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A modified particle swarm optimization rat search algorithm and its engineering application.

Manish Kumar Singla1,2, Jyoti Gupta3, Mohammed H Alsharif4

  • 1Department of Interdisciplinary Courses in Engineering, Chitkara University Institute of Engineering & Technology, Chitkara University, Rajpura, Punjab, India.

Plos One
|March 28, 2024
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Summary
This summary is machine-generated.

A new algorithm, Particle Swarm Optimization Rat Search Algorithm (PSORSA), accurately models photovoltaic (PV) systems. This method enhances solar energy optimization and reliability by improving parameter extraction for PV modules.

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Area of Science:

  • Renewable Energy
  • Electrical Engineering
  • Computational Intelligence

Background:

  • Photovoltaic (PV) system performance relies on accurate modeling, which is hindered by fluctuating and inaccessible model parameters.
  • Efficient extraction of solar module characteristics is crucial for PV system optimization, control, and simulation.

Purpose of the Study:

  • To introduce a modified Particle Swarm Optimization Rat Search Algorithm (PSORSA) for enhanced PV system modeling.
  • To improve the accuracy and efficiency of parameter extraction for solar module characterization.

Main Methods:

  • Developed a modified Particle Swarm Optimization (PSO) algorithm integrated with the Rat Search Algorithm (RSA).
  • Applied the PSORSA to extract parameters for both monocrystalline and polycrystalline solar cells using triple and four diode models.
  • Evaluated performance using Root Mean Square Error (RMSE).

Main Results:

  • PSORSA demonstrated exceptional performance for the triple diode model, with RMSE values of 3.21E-11 (monocrystalline) and 1.86E-11 (polycrystalline).
  • For the four diode model, PSORSA achieved RMSE values of 4.14E-09 (monocrystalline) and 4.72E-09 (polycrystalline).
  • PSORSA significantly outperformed existing advanced techniques in accuracy and dependability.

Conclusions:

  • The PSORSA is a highly accurate and reliable tool for solar cell and PV module data assessment.
  • The algorithm's enhanced exploration and population diversity contribute to superior performance in PV system modeling.