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Energies
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Power System Fault Diagnosis and Maintenance
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Power System Fault Diagnosis and Maintenance

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: closed (13 July 2023) | Viewed by 5491

Special Issue Editor

Dr. Jingmin Fan

E-Mail Website
Guest Editor
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Interests: power equipment condition monitoring; power equipment fault diagnosis AI algorithm; gas sensing technology

Special Issue Information

Dear Colleagues,

Most power equipment was built in the last century and has been continuously running for decades. Such power equipment is subject to component damage and insulation aging. To prevent electrical accidents, they need to be monitored and maintained.

In recent years, significant breakthroughs have been made in various research fields such as artificial intelligence algorithms and sensor technologies, which have greatly contributed to the monitoring of power system faults and their maintenance. These technologies have enabled important power equipment such as transformers to be protected and effectively prevented serious power accidents from occurring.

This Special Issue aims to promote and disseminate recent advancements in theory, design, modeling, control, and reviews related to the monitoring and fault diagnosis of power systems and electrical equipment.

Topics of interest for publication include, but are not limited to:

  • Dissolved gas analyses;
  • Fault diagnosis;
  • Fault prediction;
  • Online monitoring system;
  • Artificial intelligence algorithm;
  • Power equipment maintenance.

Dr. Jingmin Fan
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. Energies 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
  • fault diagnosis
  • fault prediction
  • dissolved gas analyses
  • artificial intelligence algorithm
  • online monitoring system

Published Papers (5 papers)

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Research

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11 pages, 496 KiB  
Article
Analysing Effective and Ineffective Impacts of Maintenance Strategies on Electric Power Plants: A Comprehensive Approach
by Tareq Ali Al Ameeri, Mohd Nizam Ab Rahman and Norhamidi Muhamad
Energies 2023, 16(17), 6243; https://0-doi-org.brum.beds.ac.uk/10.3390/en16176243 - 28 Aug 2023
Viewed by 973
Abstract
The maintenance strategy used in an electric power plant plays a crucial role in its overall performance and operational efficiency. An effective maintenance strategy describes the approach to exploiting various forms of maintenance (corrective, preventive, predictive, proactive, etc.) in an electric power plant. [...] Read more.
The maintenance strategy used in an electric power plant plays a crucial role in its overall performance and operational efficiency. An effective maintenance strategy describes the approach to exploiting various forms of maintenance (corrective, preventive, predictive, proactive, etc.) in an electric power plant. In this paper, the effective and ineffective impacts of maintenance strategies on power plants were examined. Also, the distinction between corrective, preventive, and aggressive maintenance was considered. In terms of effective impacts, a well-designed and executed maintenance strategy enhances the reliability and availability of the electric power plant by minimising unplanned downtime. It extends the lifespan of critical equipment, improves safety measures, increases energy efficiency, and contributes to long-term cost savings. However, in terms of ineffective impacts, poorly planned or executed maintenance strategies can result in increased downtime, higher repair costs, safety risks, decreased efficiency, and regulatory compliance issues. Neglecting maintenance can lead to equipment failures, reduced productivity, and potential environmental incidents. Full article
(This article belongs to the Special Issue Power System Fault Diagnosis and Maintenance)
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29 pages, 4492 KiB  
Article
Integrated Fault Detection, Classification and Section Identification (I-FDCSI) Method for Real Distribution Networks Using μPMUs
by Abdul Haleem Medattil Ibrahim, Madhu Sharma and Vetrivel Subramaniam Rajkumar
Energies 2023, 16(11), 4262; https://0-doi-org.brum.beds.ac.uk/10.3390/en16114262 - 23 May 2023
Cited by 2 | Viewed by 905
Abstract
This paper presents a rules-based integrated fault detection, classification and section identification (I-FDCSI) method for real distribution networks (DN) using micro-phasor measurement units (μPMUs). The proposed method utilizes the high-resolution synchronized realistic measurements from the strategically installed μPMUs to detect [...] Read more.
This paper presents a rules-based integrated fault detection, classification and section identification (I-FDCSI) method for real distribution networks (DN) using micro-phasor measurement units (μPMUs). The proposed method utilizes the high-resolution synchronized realistic measurements from the strategically installed μPMUs to detect and classify different types of faults and identify the faulty section of the distribution network. The I-FDCSI method is based on a set of rules developed using expert knowledge and statistical analysis of the generated realistic measurements. The algorithms mainly use line currents per phase reported by the different μPMUs to calculate the minimum and maximum short circuit current ratios. The algorithms were then fine-tuned with all the possible types and classes of fault simulations at all possible sections of the network with different fault parameter values. The proposed I-FDCSI method addresses the inherent challenges of DN by leveraging the high-precision measurements provided by μPMUs to accurately detect, classify, and sectionalise faults. To ensure the applicability of the developed IFDCSI method, it is further tested and validated with all the possible real-time events on a real distribution network and its performance has been compared with the conventional fault detection, classification and section identification methods. The results demonstrate that the I-FDCSI method has a higher accuracy and faster response time compared to the conventional methods and facilitates faster service restoration, thus improving the reliability and resiliency indices of DN. Full article
(This article belongs to the Special Issue Power System Fault Diagnosis and Maintenance)
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14 pages, 3118 KiB  
Article
Condition Forecasting of a Power Transformer Based on an Online Monitor with EL-CSO-ANN
by Jingmin Fan, Huidong Shao, Yunfei Cao, Lutao Feng, Jianpei Chen, Anbo Meng and Hao Yin
Energies 2022, 15(22), 8587; https://0-doi-org.brum.beds.ac.uk/10.3390/en15228587 - 16 Nov 2022
Cited by 1 | Viewed by 1045
Abstract
Power transformers are vital to the power grid and discovering the latent faults in advance is helpful for avoiding serious problems. This study addressed the problem of forecasting and diagnosing the faults of power transformers with small dissolved gas analysis (DGA) data samples [...] Read more.
Power transformers are vital to the power grid and discovering the latent faults in advance is helpful for avoiding serious problems. This study addressed the problem of forecasting and diagnosing the faults of power transformers with small dissolved gas analysis (DGA) data samples that arise from faults in transformers with low occurrence rates. First, an online monitor that was developed in our previous work was applied to obtain the DGA data. Second, the ensemble learning (EL) of a bagging algorithm with bootstrap resampling was used to deal with small training samples. Finally, a criss-cross-optimized neural network (i.e., CSO-NN) was applied to the short-term prediction of the DGA data, based on which the transformer status could be forecasted. The case studies showed that the proposed EL-CSO-NN algorithm integrated into the monitor was capable of achieving satisfactory classification and prediction accuracy for transformer fault forecasting. Full article
(This article belongs to the Special Issue Power System Fault Diagnosis and Maintenance)
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22 pages, 7597 KiB  
Article
Abnormal Data Cleaning Method for Wind Turbines Based on Constrained Curve Fitting
by Xiangqing Yin, Yi Liu, Li Yang and Wenchao Gao
Energies 2022, 15(17), 6373; https://doi.org/10.3390/en15176373 - 31 Aug 2022
Cited by 1 | Viewed by 1296
Abstract
With the increase of the scale of wind turbines, the problem of data quality of wind turbines has become increasingly prominent, which seriously affects the follow-up research. A large number of abnormal data exist in the historical data recorded by the wind turbine [...] Read more.
With the increase of the scale of wind turbines, the problem of data quality of wind turbines has become increasingly prominent, which seriously affects the follow-up research. A large number of abnormal data exist in the historical data recorded by the wind turbine Supervisory Control And Data Acquisition (SCADA) system. In order to improve data quality, it is necessary to clean a large number of abnormal data in the original data. Aiming at the problem that the cleaning effect is not good in the presence of a large number of abnormal data, a method for cleaning abnormal data of wind turbines based on constrained curve fitting is proposed. According to the wind speed-power characteristics of wind turbines, the constrained wind speed-power curve is fit with the least square method, and the constrained optimization problem is transformed into an unconstrained optimization problem by using the external penalty function method. Data cleaning was performed on the fitted curve using an improved 3-σ standard deviation. Experiments show that, compared with the existing methods, this method can still perform data cleaning well when the historical wind turbine data contains many abnormal data, and the method is insensitive to parameters, simple in the calculation, and easy to automate. Full article
(This article belongs to the Special Issue Power System Fault Diagnosis and Maintenance)
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Review

