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Short-Circuit Fault Diagnosis Based on Rough Sets Theory for a Single-Phase Inverter

The short-circuit (SC) fault diagnosis in inverters is an important procedure for the continuity of the performance and the extension of its useful life. The methods of diagnosis of SC failures produce good results, however, they present unfavorable aspects: they detect only one of the faults of SC,...

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Published in:IEEE transactions on power electronics 2019-05, Vol.34 (5), p.4747-4764
Main Authors: de Mello Oliveira, Andre Barros, Moreno, Robson Luiz, Ribeiro, Enio Roberto
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creator de Mello Oliveira, Andre Barros
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Ribeiro, Enio Roberto
description The short-circuit (SC) fault diagnosis in inverters is an important procedure for the continuity of the performance and the extension of its useful life. The methods of diagnosis of SC failures produce good results, however, they present unfavorable aspects: they detect only one of the faults of SC, that is to say, the hard switch fault (HSF) or the fault under load (FUL); depend on switch parameters; and use artificial intelligence (AI) techniques in their algorithms, which are executed simultaneously with the inverter operation. This article presents a method of diagnosing SC faults performed with a digital circuit. The proposed method identifies short-circuit faults: HSF and FUL; can be used with any switch, regardless of its parameters; and does not use AI algorithms and techniques concurrently with inverter operation. The digital diagnostic circuit is obtained with the use of rough sets theory (RST), which optimizes and defines a minimum set of variables necessary to diagnose faults. Applying RST to the variables obtains a set of diagnostic rules. These rules are performed with basic logic functions and, for this reason, a digital diagnostic circuit is obtained. The diagnostic variables are the command signals and the voltage source inverter switches currents. The simulation and experimental results validate the shown diagnostic method.
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The methods of diagnosis of SC failures produce good results, however, they present unfavorable aspects: they detect only one of the faults of SC, that is to say, the hard switch fault (HSF) or the fault under load (FUL); depend on switch parameters; and use artificial intelligence (AI) techniques in their algorithms, which are executed simultaneously with the inverter operation. This article presents a method of diagnosing SC faults performed with a digital circuit. The proposed method identifies short-circuit faults: HSF and FUL; can be used with any switch, regardless of its parameters; and does not use AI algorithms and techniques concurrently with inverter operation. The digital diagnostic circuit is obtained with the use of rough sets theory (RST), which optimizes and defines a minimum set of variables necessary to diagnose faults. Applying RST to the variables obtains a set of diagnostic rules. 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1941-0107
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source IEEE Electronic Library (IEL) Journals
subjects Algorithms
Artificial intelligence
Circuit faults
Computer simulation
Current measurement
Diagnostic systems
Digital electronics
Digital fault diagnosis circuit
Fault detection
fault detection and location
Fault diagnosis
fault diagnosis method
Inverters
Logic gates
Parameters
rough sets theory (RST)
Short circuits
short-circuit (SC) fault diagnosis
single-phase voltage source inverter
Switches
Voltage measurement
title Short-Circuit Fault Diagnosis Based on Rough Sets Theory for a Single-Phase Inverter
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