This book provides comprehensive insight into the fault detection techniques implemented for photovoltaic (PV) panels. It includes studies related to predictive maintenance needed to improve the performance of the solar PV systems using Artificial Intelligence (AI) techniques. The readers gain knowledge on the fault identification algorithm and the significance of all such algorithms in real-time power system applications. Gives detailed overview of fundamental concepts of fault diagnosis algorithm for solar PV system Explains AC and DC side of the solar PV system-based electricity…mehr
This book provides comprehensive insight into the fault detection techniques implemented for photovoltaic (PV) panels. It includes studies related to predictive maintenance needed to improve the performance of the solar PV systems using Artificial Intelligence (AI) techniques. The readers gain knowledge on the fault identification algorithm and the significance of all such algorithms in real-time power system applications.
Gives detailed overview of fundamental concepts of fault diagnosis algorithm for solar PV system
Explains AC and DC side of the solar PV system-based electricity generation with real-time examples
Covers effective extraction of the energy from solar radiation
Illustrates artificial intelligence techniques for detecting the faults occurring in the solar PV system
Includes MATLAB® based simulations and results on fault diagnosis including case studies
This book is aimed at researchers, professionals and graduate students in electrical engineering, artificial intelligence, control algorithms, energy engineering, photovoltaic systems, industrial electronics.
K. Mohana Sundaram is Professor in Department of EEE, KPR Institute of Engineering and Technology , Coimbatore India. He has 18 years of teaching and research experience. His current research interests include intelligent controllers, power systems, embedded systems, and power electronics. He has completed a funded project of worth Rs. 30.84 lakhs sponsored by DST, Government of India. He received his B.E. degree in Electrical and Electronics Engineering from University of Madras in 2000, M. Tech degree in High Voltage Engineering from SASTRA University in 2002, and Ph.D. degree from Anna University, India, in 2014. Under his supervision, four candidates have completed their Ph.D. from Anna University, Chennai, while nine candidates are still pursuing. He has published 47 articles in international journals. He serves as reviewer for IEEE journals, Springer journals, and Elsevier. He is a member of IE, ISTE, IAENG, etc. Sanjeevikumar Padmanaban (M'12-SM'15) received the bachelor's degree in electrical engineering from the University of Madras, Chennai, India, in 2002, the master's degree (Hons.) in electrical engineering from Pondicherry University, Puducherry, India, in 2006, and the PhD degree in electrical engineering from the University of Bologna, Bologna, Italy, in 2012. He was an Associate Professor with VIT University from 2012 to 2013. In 2013, he joined the National Institute of Technology, India, as a Faculty Member. In 2014, he was invited as a Visiting Researcher at the Department of Electrical Engineering, Qatar University, Doha, Qatar, funded by the Qatar National Research Foundation (Government of Qatar). He continued his research activities with the Dublin Institute of Technology, Dublin, Ireland, in 2014. He was an Associate Professor with the Department of Electrical and Electronics Engineering, University of Johannesburg, Johannesburg, South Africa, from 2016 to 2018. Since 2018, he has been a Faculty Member with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark. He has authored more than 300 scientific papers. S. Padmanaban was the recipient of the Best Paper cum Most Excellence Research Paper Award from IET-SEISCON'13, IET-CEAT'16, IEEE-EECSI'19, IEEE-CENCON'19 and five best paper awards from ETAEERE'16 sponsored Lecture Notes in Electrical Engineering, Springer book. He is a Fellow of the Institution of Engineers, India, the Institution of Electronics and Telecommunication Engineers, India, and the Institution of Engineering and Technology, U.K. He is an Editor/Associate Editor/Editorial Board for refereed journals, in particular the IEEE SYSTEMS JOURNAL, IEEE Transaction on Industry Applications, IEEE ACCESS, IET Power Electronics, and International Transactions on Electrical Energy Systems Journal, Wiley Publications, and the Subject Editor for the IET Renewable Power Generation, IET Generation, Transmission and Distribution, and FACTS journal (Canada). Jens Bo Holm-Nielsen currently works at the Department of Energy Technology, Aalborg University and Head of the Esbjerg Energy Section. On this research, activities established the Center for Bioenergy and Green Engineering in 2009 and serve as the Head of the research group. He has vast experience in the field of Bio-refinery concepts and Biogas production-Anaerobic Digestion. Implementation projects of Bio-energy systems in Denmark with provinces and European states. He served as the technical advisory for many industries in this field. He has executed many large scale European Union and United Nation projects in research aspects of Bioenergy, bio refinery processes, the full chain of biogas and Green Engineering. He has authored more than 100 scientific papers. He was a member on invitation with various capacities in the committee for over 250 various international conferences and Organizer of international conferences, workshops and training programmes in Europe, Central Asia and China. Focus areas Renewable Energy - Sustainability - Green jobs for all.
Inhaltsangabe
Chapter 1 Online Fault Diagnosis and Fault State Classification Methods for PV Systems
Chapter 2 Fault Diagnosis Techniques for Solar Plant Based on Unsupervised Sample Clustering Probabilistic Neural Network Model
Chapter 3 A Remote Diagnosis Using Variable Fractional Order with Reinforcement Controller for Solar-MPPT Intelligent System
Chapter 4 Challenges and Opportunities for Predictive Maintenance of Solar Plants
Chapter 5 Machine Learning-Based Predictive Maintenance for Solar Plants for Early Fault Detection and Diagnostics
Chapter 6 Optimization Modeling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants
Chapter 7 Deep Learning-Based Predictive Maintenance of Photovoltaic Panels
