Machine learning for solar array monitoring, optimization, and control / Sunil Rao, Sameeksha Katoch, Vivek Narayanaswamy, Gowtham Muniraju, Cihan Tepedelenlioglu, Andreas Spanias, Pavan Turaga, Raja Ayyanar, and Devarajan Srinivasan.Material type: TextSeries: Synthesis digital library of engineering and computer science | Synthesis lectures on power electronics ; #13.Publisher: San Rafael, California (1537 Fourth Street, 1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, Description: 1 PDF (ix, 81 pages) : illustrations (some color)Content type: text Media type: electronic Carrier type: online resourceISBN: 9781681739083Subject(s): Photovoltaic power systems -- Automatic control | Intelligent control systems | Machine learning | deep learning | photovoltaic systems | machine learning | neural networks | PV topology optimization | solar panel shading | solar array fault detection | graph signal processing | PV inverters | smart grid | computer vision in PVGenre/Form: Electronic books.Additional physical formats: Print version:: No titleDDC classification: 621.31/244 LOC classification: TK1087 | .R366 2020ebOnline resources: Abstract with links to resource | Abstract with links to full text Also available in print.
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Mode of access: World Wide Web.
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Part of: Synthesis digital library of engineering and computer science.
Includes bibliographical references (pages 65-78).
1. Introduction -- 2. Solar array research testbed -- 2.1. The SenSIP 18 kW solar array testbed -- 2.2. Design of the solar array testbed -- 2.3. The MATLAB simulink model -- 2.4. The PVWatts dataset -- 2.5. Summary
3. Fault classification using machine learning -- 3.1. Faults in PV arrays -- 3.2. Standard machine learning algorithms -- 3.3. Neural networks -- 3.4. Fault detection and computational complexity -- 3.5. Graph signal processing -- 3.6. Semi-supervised graph-based classification -- 3.7. Summary
4. Shading prediction for power optimization -- 4.1. Partial shading on photovoltaic panels -- 4.2. Prior work in cloud motion and dynamic texture synthesis -- 4.3. Dynamic texture prediction model -- 4.4. Simulation results -- 4.5. Shading and topology reconfiguration -- 4.6. Summary
5. Topology reconfiguration using neural networks -- 5.1. Need for topology reconfiguration -- 5.2. Prior work -- 5.3. Machine learning for topology reconfiguration -- 5.4. Methodology -- 5.5. Empirical evaluations -- 5.6. Summary -- 6. Summary.
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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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 features as well as parameter sensing at each panel provides information for fault detection and power output optimization using topology reconfiguration achieved using programmable actuators (relays) in the SMDs. More specifically, a custom neural network algorithm guides the selection among four standardized topologies. Accuracy in fault detection is demonstrate at the level of 90+% and topology optimization provides increase in power by as much as 16% under shading.
Also available in print.
Title from PDF title page (viewed on September 8, 2020).