Abstract
As one of the most promising forms of renewable energy, solar energy is increasingly deployed. The simulation and control of photovoltaic (PV) systems requires identification of their parameters. A Hybrid Adaptive algorithm based on JAYA and Differential Evolution (HAJAYADE) is developed to identify these parameters accurately and reliably. The HAJAYADE algorithm consists of adaptive JAYA, adaptive DE, and the chaotic perturbation method. Two adaptive coefficients are introduced in adaptive JAYA to balance the local and global search. In adaptive DE, the Rank/Best/1 mutation operator is put forward to boost the exploration and maintain the exploitation. The chaotic perturbation method is applied to reinforce the local search further. The HAJAYADE algorithm is employed to address the parameter identification of PV systems through five test cases, and the eight latest meta-heuristic algorithms are its opponents. The mean RMSE values of the HAJAYADE algorithm from five test cases are 9.8602 × 10−4, 9.8294 × 10−4, 2.4251 × 10−3, 1.7298 × 10−3, and 1.6601 × 10−2. Consequently, HAJAYADE is proven to be an efficient and reliable algorithm and could be an alternative algorithm to identify the parameters of PV systems.
Highlights
Nowadays, governments and the public are more concerned about environmental protection and the energy crisis, meaning that the unsustainable energy structure dominated by fossil energy urgently needs to be adjusted
HAJAYADE is proven to be an efficient and reliable algorithm and could be an alternative algorithm to identify the parameters of PV systems
The TLABC, MLBSA, Improved JAYA (IJAYA), PGJAYA, and HAJATo further compare the performance of the nine algorithms, the Wilcoxon Signed Rank
Summary
Governments and the public are more concerned about environmental protection and the energy crisis, meaning that the unsustainable energy structure dominated by fossil energy urgently needs to be adjusted. They have turned to renewable energy, which may be the main alternative to fossil fuels. Used models are the single-diode model (SDM) and double-diode model (DDM). The performance of these models relies on the involved parameters. They are not available directly as they vary due to the harsh environments. It is necessary to estimate the parameters of these models
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