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

Abstract

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.

Keywords

• Artificial intelligence (AI)
• diagnosis
• hydroelectric generator unit (HGU)
• monitoring
• prognosis