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Optimal parameter identification of photovoltaic systems based on enhanced differential evolution optimization

Shubhranshu Mohan Parida1, Vivekananda Pattanaik1,2, Subhasis Panda1,3

  • 1Department of Electrical Engineering, Siksha 'O' Anusandhan University, Bhubaneswar, Odisha, India.

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Summary

Accurate solar photovoltaic (PV) model parameter identification is crucial for performance assessment. An enhanced differential evolution (EDE) method with a stage-specific mutation strategy improves PV system modeling accuracy, outperforming other algorithms.

Keywords:
Enhanced differential evolutionMetaheuristic algorithmOptimization techniquePV modelParameter identification

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Area of Science:

  • Renewable Energy Engineering
  • Electrical Engineering
  • Computational Intelligence

Background:

  • Accurate solar photovoltaic (PV) model parameter identification is essential for reliable simulation, performance assessment, and design verification.
  • PV system modeling presents challenges due to inherent nonlinearity, complexity, and numerous parameters.
  • Enhancing the accuracy of PV system modeling remains an active research area.

Purpose of the Study:

  • To propose an enhanced differential evolution (EDE) algorithm with a stage-specific mutation strategy for optimal solar PV model parameter identification.
  • To improve the search process for optimal solutions by adaptively adjusting mutation factors and crossover rates.
  • To validate the accuracy and efficacy of the proposed EDE approach for various PV cells and modules.

Main Methods:

  • Formulating optimal PV system parameter identification as a single objective function based on Root Mean Square Error (RMSE).
  • Implementing a stage-specific mutation strategy within the differential evolution algorithm, adaptively varying mutation factors and crossover rates.
  • Validating identified parameters by comparing simulated I-V characteristics with experimental data for different PV cells and modules.

Main Results:

  • The proposed EDE technique achieved minimal RMSE values in parameter identification for RTC France and PVM-752-GaAs PV cells across single, double, and triple diode models.
  • RMSE values for RTC France cell models ranged from 7.419648e-4 to 7.730062e-4.
  • RMSE values for PVM-752-GaAs cell models ranged from 1.30181e-4 to 1.59256e-4.
  • The EDE method demonstrated superior performance compared to other well-accepted algorithms for PV module parameter identification (ND-R250A5, STM6 40/36, STP6 120/36), yielding RMSEs of 7.697716e-3, 1.772095e-3, and 1.224258e-2, respectively.

Conclusions:

  • The proposed EDE technique with a stage-specific mutation strategy significantly enhances the accuracy of solar PV model parameter identification.
  • The adaptive variation of mutation factor and crossover rate contributes to a more effective search process, leading to optimal solutions.
  • The validated results across diverse PV cells and modules confirm the proposed approach's accuracy and efficacy, offering a valuable tool for solar energy research and development.