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Mathematical Problems in Engineering
Volume 2014, Article ID 564517, 10 pages
http://dx.doi.org/10.1155/2014/564517
Research Article

Cloud Monitoring for Solar Plants with Support Vector Machine Based Fault Detection System

1Department of Electrical Engineering, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Road, Taipei 10607, Taiwan
2Department of Electrical Engineering, Saint John’s University, No. 499, Section 4, TamKing Road, Tamsui, Taipei 25135, Taiwan

Received 28 February 2014; Accepted 4 April 2014; Published 3 July 2014

Academic Editor: Her-Terng Yau

Copyright © 2014 Hong-Chan Chang et al. 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.

Abstract

This study endeavors to develop a cloud monitoring system for solar plants. This system incorporates numerous subsystems, such as a geographic information system, an instantaneous power-consumption information system, a reporting system, and a failure diagnosis system. Visual C# was integrated with ASP.NET and SQL technologies for the proposed monitoring system. A user interface for database management system was developed to enable users to access solar power information and management systems. In addition, by using peer-to-peer (P2P) streaming technology and audio/video encoding/decoding technology, real-time video data can be transmitted to the client end, providing instantaneous and direct information. Regarding smart failure diagnosis, the proposed system employs the support vector machine (SVM) theory to train failure mathematical models. The solar power data are provided to the SVM for analysis in order to determine the failure types and subsequently eliminate failures at an early stage. The cloud energy-management platform developed in this study not only enhances the management and maintenance efficiency of solar power plants but also increases the market competitiveness of solar power generation and renewable energy.