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Leveraging opposition-based learning for solar photovoltaic model parameter estimation with exponential distribution

Nandhini Kullampalayam Murugaiyan1, Kumar Chandrasekaran2, Premkumar Manoharan3

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This study introduces an improved Opposition-Based Exponential Distribution Optimizer (OBEDO) for photovoltaic (PV) parameter extraction. The OBEDO algorithm enhances accuracy and efficiency in estimating PV model parameters, overcoming limitations of traditional methods.

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Area of Science:

  • Renewable Energy Engineering
  • Computational Intelligence
  • Electrical Engineering

Background:

  • Parameter extraction for photovoltaic (PV) models is complex due to multi-model and nonlinear characteristics.
  • Conventional algorithms often fail due to local optima and require extensive computational resources.
  • Accurate PV parameter estimation is vital for optimizing PV system performance and energy production.

Purpose of the Study:

  • To present an improved algorithm for photovoltaic (PV) parameter extraction.
  • To address the limitations of conventional methods, including local optima entrapment and high computational cost.
  • To enhance the accuracy, reliability, and efficiency of PV model parameter identification.

Main Methods:

  • Development and application of the Opposition-Based Exponential Distribution Optimizer (OBEDO).
  • Incorporation of opposition-based learning within the OBEDO for enhanced exploration and exploitation.
  • Rigorous verification against state-of-the-art algorithms across various PV models (single-diode, double-diode, three-diode, module).

Main Results:

  • The proposed OBEDO algorithm demonstrates superior performance compared to existing methods.
  • OBEDO shows enhanced convergence speed, reliability, and accuracy in parameter estimation.
  • Validation through practical, statistical results, and several case studies confirms OBEDO's effectiveness.

Conclusions:

  • The OBEDO algorithm is a robust and computationally efficient solution for photovoltaic model parameter identification.
  • OBEDO effectively mitigates the risk of local optima entrapment in PV parameter extraction.
  • The proposed method offers a significant contribution to improving the overall performance of photovoltaic systems.