access icon free Simplex simplified swarm optimisation for the efficient optimisation of parameter identification for solar cell models

The development and application of photovoltaic (PV) systems are becoming increasingly more important as the global need for energy resources expands and environmental protection becomes more highly valued. Parameters of PV models can be identified by measuring their current–voltage (IV) characteristic curves. Identifying these parameters quickly, accurately and reliably is critical in determining the operating status of in situ PV arrays and, in turn, optimising solar energy conversions. To achieve both fast and accurate parameter identification with high reliability, a new algorithm called algorithm based on SSO and Nelder–Mead simplex (NMS) (SSSO) based on the simplified swarm optimisation (SSO) and the NMS is proposed in this study. To demonstrate the performance of SSSO in identifying solar cell system parameters, its performance on the single diode model and the double diode model was compared with existing algorithms in terms of both fitness value and run time. The experiment results indicate that SSSO outperformed the compared algorithms in both run time and standard deviation of fitness value.

Inspec keywords: optimisation; parameter estimation; photovoltaic power systems; solar cells

Other keywords: parameter identification; current-voltage characteristic curves; environmental protection; fitness value; SSSO; energy resources; in situ PV arrays; solar energy conversions; single diode model; Nelder-Mead simplex; photovoltaic systems; PV models; run time; double diode model; solar cell models; standard deviation; simplex simplified swarm optimisation

Subjects: Solar power stations and photovoltaic power systems; Photoelectric conversion; solar cells and arrays; Optimisation techniques; Solar cells and arrays

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