Improvement of Rooftop Solar Panels Efficiency using Maximum Power Point Tracking Based on an Adaptive Neural Network Fuzzy Inference System
I Made Ari Nrartha1, I Made Ginarsa2, Agung Budi Muljono2, Sultan2, Ida Ayu Sri Adnyani2, Muhammad Roil Bilad2 & Muhammad Abid3 Show affiliations
- Department of Chemical and Process Engineering, Universiti Brunei Darussalam, Brunei
- Department of Electrical Engineering, University of Mataram, Nusa Tenggara Barat, Indonesia
- Department of Integrated Technologies, University Brunei Darussalam, Brunei
American Journal of Engineering and Applied Sciences, Volume 16, Issue 1, pp. 1–11 · doi.org/10.3844/ajeassp.2023.1.11
Abstract
Rooftop solar panels are a strategy for achieving Indonesia's renewable energy goals, but their non-linear characteristics make them difficult to control, especially in the face of extreme weather changes. An effective controller is needed to optimize the power output of solar panels. This study proposes a Maximum Power Point Tracking (MPPT) controller based on an Adaptive Neural network Fuzzy Inference System (ANFIS) to address this control problem. The capacity of the rooftop solar panels is 3,430-Watt peak (Wp) and they are connected to a 220-Volt (V) grid system. The system is designed, simulated, and analyzed using the Simulink model. The proposed ANFIS MPPT control for rooftop solar panels is compared to Perturb and Observe (P&O) MPPT and no MPPT systems. The simulation results show that in rapid changes in irradiation and extreme temperature, the efficiency of MPPT based on ANFIS is better than P&O MPPT and no MPPT by 0.4523 and 0.1115%, respectively.
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Nrartha, I. M. A., Ginarsa, I. M., Muljono, A. B., Sultan, Sri Adnyani, I. A., Bilad, M. R. & Abid, M. (2023). Improvement of Rooftop Solar Panels Efficiency using Maximum Power Point Tracking Based on an Adaptive Neural Network Fuzzy Inference System. American Journal of Engineering and Applied Sciences, 16(1), 1–11. https://doi.org/10.3844/ajeassp.2023.1.11
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