DocumentCode :
3451031
Title :
A Reconfiguration Technique for Multilevel Inverters Incorporating Diagnostic System Based on Neural Network
Author :
Khomfoi, Surin ; Tolbert, Leon M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Tennessee Univ., Knoxville, TN
fYear :
2006
fDate :
16-19 July 2006
Firstpage :
317
Lastpage :
323
Abstract :
A reconfiguration technique for multilevel inverters incorporating a diagnostic system based on neural network is proposed in this paper. It is difficult to diagnose a multilevel-inverter drive (MLID) system using a mathematical model because MLID systems consist of many switching devices and their system complexity has a nonlinear factor. Therefore, a neural network (NN) classification is applied to the fault diagnosis of a MLID system. Multilayer perceptron networks are used to identify the type and location of occurring faults. The principal component analysis (PCA) is utilized in the feature extraction process to reduce the NN input size. A lower dimensional input space will also usually reduce the time necessary to train a NN, and the reduced noise may improve the mapping performance. The output phase voltage of a MLID can be used to diagnose the faults and their locations. The reconfiguration technique is also proposed. The effects of using the proposed reconfiguration technique at high modulation index are addressed. The proposed system is validated with experimental results. The experimental results show that the proposed system performs satisfactorily to detect the fault type, fault location, and reconfiguration
Keywords :
fault location; feature extraction; invertors; multilayer perceptrons; power engineering computing; principal component analysis; MLID system; NN training; PCA; fault diagnostic system; fault location; feature extraction; modulation index; multilayer perceptron network; multilevel-inverter drive; neural network; principal component analysis; reconfiguration technique; Fault diagnosis; Feature extraction; Inverters; Mathematical model; Modulation; Multilayer perceptrons; Neural networks; Noise reduction; Principal component analysis; Voltage;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computers in Power Electronics, 2006. COMPEL '06. IEEE Workshops on
Conference_Location :
Troy, NY
ISSN :
1093-5142
Print_ISBN :
0-7803-9724-X
Electronic_ISBN :
1093-5142
Type :
conf
DOI :
10.1109/COMPEL.2006.305633
Filename :
4097445
Link To Document :
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