Artificial neural network controlled DSTATCOM for mitigating power quality concerns in solar PV and wind system - Mohammed Mujahid Irfan; Mohammed Alharbi; CH Hussaian Basha (Scientific Reports)
Abstract The escalating global energy requirement, driven by population expansion and industrial development, has been met through traditional energy resources till now, many of which are now impending depletion. Renewable energy sources, specifically photovoltaic (PV) and wind power, have emerged as viable and sustainable options to fossil fuels. These systems are praised for their reliability, scalability, and cost-effectiveness, making them integral to modern energy frameworks. However, the integration of PV and wind power systems and power electronics-based loads introduces harmonic distortions, posing critical challenges to power quality and system stability. Addressing these concerns is imperative for realizing the full potential of renewable energy systems in sustainable energy development. To meet these concerns, this research proposes an ANN based DSTATCOM to mitigate power quality concerns in PV-wind power systems. Traditional DSTATCOM control appraches like “synchronous reference frame and instantaneous reactive power” often create challenges in parameter valuation and eficacy under uneven load scenarios. The model designed using XANN approach mitigates harmonics perfectly and showcase better performance even while operating under uneven non-linear loading scenarios. The model simulated using MATLAB and the results are validated using the realtime setup. The outcomes reflects the satisfactory performance interms of enhancing the power quality of the solar-wind systems.