Grid-connected PV inverter system control optimization using Grey Wolf optimized PID controller

This paper introduces a robust and adaptive control framework that integrates a Proportional-Integral-Derivative (PID) controller with the bio-inspired Grey Wolf Optimization (GWO) algorithm for real-time tuning of controller parameters in grid-connected photovoltaic (PV) inverter systems. Conventional controllers such as P and PI are widely used in PV applications due to their simplicity, but they exhibit notable limitations in dynamic environments, including increased Total Harmonic Distortion (THD), slower transient response, and poor voltage regulation-particularly under variable irradiance conditions. The proposed GWO-PID method overcomes these limitations by leveraging the GWO algorithm's global search capability to dynamically optimize the PID gains (Kp, Ki, Kd) based on a composite fitness function that minimizes Mean Squared Error (MSE) and THD. The system architecture, simulated in MATLAB/Simulink, comprises a 50 kW PV array with a boost converter employing an Incremental Conductance (INC) Maximum Power Point Tracking (MPPT) algorithm, a three-phase voltage source inverter, RLC filters, and a dual-loop (voltage and current) control system synchronized with the utility grid through a Phase-Locked Loop (PLL). The GWO algorithm iteratively refines PID parameters to achieve real-time adaptation to environmental fluctuations. Under standard irradiance (1000 W/m²), the GWO-PID controller achieved a rise time of 0.025 s, settling time of 0.035 s, THD of 3.7%, and MSE of 0.25 kW², while maintaining a stable DC-link voltage of 500 V, thereby ensuring compliance with IEEE 519-2014 power quality standards. Across irradiance levels ranging from 400 W/m² to 1000 W/m², the GWO-PID controller consistently maintained DC-link voltage stability and minimized oscillations in PV voltage and current. Compared to traditional PI and P controllers, the proposed method reduced settling time by over 45%, improved power tracking accuracy, and significantly lowered harmonic distortion. Furthermore, it ensured a power factor close to unity and exhibited excellent frequency stability under transient disturbances. Simulation results also confirm the superior performance of the GWO-PID controller in managing active and reactive power exchange, minimizing overshoot, and maintaining synchronization with the grid, even during rapid environmental transitions. By embedding intelligent metaheuristic optimization into a classical PID framework, this work advances the state of inverter control strategies for PV systems. The proposed GWO-PID technique provides a scalable, efficient, and real-time solution that enhances grid compliance, energy quality, and system stability, marking a key advancement in adaptive control for smart grid and microgrid applications.