Machine Learning for Solar Array Monitoring, Optimization, and Control
Edited by Jerry Hudgins
This book provides an overview of solar monitoring and control methods and describes machine learning and neural network algorithms for fault classification, shading prediction and topology optimization. Custom neural network architectures are described for use in PV fault detection, localization and classification. In addition, a neural network method is designed to optimize the power output under partial shading by selecting among four standardized solar array connection topologies. Accuracy in fault detection is demonstrated at the level of 90% and topology optimization provides increase in power by as much as 16% under shading. The research described in this book was supported in part by the NSF CPS award 1659871. Portions were also supported by the NSF IRES program award 1854273. Logistical support was provided by the ASU SenSIP center and NCSS I/UCRC site.
Bio
Andreas Spanias is an ASU professor of electrical engineering. He is also the director of the Sensor Signal and Information Processing center and founder of the SenSIP industry consortium. His research interests are in the areas of adaptive signal processing, speech processing, machine learning and sensor systems.
