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 introduces the FPGA technology used in the laboratory sessions, and provides a step-by-step guide for designing and simulation of digital circuits. It utilizes the VHDL language, which is one of the most common language used to describe the design of digital systems. The Quartus II, Xilinx ISE 14.7 and ModelSim software are used to process the VHDL code and make simulations, and then the Altera and Xilinx FPGA platforms are employed to implement the simulated digital designs. The book is composed of four parts. The first part of this book has two chapters and covers various aspects: FPGA architectures, ASIC vs FPGA comparison, FPGA design flow and basic VHDL concepts necessary to describe the design of digital systems. The second part of the book includes three chapters that deal with the design of digital circuits such as combinational logic circuits, sequential logic circuits and finite state machines. The third part of the book is reserved for laboratory projects carried out on the FPGA platform. It is a largely hands-on lab class for design digital circuits and implementing their designs on the Altera FPGA platform. Finally, the fourth part of this work is devoted to recent applications carried out on FPGAs, in particular advanced techniques in renewable energy systems. The book is primarily intended for students, scholars, and industrial practitioners interested in the design of modern digital systems.
This book introduces the FPGA technology used in the laboratory sessions, and provides a step-by-step guide for designing and simulation of digital circuits. It utilizes the VHDL language, which is one of the most common language used to describe the design of digital systems. The Quartus II, Xilinx ISE 14.7 and ModelSim software are used to process the VHDL code and make simulations, and then the Altera and Xilinx FPGA platforms are employed to implement the simulated digital designs. The book is composed of four parts. The first part of this book has two chapters and covers various aspects: FPGA architectures, ASIC vs FPGA comparison, FPGA design flow and basic VHDL concepts necessary to describe the design of digital systems. The second part of the book includes three chapters that deal with the design of digital circuits such as combinational logic circuits, sequential logic circuits and finite state machines. The third part of the book is reserved for laboratory projects carried out on the FPGA platform. It is a largely hands-on lab class for design digital circuits and implementing their designs on the Altera FPGA platform. Finally, the fourth part of this work is devoted to recent applications carried out on FPGAs, in particular advanced techniques in renewable energy systems. The book is primarily intended for students, scholars, and industrial practitioners interested in the design of modern digital systems.
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 presents a case study on a new approach for the optimum design of rooftop, grid-connected photovoltaic-system installation. The study includes two scenarios using different brands of commercially available PV modules and inverters. It investigates and compares several different rooftop grid-connected PV-system configurations taking into account PV modules and inverter specifications. The book also discusses the detailed dynamic MATLAB/Simulink model of the proposed rooftop grid-connected PV system, and uses this model to estimate the energy production capabilities, cost of energy (COE), simple payback time (SPBT) and greenhouse gas (GHG) emissions for each configuration. The book then presents a comprehensive small signal MATLAB/Simulink model for the DC-DC converter operated under continuous conduction mode (CCM). First, the buck converter is modeled using state-space average model and dynamic equations, depicting the converter, are derived. Then a detailed MATLAB/Simulink model utilizing SimElectronics® Toolbox is developed. Lastly, the robustness of the converter model is verified against input voltage variations and step load changes.
This book opens with a brief introduction to renewable energy and the advantages of solar energy systems, an overview of concentrated solar power (CSP) system technologies and modeling, and the application of artificial neural network (ANN) technologies in various solar field systems. Later chapters cover data and operation methods of central tower receiver power plants (CTRPP), important models of ANN techniques used in solar energy fields, accurate methods for modeling CTRPP, the economics of solar energy systems, the CSP impacts on the penetration level of photovoltaic (PV) systems, and a look at the reliability of systems using case studies on PV systems and hybrid PV and CSP systems. Provides an introduction to renewable energy and the advantages of solar energy systems Outlines methods for modeling central tower receiver power plants Includes case studies on photovoltaic (PV) and hybrid PV and concentrated solar power systems
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