RNN based SVPWM controlled grid integrated PV system with COA based MPPT for enhanced power quality and dynamic tracking

This paper proposes a hybrid control framework for a two-stage grid-connected photovoltaic (PV) system, integrating a Recurrent Neural Network (RNN)-based Space Vector Pulse Width Modulation (SVPWM) and a Crayfish Optimization Algorithm-based Maximum Power Point Tracking (COA-MPPT). The COA-MPPT governs the front-end DC-DC boost converter, achieving up to 32% faster tracking response, 28% lower overshoot, and 35% reduction in steady-state error compared to Perturb and Observe (P&O), Grey Wolf Optimization (GWO), Falcon Optimization Algorithm (FOA), Improved Coot Optimizer (ICO), and Horse Herd Optimization Algorithm (HHO). The extracted power is transferred to the grid through a Voltage Source Converter (VSC) using the RNN-SVPWM, which reduces current Total Harmonic Distortion (THD) by 71.5% compared to conventional SVPWM and by 59.5% compared to ANN-SVPWM by achieving 1.62% THD (with RNN) vs. 5.68% (conventional) and 4.0% (ANN) respectively. The proposed system is evaluated under four operating conditions: (i) constant uniform irradiance, (ii) variable uniform irradiance, (iii) variable non-uniform irradiance with partial shading, and (iv) a real-time 24-hour uniform irradiance profile from NSRDB data. Across all scenarios, the DC-link voltage is consistently regulated at 500 V with over 40% lower fluctuation under rapid irradiance changes. These results confirm that the proposed RNN-SVPWM and COA-MPPT combination delivers superior power quality, faster dynamic tracking, and improved stability, offering a robust solution for modern grid-integrated PV systems.