2024 : 2 : 26
Amir-Mohammad Golmohammadi

Amir-Mohammad Golmohammadi

Academic rank: Assistant Professor
ORCID:
Education: PhD.
ScopusId:
Faculty: Engineering
Address: Arak University
Phone:

Research

Title
A Hybrid Algorithm Based on Social Engineering and Artificial Neural Network for Fault Warning Detection in Hydraulic Turbines
Type
JournalPaper
Keywords
automated fault warning; BP neural network; artificial intelligence; optimization
Year
2023
Journal Mathematics
DOI
Researchers Amir-Mohammad Golmohammadi

Abstract

Hydraulic turbines constitute an essential component within the hydroelectric power generation industry, contributing to renewable energy production with minimal environmental pollution. Maintaining stable turbine operation presents a considerable challenge, which necessitates effective fault diagnosis and warning systems. Timely and efficient fault w arnings are particularly vital, as they enable personnel to address emerging issues promptly. Although backpropagation (BP) networks are frequently employed in fault warning systems, they exhibit several limitations, such as susceptibility to local optima. To mitigate this issue, this paper introduces an improved social engineering optimizer (ISEO) method aimed at optimizing BP networks for developing a hydraulic turbine warning system. Experimental results reveal that the ISEO-BP-based approach offers a highly effective fault warning system, as evidenced by superior performance metrics when compared toalternative methods.