Comparative Study of MPPT and Pitch Angle using PI and Fuzzy Logic Controllers

To extract the maximum power when wind speed is below rated speed, and to adapt the aerodynamic torque of wind turbine (WT) when the wind speed is above rated speed many control strategies were discussed. In this work a comparative study using conventional (PI) strategy and fuzzy logic controller (FLC) for both Maximum Power Point Tracking (MPPT) and Pitch angle control strategies is developed. As the conventional strategy requires a well-known mathematical model, the fuzzy logic does not need the system knowledge and it has a good potential with nonlinearities such as strong wind disturbances. The simulation results demonstrate that the fuzzy logic controller is robust and can reach better and improved control performance than the conventional control strategy, namely reference tracking, high sensitivity to high wind speed variation and nonlinearities.

[1]  Reza Shahnazi,et al.  An optimal fuzzy PI controller to capture the maximum power for variable-speed wind turbines , 2012, Neural Computing and Applications.

[2]  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.

[3]  J. Clare,et al.  Wind–Diesel Generation Using Doubly Fed Induction Machines , 2008, IEEE Transactions on Energy Conversion.

[4]  Mamadou Lamine Doumbia,et al.  Modelling and fuzzy logic control of DFIG based Wind Energy Conversion Systems , 2012, 2012 IEEE International Symposium on Industrial Electronics.

[5]  R.G. Harley,et al.  Wind Speed Estimation Based Sensorless Output Maximization Control for a Wind Turbine Driving a DFIG , 2008, IEEE Transactions on Power Electronics.

[6]  Tamou Nasser,et al.  Comparative Study of MPPT and Pitch Angle Control Strategies for a Wind Energy Conversion System , 2017, 2017 International Renewable and Sustainable Energy Conference (IRSEC).

[7]  Mamadou Lamine Doumbia,et al.  Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator) , 2014 .

[8]  Salma El Aimani.  Modélisation des différentes technologies d'éoliennes intégrées dans un réseau de moyenne tension , 2004 .

[9]  L. Mokrani,et al.  MPPT fuzzy logic controller of a wind energy conversion system based on a PMSG , 2016, 2016 8th International Conference on Modelling, Identification and Control (ICMIC).

[10]  W. S. Mota,et al.  Wind turbine pitch angle control using Fuzzy Logic , 2012, 2012 Sixth IEEE/PES Transmission and Distribution: Latin America Conference and Exposition (T&D-LA).

[11]  Muhammad Bilal Kadri,et al.  Pitch angle control of DFIG using self tuning neuro fuzzy controller , 2013, 2013 International Conference on Renewable Energy Research and Applications (ICRERA).

[12]  Tamou Nasser,et al.  Wind System Based on a Doubly Fed Induction Generator: Contribution to the Study of Electrical Energy Quality and Continuity of Service in the Voltage Dips Event , 2017 .

[13]  J. S. Thongam,et al.  MPPT Control Methods in Wind Energy Conversion Systems , 2011 .