Performance evaluation of Modified Genetic Algorithm over Genetic Algorithm implementation on fault diagnosis of Cascaded Multilevel Inverter

Fault diagnosis on Multilevel Inverter (MLI) has been a subject of research for about a decade. This paper is an attempt to deliver a performance analysis of Genetic Algorithm (GA) and the Modified Genetic Algorithm (MGA) working to optimize Artificial Neural Network (ANN) that trains itself on the...

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Vydáno v:2015 International Conference on Condition Assessment Techniques in Electrical Systems (CATCON) s. 51 - 56
Hlavní autoři: Manjunath, T. G., Kusagur, Ashok
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2015
Témata:
Artificial Neural Network
Artificial neural networks
Cascaded multilevel Inverter
Circuit faults
Fault detection and reconfiguration
Genetic Algorithm
Genetic algorithms
Modified Genetic Algorithm
Optimization
Parameter Optimisation
Sociology
Statistics
Training
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Shrnutí:Fault diagnosis on Multilevel Inverter (MLI) has been a subject of research for about a decade. This paper is an attempt to deliver a performance analysis of Genetic Algorithm (GA) and the Modified Genetic Algorithm (MGA) working to optimize Artificial Neural Network (ANN) that trains itself on the fault detection and reconfiguration of the Cascaded Multilevel Inverters (CMLI). The open circuit (OC) faults occurring in the CMLI is considered for this comparative analysis of the performance. The parameters that are taken for the performance evaluation are elapsed time of recovery, Mean Square Error (MSE) and the computational budgets of ANN. Matlab/Simulink is used to develop the CMLI and M-files are used to develop the ANN and optimization algorithms like GA and MGA. The results are obtained and tabulated and performance evaluation carried out.
DOI:10.1109/CATCON.2015.7449507