An enhanced adaptive differential evolution algorithm for parameter extraction of photovoltaic models. (1st February 2020)

• Record Type: Journal Article
• Authors: Li, Shuijia; Gu, Qiong; Gong, Wenyin; Ning, Bin
• Is Part Of: Energy conversion and management. Volume 205(2020)
• Publication Date: 2020-02-01
• Subjects: Parameter extraction -- Adaptive differential evolution -- Photovoltaic models -- Optimization algorithm; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2019.112443
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: An enhanced adaptive differential evolution is proposed to extract photovoltaic parameters. The accuracy, reliability, and convergence of the proposed method are confirmed. The proposed method can be an effective and efficient alternative for parameter extraction of photovoltaic models. Abstract: Parameter extraction of photovoltaic models based on measured current-voltage data plays an important role in the simulation, control, and optimization of photovoltaic systems. Although many parameter extraction techniques have been devoted to solving this problem, they may suffer from some deficiencies. In this paper, an enhanced adaptive differential evolution algorithm is proposed to extract photovoltaic parameters fast, accurately and reliably. In proposed method, the crossover rate sorting mechanism is introduced to assign each individual to an adapted crossover rate value according to their fitness values, which allows good elements to be more inherited in next generation. In addition, a dynamic population reduction strategy is used to improve the convergence speed and balance the exploration and exploitation. The performance of proposed method is confirmed by extracting parameters of different photovoltaic models, i.e., single diode, double diode, and photovoltaic modules. The simulated results show that the proposed method exhibits competitive performance on accuracy, reliability and convergence speed compared with other state-of-the-art algorithms. Further, the test results on experimental data from the manufacturers data sheet also indicate that the proposed algorithm can obtain superior solutions at different irradiance and temperature. Therefore, the proposed method can be an effective and efficient alternative for parameter extraction of photovoltaic models.