Particle Swarm Optimization with Reducing Boundaries (PSO-RB) for Maximum Power Point Tracking of Partially Shaded PV Arrays

Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.

Abstract: Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manipulated according to the rule of survival of the fittest through "genetic" operations, such as mutation, crossover and reproduction. A best solution is evolved through the generations. In contrast to evolutionary computation techniques, Eberhart and Kennedy developed a different algorithm through simulating social behavior (R.C. Eberhart et al., 1996; R.C. Eberhart and J. Kennedy, 1996; J. Kennedy and R.C. Eberhart, 1995; J. Kennedy, 1997). As in other algorithms, a population of individuals exists. This algorithm is called particle swarm optimization (PSO) since it resembles a school of flying birds. In a particle swarm optimizer, instead of using genetic operators, these individuals are "evolved" by cooperation and competition among the individuals themselves through generations. Each particle adjusts its flying according to its own flying experience and its companions' flying experience. We introduce a new parameter, called inertia weight, into the original particle swarm optimizer. Simulations have been done to illustrate the significant and effective impact of this new parameter on the particle swarm optimizer.

Abstract: Global energy demand and environmental concerns are the driving force for use of alternative, sustainable, and clean energy sources. Solar energy is the inexhaustible and CO2-emission-free energy source worldwide. The Sun provides 1.4 � 10 5 TW power as received on the surface of the Earth and about 3.6 � 10 4 TW of this power is usable. In 2012, world power consumption was 17 TW, which is less than 3.6 � 10 4 TW. Photovoltaic (PV) cells are the basic element for converting solar energy into electricity. PV cell technologies, energy conversion efficiency, economic analysis, energy policies, environmental impact, various applications, prospects, and progress have been comprehensively reviewed and presented in this paper. This work compiles the latest literature (i.e. journal articles, conference proceedings, and reports, among others) on PV power generation, economic analysis, environmental impact, and policies to increase public awareness. From the review, it was found that PV is an easy way to capture solar energy where PV based power generation has also rapidly increased.

Abstract: Maximum Power Point Tracking (MPPT) methods are used in photovoltaic (PV) systems to continually maximize the PV array output power which generally depends on solar radiation and cell temperature. MPPT methods can be roughly classified into two categories: there are conventional methods, like the Perturbation and Observation (P&O) method and the Incremental Conductance (IncCond) method and advanced methods, such as, fuzzy logic (FL) based MPPT method. This paper presents a survey of these methods in order to analyze, simulate, and evaluate a PV power supply system under varying meteorological conditions. Simulation results, obtained using MATLAB/Simulink, show that static and dynamic performances of fuzzy MPPT controller are better than those of conventional techniques based controller.

Abstract: Under partial shading conditions, photovoltaic (PV) arrays exhibit multiple local maximum power points and a single global maximum power point in the P-V characteristics curves. Therefore, efficient global maximum power point tracking (GMPPT) control techniques are the prerequisite for improving the performance of the PV array under such situations. In the literature, various GMPPT techniques have been developed to track accurately the global maximum power point among different local maxima. The objective of this study is to provide a review on recent GMPPT techniques for users to select the most appropriate method based on application requirements and system specifications. The proposed work classifies and analyzes these new strategies that are used to maximize the output power of the photovoltaic system and presents the advantages and disadvantages of each method. In addition, it gives views on the use of each method, which helps to choose the most appropriate method according to the PV application. This work will serve as a useful reference for researchers working in the field.