Modified Whale Optimization Algorithm for Solar Cell and PV Module Parameter Identification

Xiaojia Ye,Huiling Chen,Hailong Huang,Guoxi Liang,Hong Li,Wei Liu,Mingjing Wang,Chen Chi,Ramon Costa-Castelló

doi:10.1155/2021/8878686

Abstract

The whale optimization algorithm (WOA) is a powerful swarm intelligence method which has been widely used in various fields such as parameter identification of solar cells and PV modules. In order to better balance the exploration and exploitation of WOA, we propose a novel modified WOA (MWOA) in which both the mutation strategy based on Levy flight and a local search mechanism of pattern search are introduced. On the one hand, Levy flight can make the algorithm get rid of the local optimum and avoid stagnation; thus, it is able to prevent the algorithm from losing diversity and to increase the global search capability. On the other hand, pattern search, a direct search method, has not only high convergence rate but also good stability, which can boost the local optimization ability of the WOA. Therefore, the combination of these two mechanisms can greatly improve the capability of WOA to obtain the best solution. In addition, MWOA may be employed to estimate parameters in single diode model (SDM), double diode model (DDM), and PV modules and to identify unknown parameters of two different types of PV modules under diverse light irradiance and temperature conditions. The analytical results demonstrate the validity and the practicality of MWOA for estimating parameters of solar cells and PV modules.

Highlights

Solar power generation is an emerging renewable energy technology, and photovoltaic system is a type of power generation system that uses the photovoltaic effect of solar cell semiconductor materials to convert sunlight radiation energy into electricity
To verify the effectiveness of modified WOA (MWOA), parameter identification of solar cells and PV modules is carried out for the two datasets provided in the literature [72]. ese two datasets relate to RTC France cell and Photowatt-PWP 201 PV module, and they have been widely used to validate the performance of the new methods [3, 73, 74]. e dataset of RTC France contains 26 pairs of current and voltage values measured under standard test conditions (33°C, 1000 W/m2 light intensity) while that of Photowatt-PWP 201 consisting of 36 polysilicon cells in series includes 25 pairs of data measured under standard test conditions (45°C, 1000 W/m2 light intensity)
All of these results show that it is possible to identify the unknown parameters of diode model (DDM) with high precision based on MWOA

Summary

Introduction

Solar power generation is an emerging renewable energy technology, and photovoltaic system is a type of power generation system that uses the photovoltaic effect of solar cell semiconductor materials to convert sunlight radiation energy into electricity. Erefore, the modeling and the parameter searching of solar cells and PV systems is one of the hot topics in the current study. E methods for extracting parameters can be roughly classified as analytical method [5,6,7], method with Lambert W function [8, 9], method of constructing or using special function [10, 11], or method with swarm intelligence algorithm. A large number of studies have focused on using such methods or their variants for modeling and parameter extraction of solar cells and PV modules [17,18,19,20,21,22,23,24]. Both A and D are coefficient vectors given in the following equations:

Results

Discussion

Conclusion