Comprehensive overview of available objective functions for parameter identification of photovoltaic modules

Abstract

The performance of photovoltaic (PV) modules can be determined from the current–voltage characteristics. Accurate parameter extraction is vital for PV module modeling and analysis of the characteristics of PV systems, in which the objective function (OF) is essential and has an essential impact on the accuracy of the identified parameters. Therefore, many researchers have demonstrated significant interest in this over the past few years. The aim of this paper is to comprehensively describe and discuss the OFs for parameter extraction and result evaluation. The OFs are classified into four kinds: current-based, current–voltage (I–V)-based, power-based, and square-based OFs. The identification performance of each OF is deeply discussed based on three different PV models, including single-diode model, double-diode model and power-law model. Some representative criteria are used as the OFs to analyze the accuracy of parameter identification, including the current, current and voltage, power, and square-based OFs. In addition, a new evaluation criterion, called the shortest distance error, is proposed to represent the deviation more reasonably. The guaranteed convergence particle swarm optimization (GCPSO) algorithm on the basis of different objective functions is employed for many synthetic I–V curves with variable error levels and measured I–V curves under varying environmental conditions for three PV models. Based on the review and study, the performances of different objective functions are discussed and summarized thoroughly. For example, one result indicates that both OF-I and OF-I&V were suitable objective functions for the SDM. For xSi, the smallest value of SDE¯ calculated by OF-I&V, which decreases 67.87% compared with the maximum value of SDE¯ calculated by OF-P. And these results have essential references for the designing new parameter identification methods and is significant for further improving the accuracy of modeling and performance evaluation of PV modules.