A Chaotic Improved Artificial Bee Colony for Parameter Estimation of Photovoltaic Cells

Abstract

The search for new energy resources is a crucial task nowadays. Research on the use of solar energy is growing every year. The aim is the design of devices that can produce a considerable amount of energy using the Sun's radiation. The modeling of solar cells (SCs) is based on the estimation of the intrinsic parameters of electrical circuits that simulate their behavior based on the current vs. voltage characteristics. The problem of SC design is defined by highly nonlinear and multimodal objective functions. Most of the algorithms proposed to find the best solutions become trapped into local solutions. This paper introduces the Chaotic Improved Artificial Bee Colony (CIABC) algorithm for the estimation of SC parameters. It combines the use of chaotic maps instead random variables with the search capabilities of the Artificial Bee Colony approach. CIABC has also been modified to avoid the generation of new random solutions, preserving the information of previous iterations. In comparison with similar optimization methods, CIABC is able to find the global solution of complex and multimodal objective functions. Experimental results and comparisons prove that the proposed technique can design SCs, even with the presence of noise.

A Novel Improved Cuckoo Search Algorithm for Parameter Estimation of Photovoltaic (PV) Models

“…In the literature, various circuit models have been employed for describing the electrical behavior of PV cells, but in practice, only two widely used models, namely, SDM and DDM, are suitable for electrical engineering applications [4,11,24,26]. These two models will be concisely presented in the following subsections.…”

“…The modeling of PV cells or modules consists of three major processes: choice of proper electrical circuit models, the expression of mathematical models and precise estimation of values of parameters for them. Although various equivalent electronic circuit models were proposed years ago, in practice, the SDM and the DDM are two most commonly adopted models [9][10][11]. For the mathematical model, the I-V characteristic that describes PV cell/module behavior is taken into account, and the current equation of PV model is an implicit transcendental equation [10].…”

“…For the mathematical model, the I-V characteristic that describes PV cell/module behavior is taken into account, and the current equation of PV model is an implicit transcendental equation [10]. Therefore, under the circumstances, a precise parameter estimation of such models is extremely essential and hard work and has drawn much attention recently [11].…”

“…The metaheuristic methods have been widely used for the PV models parameter estimation problem [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Such methods include genetic algorithm (GA) [19], chaos particle swarm optimization (CPSO) [20], pattern search (PS) [21,22], simulated annealing (SA) [23], harmony search (HS) [1], artificial bee swarm optimization (ABSO) [24], R cr -IJADE [25], mutative-scale parallel chaos optimization algorithm (MPCOA) [26], biogeography-based optimization algorithm with mutation strategies (BBO-M) [27], artificial bee colony (ABC) [2], modified artificial bee colony (MABC) [28], improved artificial bee colony (IABC) [29], chaotic asexual reproduction optimization (CARO) [4], EHA-NMS [30], generalized oppositional teaching learning based optimization (GOTLBO) [10], self-adaptive teaching-learning-based optimization (SATLBO) [31], improved JAYA (IJAYA) [32], modified simplified swarm optimization (MSSO) [33], chaotic improved artificial bee colony (CIABC) [11], and teaching-learning-based artificial bee colony (TLABC) [34]. These metaheuristic methods are very flexible and can achieve satisfied results, however, in the light of "no free lunch" (NFL) theorem, there is no single metaheuristic method best suited for all optimization problems [35].…”

Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems

“…Therefore, to a certain extent, the traditional methods cannot solve the OPF problem successfully. With the development of computers, the intelligent optimization methods based on the combination of computer technology and biological simulation are proposed to overcome many drawbacks of the traditional methods; and these heuristic algorithms include artificial bee colony algorithm (ABC) [7], particle swarm optimization (PSO) [8], differential search algorithm (DSA) [9], differential evolution algorithm (DE) [10], gravitational search algorithm (GSA) [11], etc. Numerous studies indicate that each algorithm has different performance, pros and cons in different cases, so there are more and more modified methods based on intelligent algorithms to solve the OPF problem effectively.…”