Modeling Identification And Control Methods In Renewable Energy Systems

Most of the research and experiments in the fields of modeling and control systems have spent significant efforts to find rules from various complicated phenomena by principles, observations, measured data, logic derivations. The rules are normally summarized as concise and quantitative expressions or “models”. “Identification” provides mechanisms to establish the models and “control” provides mechanisms to improve system performances. This book reflects the relevant studies and applications in the area of renewable energies, with the latest research from interdisciplinary theoretical studies, computational algorithm development to exemplary applications. It discusses how modeling and control methods such as recurrent neural network, Pitch Angle Control, Fuzzy control, Sliding Mode Control and others are used in renewable systems. It covers topics as photovoltaic systems, wind turbines, maximum power point tracking, batteries for renewable energies, solar energy, thermal energy and so on. This book is edited and written by leading experts in the field and offers an ideal reference guide for researchers and engineers in the fields of electrical/electronic engineering, control system and energy.

Standalone (off-grid) renewable energy systems supply electricity in places where there is no access to a standard electrical grid. These systems may include photovoltaic generators, wind turbines, hydro turbines or any other renewable electrical generator. Usually, this kind of system includes electricity storage (commonly lead-acid batteries, but also other types of storage can be used). In some cases, a backup generator (usually powered by fossil fuel, diesel or gasoline) is part of the hybrid system. The modelling of the components, the control of the system and the simulation of the performance of the whole system are necessary to evaluate the system technically and economically. The optimization of the sizing and/or the control is also an important task in this kind of system.

Renewable Energy Systems: Modelling, Optimization and Control aims to cross-pollinate recent advances in the study of renewable energy control systems by bringing together diverse scientific breakthroughs on the modeling, control and optimization of renewable energy systems by leading researchers. The book brings together the most comprehensive collection of modeling, control theorems and optimization techniques to help solve many scientific issues for researchers in renewable energy and control engineering. Many multidisciplinary applications are discussed, including new fundamentals, modeling, analysis, design, realization and experimental results. The book also covers new circuits and systems to help researchers solve many nonlinear problems. This book fills the gaps between different interdisciplinary applications, ranging from mathematical concepts, modeling, and analysis, up to the realization and experimental work. Covers modeling, control theorems and optimization techniques which will solve many scientific issues for researchers in renewable energy Discusses many multidisciplinary applications with new fundamentals, modeling, analysis, design, realization and experimental results Includes new circuits and systems, helping researchers solve many nonlinear problems

Fault detection, control, and forecasting have a vital role in renewable energy systems (Photovoltaics (PV) and wind turbines (WTs)) to improve their productivity, ef?ciency, and safety, and to avoid expensive maintenance. For instance, the main crucial and challenging issue in solar and wind energy production is the volatility of intermittent power generation due mainly to weather conditions. This fact usually limits the integration of PV systems and WTs into the power grid. Hence, accurately forecasting power generation in PV and WTs is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. Also, accurate and prompt fault detection and diagnosis strategies are required to improve efficiencies of renewable energy systems, avoid the high cost of maintenance, and reduce risks of fire hazards, which could affect both personnel and installed equipment. This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems.

Modelling and Control Dynamics in Microgrid Systems with Renewable Energy Resources looks at complete microgrid systems integrated with renewable energy resources (RERs) such as solar, wind, biomass or fuel cells that facilitate remote applications and allow access to pollution-free energy. Designed and dedicated to providing a complete package on microgrid systems modelling and control dynamics, this book elaborates several aspects of control systems from classical approach to advanced techniques based on artificial intelligence. It captures the typical modes of operation of microgrid systems with distributed energy storage applications like battery, flywheel, electrical vehicles infrastructures that are integrated within microgrids with desired targets. More importantly, the techno-economics of these microgrid systems are well addressed to accelerate the process of achieving the SDG7 i.e., affordable and clean energy for all (E4ALL). This reference presents the latest developments including step by step modelling processes, data security and standards protocol for commissioning of microgrid projects, making this a useful tool for researchers, engineers and industrialists wanting a comprehensive reference on energy systems models. Includes simulations with case studies and real-world applications of energy system models Detailed systematic modeling with mathematical analysis is covered Features possible operating scenarios with solutions to the encountered issues

This book presents state of the art applications of artificial intelligence in energy and renewable energy systems design and modelling. It covers such topics as solar energy, wind energy, biomass and hydrogen as well as building services systems, power generation systems, combustion processes and refrigeration. In all these areas applications of artificial intelligence methods such as artificial neural networks, genetic algorithms, fuzzy logic and a combination of the above, called hybrid systems, are included. The book is intended for a wide audience ranging from the undergraduate level up to the research academic and industrial communities dealing with modelling and performance prediction of energy and renewable energy systems.

Presents the principles, derivations, and equations of renewable energy power plants, including MATLAB code Advanced Control of Grid-Integrated Renewable Energy Power Plants presents a comprehensive introduction to the power system dynamics and stability of renewable energy power plants (RPPs), such as wind turbines, wind power plants, and photovoltaic systems. The author—a noted expert on the topic—takes a rigorous approach to the analysis and modelling of RPPs, such as turbine rotors, PV cells, electronic converters, transformers, and aggregated grid models. This approach allows for the validation of requirements for sustainable power systems based on formal methods. The text deals with nonlinear model-based observer and control design techniques in the Takagi-Sugeno (TS) framework. It explores the Takagi-Sugeno fuzzy (TSF) models which are nonlinear systems, in which the consequent part of a fuzzy rule is a mathematical formula, representing local dynamics or limited nonlinearities by sector functions. The strong property of the TSF finds several applications modelling dynamical systems that can be described by differential equations. The book’s practical exercises use MATLAB code to help model simulation models of single large-scale wind turbines, wind farms, and photovoltaic plants. This important book: Provides a complete introduction to the power system dynamics and stability of renewable energy power plants Includes a detailed discussion of how to design model model-based controllers for RPPs Takes a rigorous approach to the analysis and modelling of RPPs, including turbine rotors, PV cells, electronic converters, transformers, aggregated grid models, and more Includes MATLAB code to model simulation models of single large-scale wind turbines, wind farms, and photovoltaic plants Written for students and researchers of renewable energy, Advanced Control of Grid-Integrated Renewable Energy Power Plants offers an authoritative text to the topic.