Handbook of Artificial Intelligence Techniques in Photovoltaic Systems: Modelling, Control, Optimization, Forecasting and Fault Diagnosis provides readers with a comprehensive and detailed overview of the role of artificial intelligence in PV systems. Covering up-to-date research and methods on how, when and why to use and apply AI techniques in solving most photovoltaic problems, this book will serve as a complete reference in applying intelligent techniques and algorithms to increase PV system efficiency. Sections cover problem-solving data for challenges, including optimization, advanced control, output power forecasting, fault detection identification and localization, and more.Supported by the use of MATLAB and Simulink examples, this comprehensive illustration of AI-techniques and their applications in photovoltaic systems will provide valuable guidance for scientists and researchers working in this area. - Includes intelligent methods in real-time using reconfigurable circuits FPGAs, DSPs and MCs - Discusses the newest trends in AI forecasting, optimization and control applications - Features MATLAB and Simulink examples highlighted throughout

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.

Introduction to AI techniques for Renewable Energy System Artificial Intelligence (AI) techniques play an essential role in modeling, analysis, and prediction of the performance and control of renewable energy. The algorithms used to model, control, or predict performances of the energy systems are complicated, involving differential equations, enormous computing power, and time requirements. Instead of complex rules and mathematical routines, AI techniques can learn critical information patterns within a multidimensional information domain. Design, control, and operation of renewable energy systems require a long-term series of meteorological data such as solar radiation, temperature, or wind data. Such long-term measurements are often non-existent for most of the interest locations or, wherever they are available, they suffer from several shortcomings, like inferior quality of data, and in-sufficient long series. The book focuses on AI techniques to overcome these problems. It summarizes commonly used AI methodologies in renewal energy, with a particular emphasis on neural networks, fuzzy logic, and genetic algorithms. It outlines selected AI applications for renewable energy. In particular, it discusses methods using the AI approach for prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems. Features Focuses on a significant area of concern to develop a foundation for the implementation of renewable energy system with intelligent techniques Showcases how researchers working on renewable energy systems can correlate their work with intelligent and machine learning approaches Highlights international standards for intelligent renewable energy systems design, reliability, and maintenance Provides insights on solar cell, biofuels, wind, and other renewable energy systems design and characterization, including the equipment for smart energy systems This book, which includes real-life examples, is aimed at undergraduate and graduate students and academicians studying AI techniques used in renewal energy systems.

The present book focuses on recent advances methods and applications in photovoltaic (PV) systems. The book is divided into two parts: the first part deals with some theoretical, simulation and experiments on solar cells, including efficiency improvement, new materials and behavior performances. While the second part of the book devoted mainly on the application of advanced methods in PV systems, including advanced control, FPGA implementation, output power forecasting based artificial intelligence technique (AI), high PV penetration, reconfigurable PV architectures and fault detection and diagnosis based AI. The authors of the book trying to show to readers more details about some theoretical methods and applications in solar cells and PV systems (eg. advanced algorithms for control, optimization, power forecasting, monitoring and fault diagnosis methods). The applications are mainly carried out in different laboratories and location around the world as projects (Algeria, KSA, Turkey, Morocco, Italy and France). The book will be addressed to scientists, academics, researchers and PhD students working in this topic. The book will help readers to understand some applications including control, forecasting, monitoring, fault diagnosis of photovoltaic plants, as well as in solar cells such as behavior performances and efficiency improvement. It could be also be used as a reference and help industry sectors interested by prototype development.

The last ten years have seen rapid advances in nanoscience and nanotechnology, allowing unprecedented manipulation of the nanoscale structures controlling solar capture, conversion, and storage. Filled with cutting-edge solar energy research and reference materials, the Handbook of Research on Solar Energy Systems and Technologies serves as a one-stop resource for the latest information regarding different topical areas within solar energy. This handbook will emphasize the application of nanotechnology innovations to solar energy technologies, explore current and future developments in third generation solar cells, and provide a detailed economic analysis of solar energy applications.

Practical Handbook of Photovoltaics, Third Edition, is a 'benchmark' publication for those involved in the design, manufacture and use of these devices. This fully revised handbook includes brand new sections on smart grids, net metering and the modeling of photovoltaic systems, as well as fully revised content on developments in photovoltaic applications, the economics of PV manufacturing and updated chapters on solar cell function, raw materials, photovoltaic standards, calibration and testing, all with new examples and case studies. The editor has assembled internationally-respected contributors from industry and academia around the world to make this a truly global reference. It is essential reading for electrical engineers, designers of systems, installers, architects, policymakers and physicists working with photovoltaics. - Presents a cast of international experts from industry and academia to ensure the highest quality information from multiple stakeholder perspectives - Covers all things photovoltaics, from the principles of solar cell function and their raw materials, to the installation and design of full photovoltaic systems - Includes case studies, practical examples, and reports on the latest advances and worldwide applications

This book provides a clear explanation of how to apply artificial intelligence (AI) to solve the challenges in solar photovoltaic technology. It introduces readers to new AI-based approaches and technologies that help manage and operate solar photovoltaic systems effectively. It also motivates readers to find new AI-based solutions for these challenges by providing a comprehensive collection of findings on AI techniques. It covers important topics including solar irradiance variability, solar power forecasting, solar irradiance forecasting, maximum power point tracking, hybrid algorithms, swarm optimization, evolutionary optimization, sensor-based sun- tracking systems, single-axis and dual-axis sun-tracking systems, smart metering, frequency regulation using AI, emerging multilevel inverter topologies, and voltage and reactive power control using AI. This book is useful for senior undergraduate students, graduate students, and academic researchers in areas such as electrical engineering, electronics and communication engineering, computer science, and renewable energy.