文件名称:R1
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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 the wind.-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 the wind.
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 the wind.-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 the wind.
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