Power quality improvement in distribution generation with renewable energy sources using hybrid GBDT-BCMO approach

Abstract

ABSTRACT This paper presents a hybrid control system designed to improve power quality in grid-integrated hybrid renewable energy sources (HRES). The proposed hybrid control scheme is a hybrid approach gradient boosting decision tree and balancing composite motion optimisation, named GBDT-BCMO. The goals outlined above are the focus of GBDT-BCMO. The GBDT machine learning technique is enhanced using the BCMO method. GBDT method is trained on inputs like previous instantaneous energy of obtainable sources and current time required load demand based on target reference power. Based on load variation, GBDT-BCMO control system creates PI controller gain parameters to generate optimal control signal and manage HRES energy. The prediction process of the present method undertakes variations on system parameters, like reactive and real power and DC voltage. The proposed GBDT-BCMO system improves the power system damping and creates line voltage, giving reactive power compensation. The proposed methodology is then carried out in MATLAB/Simulink platform, and the performance is analysed. The behaviour of the proposed system is related with other systems. The proposed method ranges from 0 to 22.1 V. The BCMO system ranges from 0 to 22. The ALO system ranges from 0 to 22 V. The BAT Algorithm ranges from 0 to 22.0 V.