A XGBOOST-MRFO control scheme for power quality improvement in grid integrated hybrid renewable energy sources using STATCOM

A hybrid control system is presented for the improvement of power quality in grid-integrated hybrid renewable energy sources (HRES) like hybrid photovoltaic-wind solar energy system using STATCOM. The proposed hybrid control system is the hybrid wrapper of both the extreme gradient boosting (XGBOOST) and Manta Ray Foraging Optimization (MRFO) and hence it is said to be as XGBOOST-MRFO control scheme. The innovation of the proposed hybrid system is the improved predictability through the participation of MRFO, reliability, adaptation for the entire parameter variations, and shorter implementation time. Depending on the minimal error objective function, MRFO improves the XGBOOST learning process. The proposed approach relies on XGBOOST-MRFO which is serving control algorithm to generate STATCOM reference signals. Here, the XGBOOST system is qualified with inputs like instantaneous previous power of the obtainable sources and the current time necessary load demand and the equivalent target reference power of the sources. Depending on the load variation, the XGBOOST-MRFO control scheme makes the gain parameters of the PI controller give the optimal control signal for the STATCOM and to direct the power of the HRES. The prediction process of the proposed system assumes the entire kinds of differences on system parameters such as DC voltage, real and reactive power. Therefore, the proposed XGBOOST-MRFO strategy gives that optimal control of STATCOM that attempts to recover the damping of the power system and direct line voltage by giving reactive power compensation. The proposed system is executed on the MATLAB platform and performance is evaluated. The performance of the proposed system is compared with existing methods.