H Bridge Inverter System Fault Diagnosis Using a PCA 

Multilevel inverters, for their distinctive performance, have been widely used in high voltage and high power applications in recent years. As power electronicsequipment reliability is very important and to ensure multilevel inverter systems stable operation, it is important to detect and locate faults as quickly as possible. In this context and to improve fault diagnosis accuracy and efficiency of a Cascaded H-bridge Multi-Level Inverter System (CHMLIS), a fault diagnosis strategy based on the Principle Component Analysis and the multi-class Relevance Vector Machine (PCA-mRVM), is elaborated and proposed in this paper.

Firstly, CHMLIS output voltage signals are selected as input fault classification characteristic signals. Then, a Fast Fourier Transform (FFT) is used to pre-process these signals. PCA is used to extract fault signals features and to reduce samples dimensions. Finally, mRVM model is used to classify faulty samples. Compared to traditional approaches, the proposed PCA-mRVM strategy not only achieves higher model sparsity and shorter diagnosis time, but also provides probabilistic outputs for every class membership. Experimental tests are carried-out to highlight the proposed PCA-mRVM diagnosis performances.