Parameter extraction of photovoltaic single-diode model using integrated current–voltage error criterion

Abstract

An error criterion is essential in the process of parameter extraction of photovoltaic (PV) modules by fitting I–V curves, which exerts a huge influence on the accuracy of the extracted parameters. This paper proposes a new integrated current–voltage error criterion, named EC-I&V(x), which takes into account the intrinsic I–V properties of the PV module. The deviation in both current and voltage is considered by combining the mean squared error of the current and voltage in different data regions. Four optimization methods are used to validate the proposed error criterion, including guaranteed convergence particle swarm optimization, differential evolution, shuffled complex evolution, and an artificial bee colony algorithm. Different methods with the proposed error criterion are applied to synthetic I–V curves with variable error levels and measured I–V data under different operating conditions. Comparing with the traditional current based error criterion, more accurate results are obtained by using the proposed EC-I&V(x) at different error levels for different optimization methods. The proposed EC-I&V(x) not only improves the accuracy of each extracted parameter but also improves the accuracy of the estimated I–V property near maximum power points.

Noise-Scaled Euclidean Distance: A Metric for Maximum Likelihood Estimation of the PV Model Parameters

“…In such problems, the fundamental assumption of ordinary least squares (OLS) optimization for observation error only on the dependent variable, i.e., the PV current, does not hold true. This challenge is reaffirmed by more recent studies as well [14], [17], [18]. This work is licensed under a Creative Commons Attribution 4.0 License.…”

“…The alternatives that employ the entire I-V curve dataset are generally considered more complex, but also more reliable [7]. This cluster, referred to as optimization methods (or curve fitting, or metaheuristic), aims to fit the PV model on the I-V dataset by minimizing an objective function [1], [8]- [14]. Although there is abundant literature on the minimization algorithm employed, most commonly an evolutionary-based technique [7], [10]- [14], the objective function itself has been overlooked and taken for granted in most cases.…”

“…This cluster, referred to as optimization methods (or curve fitting, or metaheuristic), aims to fit the PV model on the I-V dataset by minimizing an objective function [1], [8]- [14]. Although there is abundant literature on the minimization algorithm employed, most commonly an evolutionary-based technique [7], [10]- [14], the objective function itself has been overlooked and taken for granted in most cases. In the majority of relevant methods, the objective function (or metric, or fitness function) is based on the PV current, expressed in various forms: most commonly root mean square error (RMSE) [8], [14], sum of squared errors (SSE) [9], [10], and absolute error (AE) [13].…”

“…Although there is abundant literature on the minimization algorithm employed, most commonly an evolutionary-based technique [7], [10]- [14], the objective function itself has been overlooked and taken for granted in most cases. In the majority of relevant methods, the objective function (or metric, or fitness function) is based on the PV current, expressed in various forms: most commonly root mean square error (RMSE) [8], [14], sum of squared errors (SSE) [9], [10], and absolute error (AE) [13]. It is indicative that almost all methods reviewed in [7] adopt such a current-based criterion, either one of the above metrics or a normalized/mean variation.…”

“…The area between the measured and reconstructed I-V curve is adopted as objective function in [15] and as an evaluation criterion in [6] and [19]. Other alternatives include: the power absolute error in [10], a metric based on the derivative of power with respect to voltage in [11], a combination of the SC, OC, and MPP absolute errors in [12], and a hybrid current-voltage criterion in [14]. Although these studies back the superiority of their metrics via results, there is no theoretical justification on their optimality.…”