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Power System Protection and Fault Location Technologies in Smart Grid...
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Power System Protection and Fault Location Technologies in Smart Grid Systems

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 9961

Special Issue Editors

Dr. Shuiqing Xu

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, China
Interests: fault diagnosis; fault estimation; their applications in power electronics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Darong Huang

E-Mail Website
Guest Editor
School of Artificial Intelligence, Anhui University, Hefei 230039, China
Interests: fault diagnosis and information processing
Prof. Dr. Ke Zhang

E-Mail Website
Guest Editor
School of Automation, Chongqing University, Chongqing 440004, China
Interests: power system fault diagnosis
Dr. Zheren Zhu

E-Mail Website
Guest Editor
School of Automation, Zhejiang University, Hangzhou 230039, China
Interests: fault diagnosis and safety control

Special Issue Information

Dear Colleagues,

In the past few decades, the global demand for power systems has been growing steadily. In terms of practical applications, how to ensure the efficient and stable operation of power systems has always been a key problem in power system development. Therefore, many scholars have proposed fault diagnosis methods for power system derived from data-driven and model methods. However, with access to large-scale sources of new energy and the use of UHV transmission technology, in addition to many other factors, it is difficult for the existing data-driven methods and model methods to meet the real-time online monitoring of power system faults. There is an urgent need for new fault detection and location technology to be developed to ensure the efficient, safe, and stable operation of power systems. This Special Issue intends to collect advanced and updated designs of fault diagnosis methods for power system from both academia and industry. Topics of interest to this Special Issue include but are not limited to:

  • Performance evaluation and optimization of power systems and electrical equipment;
  • Online monitoring and fault diagnosis for power systems and electrical equipment;
  • Fault-tolerant control and performance recovery of power systems and electrical equipment;
  • Lifecycle management and remaining useful life prediction of electrical equipment;
  • Fault identification and prognosis for power systems and electrical equipment;
  • Fault simulation and injection for power systems.

Dr. Shuiqing Xu
Prof. Dr. Darong Huang
Prof. Dr. Ke Zhang
Dr. Zheren Zhu
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

  • fault detection
  • fault location
  • system protection
  • fault-tolerant control

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Published Papers (7 papers)

