Y. Away, A. Novandri, M.S. Rizal
Sun tracker is a system designed to optimize the position of Photovoltaic (PV) panels so that they continuously face the sun, thereby increasing the efficiency of solar energy harvesting. The energy generated by PV is divided into two categories: energy-proportional and energy-operational. Energy-proportional is the energy produced by the PV and stored in the battery. Meanwhile, energyoperational is used to operate the mechanical and electronic components of the sun tracker system. However, current sun tracker systems have yet to achieve optimal efficiency because a portion of the captured energy is used to operate the tracker. To address this issue, a dual-axis sun-tracking system was developed, incorporating an Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm to improve the overall efficiency of solar panels. ANFIS is a hybrid model combining Artificial Neural Networks (ANN) principles and fuzzy logic. ANFIS leverages the strengths of both approaches, making it more effective for applications that require handling uncertain data and can model complex and non-linear systems. The system detects the sun's position using three Light Dependent Resistor (LDR) sensors arranged in a tetrahedron geometry. Using the ANFIS algorithm, the system continuously adjusts the azimuth and altitude angles to stay aligned with the sun. Energy-proportional is calculated by subtracting the tracker's energy consumption from the total solar energy. Testing showed that the ANFIS-based tracker system more accurate follows the sun's path, demonstrating high performance. When tested with a 10 Wp solar panel, the system achieved energyproportional levels between 87.99% and 94.84% due to optimized energy management in the tracker's motor and controller. These results show that the ANFIS-based sun tracker system can minimize energy-operational consumption while increasing energy-proportional, thus improving overall system efficiency and optimizing PV performance in absorbing solar energy. © Published under licence by IOP Publishing Ltd.
Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia; Department of Computer Engineering, Universitas Serambi Mekkah, Banda Aceh, 23245, Indonesia; Department of Information Technology, Universitas Islam Negeri Ar-Raniry, Banda Aceh, 23111, Indonesia
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