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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	2015 International Conference on Condition Assessment Techniques in Electrical Systems (CATCON) s. 51 - 56
Hlavní autori:	Manjunath, T. G., Kusagur, Ashok
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.12.2015
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
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