Intelligent power quality enhancement in HRES -microgrids: an RNN-HHO optimized STATCOM approach.

The increasing demand for reliable and clean electricity necessitates effective power-quality control in hybrid renewable energy systems (HRES). This study presents the modeling and MATLAB/Simulink simulation of a grid-connected HRES comprising photovoltaic (PV) generation, a proton-exchange membrane fuel cell (PEMFC), and battery energy storage. Variations in renewable power generation and load conditions can cause voltage deviations and harmonic distortion, affecting the power quality at the point of common coupling (PCC). To mitigate these disturbances, a static synchronous compensator (STATCOM) integrated with a proportional–integral–derivative (PID) controller is investigated, with its controller parameters optimized using a hybrid recurrent neural network and Harris Hawks Optimization (RNN–HHO) framework. The approach combines neural-network-based learning with population-based optimization to support PID parameter tuning and reactive-power compensation. MATLAB/Simulink results indicate that the compensated system achieves a reported total harmonic distortion (THD) of 1.57% under the evaluated simulation conditions. This result demonstrates the harmonic performance obtained with the proposed controller in the investigated case. The controller’s voltage-regulation and dynamic-response performance should be assessed alongside the corresponding uncompensated THD and measured response metrics. The study provides a simulation-based assessment of RNN–HHO-assisted STATCOM control for power-quality improvement in grid-connected hybrid renewable energy systems.The growing global demand for clean and reliable energy, driven by concerns about climate change and increasing electricity consumption, necessitates advanced control strategies for hybrid renewable energy systems (HRES). This research presents the design, modeling, and simulation of a grid-connected HRES comprising photovoltaic (PV), fuel cell (FC), and battery energy storage systems to enhance power quality and voltage stability under intermittent operating conditions. Due to the stochastic nature of renewable sources, the system is prone to power quality disturbances, including voltage sags, swells, fluctuations, and harmonic distortion.To address these challenges, a Static Synchronous Compensator (STATCOM) with an intelligent control framework is integrated into the HRES. The proportional-integral-derivative (PID) parameters of the STATCOM are optimally tuned using a hybrid Recurrent Neural Network and Harris Hawks Optimization (RNN-HHO) algorithm to achieve fast dynamic response and effective reactive power compensation. Simulation results demonstrate a significant reduction in harmonic distortion, with the Total Harmonic Distortion (THD) reduced to 1.57%, indicating superior power quality improvement compared to existing optimization-based controllers. The findings confirm that the proposed RNN-HHO-based STATCOM control strategy effectively enhances voltage stability, mitigates power quality issues, and ensures reliable operation of gridconnected hybrid renewable energy systems.