Bacterial Foraging Algorithm based solar PV parameter estimation

• Published in: Solar energy Vol. 97; pp. 255 - 265
• Main Authors: Rajasekar, N., Krishna Kumar, Neeraja, Venugopalan, Rini
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.11.2013; Elsevier; Pergamon Press Inc
• Subjects: Applied sciences; Artificial Immune System; Bacteria; Bacterial Foraging Algorithm; Convergence; Direct energy conversion and energy accumulation; Electrical engineering. Electrical power engineering; Electrical power engineering; Energy; Environmental conditions; Equipments, installations and applications; Exact sciences and technology; Genetic Algorithm; Natural energy; Optimization algorithms; Photoelectric conversion; Photovoltaic cells; Photovoltaic conversion; Simulation; Solar cells. Photoelectrochemical cells; Solar energy; Solar PV modeling; Solar PV modeling; Artificial Immune System; Bacterial Foraging Algorithm; Genetic Algorithm; Performance evaluation; Parameter estimation; Photovoltaic system; Nonlinear problems; Modeling; Optimization; Accuracy; Genetic algorithm; Solar energy; Photovoltaic array; Manufacturer; Comparative study
• ISSN: 0038-092X; 1471-1257
• DOI: 10.1016/j.solener.2013.08.019

•A modified approach using Bacterial Foraging Algorithm is presented for modeling the Solar PV characteristics.•New equations are evolved to predict the characteristics accurately.•Simulations are carried out in MATLAB/SIMULNIK environment.•Simulated results are compared with experimental data given in manufacturer’s datasheet.•BFA results are compared with Genetic Algorithm and Artificial Immune Algorithm. The abundance and non-polluting nature of solar energy has aroused the interest of many researchers. This worldwide attention of photovoltaic panels has led to the need of generating accurate model for solar photovoltaic (PV) module before proceeding to the installation part. However, accurate modeling of solar PV characteristics is difficult; since the manufacturer’s datasheet provides only four values such as Vmp, Imp, Voc, and Isc. Further, for accurate modeling precise estimation of model parameters at different environmental conditions are very essential. On the other hand, optimization technique is a very powerful tool to obtain solutions to complex non-linear problems. Hence, in this paper, Bacterial Foraging Algorithm is proposed to model the solar PV characteristics accurately. A new equation has been evolved to determine the values of Voc, Vmp accurately; since these values decides the closeness of the simulated characteristics. Model parameters are extracted for three different types of solar PV panels. A systematic evaluation and performance comparison of Bacterial Foraging Algorithm with other optimization techniques such as Genetic Algorithm and Artificial Immune System has been done and the best computational technique is derived based on performance criteria such as accuracy, consistency, speed of convergence and absolute error. Extensive computations are carried out for the proposed method, as well as for Genetic Algorithm and Artificial Immune System to substantiate the findings.