Hybrid ANN-PSO Based MPPT Optimization for Enhanced Solar Panel Efficiency

Authors

  • Muhammad ilham hasby Hamzah Department of Electrical Engineering, Institut Teknologi Kalimantan, Indonesia
  • Happy Aprillia Department of Electrical Engineering, Institut Teknologi Kalimantan, Indonesia https://orcid.org/0000-0002-5263-0608
  • Andhika Giyantara Department of Electrical Engineering, Institut Teknologi Kalimantan, Indonesia

DOI:

https://doi.org/10.26418/elkha.v17i1.88711

Keywords:

photovoltaic, PSO, neural network, MPPT

Abstract

In some cases of Solar Power Generation System (PLTS) optimization, AI algorithms can be used to solve complex problems such as efficiency problems. In this research, a hybrid approach that combines Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) algorithms is used to optimize the Maximum Power Point Tracking (MPPT) system for solar panels. The hybrid technique seeks to maximize power output by precisely determining the ideal voltage and current points, which will increase the efficiency of solar panels. This study includes the measurement of parameters such as current (I), voltage (V), and power (W) in the MPPT system. The research shows that the hybrid ANN-PSO approach performs better than the traditional ANN method, producing mean squared error (MSE) and root mean squared error (RMSE) values that are lower. Moreover, research results show that the hybrid system maintains a load efficiency of approximately 51% in real-world measurements and about 67% in simulation data, indicating better performance and implementation ease.

Author Biography

Happy Aprillia, Department of Electrical Engineering, Institut Teknologi Kalimantan, Indonesia

Penelitian/Keahlian :
  • Forecasting Methods
  • Energy Management System
  • Artificial Intelligent

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Volume 17, Number 1, April 2025

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2025-10-13

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Vol. 17 No.1 April 2025

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Vol 17 No 1 April 2025

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