Computational Intelligence in Photovoltaic Systems

• Main Author: Ogliari , Emanuele (auth)
• Other Authors: Leva, Sonia (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2019
• Subjects: History of engineering & technology; artificial neural network; online diagnosis; genetic algorithm; renewable energy; unit commitment; photovoltaic panel; power forecasting; metaheuristic; monitoring system; embedded systems; firefly algorithm; tracking system; MPPT algorithm; integrated storage; day-ahead forecast; solar radiation; prototype model; artificial neural networks; parameter extraction; thermal image; thermal model; solar cell; PV cell temperature; evolutionary algorithms; uncertainty; battery; harmony search meta-heuristic algorithm; single-diode photovoltaic model; symbiotic organisms search; photovoltaics; tilt angle; smart photovoltaic system blind; orientation; photovoltaic; particle swarm optimization; analytical methods; computational intelligence; statistical errors; ensemble methods; solar photovoltaic; electrical parameters; demand response; metaheuristic algorithm
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.