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Research

16 pages, 6382 KiB  
Article
Method for Fault Diagnosis of Track Circuits Based on a Time–Frequency Intelligent Network
by Feitong Peng and Tangzhi Liu
Electronics 2024, 13(5), 859; https://doi.org/10.3390/electronics13050859 - 23 Feb 2024
Viewed by 1046
Abstract
In response to the limitations posed by noise interference in complex environments and the narrow focus of existing diagnosis methods for jointless track circuit faults, an innovative approach is put forward in this study. It involves the application of the continuous wavelet transform [...] Read more.
In response to the limitations posed by noise interference in complex environments and the narrow focus of existing diagnosis methods for jointless track circuit faults, an innovative approach is put forward in this study. It involves the application of the continuous wavelet transform (CWT) for signal preprocessing, along with the integration of a deep belief network (DBN) and a genetic algorithm (GA) to improve the least-squares support vector machine (LSSVM) model for intelligent time–frequency fault diagnosis. Initially, the raw induced voltage signals are transformed using continuous wavelet transformation resulting in wavelet time–frequency representations that combine temporal and spectral information. Subsequently, these time–frequency representations are fed into the deep belief networks, which perform semi-supervised dimensionality reduction and feature extraction, thereby uncovering distinct fault characteristics in the track circuit. Finally, the genetic algorithms are employed to improve the kernel function and penalty factor parameters of the least-squares support vector machine, thus establishing an optimal DBN-GA-LSSVM diagnostic model. Experimental validation demonstrates the effectiveness of the proposed time–frequency intelligent network model by leveraging the advantages of deep belief networks in hierarchical feature extraction and the superior performance of the least-squares support vector machine in addressing high-dimensional pattern recognition problems with limited samples. The achieved accuracy rate on the testing dataset reaches an impressive 99.6%. Consequently, this comprehensive approach provides a viable solution for data-driven track circuit fault diagnosis. Full article
(This article belongs to the Special Issue Power System Protection and Fault Location Technologies in Smart Grid Systems)
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37 pages, 2761 KiB  
Article
Fault Tracing Method for Relay Protection System–Circuit Breaker Based on Improved Random Forest
by Ning Shao, Qing Chen, Chengao Yu, Dan Xie and Ye Sun
Electronics 2024, 13(3), 582; https://doi.org/10.3390/electronics13030582 - 31 Jan 2024
Viewed by 1372
Abstract
The incorrect operation of protective relays and circuit breakers will significantly compromise the safety and stability of power systems. To promptly detect the faults of the relay protection system and the circuit breakers in time and to ensure the operational reliability of these [...] Read more.
The incorrect operation of protective relays and circuit breakers will significantly compromise the safety and stability of power systems. To promptly detect the faults of the relay protection system and the circuit breakers in time and to ensure the operational reliability of these protective devices, this paper proposes a fault tracing method for a relay protection system–circuit breaker based on improved Random Forest. Firstly, an analysis is conducted to identify the causes of incorrect operation of the protective relay and the circuit breaker. The fault types and corresponding alarm messages for the relay protection system and the circuit breaker are categorized, and the alarm feature set is constructed. Then, the Random Forest is improved and trained to develop the fault tracking model. Finally, the operation evaluation process is developed to determine the incorrect operations of the protective relay and the circuit breaker, and the fault tracking model and fault tracking process are then employed to locate the faults of the relay protection system and the circuit breaker. The experimental results demonstrate the method’s capability to accurately track faults in the relay protection system and the circuit breaker, thereby assisting operation and maintenance personnel in troubleshooting and highlighting its promising practical potential. Full article
(This article belongs to the Special Issue Power System Protection and Fault Location Technologies in Smart Grid Systems)
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21 pages, 1647 KiB  
Article
Inverter Fault Diagnosis for a Three-Phase Permanent-Magnet Synchronous Motor Drive System Based on SDAE-GAN-LSTM
by Li Feng, Honglin Luo, Shuiqing Xu and Kenan Du
Electronics 2023, 12(19), 4172; https://doi.org/10.3390/electronics12194172 - 8 Oct 2023
Cited by 5 | Viewed by 1448
Abstract
In this study, a novel intelligent inverter fault diagnosis approach based on a stacked denoising autoencoder–generative adversarial network–long short-term memory (SDAE-GAN-LSTM) under an imbalanced sample is proposed for a three-phase permanent-magnet synchronous motor (PMSM) drive system. The proposed method can address the problem [...] Read more.
In this study, a novel intelligent inverter fault diagnosis approach based on a stacked denoising autoencoder–generative adversarial network–long short-term memory (SDAE-GAN-LSTM) under an imbalanced sample is proposed for a three-phase permanent-magnet synchronous motor (PMSM) drive system. The proposed method can address the problem of unbalanced fault data samples and improve the accuracy of fault classification. Concretely speaking, firstly, the stacked denoising autoencoder (SDAE) is pre-trained to obtain the optimum decoder network. Afterward, a new generator of generative adversarial networks (GANs) is designed to generate high-quality samples by migrating the pre-trained optimal decoder network to the hidden layer and output layer of the generator of GANs. Additionally, a new model of long short-term memory (LSTM) based on the second discriminator of the GANs is presented for fault diagnosis. The generator of GANs is cross-trained using the reconstruction error gained by SDAE and the fault diagnosis error obtained by LSTM, resulting in the generation of high-quality samples for fault discrimination. Simulation and experimental results demonstrate the effectiveness of the proposed fault diagnosis approach, and the average fault identification accuracy reaches 98.63%. Full article
(This article belongs to the Special Issue Power System Protection and Fault Location Technologies in Smart Grid Systems)
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14 pages, 2584 KiB  
Article
AVOA-LightGBM Power Fiber Optic Cable Event Pattern Recognition Method Based on Wavelet Packet Decomposition
by Xiaojuan Chen, Wenbo Cui and Tiantong Zhang
Electronics 2023, 12(18), 3743; https://doi.org/10.3390/electronics12183743 - 5 Sep 2023
Cited by 2 | Viewed by 1041
Abstract
