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

Hong-Chan Chang, Shang-Chih Lin, Cheng-Chien Kuo and Hao-Ping Yu

Mathematical Problems in Engineering, 2014, vol. 2014, 1-10

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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:564517

DOI: 10.1155/2014/564517

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