No abstract

There are concerns in regards to wind power generation as its output constantly, as well as considerably, varies. This article presents a control strategy for wind farms consisting of a wind turbine with variable pitch. In order to reduce output power fluctuation of wind farms, smoothed wind-farm output power command is determined by wind condition and fuzzy neural network. In addition, individual wind turbine generators are controlled by output power command derived from wind-farm output power command and coordination control for each wind turbine generator. Simulation results using an actual detailed model for wind-farm systems show the effectiveness of the proposed method.

SUMMARYEffective utilization of renewable energies such as wind energy is expected instead of the fossil fuels. Wind energy is not constant and windmill output is proportional to the cube of wind speed, which causes fluctuating power of wind turbine generator (WTG). In order to reduce the fluctuating power of WTG, this paper presents an output power leveling technique of WTG by pitch angle control using H ∞ control, and the control input of WTG linear model is separated from the disturbance. The simulation results using actual detailed model for WTG show the effectiveness of the proposed method.

In recent years, there have been problems such as exhaustion of fossil fuels, e.g., coal and oil, and environmental pollution resulting from consumption. Effective utilization of renewable energies such as wind energy is expected instead of the fossil fuel. Wind energy is not constant and windmill output is proportional to the cube of wind speed, which cause the generated power of wind turbine generators (WTGs) to fluctuate. In order to reduce fluctuating components, there is a method to control pitch angle of blades of the windmill. In this paper, output power leveling of wind turbine generator by pitch angle control using an adaptive control is proposed. A self-tuning regulator is used in adaptive control. The control input is determined by the minimum variance control. It is possible to compensate control input to alleviate generating power fluctuation with using proposed controller. The simulation results with using actual detailed model for wind power system show effectiveness of the proposed controller.

No abstract