Open Circuit Fault Diagnosis of Cascaded H-Bridge MLI Using k-NN Classifier Based on PPCA

Abstract

Nowadays, great progress has been made in the development of multilevel inverters in renewable energy sources and other electrical drive applications. A k-Nearest Neighbors (k-NN) algorithm is applied to fault diagnosis of Cascaded H-Bridge Multilevel Inverter (CHMLI), this new fault diagnosis method is based on Probabilistic Principle Component Analysis (PPCA). The output voltage signals under different fault conditions of CHMLI are taken as the fault characteristics signals to avoid the effect of load variation on fault diagnosis. PPCA is used to optimize the data without changing the original properties of the input data, and k-NN is used to identify the accurate fault location and diagnosis the fault. The proposed technique is validated by conducting the experiment using Field-Programmable Gate Array (FPGA) controller. The simulation and experimental results shows that the proposed fault diagnosis method reduced the fault diagnosis time and improved the accuracy.