Parameters Identification of Photovoltaic Models Using a Multi-Strategy Success-History-Based Adaptive Differential Evolution

Qinzhi Hao,Guanghui Chen,Zhenglei Wei,Zhongliang Zhou

doi:10.1109/access.2020.2975078

Abstract

Parameters identification of photovoltaic (PV) models based on measured current-voltage characteristics curves is significant for the simulation, evaluation and control of PV systems. To accurately and reliably identify the parameters of different PV models, a novel optimization algorithm, multi-strategy success-history based adaptive differential evolution with linear population size reduction (MLSHADE), is proposed. MLSHADE mainly divides evolutionary process into two phases during every generation. According to the definition of class probability variable, the population individuals of first phase are assigned to different two populations for exploration and exploitation, respectively. The novelty of MLSHADE algorithm lies primarily in three improvements: (i) a new weighted mutation strategy is used to enrich the population diversity of later iterations for differential evolution population in the first phase; (ii) inferior solutions search (ISS) technique is presented to avoid falling into local optimum for covariance matrix adaptation evolution strategy population in the first phase; and (iii) Eigen Gaussian random walk strategy is proposed to help maintain effectively the balance between the global exploration and local exploitation abilities in the second phase. The experiments on CEC 2018 test suite illustrate that the proposed MLSHADE exerts the better performances against the stat-of-the-art algorithms in terms of accuracy, reliability and time consumption. The proposed MLSHADE is used to solve the parameters identification problems of different PV models including single diode, double diode, and PV module. Comprehensive experiment results and analyses indicate that MLSHADE can obtain a highly competitive performance compared with other state-of-the-art algorithms, especially in terms of accuracy and reliability.

Highlights

Due to richness, cleanliness and pollution-free of the solar energy, it is considered as one of the most promising renewable energy sources [1]
MLSHADE we present the reviews of success-history based adaptive differential evolution (DE) with linear population size reduction (LSHADE) and covariance matrix adaptation evolution strategy (CMA-ES)
This paper introduces a powerful optimization technique, multi-strategy success-history based adaptive DE with linear population size reduction, which is a variant of LSHADE, to accurately and steadily estimate the parameters of different PV models

Summary

Introduction

Cleanliness and pollution-free of the solar energy, it is considered as one of the most promising renewable energy sources [1]. Through photovoltaic (PV) systems such as solar cell, solar energy is transformed into electrical energy. To develop the photovoltaic power generation and apply the photovoltaic power generation to comprehensive field, photovoltaic model is one of the most important parts in photovoltaic power generation system. Optimize the parameters identification of photovoltaic models, it is vital to evaluate the actual behavior of PV arrays in operation using accurate model based on measured current-voltage data. In addition to accurate parameters identification, automatic fault detection and diagnosis techniques for photovoltaic arrays are crucial to promote the efficiency and reliability of photovoltaic models. Many conventional artificial intelligence approaches have been successfully applied to automatically establish fault detection and diagnosis model using fault data samples [2]. In [3], based on the output I-V characteristic of the PV arrays, kernel based extreme learning machine (KELM), is explored for the first

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