The type of power fiber optic cable fault event obtained by analyzing the optical time domain reflectometer (OTDR) detection curve is an important basis for ensuring the operation quality of communication lines. To address the issue of low accuracy in recognizing fault event [...] Read more.
The type of power fiber optic cable fault event obtained by analyzing the optical time domain reflectometer (OTDR) detection curve is an important basis for ensuring the operation quality of communication lines. To address the issue of low accuracy in recognizing fault event patterns, this research proposes the AVOA-LightGBM method for optical cable fault event pattern recognition based on wavelet packet decomposition. Initially, a three-layer wavelet packet decomposition is performed on different fault events, resulting in eight characteristic signals. These signals are then normalized and used as input for each recognition model. The Light Gradient Boosting Machine (LightGBM) is optimized using the African vulture optimization algorithm (AVOA) for pattern recognition. The experimental results demonstrate that this method achieves a recognition accuracy of 98.24%. It outperforms LightGBM, support vector machine (SVM), and extreme learning machine (ELM) by 3.7%, 19.15%, and 5.67%, respectively, in terms of accuracy. Moreover, it shows a 1.8% improvement compared with the combined model PSO-LightGBM. Full article
(This article belongs to the Special Issue Power System Protection and Fault Location Technologies in Smart Grid Systems)
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11 pages, 543 KiB  
Article
Analysis of Factors Influencing Measurement Accuracy for High-Temperature Liquid Metal Flow Sensors in Nuclear Power
by Gang Wang, Wenyi Lin, Yutao Jiang, Lixin Deng and Yi Chai
Electronics 2023, 12(13), 2944; https://doi.org/10.3390/electronics12132944 - 4 Jul 2023
Viewed by 1118
Abstract
Permanent magnet metal flowmeters are implemented to monitor the flow of liquid metal coolant in a nuclear reactor, whose measurement accuracy plays a significant role to ensure the safety and normal operation of the nuclear reactor. According to a theoretical analysis of the [...] Read more.
Permanent magnet metal flowmeters are implemented to monitor the flow of liquid metal coolant in a nuclear reactor, whose measurement accuracy plays a significant role to ensure the safety and normal operation of the nuclear reactor. According to a theoretical analysis of the permanent magnet metal flowmeter, several factors such as temperature, nonlinear degree, and zero potential will affect the accuracy of measuring the flow. However, for a heavy-caliber permanent magnet metal flowmeter, the influence of the magnetic Reynolds number provides obvious nonlinearity, which affects the measurement accuracy of the flowmeter. Consequently, we use the finite element method to calculate the magnetic field of the flowmeter and analyze the cause of the nonlinearity. Additionally, the influence of nonlinear error is significantly reduced by designing the structure of the flowmeter and the appropriate arrangement of the electrodes. Full article
(This article belongs to the Special Issue Power System Protection and Fault Location Technologies in Smart Grid Systems)
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14 pages, 972 KiB  
Article
Intelligent Fault Diagnosis Method for Industrial Processing Equipment by ICECNN-1D
by Zhaofei Li, Yutao Jiang, Bowen Liu, Le Ma, Jianfeng Qu and Yi Chai
Electronics 2022, 11(24), 4207; https://doi.org/10.3390/electronics11244207 - 16 Dec 2022
Cited by 3 | Viewed by 1498
Abstract
Intelligent algorithm has been widely implemented to effectively diagnose faults in industrial instrument, electrical equipment and mechanical equipment. In addition, the rapid development of sensing technology generated enormous time series signal. Accordingly, diagnosing faults by analyzing time series signal has been widely developed. [...] Read more.
Intelligent algorithm has been widely implemented to effectively diagnose faults in industrial instrument, electrical equipment and mechanical equipment. In addition, the rapid development of sensing technology generated enormous time series signal. Accordingly, diagnosing faults by analyzing time series signal has been widely developed. This paper aims to diagnose faults by applying improved Convolution Neural Network with Compression Enhancement (ICECNN-1D) to analyze time series signal, which effectively considers time series property of signal while diagnosing faults by artificial intelligence. Additionally, a large number of trend features and fluctuation features in high-frequency time series are also considered. the recognition rates of almost other machine learning algorithm are less than 90% in the experiments. Other methods may provide high rate of recognition, but their fluctuation of the recognition rate has varied obviously with different loads, and results provide undesirable ability of generalization under different working conditions. Comparatively, ICECNN-1D model provides high recognition rate and terrific ability of generation while processing time series with high frequency, and its accuracy of the recognition rate fluctuates inconspicuously with different loads. Full article
(This article belongs to the Special Issue Power System Protection and Fault Location Technologies in Smart Grid Systems)
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15 pages, 1560 KiB  
Article
Industrial Fault Detection Based on Discriminant Enhanced Stacking Auto-Encoder Model
by Bowen Liu, Yi Chai, Yutao Jiang and Yiming Wang
Electronics 2022, 11(23), 3993; https://doi.org/10.3390/electronics11233993 - 2 Dec 2022
Cited by 5 | Viewed by 1543
Abstract
In the recent years, deep learning has been widely used in process monitoring due to its strong ability to extract features. However, with the increasing layers of the deep network, the compression of features by the deep model will lead to the loss [...] Read more.
In the recent years, deep learning has been widely used in process monitoring due to its strong ability to extract features. However, with the increasing layers of the deep network, the compression of features by the deep model will lead to the loss of some valuable information and affect the model’s performance. To solve this problem, a fault detection method based on a discriminant enhanced stacked auto-encoder is proposed. An enhanced stacked auto-encoder network structure is designed, and the original data is added to each hidden layer in the model pre-training process to solve the problem of information loss in the feature extraction process. Then the self-encoding network is combined with spectral regression kernel discriminant analysis. The fault category information is introduced into the features to optimize the features and enhance the discrimination of the extracted features. The Euclidean distance is used for fault detection based on the extracted features. From the Tennessee Eastman process experiment, it can be found that the detection accuracy of this method is about 9.4% higher than that of the traditional stacked auto-encoder method. Full article
(This article belongs to the Special Issue Power System Protection and Fault Location Technologies in Smart Grid Systems)
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