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Diagnosis and Risk Analysis of Electrical Systems
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Diagnosis and Risk Analysis of Electrical Systems

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

Deadline for manuscript submissions: 20 September 2025 | Viewed by 808

Special Issue Editors

Prof. Dr. Zhenbing Zhao

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Interests: fault detection
Special Issues, Collections and Topics in MDPI journals
Dr. Bing Li

E-Mail Website
Guest Editor
Department of Automation, North China Electric Power University, Baoding 071003, China
Interests: power systems
Dr. Haopeng Li

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Interests: power systems; information processing

Special Issue Information

Dear Colleagues,

The power system provides essential energy security for the normal operation of modern society; therefore, the safe and stable operation of systems is crucial. However, during operation, the system can be affected by various factors, leading to equipment failures that disrupt the stable supply of electricity. To address this, we are launching this Special Issue, titled "Electrical Equipment Diagnosis and Risk Analysis", which is aimed at gathering original research and review articles on the latest advancements, technologies, solutions, applications, and new challenges in the field of electrical equipment fault diagnosis and operational risk analysis.

This Special Issue will delve deeply into the critical role of sensors in diagnosing electrical equipment, while also exploring the integration of other advanced technologies to provide robust support for precise diagnosis and efficient maintenance. As the "nervous endings" of modern industry, sensors can monitor the operating status of electrical equipment in real-time, capturing abnormal signals and providing crucial data for fault diagnosis. Building on this foundation, this Special Issue will showcase how advanced technologies such as artificial intelligence, big data analysis, the Internet of Things, and cloud computing can be closely integrated with sensor technology to drive the intelligence and precision of electrical equipment diagnostics.

The topics for submission include, but are not limited to, the following:

(1) Diagnosis and Risk Analysis Techniques and Methods for Electrical Equipment:

  • Sensor Technology: Discuss the core role of sensors in electrical equipment diagnosis and their latest applications.
  • Artificial Intelligence: Showcase the intelligent applications of AI in electrical equipment diagnosis and risk analysis.
  • Big Data Analysis: Explore the value of big data analysis in electrical equipment fault diagnosis and analysis.
  • Internet of Things: Study how the Internet of Things supports electrical equipment diagnosis and risk analysis.

(2) Electrical Equipment Diagnosis:

Electrical equipment fault diagnosis refers to the process of determining whether a device is operating normally or experiencing a fault based on the various information generated during its operation, using advanced technological methods. Common types of electrical equipment faults include the following:

  • Mechanical Failures: Faults caused by damage or wear of mechanical components.
  • Electrical Failures: Issues related to power supply or circuit malfunctions.
  • Equipment and Component Failures: Failures resulting from overheating, electrical breakdown, or degradation of electrical components.
  • Environmental Failures: Faults caused by environmental factors such as temperature and humidity surrounding the equipment.

(3) Electrical Equipment Risk Analysis:

Electrical equipment risk analysis involves a comprehensive assessment of factors such as the operational status of the equipment, its operating environment, and maintenance history. By utilizing advanced technological methods, this analysis aims to predict potential future faults or issues and implement corresponding preventive or corrective measures. Common types of equipment risks include the following:

  • Personal Safety Risks: Risks associated with electrical equipment, including electric shock, fire, and explosion hazards.
  • Equipment Damage Risks: Risks of component damage or performance degradation due to equipment aging, overloads, short circuits, or external environmental factors.
  • Production Interruption Risks: Risks of production halts caused by electrical equipment failures, leading to increased downtime, higher maintenance costs, and potential declines in output and quality.

Electrical equipment diagnosis and risk analysis play a crucial role in ensuring the stable operation of power systems, extending equipment lifespan, reducing maintenance costs, and ensuring personnel safety. We warmly invite researchers, engineers, and technicians to collaborate and focus on driving innovative developments in the field of electrical equipment diagnosis and risk analysis.

Prof. Dr. Zhenbing Zhao
Dr. Bing Li
Dr. Haopeng Li
Guest Editors

Manuscript Submission Information

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

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

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

Keywords

  • electrical equipment
  • fault diagnosis
  • risk analysis
  • sensor technology
  • AI and big data
  • Internet of Things

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

Published Papers (2 papers)

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Research

19 pages, 6097 KiB  
Article
Forced Oscillation Detection via a Hybrid Network of a Spiking Recurrent Neural Network and LSTM
by Xiaomei Yang, Jinfei Wang, Xingrui Huang, Yang Wang and Xianyong Xiao
Sensors 2025, 25(8), 2607; https://doi.org/10.3390/s25082607 - 20 Apr 2025
Viewed by 129
Abstract
The detection of forced oscillations, especially distinguishing them from natural oscillations, has emerged as a major concern in power system stability monitoring. Deep learning (DL) holds significant potential for detecting forced oscillations correctly. However, existing artificial neural networks (ANNs) face challenges when employed [...] Read more.
The detection of forced oscillations, especially distinguishing them from natural oscillations, has emerged as a major concern in power system stability monitoring. Deep learning (DL) holds significant potential for detecting forced oscillations correctly. However, existing artificial neural networks (ANNs) face challenges when employed in edge devices for timely detection due to their inherent complex computations and high power consumption. This paper proposes a novel hybrid network that integrates a spiking recurrent neural network (SRNN) with long short-term memory (LSTM). The SRNN achieves computational and energy efficiency, while the integration with LSTM is conducive to effectively capturing temporal dependencies in time-series oscillation data. The proposed hybrid network is trained using the backpropagation-through-time (BPTT) optimization algorithm, with adjustments made to address the discontinuous gradient in the SRNN. We evaluate our proposed model on both simulated and real-world oscillation datasets. Overall, the experimental results demonstrate that the proposed model can achieve higher accuracy and superior performance in distinguishing forced oscillations from natural oscillations, even in the presence of strong noise, compared to pure LSTM and other SRNN-related models. Full article
(This article belongs to the Special Issue Diagnosis and Risk Analysis of Electrical Systems)
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17 pages, 1096 KiB  
Article
Secondary Operation Risk Assessment Method Integrating Graph Convolutional Networks and Semantic Embeddings
by Pengyu Zhu, Youwei Li, Peidong Xu, Ping Li, Zhenbing Zhao and Gang Li
Sensors 2025, 25(6), 1934; https://doi.org/10.3390/s25061934 - 20 Mar 2025
Viewed by 202
Abstract
In the power industry, secondary operation risk assessment is a critical step in ensuring operational safety. However, traditional assessment methods often rely on expert judgment, making it difficult to efficiently address the challenges posed by unstructured textual data and complex equipment relationships. To [...] Read more.
In the power industry, secondary operation risk assessment is a critical step in ensuring operational safety. However, traditional assessment methods often rely on expert judgment, making it difficult to efficiently address the challenges posed by unstructured textual data and complex equipment relationships. To address this issue, this paper proposes a hybrid model that integrates graph convolutional networks (GCNs) with semantic embedding techniques. The model consists of two main components: the first constructs a domain-specific knowledge graph for the power industry and uses a GCN to extract structural information, while the second fine-tunes the RoBERTa pre-trained model to generate semantic embeddings for textual data. Finally, the model employs a hybrid similarity measurement mechanism that comprehensively considers both semantic and structural features, combining K-means clustering similarity search with a multi-node weighted evaluation method to achieve efficient and accurate risk assessment. The experimental results demonstrate that the proposed model significantly outperforms the traditional methods in key metrics, such as accuracy, recall, and F1 score, fully validating its practical application value in secondary operation scenarios within the power industry. Full article
(This article belongs to the Special Issue Diagnosis and Risk Analysis of Electrical Systems)
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