Review of Artificial Intelligence Methods for Faults Monitoring, Diagnosis, and Prognosis in Hydroelectric Synchronous Generators

被引：0

作者：

Bechara, Helene ^{[1
]}

Ibrahim, Rony ^{[2
]}

Zemouri, Ryad ^{[3
]}

Kedjar, Bachir ^{[1
]}

Merkhouf, Arezki ^{[3
]}

Tahan, Antoine ^{[2
]}

Al-Haddad, Kamal ^{[1
]}

机构：

[1] Ecole Technol Super ETS, Dept Elect Engn, Montreal, PQ H3C 1K3, Canada

[2] Ecole Technol Super ETS, Dept Mech Engn, Montreal, PQ H3C 1K3, Canada

[3] Hydro Quebec Res Inst IREQ, Varennes, PQ J3X 1S1, Canada

来源：

IEEE ACCESS | 2024年 / 12卷

关键词：

Monitoring; Prognostics and health management; Artificial intelligence; Vibrations; Generators; Reviews; Fault diagnosis; Accuracy; Support vector machines; Feature extraction; Artificial intelligence (AI); diagnosis; hydroelectric generator unit (HGU); monitoring; prognosis; NEURAL-NETWORK; EXPERT-SYSTEM; MAINTENANCE; COMBINATION; ANFIS; TOOLS; MODEL;

D O I：

10.1109/ACCESS.2024.3502546

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This scientific article aims to provide a comprehensive review of fault monitoring, diagnosis, and prognosis methods based on Artificial Intelligence (AI) for Hydroelectric Generator Units (HGUs). It presents a compilation of research studies that have utilized AI models for fault monitoring, diagnosis, and prognosis in HGUs. Additionally, it outlines the process for building an AI model in the context of fault management in HGUs and discusses the advantages and disadvantages associated with AI methods in this domain. Furthermore, the article examines the research prospects and trends of AI models for fault management in HGUs. By synthesizing existing literature and highlighting future directions, this article serves as a valuable resource for researchers and practitioners seeking to leverage AI techniques for effective fault management in HGUs.

引用

页码：173599 / 173617

页数：19

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