Ecology, Environment and Conservation Paper

DESIGN OF HYBRID POWER CONTROL USING EXOTIC ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR HYBRID RENEWABLE APPLICATIONS

K. Mahendran and S.U. Prabha

Abstract

The idea of an exotic adaptive neuro-fuzzy inference system (EANFIS) - based hybrid boost converter system (HBC) for high power applications is presented by hybrid renewable energy. To interface them to a central dc bus multiple renewable energy sources utilize HBC power converters. A HBC-EANFIS-based supervisory control system measures the power that must be produced by the accessible resource power and state-of-charge (SOC) of the battery to deal with the power demand. This framework makes a reference to dynamic operating points to low-level individual subsystems anticipating on the wind and load conditions esteem. Under low energy storage to dodge a system power outage the energy management system and electricity regulation system additionally controls the booking on charging operation amid ominous wind conditions. The neighborhood controllers control the wind turbine, PV cell, fuel cell (FC) and storage battery units in light of the reference dynamic operating points of the individual subsystems. To upgrade the gain ability with lessened switching loss the proposed exotic adaptive neuro-fuzzy inference system (EANFIS)control is appropriate. Under variable load conditions utilizing MATLAB/Simulink tool the proposed controller strategy for operation, controller design, are inspected and tried.