Power Flow Management of the Grid-Connected Hybrid Renewable Energy System: A PLSANN Control Approach

Abstract

This paper clarifies the optimal control strategy of Hybrid Renewable Energy System (HRES), which has the parallel execution of Lightning Search algorithm with Artificial Neural Network and Recurrent Neural Network (PLSANN). The projected system is composed of Photovoltaic (PV), Wind Turbine (WT), Fuel Cell (FC), and Battery, which can associate DC link and competent to balance the real and reactive power. The introduction of wind/PV power within an electric grid origin the PQ troubles are considered. At this point, the recompense policy of DC/DC converter is examined by PLSANN/RNN technique to provide the optimal power flow management of HRES. Here, LSA is exploited for the optimization process of real power and the Recurrent Neural Network (RNN) is utilized for the optimal reactive management. The projected process recognizes the finest control pulses of the DC/DC converter derived from the foundation part and load part limitation. The projected system is competent to establish the active power into the grid and additionally it is capable of enhancing the PQ. With proper control, HRES significantly enhance the dynamic security of the power system. The proposed method is implemented in MATLAB/Simulink working platform and analyzed their performances. The statistical analysis of proposed method is analyzed in terms of mean, median and standard deviation factors. In order to prove the effectiveness, the proposed method is compared with current techniques such as Combined Modified Bat Search algorithm–artificial neural network (CMBSNN) technique, Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and PI controller.