Volume 8 Number 6 (Jun. 2013)
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JCP 2013 Vol.8(6): 1464-1471 ISSN: 1796-203X
doi: 10.4304/jcp.8.6.1464-1471

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

Danjiang Chen1, Yinzhong Ye2, and Rong Hua2
1 Shanghai Maritime University, Shanghai, China; Zhejiang Wanli University, Ningbo Zhejiang, China
2 Shanghai Institute of Technology, Shanghai, China


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.

Index Terms—three level inverter, fault diagnosis, MANN, PCA

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Cite: Danjiang Chen, Yinzhong Ye, and Rong Hua, " Fault Diagnosis System for NPC Inverter based on Multi-Layer Principal Component Neural Network," Journal of Computers vol. 8, no. 6, pp. 1464-1471, 2013.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Monthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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