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.
SUMMARYEffective utilization of renewable energies such as wind energy as a replacement for fossil fuels is highly desirable. Wind energy is not constant and wind generator output is proportional to the cube of the wind speed, which causes the power output of wind turbine generators (WTGs) to fluctuate. In order to reduce output power fluctuations of wind farms, this paper presents an output power leveling control strategy for wind farms based on both the mean and the standard deviation of wind farm output power, a cooperative control strategy for WTGs, and a pitch angle control method using a generalized predictive controller (GPC) intended for all operating regions of WTGs. Simulation results using an actual detailed model for wind farm systems show the effectiveness of the proposed method.
Wind turbine generators and PV system generate fluctuating power condition. Therefore, the fluctuating power causes frequency and voltage fluctuations. To solve this problem, we propose a new power supply system with using renewable energy in isolated island. The feature of this system is to use an aqua electrolyzer and fuel cell. The operation of suggested system absorbs fluctuating power of renewable energy. Furthermore, the proposed system is able to generate reactive power and active power with using three-phase inverter. The effectiveness of the proposed power supply system is shown through simulation results in this paper.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.