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Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems.

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Grey wolf optimizer (GWO) best enhances artificial neural network (ANN) Maximum Power Point Tracking (MPPT) for solar energy under partial shading. GWO balances prediction accuracy and computational speed, improving photovoltaic system efficiency.

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ANNCSGWOMPPTPSOSSAbioinspiredoptimization algorithm

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Area of Science:

  • Renewable Energy Systems
  • Artificial Intelligence in Engineering
  • Optimization Algorithms

Background:

  • Photovoltaic (PV) systems face challenges in maintaining peak energy output under partial shading conditions.
  • Artificial Neural Networks (ANNs) are used for Maximum Power Point Tracking (MPPT), but their performance can be suboptimal without advanced optimization.
  • Bio-inspired algorithms offer potential for enhancing ANN-based MPPT system accuracy and efficiency.

Purpose of the Study:

  • To compare the effectiveness of four bio-inspired optimization algorithms: Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Squirrel Search Algorithm (SSA), and Cuckoo Search (CS).
  • To enhance an ANN-based MPPT system for improved performance under simulated partial shading conditions in PV systems.
  • To identify the most computationally efficient and accurate algorithm for real-world application.

Main Methods:

  • Implemented and evaluated GWO, PSO, SSA, and CS to optimize an ANN for MPPT in PV systems.
  • Augmented a dataset with perturbations to simulate realistic partial shading scenarios.
  • Assessed algorithm performance based on prediction accuracy (Mean Squared Error - MSE, Mean Absolute Error - MAE) and computational execution time.

Main Results:

  • The standard ANN exhibited poor performance (MSE: 159.94, MAE: 8.08).
  • GWO achieved the best prediction accuracy (MSE: 11.95, MAE: 2.46) with good computational efficiency (1199 s).
  • SSA was the fastest (987 s) with competitive accuracy (MSE: 12.15, MAE: 2.70), while PSO minimized MAE (2.17) but was slower (1418 s). CS showed the least favorable results.

Conclusions:

  • Grey Wolf Optimizer (GWO) provides the optimal balance between prediction accuracy and computational speed for ANN-based MPPT under partial shading.
  • The optimized ANN using GWO significantly improves energy harvesting efficiency in photovoltaic systems.
  • This methodology holds promise for real-world applications in solar farms and residential solar panels, with potential for integration into smart grid systems.