Power quality management is a widespread requirement in this renewal energy source (RES) integrated environment. A few of the key challenges faced during power quality management are total harmonic distortion (THD), poor power factor and reactive power compensation. This paper tries to improve these power quality issues using synchronous reference frame (SRF) theory-based distributed static compensator (DSTATCOM). An Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm has been used to optimize the operation of the DSTATCOM and to obtain maximum power from a PV array. This algorithm optimizes the requirement of compensating current from the PV fed DSTATCOM to make the source current smoother. System dynamic performance is investigated in the presence of balanced/unbalanced nonlinear and reactive loads. The results obtained using ANFIS- and SRF-based methods are compared to other conventional/AI-based methods and found to be superior. The proposed method is validated in real time using Opal-RT on FPGA computation engine to closely emulate actual hardware.

在这种可再生能源 (RES) 集成环境中，电能质量管理是一项普遍要求。电能质量管理过程中面临的一些主要挑战是总谐波失真 (THD)、功率因数差和无功功率补偿。本文尝试使用基于同步参考框架 (SRF) 理论的分布式静态补偿器 (DSTATCOM) 来改善这些电能质量问题。自适应神经模糊推理系统 (ANFIS) 算法已用于优化 DSTATCOM 的操作并从 PV 阵列获得最大功率。该算法优化了从 PV 馈电 DSTATCOM 补偿电流的要求，以使源电流更平滑。在存在平衡/不平衡非线性和无功负载的情况下研究系统动态性能。将使用基于 ANFIS 和 SRF 的方法获得的结果与其他传统/基于 AI 的方法进行比较，发现结果更好。所提出的方法在 FPGA 计算引擎上使用 Opal-RT 进行实时验证，以密切模拟实际硬件。