Synopsis
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.
À propos de l?auteur
Sunil Rao received a B.E. degree in electronics and communications engineering from Visvesvaraya Technological University, India, in 2013, and an M.S. degree in electrical engineering from Arizona State University, Tempe, AZ, USA, in 2018. He is currently pursuing a Ph.D. degree at the School of Electrical, Computer, and Energy Engineering, Arizona State University. His research interests include solar array fault classification using machine learning, signal processing, and deep learning. Sunil is a recipient of the IEEE Irv Kaufman student award in 2019.
Sameeksha Katoch is a Ph.D. student in the School of Electrical, Computer and Energy Engineering at Arizona State University. She received a Bachelor's degree in electronics and communication engineering from National Institute of Technology, Srinagar, India, in 2015 and an M.S. degree in electrical engineering from Arizona State University in 2018. She has received the IEEE Al Gross student award. Her research interests are in computer vision, deep learning, and signal processing for solar array monitoring.
Vivek Narayanaswamy received his Bachelor's degree in electronics and communication engineering from S.S.N College of Engineering, Anna University, Tamil Nadu, India, in 2017. He is currently a Ph.D. student in the school of electrical, computer and energy engineering at ASU, Tempe, AZ. He had completed an internship with Qualcomm R&D in summer 2018 and with Lawrence Livermore National Labs in summer 2019. His research interests include applications of machine learning for signal processing applications. In particular, he works in using machine leaning for speech and audio applications and solar array monitoring.
Gowtham Muniraju received a B.E. degree in electronics and communications engineering from Visvesvaraya Technological University, India, in 2016, and an M.S. degree in electrical engineering from Arizona State University, Tempe, AZ, USA, in 2019. He is currently pursuing a Ph.D. degree with the School of Electrical, Computer, and Energy Engineering, Arizona State University. His research interests include distributed computation in wireless sensor networks, distributed optimization, computer vision, and deep learning.
Cihan Tepedelenlioglu (S'97–M'01) was born in Ankara, Turkey, in 1973. He received a B.S. degree in electrical engineering (highest honors) from the Florida Institute of Technology, Melbourne, in 1995, an M.S. degree in electrical engineering from the University of Virginia, Charlottesville, in 1998, and a Ph.D. degree in electrical and computer engineering from the University of Minnesota, Minneapolis. From January 1999 to May 2001 he was a Research Assistant with the University of Minnesota. He is currently an Associate Professor of electrical engineering at Arizona State University, Tempe. His research interests include statistical signal processing, system identification, wireless communications, estimation and equalization algorithms for wireless systems, multiantenna communications, filter banks and multirate systems, orthogonal frequency division multiplexing, ultrawideband systems, and distributed detection and estimation. Dr. TepedelenlioMglu was a recipient of the 2001 National Science Foundation (early) Career award. He has served as an Associate Editor for several IEEE Transactions, including the IEEE Transactions on Communications, and the IEEE Signal Processing Letters.
Andreas Spanias is a Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University (ASU). He is also the director of the Sensor Signal and Information Processing (SenSIP) center and the founder of the SenSIP industry consortium (now an NSF I/UCRC site). His research interests are in the areas of adaptive signal processing, speech processing, and sensor systems. He and his student team developed the computer simulation software Java-DSP and its award-winning iPhone/iPad and Android versions. He is the author of two textbooks: Audio Processing and Coding by Wiley and DSP: An Interactive Approach (2nd ed.). He contributed to more than 400 papers, 10 monographs, 11 full patents, and 14 patent pre-disclosures. He served as Associate Editor of the IEEE Transactions on Signal Processing and as General Cochair of IEEE ICASSP-99. He also served as the IEEE Signal Processing Vice- President for Conferences. Andreas Spanias is co-recipient of the 2002 IEEE Donald G. Fink paper prize award and was elected Fellow of the IEEE in 2003. He served as Distinguished Lecturer for the IEEE Signal Processing Society in 2004. He is a series editor for the Morgan & Claypool lecture series on algorithms and software. He received the 2018 IEEE Phoenix Chapter award "For significant innovations and patents in signal processing for sensor systems." He also received the IEEE Region 6 Outstanding Educator award in September 2018. He is a Senior Member of the National Academy of Inventors (NAI).
Pavan Turaga (S'05, M'09, SM'14) is an Associate Professor in the School of Arts, Media Engineering, and Electrical Engineering at Arizona State University. He received a B.Tech. degree in electronics and communication engineering from the Indian Institute of Technology Guwahati, India, in 2004, and M.S. and Ph.D. degrees in electrical engineering from the University of Maryland, College Park in 2008 and 2009, respectively. He then spent two years as a research associate at the Center for Automation Research, University of Maryland, College Park. His research interests are in imaging and sensor analytics with a theoretical focus on non-Euclidean and high-dimensional geometric and statistical techniques. He was awarded the Distinguished Dissertation Fellowship in 2009. He was selected to participate in the Emerging Leaders in Multimedia Workshop by IBM, New York, in 2008. He received the National Science Foundation CAREER award in 2015.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
