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An adaptive snake optimization algorithm incorporating Subtraction-Average-Based Optimizer for photovoltaic cell

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Summary
This summary is machine-generated.

This study introduces an enhanced Snake algorithm (ISASO) for accurate photovoltaic (PV) model parameter identification. ISASO significantly improves accuracy and reliability in estimating solar PV parameters, outperforming existing methods.

Keywords:
Metaheuristic algorithmsPV cellsPV modulesSnake optimization algorithmSubtraction average-based optimization

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Area of Science:

  • Renewable Energy Systems
  • Computational Intelligence
  • Optimization Algorithms

Background:

  • Photovoltaic (PV) model parameter identification is crucial for efficient solar energy utilization.
  • Traditional methods struggle with nonlinearity, numerous parameters, and local optima, hindering PV system performance.
  • Accurate parameter estimation is key to maximizing the efficiency and reliability of solar energy conversion.

Purpose of the Study:

  • To develop an advanced optimization algorithm for accurate photovoltaic model parameter identification.
  • To overcome the limitations of existing methods, including low accuracy, slow convergence, and susceptibility to local optima.
  • To enhance the global search capability and population diversity for robust parameter estimation.

Main Methods:

  • Proposes an enhanced Snake algorithm (ISASO) integrating Subtraction Average-Based Optimization (SABO).
  • Incorporates Tent chaotic map for improved initial population quality and diversity.
  • Employs dynamic learning factor and adaptive inertia weight for accelerated convergence.
  • Validates ISASO on CEC2005 benchmark functions and diverse PV models.

Main Results:

  • ISASO demonstrates superior accuracy and reliability in PV model parameter identification compared to existing methods.
  • Achieves the lowest Root Mean Square Error (RMSE) values between standard and simulated PV data.
  • Comparative analysis confirms ISASO's effectiveness against other meta-heuristic algorithms.

Conclusions:

  • ISASO offers a reliable and effective solution for accurate solar PV model parameter estimation.
  • The proposed method enhances optimization performance by improving global search and convergence speed.
  • ISASO represents a significant advancement in computational methods for renewable energy systems.