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27 pages, 1576 KiB  
Review
Multi-Power System Electrical Source Fault Review
by Mariem Hadj Salem, Karim Mansouri, Eric Chauveau, Yemna Ben Salem and Mohamed Naceur Abdelkrim
Energies 2024, 17(5), 1187; https://0-doi-org.brum.beds.ac.uk/10.3390/en17051187 - 01 Mar 2024
Viewed by 609
Abstract
The phrase “Multi-Power System (MPS)” refers to an application that combines different energy conversion technologies to meet a specific energy need. These integrated power systems are rapidly being lauded as essential for future decarbonized grids to achieve optimum efficiency and cost reduction. The [...] Read more.
The phrase “Multi-Power System (MPS)” refers to an application that combines different energy conversion technologies to meet a specific energy need. These integrated power systems are rapidly being lauded as essential for future decarbonized grids to achieve optimum efficiency and cost reduction. The fact that MPSs multiply several sources also multiplies their advantages to be environmentally friendly and increases the possibility of energy autonomy as they do not depend on a single source. Consequently, this increases the reliability and reduces the production costs and the size of the storage system. However, the main disadvantages of such a system are the complexity of its architecture and the difficulty in managing the power level, which leads the system to face many faults and sometimes failure. In this case, a fault-tolerant control (FTC) system can automatically adapt to component malfunctions while maintaining closed-loop system stability to achieve acceptable performance. However, on the way to build efficient FTC, one first needs to study the faults that may occur in the system in order to tolerate them. This review paper presents the faults of the MPS electrical sources used in a hybrid system, including a photovoltaic generator and a diesel generator, plus a lead–acid battery as a storage device. Only the most-encountered faults are treated. Full article
(This article belongs to the Special Issue Power System Fault Diagnosis and Maintenance)
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