近年來世界先進各國正積極投入綠能源產業，如太陽光電、風力發電及生質能源等替代能源，以解決石化燃料耗竭、空氣及環境汙染等問題。因此，足見太陽光電的發展在國際皆受到的重視，這也可從近年來太陽光電專利數急遽成長看出此趨勢。欲使太陽能發電廠的發展逐步普及化，則一套合適且容易使用的監測技術及智慧型故障診斷系統就顯得相當重要。
基於此，本文旨在開發太陽能雲端監測系統，並可加入地理資訊系統、即時用電資訊、報表系統及故障診斷等子系統。其中，監測系統將以Visual C#結合ASP.NET與SQL技術，開發使用介面之資料庫管理系統，提供使用者或管理者查詢相關電能資訊及管理系統或資料庫，接著，透過Peer to Peer (P2P)串流技術、視訊及音訊編解碼技術，將即時視訊畫面傳送給予用戶端，以掌握第一現場即時狀況。智慧型故障診斷方面，本系統採用支撐向量機理論(SVM)來訓練故障數學模型，透過資料擷取系統的收集，將相關電能數據給予支撐向量機分析並得到結果，藉此判斷故障類型，期在故障初期即時排除故障。本文所建立之雲端能源管理平台，除將提昇太陽能電廠之管理及維護效率，亦能增進太陽能發電或再生能源市場上的競爭力。

Recently, numerous advanced countries have been actively involved in developing the green energy industry, specifically alternative energy sources, such as solar and wind power and biomass energy, to resolve various environmental issues (e.g., fossil fuel depletion and air and environmental pollution). The development of solar power generation has received international attention, which can be evidenced by the rapid growth in the number of photovoltaic patents that have been granted in recent years. Consequently, to gradually popularize the development of solar power plants, the creation of a suitable and easy-to-use monitoring technique and smart failure diagnosis method is vital.
In this context, this study endeavors to develop a solar-powered cloud monitoring system that incorporates numerous sub-systems, 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, and a database management system for the user interface was developed to enable users or managers to access power information, management systems, or databases. 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 the smart failure diagnosis, the proposed system employs support vector machine (SVM) theory to train failure mathematical models and, using a data acquisition system, relevant power data are provided to the SVM for analysis. The results can be used 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.

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