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Accurate determination of photovoltaic (PV) model parameters is essential for studying the variables that affect PV power generation efficiency. The extraction of parameters from the PV model presents a challenging problem due to its multi-model as well as nonlinear characteristics. This paper presents a solution to this challenge through the utilization of the Lungs Performance-Based Optimization (LPO) algorithm. The parameter identification technique has been described as an optimization problem focused on minimizing the current-based root mean squared error (RMSE). Additionally, the LPO algorithm is adopted in triple diode model (TDM) parameter extraction for RTC France, and Photowatt-PWP201 solar PV modules. The outcomes indicate that the LPO algorithm combined with the Newton–Raphson method demonstrates superior robustness and convergence accuracy contrasted to the other meta-heuristics (MH) algorithm with obtained minimum RMSE value of 7.3478E-04 and 2.0528E-03 for RTC France and Photowatt-PWP201 solar PV modules, respectively. Furthermore, an extensive performance evaluation of the MH algorithms has been performed, utilizing convergence curves, boxplots, as well as characteristic curves for current (I) versus voltage (V) and power (P) versus voltage (V), derived from the experimental outcomes.

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An Optimized Solar PV Model Parameter Extraction Technique Using Lungs Performance-Based Optimization

  • 会议论文

  • 发表日期：

    2025年

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  • Rahul Khajuria,
  • Ananad Krishan Sharma,
  • Pankaj Sharma,
  • Rajesh Kumar,
  • Ravita Lamba,
  • Saravanakumar Raju

摘要

Accurate determination of photovoltaic (PV) model parameters is essential for studying the variables that affect PV power generation efficiency. The extraction of parameters from the PV model presents a challenging problem due to its multi-model as well as nonlinear characteristics. This paper presents a solution to this challenge through the utilization of the Lungs Performance-Based Optimization (LPO) algorithm. The parameter identification technique has been described as an optimization problem focused on minimizing the current-based root mean squared error (RMSE). Additionally, the LPO algorithm is adopted in triple diode model (TDM) parameter extraction for RTC France, and Photowatt-PWP201 solar PV modules. The outcomes indicate that the LPO algorithm combined with the Newton–Raphson method demonstrates superior robustness and convergence accuracy contrasted to the other meta-heuristics (MH) algorithm with obtained minimum RMSE value of 7.3478E-04 and 2.0528E-03 for RTC France and Photowatt-PWP201 solar PV modules, respectively. Furthermore, an extensive performance evaluation of the MH algorithms has been performed, utilizing convergence curves, boxplots, as well as characteristic curves for current (I) versus voltage (V) and power (P) versus voltage (V), derived from the experimental outcomes.