This paper proposes a novel hybrid technique for enhancing power quality (PQ) in distributed generation (DG) systems by deploying a unified power quality conditioner (UPQC). Here, the proposed hybrid method is the joint execution of white shark optimizer (WSO) and recalling-enhanced recurrent neural network (RERNN), called the WSO-RERNN technique. The primary objective of this novel approach is to effectively mitigate voltage sag and reduce voltage harmonics under varying load conditions. It is important to investigate the voltage sag, swell and harmonic distortion of the system to obtain an enhanced PQ of the energy supply. Therefore, this paper shows the brief impact of PQ in DG utilizing the proposed unified PQ conditioner controller. The WSO-RERNN control technique enhances the performance of the UPQC controller by providing the optimal control signal. By then, the efficiency of the proposed approach is done in MATLAB, and the performance is compared with those of existing optimization techniques, including Ant Lion Optimizer (ALO), Grey wolf optimization (GWO) and Salp swarm algorithm (SSA) methods.

本文提出了一种新型混合技术，通过部署统一电能质量调节器（UPQC）来增强分布式发电（DG）系统中的电能质量（PQ）。这里，提出的混合方法是白鲨优化器（WSO）和召回增强循环神经网络（RERNN）的联合执行，称为WSO-RERNN技术。这种新颖方法的主要目标是有效减轻电压暂降并减少变化负载条件下的电压谐波。研究系统的电压暂降、暂升和谐波失真对于获得增强的供电 PQ 非常重要。因此，本文利用所提出的统一 PQ 调节器控制器展示了 PQ 对 DG 的简要影响。 WSO-RERNN 控制技术通过提供最优控制信号来增强 UPQC 控制器的性能。然后，在MATLAB中对所提出的方法进行了效率测试，并将其性能与现有的优化技术进行了比较，包括蚁狮优化器（ALO）、灰狼优化（GWO）和Salp群算法（SSA）方法。