Enhancing power quality through the integration of hybrid renewable energy sources and multi-level inverters with unified power quality conditioner - Bharathi, S. Lakshmi Kanthan; Manjula, A.; Veeramanikandan, P. (Electrical engineering, 2025)
Integrating a hybrid renewable energy system (HRES) into a grid-associated load system enhances reliability and efficiency while meeting diverse load demands. However, this integration can introduce power quality (PQ) issues due to unbalanced, critical, and nonlinear load conditions. This manuscript proposes a novel approach for improving PQ in HRES through the integration of a Hiking Optimization Algorithm (HOA) and a Spike-Induced Graph Neural Network (SIGNN), referred to as the HOA–SIGNN method. The primary objective is to mitigate the effects of nonlinear, unbalanced, and critical load conditions by optimizing the 2DOF-PIDF (Two-Degree-of-Freedom Proportional–Integral–Derivative with Filter) controller parameters, ensuring superior PQ management. Unlike conventional methods, HOA leverages Tobler’s Hiking Function to dynamically balance exploration and exploitation, achieving faster convergence and reducing computational complexity. SIGNN further enhances system adaptability by capturing spatial–temporal dependencies using its temporal activation mechanism, leading to accurate gain parameter estimation. The proposed technique is excluded in MATLAB and compared with other strategies that have already been used, like Puzzle Optimization Algorithm, Artificial Neural Network, and Cuckoo Search Optimization. Results demonstrate that the HOA–SIGNN technique achieves an extremely low Total Harmonic Distortion of 0.12% and reduces computational time to 483 ms while maintaining an efficiency of 98.74% and minimizing the Integral Square Error to 0.01%. These significant improvements establish the HOA–SIGNN method as a robust and efficient solution for real-time PQ enhancement in grid-connected HRES.