Improvement of Grid-Connected Photovoltaic System Using Artificial Neural Network and Genetic Algorithm Under Different Condition


Book Description

Photovoltaic (PV) systems have one of the highest potentials and operating ways for generating electrical power by converting solar irradiation directly into the electrical energy. In order to control maximum output power, using maximum power point tracking (MPPT) system is highly recommended. This paper simulates and controls the photovoltaic source by using artificial neural network (ANN) and genetic algorithm (GA) controller. Also, for tracking the maximum point the ANN and GA are used. Data are optimized by GA and then these optimum values are used in neural network training. The simulation results are presented by using Matlab/Simulink and show that the neural network-GA controller of grid-connected mode can meet the need of load easily and have fewer fluctuations around the maximum power point, also it can increase convergence speed to achieve the maximum power point (MPP) rather than conventional method. Moreover, to control both line voltage and current, a grid side p-q controller has been applied.




Enhancement of Grid-Connected Photovoltaic Systems Using Artificial Intelligence


Book Description

​Enhancement of Grid-Connected Photovoltaic Systems Using Artificial Intelligence presents methods for monitoring transmission systems and enhancing distribution system performance using modern optimization techniques considering different multi-objective functions such as voltage loss sensitivity indexes, reducing total annual cost, and voltage deviation. The authors offer a comprehensive survey of distributed energy resources (DERs), explain the backward/forward sweep (BFS) power flow algorithm, and present simulation results on the optimal integration of photovoltaic-based distributed generators (PV-DG) and distribution static synchronous compensators (DSTATCOM) in different transmission and distribution systems. This book will be a valuable academic and industry resource for electrical engineers, students, and researchers working on optimization techniques, photovoltaic systems, energy engineering, and artificial intelligence.







Wind and Solar Energy Applications


Book Description

This book examines the recent advances, from theoretical and applied perspectives, addressing the major issues associated with renewable energy systems, with each chapter covering fundamental issues and latest developments. This book covers important themes, including solar energy equipment, wind and solar energy systems, energy storage and bioenergy applications, hybrid renewable energy systems, as well as the measurement techniques that are used for these systems. Further, it focusses on original research outcomes on various technological developments and provides insights to taxonomy of challenges, issues, and research directions in renewable energy applications. Features: Covers research and technological developments in wind and solar energy applications Proposes resolution of limitations and performance issues of existing system models and design Incorporates the challenges of adoption of renewable energies system Provides hypotheses, mathematical analysis, and real-time practical applications to practical problems Includes case studies of implementation of solar and wind systems in remote areas This book is aimed at researchers, professionals, and graduate students in electrical and mechanical engineering and renewable energy.







Advances in Data and Information Sciences


Book Description

This book gathers a collection of high-quality peer-reviewed research papers presented at the 2nd International Conference on Data and Information Sciences (ICDIS 2019), held at Raja Balwant Singh Engineering Technical Campus, Agra, India, on March 29–30, 2019. In chapters written by leading researchers, developers, and practitioner from academia and industry, it covers virtually all aspects of computational sciences and information security, including central topics like artificial intelligence, cloud computing, and big data. Highlighting the latest developments and technical solutions, it will show readers from the computer industry how to capitalize on key advances in next-generation computer and communication technology.




Computational Intelligence in Photovoltaic Systems


Book Description

Photovoltaics, among the different renewable energy sources (RES), has become more popular. In recent years, however, many research topics have arisen as a result of the problems that are constantly faced in smart-grid and microgrid operations, such as forecasting of the output of power plant production, storage sizing, modeling, and control optimization of photovoltaic systems. Computational intelligence algorithms (evolutionary optimization, neural networks, fuzzy logic, etc.) have become more and more popular as alternative approaches to conventional techniques for solving problems such as modeling, identification, optimization, availability prediction, forecasting, sizing, and control of stand-alone, grid-connected, and hybrid photovoltaic systems. This Special Issue will investigate the most recent developments and research on solar power systems. This Special Issue “Computational Intelligence in Photovoltaic Systems” is highly recommended for readers with an interest in the various aspects of solar power systems, and includes 10 original research papers covering relevant progress in the following (non-exhaustive) fields: Forecasting techniques (deterministic, stochastic, etc.); DC/AC converter control and maximum power point tracking techniques; Sizing and optimization of photovoltaic system components; Photovoltaics modeling and parameter estimation; Maintenance and reliability modeling; Decision processes for grid operators.




Optimization in Renewable Energy Systems


Book Description

Optimization in Renewable Energy Systems: Recent Perspectives covers all major areas where optimization techniques have been applied to reduce uncertainty or improve results in renewable energy systems (RES). Production of power with RES is highly variable and unpredictable, leading to the need for optimization-based planning and operation in order to maximize economies while sustaining performance. This self-contained book begins with an introduction to optimization, then covers a wide range of applications in both large and small scale operations, including optimum operation of electric power systems with large penetration of RES, power forecasting, transmission system planning, and DG sizing and siting for distribution and end-user premises. This book is an excellent choice for energy engineers, researchers, system operators, system regulators, and graduate students. Provides chapters written by experts in the field Goes beyond forecasting to apply optimization techniques to a wide variety of renewable energy system issues, from large scale to relatively small scale systems Provides accompanying computer code for related chapters




Design and Power Quality Improvement of Photovoltaic Power System


Book Description

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.




Artificial Intelligence and Heuristics for Smart Energy Efficiency in Smart Cities


Book Description

This book emphasizes the role of micro-grid systems and connected networks for the strategic storage of energy through the use of information and communication techniques, big data, the cloud, and meta-heuristics to support the greed for artificial intelligence techniques in data and the implementation of global strategies to meet the challenges of the city in the broad sense. The intelligent management of renewable energy in the context of the energy transition requires the use of techniques and tools based on artificial intelligence (AI) to overcome the challenges of the intermittence of resources and the cost of energy. The advent of the smart city makes an increased call for the integration of artificial intelligence and heuristics to meet the challenge of the increasing migration of populations to the city, in order to ensure food, energy, and environmental security of the citizen of the city and his well-being. This book is intended for policymakers, academics, practitioners, and students. Several real cases are exposed throughout the book to illustrate the concepts and methods of the networks and systems presented. This book proposes the development of new technological innovations—mainly ICT—the concept of “Smart City” appears as a means of achieving more efficient and sustainable cities. The overall goal of the book is to develop a comprehensive framework to help public and private stakeholders make informed decisions on smart city investment strategies and develop skills for assessment and prioritization, including resolution of difficulties with deployment and reproducibility.