Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models

By Kunjie Yu, Jing J. Liang, B. Qu, Zhiping Cheng, Heshan Wang. Applied Energy, 2018

357 citations recorded by the source corpus.

Abstract Obtaining appropriate parameters of photovoltaic models based on measured current-voltage data is crucial for the evaluation, control, and optimization of photovoltaic systems. Although many techniques have been developed to solve this problem, it is still challenging to identify the model parameters accurately and reliably. To improve parameters identification of different photovoltaic models, a multiple learning backtracking search algorithm (MLBSA) is proposed in this paper. In MLBSA, some individuals learn from the current population information and historical population information simultaneously, which aims to maintain population diversity and enhance the exploration ability. While other individuals learn from the best individual of current population to improve the convergence speed and thus enhance the exploitation ability. In addition, an elite strategy based on chaotic local search is developed to further refine the quality of current population. The proposed MLBSA is employed to solve the parameters identification problems of different photovoltaic models, i.e., single diode, double diode, and photovoltaic module. Comprehensive experimental results and analyses demonstrate that MLBSA outperforms other state-of-the-art algorithms in terms of accuracy, reliability, and computational efficiency.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/a345e7ae8106f99ac2d287c70138dfbf889ded8b

How researchers see this paper

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In Electrical and Electronic Engineering

1. Vincenzo Stornelli
2. Mirco Muttillo
3. Tullio de Rubeis
4. Iole Nardi

In Renewable Energy, Sustainability and the Environment

1. Vincenzo Stornelli
2. Mirco Muttillo
3. Tullio de Rubeis
4. Iole Nardi

Citations in the corpus

• A New Simplified Five-Parameter Estimation Method for Single-Diode Model of Photovoltaic Panels (2019, methodology): “Other methods used time-varying acceleration coefficients particle swarm optimization (TVACPSO) [24], the hybrid adaptive Nelder–Mead simplex algorithm based on the eagle strategy [25], algorithms and adapting control parameters [27], genetic algorithms to estimate parameters [28], a chaotic whale o”