Power flow management of grid connected HRES system using a CFANN technique

Abstract

ABSTRACT This manuscript presents a hybrid technique-based power flow management of grid-connected Hybrid Renewable energy sources (HRES). HRES has Photovoltaic (PV), Wind Turbine (WT) as well as Diesel Generator (DG). The proposed hybrid technique is the combined implementation of Cuttlefish Algorithm (CFA) and artificial neural network (ANN) and commonly called CFANN technique. Atproposed technique, CFA is used to create the precise control signals for system and builds the control signal database off-line based on power range among source side and load side. Dataset made to operate ANN online and lead the control procedure at less implementation time. The behaviours of system are examined depend on objective function. To analyse power flow management, equality and inequality restrictions are distinct that specifies the renewable energy source availability and power demand. By this appropriate control, HRES can considerably improve the dynamic security of power system. The result shows that the energy sources of renewable energy system and its power converters are controlled and the error is reduced by 92.42%. At that time, proposed model is implemented on MATLAB/Simulink work platform and implementation is evaluated to existing methods.