Development of intelligent MPPT (maximum power point tracking) control for a grid-connected hybrid power generation system

A hybrid power control system is proposed in the paper, consisting of solar power, wind power, and a diesel-engine. To achieve a fast and stable response for the real power control, an intelligent controller was proposed, which consists of the Wilcoxon (radial basis function network) RBFN and the improved (Elman neural network) ENN for (maximum power point tracking) MPPT. The pitch angle control of wind power uses improved ENN controller, and the output is fed to the wind turbine to achieve the MPPT. The solar array is integrated with an RBFN control algorithm to track the maximum power. MATLAB (MATrix LABoratory)/Simulink was used to build the dynamic model and simulate the solar and diesel-wind hybrid power system.

► To achieve a fast and stable response for the real power control.
► The pitch control of wind power uses improved ENN (Elman neural network) controller to achieve the MPPT (maximum power point tracking).
► The RBFN (radial basis function network) can quickly and accurately track the maximum power output for PV (photovoltaic) array.
► MATLAB was used to build the dynamic model and simulate the hybrid power system.
► This method can reach the desired performance even under different load conditions.