Fault diagnosis of H-bridge cascaded five-level inverter based on improved support vector machine with gray wolf algorithm

Abstract

The reliability of power electronics is very important to the safety and efficiency operation of the multilevel inverters, and it is very necessary to quickly detect and locate the faults. To solve the difficulty in locating open-circuit faults and improve the diagnosis accuracy for the H-bridge cascaded five-level inverters, an improved support vector machine (SVM) with gray wolf (GWO) optimization method has been elaborated and proposed in this paper especially for single switch faults and double switches faults in one phase. Firstly, the output voltage signals are selected as the sampling data, and whose eigenvectors have been taken as the input signals of the wavelet packet energy entropy method, and the dimensionality of these extracted eigenvectors is furtherly reduced with a probabilistic principle component analysis (PPCA) method to reduce the calculations and improve the diagnosis efficiency. Then, the SVM method is used for the fault classification, during which the GWO optimization has been introduced to find the optimal value of SVM, so as to improve the diagnostic efficiency. Experimental results show that this diagnosis method can not only achieve higher diagnostic accuracy, smaller identification error, and has greater efficiency compared with ordinary SVM or some other traditional diagnosis methods and other intelligent optimization algorithms, which promotes the fault diagnosis research for these multilevel inverters.