AN IMPROVED GREY WOLF OPTIMIZATION TECHNIQUE FOR ESTIMATION OF SOLAR PHOTOVOLTAIC PARAMETERS

IF 0.3 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Power and Energy Systems Pub Date : 2021-01-01 DOI:10.2316/j.2021.203-0318

Pijush Dutta∗, Madhurima Majumder∗∗

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引用次数: 2

Abstract

Modelling and parameter extraction of the solar cell is difficult for the researcher due to the nonlinear characteristics of voltage and current. Optimization is the best technique through which we can obtain the optimum parameters from a nonlinear model. Recently, there are a number of optimization techniques that are applied for the estimation of the optimum parameter from solar, but still it is not achieved to date. In the present research, we proposed Improved Grey Wolf Optimization (HPSOGWO) for identifying the optimum parameter of a solar cell. Relative error, convergence speed, accuracy, and stability of the final solution are the statistical result which is compared with the particle swarm optimization (PSO) and Grey wolf optimization (GWO) for a single diode model and double diode model of a solar cell. A comparative study reveals that the improved version of the GWO tool provides a more accurate model for the estimation of the optimum parameter of a solar cell with less number of iteration. Hence, we recommended that HPSOGWO is the best optimization tool for providing the perfect promising performance.

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一种改进的灰狼优化技术估算太阳能光伏参数

由于电压和电流的非线性特性，太阳能电池的建模和参数提取一直是研究人员的难题。优化是一种从非线性模型中获得最优参数的最佳方法。近年来，有许多优化技术应用于太阳最优参数的估计，但至今仍未实现。在本研究中，我们提出了改进的灰狼优化算法(HPSOGWO)来确定太阳能电池的最佳参数。将最终解的相对误差、收敛速度、精度和稳定性与单二极管模型和双二极管模型的粒子群优化(PSO)和灰狼优化(GWO)进行了统计比较。对比研究表明，改进后的GWO工具能够以更少的迭代次数为太阳电池最优参数的估计提供更精确的模型。因此，我们建议将HPSOGWO作为最佳的优化工具，以提供理想的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Power and Energy Systems ENGINEERING, ELECTRICAL & ELECTRONIC-

CiteScore

1.00

自引率

0.00%

发文量

期刊介绍： First published in 1972, this journal serves a worldwide readership of power and energy professionals. As one of the premier referred publications in the field, this journal strives to be the first to explore emerging energy issues, featuring only papers of the highest scientific merit. The subject areas of this journal include power transmission, distribution and generation, electric power quality, education, energy development, competition and regulation, power electronics, communication, electric machinery, power engineering systems, protection, reliability and security, energy management systems and supervisory control, economics, dispatching and scheduling, energy systems modelling and simulation, alternative energy sources, policy and planning.