PITCH ANGLE CONTROL OF VARIABLE LOW RATED SPEED WIND TURBINE USING FUZZY LOGIC CONTROLLER

Pitch angle control of wind turbine has been used widely to reduce torque and output power variation in high rated wind speed areas. It is a challenge to maximize available energy in the low rated wind speed areas. In this paper, a wind turbine prototype with a pitch angle control based on fuzzy logic to maximize the output power is built and demonstrated. In the varying low rated wind speed of 4-6 m/s, the use of fuzzy logic controller can maximize the average output power of 14.5 watt compared to 14.0 watt at a fixed pitch angle of the blade. Implementation of pitch angle fuzzy logic-based control to the wind turbine is suitable for the low rated wind speed areas

[1]  Mona N. Eskander,et al.  Fuzzy logic control based maximum power tracking of a wind energy system , 2001 .

[2]  Onder Ozgener,et al.  A small wind turbine system (SWTS) application and its performance analysis , 2006 .

[3]  Eduard Muljadi,et al.  Pitch-controlled variable-speed wind turbine generation , 1999, Conference Record of the 1999 IEEE Industry Applications Conference. Thirty-Forth IAS Annual Meeting (Cat. No.99CH36370).

[4]  Mouna Ben Smida,et al.  Pitch Angle Control for Variable Speed Wind Turbines , 2015, Renewable Energy and Sustainable Development.

[5]  Ming Cheng,et al.  Pitch angle control for variable speed wind turbines , 2008, 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies.

[6]  Fernando D. Bianchi,et al.  Power regulation in pitch-controlled variable-speed WECS above rated wind speed , 2004 .

[7]  Xiao Cheng,et al.  Fuzzy PID Controller for Wind Turbines , 2009, 2009 Second International Conference on Intelligent Networks and Intelligent Systems.

[8]  Yousif El-Tous.  Pitch Angle Control of Variable Speed Wind Turbine , 2008 .

[9]  F. Martin McNeill,et al.  Fuzzy Logic: A Practical Approach , 1994 .

[10]  Ahmet Serdar Yilmaz,et al.  Pitch angle control in wind turbines above the rated wind speed by multi-layer perceptron and radial basis function neural networks , 2009, Expert Syst. Appl..