Voiceprint Detection Based on Poly-Phase Filtering and Complex Variational Modal Decomposition
by Hualiang Zhou, Lu Lu, Mingwei Shen, Zhantao Su and Yuxuan Huang
Electronics 2024, 13(2), 338; https://doi.org/10.3390/electronics13020338 - 12 Jan 2024
Cited by 1 | Viewed by 1691
Abstract
The transformer is a core component in power systems, and its reliable operation is crucial for the safety and stability of the power grid. Transformer faults can be diagnosed early using acoustic signals. However, effective acoustic features are often affected by complex environmental [...] Read more.
The transformer is a core component in power systems, and its reliable operation is crucial for the safety and stability of the power grid. Transformer faults can be diagnosed early using acoustic signals. However, effective acoustic features are often affected by complex environmental noise, which reduces the accuracy of fault identification. As a solution, this study proposes a poly-phase filtering (PF)-based noise reduction algorithm for complex variational mode decomposition (CVMD) of multiple acoustic sources in power transformers. The algorithm dissects the received signal from the power transformer into subbands, downsizing their sampling rates via PF. Subsequently, it independently targets noise reduction within these subbands, focusing on specific acoustic sources. Leveraging complex signal transformations, we extend the variational mode decomposition (VMD) to mitigate the field of complex signals and utilize the CVMD to reduce the noise of each acoustic source within each subband for every acoustic source. The experimental results reveal that the proposed method effectively separates and denoises the sound signal of transformer operation under the interference of multiple sound sources in the substation. Its powerful noise reduction ability, combined with minimal computational complexity, greatly improves the accuracy of transformer fault identification and the reliability of the system. Full article
(This article belongs to the Special Issue Power System Fault Detection and Location Based on Machine Learning)
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18 pages, 6684 KiB  
Article
Transmission Line Fault Detection and Classification Based on Improved YOLOv8s
by Hao Qiang, Zixin Tao, Bo Ye, Ruxue Yang and Weiyue Xu
Electronics 2023, 12(21), 4537; https://doi.org/10.3390/electronics12214537 - 4 Nov 2023
Cited by 14 | Viewed by 4750
Abstract
Transmission lines are an important component of the power grid, while complex natural conditions can cause fault and delayed maintenance, which makes it quite important to locate and collect the fault parts efficiently. The current unmanned aerial vehicle (UAV) inspection on transmission lines [...] Read more.
Transmission lines are an important component of the power grid, while complex natural conditions can cause fault and delayed maintenance, which makes it quite important to locate and collect the fault parts efficiently. The current unmanned aerial vehicle (UAV) inspection on transmission lines makes up for these problems to some extent. However, the complex background information contained in the images collected by power inspection and the existing deep learning methods are mostly highly sensitive to complex backgrounds, making the detection of multi-scale targets more difficult. Therefore, this article proposes an improved transmission line fault detection method based on YOLOv8s. The model not only detects defects in the insulators of power transmission lines but also adds the identification of birds’ nests, which makes the power inspection more comprehensive in detecting faults. This article uses Triplet Attention (TA) and an improved Bidirectional Feature Pyramid Network (BiFPN) to enhance the ability to extract discriminative features, enabling higher semantic information to be obtained after cross-layer fusion. Then, we introduce Wise-IoU (WIoU), a monotonic focus mechanism for cross-entropy, which enables the model to focus on difficult examples and improve the bounding box loss and classification loss. After deploying the improved method in the Win10 operating system and detecting insulator flashover, insulator broken, and nest faults, this article achieves a Precision of 92.1%, a Recall of 88.4%, and an mAP of 92.4%. Finally, we conclude that in complex background images, this method can not only detect insulator defects but also identify power tower birds’ nests. Full article
(This article belongs to the Special Issue Power System Fault Detection and Location Based on Machine Learning)
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