Chaos Game Optimization-Least Squares Algorithm for Photovoltaic Parameter Estimation

Estimating the parameters of photovoltaic (PV) models accurately is vital to increase the effectiveness of PV systems. During the past few years, many approaches have been developed to solve this problem. However, due to the presence of nonlinearity and multi-modality in the problem, the estimated parameters are usually not very accurate and reliable. Therefore, this paper proposes a novel hybrid algorithm called chaos game optimization-least squares (CGO-LS) algorithm. The novelty of CGO-LS is that it adopts a cascade estimation strategy based on parameter decomposition. By the aid of this decomposition, CGO-LS combines a sophisticated nonlinear optimization capability of chaos game optimization (CGO) and the power of the optimal linear least squares (LS) estimator. LS focuses directly on estimating linear parameters, thus reducing the workload of CGO and helping to increase its convergence speed. To validate the performance of CGO-LS, it is employed to estimate the parameters of four PV models, including single-diode, double-diode, three-diode models, and PV module model. The results obtained by CGO-LS are compared with those of CGO, six state-of-the-art metaheuristics, their hybridized versions with LS, as well as some reported results in the literature. The overall results show that CGO-LS possesses superior estimation performance and excellent robustness in emulating experimental datasets.