Enhancement of Electric Power Quality using UPQC with Adaptive Neural Network Model Predictive Control

Advanced controllers for harmonic suppression alongside unified power quality conditioner (UPQC) for the power distribution network is proposed in this research. Adaptive neural network based model predictive control (ANNMPC) design is the auxiliary unit to enhance the power quality using UPQC. The unit is composed of shunt and series active filters to ensure the better quality of power across the power system. Supply current and the source potential is normalized by controller despite the subsistence impact of harmonic agents owing to the sag and swell phenomena and issues associated with the inductive loads and thereby power factor. State space model is developed for the power quality conditioner that assists ANNMPC for the optimal power flow in the distribution system. The optimal control strategy accounts to sustain the constant DC link voltage and regulates the switching pulses as well. The recital of the UPQC is extensively advanced with the proposed ANNMPC respectively. Ideology of this ace research decreases the stress over the power conditioners and predictive controllers. Real time simulations are carried out through the MATLAB/SIMULINK platform to corroborate the validity of the proposal. Model is subjected to operate under various circuit conditions and the results are compared to show the genuineness.