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Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis II

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (25 July 2024) | Viewed by 3787

Special Issue Editors

Dr. Guang Wang

E-Mail Website
Guest Editor
Automation Department, North China Electric Power University, Baoding Campus, Baoding 071051, China
Interests: process monitoring; fault diagnosis; fault prediction; power system modeling, control and optimization; battery energy storage systems; integrated energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Jiale Xie

E-Mail Website
Guest Editor
Automation Department, North China Electric Power University, Baoding Campus, Baoding 071051, China
Interests: battery characteristic modeling; fault diagnosis; states estimation; thermal management; energy equilibrium
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shunli Wang

E-Mail Website
Guest Editor
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China
Interests: lithium battery modeling; state estimation; battery balancing; battery management system; new energy system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ever-increasing demands of automatic management in power systems and energy storage systems have garnered significant attention in both industry and academia. To systematically present the recent developments in related fields, this Special Issue focuses on the latest advances in operation monitoring and safety control, most notably using emerging techniques such as artificial intelligence, big data analysis, deep learning, characteristic modeling, performance control and fault-diagnosing applications.

The scope of this Special Issue includes, but is not limited to, the following:

  • Data-based abnormalities analysis of thermal power system and nuclear power system;
  • Fault diagnosis and prediction of wind turbines based on SCADA data;
  • Modeling, monitoring and diagnosing of waste-to-energy, biomass power, and tidal power systems;
  • Data-based fault characteristics analysis of power generation equipment;
  • Power equipment health monitoring based on vibration signal, sound signal, image signal, thermal infrared signal, etc.;
  • Control and performance monitoring of photovoltaic power generation systems;
  • Modeling, scheduling, control and monitoring of microgrid systems;
  • SOC estimation, SOH estimation, fault detection, isolation and localization of lithium battery systems;
  • State estimation and performance evaluation of large-scale energy storage systems

Dr. Guang Wang
Dr. Jiale Xie
Prof. Dr. Shunli Wang
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 systems
  • new advances
  • artificial intelligence
  • big data
  • deep learning
  • modeling
  • monitoring
  • fault detection
  • fault diagnosis

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Related Special Issue

Published Papers (4 papers)

