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- W4308797348 abstract "Photovoltaic (PV) systems play a significant role in power systems since they can convert solar energy directly into electricity. Their conversion performance depends mainly on the unknown parameters of the PV model. Due to the multimodal, nonlinear, and multivariate characteristics of solar cells and modules, parameter identification of PV models is still a popular and challenging task in PV systems. However, most of the existing approaches face the problem of falling into local optima, high computational costs, and insufficient utilization of landscape information in the evolutionary process. In this study, a landscape-aware particle swarm optimization algorithm (LaPSO) is proposed to estimate the parameters of the PV system. LaPSO includes two main improvements: (i) A landscape-based adaptive operator selection mechanism is proposed to quantify the landscape modality and assign the most appropriate evolutionary strategy to the current population to improve population quality. (ii) The problem of getting trapped in local optima is alleviated by using the mirrored boundary handling method. The experimental results tested on various PV models demonstrate the excellence of LaPSO in terms of accuracy, stability, and convergence speed. Moreover, LaPSO has shown superior practicality and reliability at different temperatures and irradiances on other PV modules. Consequently, LaPSO is well-suited as an alternative for the parameter identification of solar cells and modules under various practical conditions. • PSO is proposed to estimate the model parameters of solar cells and modules. • The landscape-based adaptive operator selection mechanism without increasing the computational complexity is proposed to satisfy the needs of the population at different evolutionary stages by selecting the most appropriate evolutionary operator. • The mirrored boundary handling approach for constraining particles beyond the boundary is used to alleviate the premature convergence in the local region. • Experimental results indicate the superior performance of LaPSO." @default.
- W4308797348 created "2022-11-15" @default.
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- W4308797348 date "2022-12-01" @default.
- W4308797348 modified "2023-10-16" @default.
- W4308797348 title "A landscape-aware particle swarm optimization for parameter identification of photovoltaic models" @default.
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- W4308797348 doi "https://doi.org/10.1016/j.asoc.2022.109793" @default.
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