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Modeling, Analysis, of Induction Motor's Stator Turns Fault Using Neuro-Fuzzy
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Author(s): Hussein. A. Taha (Department of Electrical Engineering, Sohag University, Egypt), M. E. Ammar (Electric Power Department, Cairo University, Egypt)and M. A. Moustafa Hassan (Electric Power Department, Cairo University, Egypt)
Copyright: 2021
Pages: 23
Source title:
Handbook of Research on Modeling, Analysis, and Control of Complex Systems
Source Author(s)/Editor(s): Ahmad Taher Azar (Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt & College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia)and Nashwa Ahmad Kamal (Faculty of Engineering, Cairo University, Giza, Egypt)
DOI: 10.4018/978-1-7998-5788-4.ch020
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Abstract
This chapter discusses modeling and analysis methods for fault detection and diagnosis of stator inter-turn short circuit in three-phase induction machines. dq frame was used to model the induction motor for both health and fault cases to facilitate recognition of motor current and simulate motor environment. Fault diagnosis system was designed with adaptive neuro-fuzzy inference system (ANFIS) to provide an efficient online diagnostic tool. ANFIS diagnostic tool was trained with simulated data that generated by induction motor healthy and faulty models. Approached tool is verified online with a motor under different loading conditions. It determines the fault severity values using the motor current signature analysis (MCSA). Developed tool performance is investigated with a case study of two HP three-phase induction motor using Matlab/Simulink® software.
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