Fault Diagnosis System for NPC Inverter based on Multi-Layer Principal Component Neural Network

Abstract

This paper presents a fault diagnosis method for a neutral point clamped (NPC) inverter using a multi-layer artificial neural network (MANN). The considered possible faults of NPC inverter include the open-circuit fault occurring in one single device or more devices. The upper, middle and down bridge voltages are adopted the test signals because of the difficulties in isolating some fault modes. A novel multi-layer neural network is proposed to diagnose all possible open-circuit faults. Furthermore, the principal component analysis (PCA) is utilized to reduce the input size of neural network. The comparison between neural network with and without PCA is performed. The simulation and experimental results prove the feasibility of the diagnostic method and show that the proposed method has the advantages of good classification performance and high reliability.