Adaptive RNN with CSOA controlled based MMC-DSTATCOM for PQ enhancement in Distribution System

Abstract

In this paper, a hybrid convergence method is used to evaluate the development of PQ in a distribution device. For PQ analysis, a D-STATCOM-based Modular Multilevel Converter device is being analyzed. This proposed hybrid adapter is named as the Adaptive Recurrent Neural Network with a Crow Search Optimization Algorithm (ARNN-CSOA), which is used for Modular Multilevel Converter (MMC) optimization based on the device D-STATCOM. The proposed D-STATCOM advanced method will now go by providing a fast watt controller with invisible power to compensate for loads, modern day imbalances, flicker power reductions and voltage regulation. The proposed hybrid control strategy is to maximize the power of participation through the RNN approach. By using the proposed hybrid adapter method, the PI controller barriers are identified in advance to deliver appropriate MMC based DSTATCOM action. The proposed process learns all types of switches for mechanical problems such as DC power, real and active power. Based on the proposed procedure, the appropriate MM-based D-STATCOM cones are produced and obtain the correct results. The proposed method employed in the MATLAB / Simulink platform and is associated with different PWM methods such as SVM and ANN process.