Parameter Optimization of Photovoltaic Solar Cell and Panel Using Genetic Algorithms Strategy

Parameter Optimization of Photovoltaic Solar Cell and Panel Using Genetic Algorithms Strategy

Benmessaoud Mohammed Tarik (University of Science and Technology of Oran, Algeria), Fatima Zohra Zerhouni (University of Science and Technology of Oran, Algeria), Amine Boudghene Stambouli (University of Science and Technology of Oran, Algeria), Mustapha Tioursi (University of Science and Technology of Oran, Algeria) and Aouad M'harer (University of Tissemsilt, Algeria)
DOI: 10.4018/978-1-4666-9644-0.ch022
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Abstract

In this chapter, we propose to perform a numerical technique based on genetic algorithms (GAs) to identify the electrical parameters (Is, Iph, Rs, Rsh, and n) of photovoltaic (PV) solar cells and modules. The one diode type approach is used to model the I–V characteristic of the solar cell. To extract electrical parameters, the approach is formulated as optimization problem. The GAs approach was used as a numerical technique in order to overcome problems involved in the local minima in the case optimization criteria. Compared to other methods, we find that the GAs is a very efficient technique to estimate the electrical parameters of photovoltaic solar cells and modules. Compared with other parameter extraction techniques, based on statistical study, results indicate the consistency and uniformity of method in terms of the quality of final solutions. In parallel, the simulated data with the extracted parameters of method base with GAs are in very good agreement with the experimental data in all cases.
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Introduction

Metaheuristic algorithms have become high powerful tools for modeling and optimization. Metaheuristic optimization algorithms are used extensively for solving complex optimization problems. Compared to conventional methods based on formal logics or mathematical programming, these metaheuristic algorithms are generally more powerful (Sahab, Toropov & Gandomi, 2013).

Optimization techniques can be divided in two groups, mathematical programming and metaheuristic algorithms. In general, the existing metaheuristic algorithms may be divided into two main categories as follows, Evolutionary algorithm and Evolutionary algorithms (Gandomi, 2014).

An evolutionary algorithm (EA) is a population-based meta-heuristic optimization algorithm inspired by the concepts of biological evolution such as reproduction, mutation, recombination and selection. They are randomized and stochastic techniques which simulate the evolutionary pressure of selection to select high fit individuals and use the evolutionary operators “crossover” and “mutation” to evolve better individuals. EAs generally start with a set (population) of random candidate solutions called individuals or chromosomes. In case of de novo drug design, chromosomes are represented by candidate chemical compounds. Evolutionary algorithms use the scoring function or the fitness function over the current population to determine the quality of the candidate solutions. Various selection methods such as “Roulette wheel”, “Tournament”, and “Rank” selection are used to populate the right candidates into the mating pool, where they are subjected to crossover and mutation. The crossover operator combines the genetic traits of two individuals (parents) and produces a new off-spring. A mutation event introduces new piece of information into an existing population. Thus, the crossover and mutation operators provide variations among the current population and evolve better and better solutions. The evolution process continues tilluser-specified termination criterion (Surbey, Devia, Sathyaa & Coumarb, 2015). Evolutionary strategy (ES) (Fogell, Owens & Walsh, 1966), genetic algorithm (GA) (Holland, 1975), and differential evolution (DE) (Storn & Price, 1997) are the most well-known paradigms of the evolutionary algorithms which use biological mechanisms such as crossover and mutation. These algorithms have been improved a lot and new variants of them have been introduced (e.g. cellular GA (Canyurt & Hajela, 2010)). Genetic programming (GP) is known as an extension of GAs (Koza, 1992). Several attempts have been made to improve the performance of GP (e.g.multi-stage GP (Gandomi & Alavi, 2011)). The mentioned evolutionary algorithms have been widely used to solve engineering optimization problems (e.g. Yüzgeç, Türker and Hocalar (2009)). Harmony search (HS) algorithm is another evolutionary algorithm proposed by Geemetal (Hedar & Fukushima, 2006). This algorithm simulates the musicians’ behavior when they are searching for better harmonies.

Genetic algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics. As such they represent an intelligent exploitation of a random search used to solve optimization problems (Singh, Agrawal, Tiwari, Al-Helal & Avasthi, 2015). The performance of genetic algorithms depends to a large degree on the parameters which are under the control of the researcher, requiring adjustments to deal with the specific problem at hand. These parameters and namely the fitness function there- fore have to be carefully selected to match the specifics of credit scoring (Kozeny, 2015).

Key Terms in this Chapter

Solar Cell: A solar cell, or photovoltaic cell, is an electrical device that converts the energy of light directly into electricity by the photovoltaic effect, which is a physical and chemical phenomenon (Chemistry explained, 2015). It is a form of photoelectric cell, defined as a device whose electrical characteristics, such as current, voltage, or resistance, vary when exposed to light. Solar cells are the building blocks of photovoltaic modules, otherwise known as solar panels.

Optimization: In mathematics, computer science, economics, or management science, mathematical optimization (alternatively, optimization or mathematical programming) is the selection of a best element (with regard to some criteria) from some set of available alternatives. In the simplest case, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of the function. The generalization of optimization theory and techniques to other formulations comprises a large area of applied mathematics. More generally, optimization includes finding “best available” values of some objective function given a defined domain (or a set of constraints), including a variety of different types of objective functions and different types of domains (Wikipedia, 2015 AU159: The in-text citation "Wikipedia, 2015" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).

Metaheuristic Algorithms: In computer science, artificial intelligence, and mathematical optimization, a heuristic is a technique designed for solving a problem more quickly when classic methods are too slow, or for finding an approximate solution when classic methods fail to find any exact solution (Wikipedia, 2015 AU157: The in-text citation "Wikipedia, 2015" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).

Modeling: Scientific modelling is a scientific activity, the aim of which is to make a particular part or feature of the world easier to understand, define, quantify, visualize, or simulate by referencing it to existing and usually commonly accepted knowledge. It requires selecting and identifying relevant aspects of a situation in the real world and then using different types of models for different aims, such as conceptual models to better understand, operational models to operationalize, mathematical models to quantify, and graphical models to visualize the subject. Modelling is an essential and inseparable part of scientific activity, and many scientific disciplines have their own ideas about specific types of modelling (Mathematical Programming; Wikipedia, 2015 AU158: The in-text citation "Wikipedia, 2015" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Cartwright, 2983; Hacking, 1983 ).

Photovoltaic: Photovoltaics (PV) is a method of converting solar energy into direct current electricity using semiconducting materials that exhibit the photovoltaic effect. A photovoltaic system employs solar panels composed of a number of solar cells to supply usable solar power. Power generation from solar PV has long been seen as a clean sustainable (Wikipedia, 2015 AU160: The in-text citation "Wikipedia, 2015" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Pearce, 2002 ).

Genetic Algorithm: In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover (Wikipedia, 2015 AU156: The in-text citation "Wikipedia, 2015" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ; Mitchell, 1996 ).

Solar Panel: Solar panel refers either to a photovoltaics (PV) module, a solar hot water panel, or to a set of solar photovoltaics modules electrically connected and mounted on a supporting structure. A PV module is a packaged, connected assembly of solar cells. Solar panels can be used as a component of a larger photovoltaic system to generate and supply electricity in commercial and residential applications (Wikipedia, 2015 AU161: The in-text citation "Wikipedia, 2015" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).

The Five-Parameter Model: The five-parameter model used in this work evaluates individually each parameter (I s , I ph , R s , R sh , and n) of the nonlinear equation of I-V characteristic at STC and any other operating condition from analytical calculations.

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