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Research

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11 pages, 873 KiB  
Article
An Ensemble Method for Non-Intrusive Load Monitoring (NILM) Applied to Deep Learning Approaches
by Silvia Moreno, Hector Teran, Reynaldo Villarreal, Yolanda Vega-Sampayo, Jheifer Paez, Carlos Ochoa, Carlos Alejandro Espejo, Sindy Chamorro-Solano and Camilo Montoya
Energies 2024, 17(18), 4548; https://doi.org/10.3390/en17184548 - 11 Sep 2024
Viewed by 916
Abstract
Climate change, primarily driven by human activities such as burning fossil fuels, is causing significant long-term changes in temperature and weather patterns. To mitigate these impacts, there is an increased focus on renewable energy sources. However, optimizing power consumption through effective usage control [...] Read more.
Climate change, primarily driven by human activities such as burning fossil fuels, is causing significant long-term changes in temperature and weather patterns. To mitigate these impacts, there is an increased focus on renewable energy sources. However, optimizing power consumption through effective usage control and waste recycling also offers substantial potential for reducing energy demands. This study explores non-intrusive load monitoring (NILM) to estimate disaggregated energy consumption from a single household meter, leveraging advancements in deep learning such as convolutional neural networks. The study uses the UK-DALE dataset to extract and plot power consumption data from the main meter and identify five household appliances. Convolutional neural networks (CNNs) are trained with transfer learning using VGG16 and MobileNet. The models are validated, tested on split datasets, and combined using ensemble methods for improved performance. A new voting scheme for ensembles is proposed, named weighted average confidence voting (WeCV), and it is used to create combinations of the best 3 and 5 models and applied to NILM. The base models achieve up to 97% accuracy. The ensemble methods applying WeCV show an increased accuracy of 98%, surpassing previous state-of-the-art results. This study shows that CNNs with transfer learning effectively disaggregate household energy use, achieving high accuracy. Ensemble methods further improve performance, offering a promising approach for optimizing energy use and mitigating climate change. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis II)
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15 pages, 3966 KiB  
Article
Infrared Image Object Detection Algorithm for Substation Equipment Based on Improved YOLOv8
by Siyu Xiang, Zhengwei Chang, Xueyuan Liu, Lei Luo, Yang Mao, Xiying Du, Bing Li and Zhenbing Zhao
Energies 2024, 17(17), 4359; https://doi.org/10.3390/en17174359 - 31 Aug 2024
Viewed by 960
Abstract
Substations play a crucial role in the proper operation of power systems. Online fault diagnosis of substation equipment is critical for improving the safety and intelligence of power systems. Detecting the target equipment from an infrared image of substation equipment constitutes a pivotal [...] Read more.
Substations play a crucial role in the proper operation of power systems. Online fault diagnosis of substation equipment is critical for improving the safety and intelligence of power systems. Detecting the target equipment from an infrared image of substation equipment constitutes a pivotal step in online fault diagnosis. To address the challenges of missed detection, false detection, and low detection accuracy in the infrared image object detection in substation equipment, this paper proposes an infrared image object detection algorithm for substation equipment based on an improved YOLOv8n. Firstly, the DCNC2f module is built by combining deformable convolution with the C2f module, and the C2f module in the backbone is replaced by the DCNC2f module to enhance the ability of the model to extract relevant equipment features. Subsequently, the multi-scale convolutional attention module is introduced to improve the ability of the model to capture multi-scale information and enhance detection accuracy. The experimental results on the infrared image dataset of the substation equipment demonstrate that the improved YOLOv8n model achieves [email protected] and [email protected]:0.95 of 92.7% and 68.5%, respectively, representing a 2.6% and 3.9% improvement over the baseline model. The improved model significantly enhances object detection accuracy and exhibits superior performance in infrared image object detection in substation equipment. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis II)
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20 pages, 444 KiB  
Article
New Insights into Gas-in-Oil-Based Fault Diagnosis of Power Transformers
by Felipe M. Laburú, Thales W. Cabral, Felippe V. Gomes, Eduardo R. de Lima, José C. S. S. Filho and Luís G. P. Meloni
Energies 2024, 17(12), 2889; https://doi.org/10.3390/en17122889 - 12 Jun 2024
Cited by 1 | Viewed by 644
Abstract
The dissolved gas analysis of insulating oil in power transformers can provide valuable information about fault diagnosis. Power transformer datasets are often imbalanced, worsening the performance of machine learning-based fault classifiers. A critical step is choosing the proper evaluation metric to select features, [...] Read more.
The dissolved gas analysis of insulating oil in power transformers can provide valuable information about fault diagnosis. Power transformer datasets are often imbalanced, worsening the performance of machine learning-based fault classifiers. A critical step is choosing the proper evaluation metric to select features, models, and oversampling techniques. However, no clear-cut, thorough guidance on that choice is available to date. In this work, we shed light on this subject by introducing new tailored evaluation metrics. Our results and discussions bring fresh insights into which learning setups are more effective for imbalanced datasets. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis II)
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Review

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15 pages, 1055 KiB  
Review
A Review on the Application of Artificial Intelligence in Anomaly Analysis Detection and Fault Location in Grid Indicator Calculation Data
by Shiming Sun, Yuanhe Tang, Tong Tai, Xueyun Wei and Wei Fang
Energies 2024, 17(15), 3747; https://doi.org/10.3390/en17153747 - 29 Jul 2024
Viewed by 808
Abstract
With the rapid development of artificial intelligence (AI), AI has been widely applied in anomaly analysis detection and fault location in power grid data and has made significant research progress. Through looking back on traditional methods and deep learning methods in anomaly analysis [...] Read more.
With the rapid development of artificial intelligence (AI), AI has been widely applied in anomaly analysis detection and fault location in power grid data and has made significant research progress. Through looking back on traditional methods and deep learning methods in anomaly analysis detection and fault location of power grid data, we aim to provide readers with a comprehensive understanding of the existing knowledge and research advancements in this field. Firstly, we introduce the importance of anomaly analysis detection and fault location in power grid data for the safety and stability of power system operations and review traditional methods for anomaly analysis detection and fault location in power grid data, analyzing their advantages and disadvantages. Next, the paper briefly introduces the concepts of commonly used deep learning models in this field and explores, in depth, the application of deep learning methods in anomaly analysis detection and fault location of power grid data, summarizes the current research progress, and highlights the advantages of deep learning over traditional methods. Finally, we summarize the current issues and challenges faced by deep learning in this field and provide an outlook on future research direction. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis II)
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