| 書目名稱 | Machine Learning for Solar Array Monitoring, Optimization, and Control | | 編輯 | Sunil Rao,Sameeksha Katoch,Devarajan Srinivasan | | 視頻video | http://file.papertrans.cn/621/620652/620652.mp4 | | 叢書名稱 | Synthesis Lectures on Power Electronics | | 圖書封面 |  | | 描述 | The efficiency of solar energy farms requires detailed analytics and information on each panel regarding voltage, current, temperature, and irradiance. Monitoring utility-scale solar arrays was shown to minimize the cost of maintenance and help optimize the performance of the photo-voltaic arrays under various conditions. We describe a project that includes development of machine learning and signal processing algorithms along with a solar array testbed for the purpose of PV monitoring and control. The 18kW PV array testbed consists of 104 panels fitted with smart monitoring devices. Each of these devices embeds sensors, wireless transceivers, and relays that enable continuous monitoring, fault detection, and real-time connection topology changes. The facility enables networked data exchanges via the use of wireless data sharing with servers, fusion and control centers, and mobile devices. We develop machine learning and neural network algorithms for fault classification. In addition, we use weather camera data for cloud movement prediction using kernel regression techniques which serves as the input that guides topology reconfiguration. Camera and satellite sensing of skyline feat | | 出版日期 | Book 2020 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-02505-1 | | isbn_softcover | 978-3-031-01377-5 | | isbn_ebook | 978-3-031-02505-1Series ISSN 1931-9525 Series E-ISSN 1931-9533 | | issn_series | 1931-9525 | | copyright | Springer Nature Switzerland AG 2020 |
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