Intelligent control of Grid-Connected Wind Photovoltaic Hybrid Power Systems to Improve Efficiency

Ms. Simran R. Sayyad, Prof. V. J. Patil

Maximum power point tracking (MPPT), Photovoltaic (PV), Wind turbine, Hybrid power system, Radial basis function network system model (RBFNSM), General Regression Neural Network (GRNN).

This work proposes a grid-connected wind-photovoltaic (PV) hybrid power system, as well as the system's steady-state model analysis and control method. PV power, wind power, and an intelligent power controller make up the system. The General Regression Neural Network (GRNN) algorithm was used to examine the performance of a PV generation system with non-linear characteristics. The turbine speed is calculated using a high-performance on-line training radial basis function network-sliding mode (RBFNSM) method to capture maximum power from the wind. The intelligent controller consists of an RBFNSM and a GRNN for maximum power point tracking (MPPT) control to achieve a fast and stable response for power control. The wind turbine pitch angle is regulated by RBFNSM, and the PV system is controlled by GRNN, with the output signal controlling the boost converters to achieve the MPPT. The simulation results show that using MPPT, the suggested hybrid generation system may achieve high efficiency.

https://doi.org/10.31871/IJNTR.11.2.3