Photovoltaic cell model parameter optimization using micro-charge field effect P systems. (September 2021)

• Record Type: Journal Article
• Authors: Yang, Shipin; Max, Nelson; Xie, Shengdong; Li, Lijuan; Zhao, Tingting
• Is Part Of: Engineering applications of artificial intelligence. Volume 104(2021)
• Publication Date: 2021-09
• Subjects: Photovoltaic cell -- P systems optimization -- Micro-charge field effect -- Parameter estimation -- Prediction accuracy; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104374
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Building a highly accurate model for photovoltaic (PV) cells based on actual sampled data is essential for the simulation, optimization, evaluation, and control of photovoltaic power generation systems. But finding globally optimal model parameters, which give the best fit to experimental data, is a great challenge. In this paper, a new optimization algorithm, called the micro-charge field effect P systems optimization algorithm (MFE-POA) is proposed. Though the analysis of the interaction among ionic substances inside a living cell membrane and considering the algorithm's dual needs of exploration and convergence accuracy, combined with the law of interaction between charges, we designed a novel micro-charge interaction rule based on distance, force characteristics, spatial location, and percentage of search completion, and embedded it into our existing P systems optimization algorithm (POA). Numerical studies and results analysis on some benchmark test functions demonstrate that MFE-POA can produce solutions of high quality and has great stability. The proposed method is applied to the model parameter estimation of the two types of PV cell models and multi-cell PV modules in different environmental conditions. The experimental results clearly argue the effectiveness of our proposed MFE-POA. Comparisons with other methods are presented and the results show that the proposed optimization algorithm is helpful and worth great promoting for parameter estimation in renewable energy modeling and prediction. Graphical abstract: Highlights: Micro-charge field effect among ionic substances in living cells were discussed. A novel P systems based optimization algorithm, called MFE-POA, is proposed. The global search accuracy and stability of MFE-POA were validated. Parameters of two type of PV cell models were estimated successfully by MFE_POA. i – v features of PV modules under different conditions were predicted successfully by MFE_POA.