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Full Description
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.
Contents
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
