DBSCAN Based inductively coupled DSTATCOM for PQ improvement

Abstract

This research study explains the feasibility of implementing machine learning (ML) oriented inductively coupled distributed static compensator (IC-DSTATCOM) for power quality (PQ) enhancement. The issue involved in declining the power quality (PQ) using direct coupled static compensator (DC-DSTATCOM) was identified as a hazardous disappointment. Hence, to improve the quality, coupling transformer is served in unification with DC-DSTATCOM. Also, the recent growth of machine learning (ML) systems and progression of computational resources, with unpredicted data obtainability, has inspired the researchers. In this study, density based spatial clustering of application with noise (DBSCAN) is employed by using its own learning mechanism (LM) using MATLAB/ Simulink. This controller contains six subnets. Among them, six subnets are employed for active tuned weight extraction whereas other three subnets are used for reactive part. Moreover, the abovesaid devices are triggered with the help of generated reference supply current. A case education is reviewed in detail to demonstrate the operation of both DC-DSTATCOM & IC-DSTATCOM. Finally, the IC-DSTATCOM is amplified healthier as compared to other in terms of harmonics shortening, upgrading in power factor, load balancing, and potential regulation etc. To examine the effectiveness, simulation outputs of the IC-DSTATCOM is presented by following the benchmark measure of IEEE-2030-7-2017 and IEC-61000-1 grid code.