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A Matlab and Simulink Based Three-Phase Inverter Fault Diagnosis Method Using Three-Dimensional Features
Author(s) -
Muhammad Talha,
Furqan Asghar,
Sung Ho Kim
Publication year - 2016
Publication title -
international journal of fuzzy logic and intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.296
H-Index - 9
eISSN - 2093-744X
pISSN - 1598-2645
DOI - 10.5391/ijfis.2016.16.3.173
Subject(s) - fault (geology) , artificial neural network , stuck at fault , inverter , matlab , computer science , fault indicator , fault detection and isolation , fault coverage , fault model , feature extraction , power (physics) , real time computing , pattern recognition (psychology) , engineering , artificial intelligence , voltage , electronic circuit , electrical engineering , physics , quantum mechanics , seismology , actuator , geology , operating system
Fault detection and diagnosis is a task to monitor the occurrence of faults and pinpoint the exact location of faults in the system. Fault detection and diagnosis is gaining importance in development of efficient, advanced and safe industrial systems. Three phase inverter is one of the most common and excessively used power electronic system in industries. A fault diagnosis system is essential for safe and efficient usage of these inverters. This paper presents a fault detection technique and fault classification algorithm. A new feature extraction approach is proposed by using three-phase load current in three-dimensional space and neural network is used to diagnose the fault. Neural network is responsible of pinpointing the fault location. Proposed method and experiment results are presented in detail.

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