Neural-Network-Based MPPT Control of a Stand-Alone Hybrid Power Generation System

Abstract

A stand-alone hybrid power system is proposed in this paper. The system consists of solar power, wind power, diesel engine, and an intelligent power controller. MATLAB/Simulink was used to build the dynamic model and simulate the system. To achieve a fast and stable response for the real power control, the intelligent controller consists of a radial basis function network (RBFN) and an improved Elman neural network (ENN) for maximum power point tracking (MPPT). The pitch angle of wind turbine is controlled by the ENN, and the solar system uses RBFN, where the output signal is used to control the dc/dc boost converters to achieve the MPPT.