Title :
Output power leveling of wind farm using pitch angle control with fuzzy neural network
Author :
Senjyu, Tomonobu ; Sakamoto, Ryosei ; Urasaki, Naomitsu ; Funabashi, Toshihisa ; Sekine, Hideomi
Author_Institution :
Dept. of Electr. & Electron. Eng., Ryukyus Univ., Okinawa
Abstract :
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 output power fluctuation of wind farm, this paper presents a output power leveling control strategy of wind farm based on both average wind farm output power and standard deviation of wind farm output power, cooperative control strategy for WTGs, and pitch angle control using a generalized predictive controller (GPC) in all operating regions for WTG. The simulation results with using actual detailed model for wind farm systems show effectiveness of the proposed method
Keywords :
fuzzy neural nets; power control; power engineering computing; power generation control; predictive control; wind power plants; cooperative control strategy; fuzzy neural network; generalized predictive controller; output power fluctuation reduction; output power leveling; pitch angle control; renewable energies utilization; wind energy; wind farm; wind turbine generators; windmill output; Fluctuations; Fossil fuels; Fuzzy control; Fuzzy neural networks; Power generation; Wind energy; Wind energy generation; Wind farms; Wind speed; Wind turbines; Fuzzy neural network (FNN); generalized predictive controller (GPC); output power fluctuation; pitch angle control; wind farm;
Conference_Titel :
Power Engineering Society General Meeting, 2006. IEEE
Conference_Location :
Montreal, Que.
Print_ISBN :
1-4244-0493-2
DOI :
10.1109/PES.2006.1709377
