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Mathematical Problems in Engineering
Volume 2013, Article ID 816296, 6 pages
Research Article

Study of Intelligent Photovoltaic System Fault Diagnostic Scheme Based on Chaotic Signal Synchronization

Department of Electrical Engineering, National Chin-Yi University of Technology, No. 57, Section 2, Zhongshan Road, Taiping District, Taichung 41170, Taiwan

Received 14 July 2013; Revised 10 September 2013; Accepted 18 September 2013

Academic Editor: Dumitru Baleanu

Copyright © 2013 Chin-Tsung Hsieh and Jen Shiu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


As the photovoltaic system consists of many equipment components, manual inspection will be very costly. This study proposes the photovoltaic system fault diagnosis based on chaotic signal synchronization. First, MATLAB was used to simulate the fault conditions of solar system, and the maximum power point tracking (MPPT) was used to ensure the system's stable power and capture and record the system fault feature signals. The dynamic errors of various fault signals were extracted by chaotic signal synchronization, and the dynamic error data of various fault signals were recorded completely. In the photovoltaic system, the captured output voltage signal was used as the characteristic values for fault recognition, and the extension theory was used to create the joint domain and classical domain of various fault conditions according to the collected feature data. The matter-element model of extension engineering was constructed. Finally, the whole fault diagnosis system is only needed to capture the voltage signal of the solar photovoltaic system, so as to know the exact fault condition effectively and rapidly. The proposed fault diagnostor can be implemented by embedded system and be combined with ZigBee wireless network module in the future, thus reducing labor cost and building a complete portable renewable energy system fault diagnostor.