article · Scientific Reports
Solar panels require maximum power point tracking to maximise their electrical output across changing environmental conditions. Conventional tracking methods, such as perturb and observe or incremental conductance, often struggle with slow response speeds and sluggish dynamics during sudden weather shifts. To resolve these performance issues, an optimisation method using an artificial neural network was developed and evaluated. This data-driven model learns to adjust the solar panel operating point rapidly in response to variations in solar irradiation and ambient temperature. The approach was evaluated through numerical simulations and confirmed through physical experiments alongside standard tracking techniques. Across the examined methods, the neural network approach delivered the strongest overall performance, achieving a tracking efficiency of 98.16 per cent and finding the maximum power point within 1.3 seconds.
Solar power generation loses potential output when weather conditions shift quickly and control systems cannot adjust promptly. Applying an artificial neural network to continuously adapt to sunlight and temperature variations ensures solar systems harvest the maximum available energy. This faster tracking improves the overall energy yield and responsiveness of solar energy systems during dynamic weather conditions.
This technique applies to photovoltaic systems requiring enhanced power extraction, which could serve solar inverter manufacturers and solar energy operators. Having undergone both simulation verification and experimental testing, the technology is at an applied and tested stage of development. Moving towards practical commercial use would require integrating the neural network algorithm into standard embedded hardware or microcontrollers used in commercial solar installations.
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Maximum power point tracking (MPPT) is a technique involved in photovoltaic (PV) systems for optimizing the output power of solar panels. Traditional solutions like perturb and observe (P&O) and Incremental Conductance (IC) are commonly utilized to follow the MPP under various environmental circumstances. However, these algorithms suffer from slow tracking speed and low dynamics under fast-changing environment conditions. To cope with these demerits, a data-driven artificial neural network (ANN) algorithm for MPPT is proposed in this paper. By leveraging the learning capabilities of the ANN, the PV operating point can be adapted to dynamic changes in solar irradiation and temperature. Consequently, it offers promising solutions for MPPT in fast-changing environments as well as overcoming the limitations of traditional MPPT techniques. In this paper, simulations verification and experimental validation of a proposed data-driven ANN-MPPT technique are presented. Additionally, the proposed technique is analyzed and compared to traditional MPPT methods. The numerical and experimental findings indicate that, of the examined MPPT methods, the proposed ANN-MPPT approach achieves the highest MPPT efficiency at 98.16% and the shortest tracking time of 1.3 s.
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DOI: 10.1038/s41598-024-67306-0
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