Academic Journal
A Comprehensive Review of AI Integration for Fault Detection in Modern Power Systems: Data Processing, Modeling, and Optimization.
| Title: | A Comprehensive Review of AI Integration for Fault Detection in Modern Power Systems: Data Processing, Modeling, and Optimization. |
|---|---|
| Authors: | Liu, Youping, Li, Pin, Si, Yang, Ma, Linrui |
| Source: | Energies (19961073); Sep2025, Vol. 18 Issue 18, p4983, 32p |
| Subject Terms: | Artificial intelligence, Fault diagnosis, Computer simulation, Deep learning, Renewable energy sources, Electric power systems, Electronic data processing, Mathematical optimization |
| Abstract: | Driven by the high penetration of renewable energy sources and power electronic devices, modern power systems have become increasingly complex, intensifying the demand for accurate and intelligent fault detection. This paper analyzes a total of 81 references to explore the integrated application of artificial intelligence (AI) technologies across all stages of fault data processing, modeling, and optimization. The application potential of AI in fault data processing is firstly analyzed in terms of its performance in mitigating class imbalance, extracting feature information, handling data noise and classification. Then, the modeling of fault detection is classified into rule-driven, data-driven and hybrid-driven methods to evaluate their applicability in scenarios such as transmission lines and distribution networks. The accuracy of fault detection models is also investigated by studying the hyperparameter optimization (HPO) methods. The results indicate that the utilization of AI-driven imbalance handling enhances model accuracy by a range of 16.2% to 26.2%, while deep learning-based feature extraction techniques sustain accuracy levels exceeding 98.5% under a signal-to-noise ratio (SNR) of 10 dB. With a 99.96% detection accuracy, hybrid-driven models applied in fault detection perform the best. For the optimization of fault detection models, heuristic algorithms provide 6.92–19.375% improvement over the baseline models. The findings suggest that AI-driven methodologies in data processing demonstrate notable noise resilience and other benefits. For modeling fault detection, data-driven and hybrid-driven models are presently extensively employed for detecting short-circuit faults, predicting transformer gas trends, and identifying faults in complex and uncertain scenarios. Conversely, rule-driven models are better suited for scenarios possessing a comprehensive experience library and are utilized with less frequency. In the optimization of fault detection models, heuristic algorithms occupy a pivotal position, whereas hyperparameter optimization incorporating reinforcement learning (RL) is better suited for real-time fault detection. The discoveries presented in this paper facilitate the seamless integration of AI with fault detection in modern power systems, thereby advancing their intelligent evolution. [ABSTRACT FROM AUTHOR] |
| Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=19961073&ISBN=&volume=18&issue=18&date=20250915&spage=4983&pages=4983-5014&title=Energies (19961073)&atitle=A%20Comprehensive%20Review%20of%20AI%20Integration%20for%20Fault%20Detection%20in%20Modern%20Power%20Systems%3A%20Data%20Processing%2C%20Modeling%2C%20and%20Optimization.&aulast=Liu%2C%20Youping&id=DOI:10.3390/en18184983 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 188282436 RelevancyScore: 1023 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1023.087890625 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: A Comprehensive Review of AI Integration for Fault Detection in Modern Power Systems: Data Processing, Modeling, and Optimization. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Youping%22">Liu, Youping</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Pin%22">Li, Pin</searchLink><br /><searchLink fieldCode="AR" term="%22Si%2C+Yang%22">Si, Yang</searchLink><br /><searchLink fieldCode="AR" term="%22Ma%2C+Linrui%22">Ma, Linrui</searchLink> – Name: TitleSource Label: Source Group: Src Data: Energies (19961073); Sep2025, Vol. 18 Issue 18, p4983, 32p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+systems%22">Electric power systems</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Driven by the high penetration of renewable energy sources and power electronic devices, modern power systems have become increasingly complex, intensifying the demand for accurate and intelligent fault detection. This paper analyzes a total of 81 references to explore the integrated application of artificial intelligence (AI) technologies across all stages of fault data processing, modeling, and optimization. The application potential of AI in fault data processing is firstly analyzed in terms of its performance in mitigating class imbalance, extracting feature information, handling data noise and classification. Then, the modeling of fault detection is classified into rule-driven, data-driven and hybrid-driven methods to evaluate their applicability in scenarios such as transmission lines and distribution networks. The accuracy of fault detection models is also investigated by studying the hyperparameter optimization (HPO) methods. The results indicate that the utilization of AI-driven imbalance handling enhances model accuracy by a range of 16.2% to 26.2%, while deep learning-based feature extraction techniques sustain accuracy levels exceeding 98.5% under a signal-to-noise ratio (SNR) of 10 dB. With a 99.96% detection accuracy, hybrid-driven models applied in fault detection perform the best. For the optimization of fault detection models, heuristic algorithms provide 6.92–19.375% improvement over the baseline models. The findings suggest that AI-driven methodologies in data processing demonstrate notable noise resilience and other benefits. For modeling fault detection, data-driven and hybrid-driven models are presently extensively employed for detecting short-circuit faults, predicting transformer gas trends, and identifying faults in complex and uncertain scenarios. Conversely, rule-driven models are better suited for scenarios possessing a comprehensive experience library and are utilized with less frequency. In the optimization of fault detection models, heuristic algorithms occupy a pivotal position, whereas hyperparameter optimization incorporating reinforcement learning (RL) is better suited for real-time fault detection. The discoveries presented in this paper facilitate the seamless integration of AI with fault detection in modern power systems, thereby advancing their intelligent evolution. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=188282436 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en18184983 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 32 StartPage: 4983 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Fault diagnosis Type: general – SubjectFull: Computer simulation Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Renewable energy sources Type: general – SubjectFull: Electric power systems Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Mathematical optimization Type: general Titles: – TitleFull: A Comprehensive Review of AI Integration for Fault Detection in Modern Power Systems: Data Processing, Modeling, and Optimization. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Youping – PersonEntity: Name: NameFull: Li, Pin – PersonEntity: Name: NameFull: Si, Yang – PersonEntity: Name: NameFull: Ma, Linrui IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 18 – Type: issue Value: 18 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |