Intelligent control of a grid-connected wind-photovoltaic hybrid power systems

A grid-connected wind-photovoltaic (PV) hybrid power system is proposed, and the steady-state model analysis and the control strategy of the system are presented in this paper. The system consists of the PV power, wind power, and an intelligent power controller. The General Regression Neural Network (GRNN) algorithm applied to PV generation system which has non-linear characteristic and analyzed performance. A high-performance on-line training radial basis function network-sliding mode (RBFNSM) algorithm is designed to derive the turbine speed to extract maximum power from the wind. To achieve a fast and stable response for the power control, the intelligent controller consists of a RBFNSM and a GRNN for maximum power point tracking (MPPT) control. The pitch angle of wind turbine is controlled by RBFNSM, and the PV system uses GRNN, where the output signal is used to control the boost converters to achieve the MPPT. The simulation results confirm that the proposed hybrid generation system can provide high efficiency with the use of MPPT.

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