Wind Turbine Pitch Angle Control Based on ANFIS

Jun Xiao; Jiang Xiao; Wei Chen

doi:10.4028/www.scientific.net/AMM.291-294.507

Paper Titles

Using Dynamic Reactive Power Compensation Equipments to Enhance Low Voltage Ride-Through Capability of Fixed Speed Asynchronous Wind Farms
p.481

Numerical Simulation of Unsteady Flow for Variable-Pitch Vertical Axis Wind Turbine
p.490

Stall Nonlinear Flutter of Wind Turbine Blade Modeled as Thin-Walled Composite Beam Integrated with Structural Damping
p.496

Variation of Aerodynamic Load Engineering Analysis during Wind Turbine Run
p.501

Wind Turbine Pitch Angle Control Based on ANFIS
p.507

Research on Matching of Main Engine and Turbocharger on Sail-Assisted Ship
p.513

The Applicability of Two-Equation Turbulence Models in Wind Velocity Prediction
p.518

WTG’s Gearbox Condition Assessment by Matching the Hilbert-Huang Spectrum of Vibration Signal
p.522

Aerodynamic Performance Analysis of a Large Scale Wind Turbine Blade under Variable Condition
p.527

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 291-294Wind Turbine Pitch Angle Control Based on ANFIS

Wind Turbine Pitch Angle Control Based on ANFIS

Abstract:

Wind turbine pitch control need a higher demand due to the random changes in wind speed, this paper proposes using adaptive neural fuzzy inference system to control wind turbine pitch, and constructs a mathematical model of the wind turbine. Consider the error between the measured and the actual value of generator speed as the input to the controller. Take a simulation analysis to the adaptive neural fuzzy inference system controller under random wind speed. The simulation results show that the adaptive neural fuzzy inference system control strategy has good robustness and dynamic performance, to improve wind turbine pitch control is feasible and effective.

You might also be interested in these eBooks

Advances in Energy Science and Technology

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 291-294)

Pages:

507-512

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.291-294.507

Citation:

Cite this paper

Online since:

February 2013

Authors:

Jun Xiao, Jiang Xiao, Wei Chen

Keywords:

ANFIS, Pitch Angle, Wind Turbine (WT)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Lin L, Zhang J, Yang Y H. Comparison of pitch angle control models of wind farm for power system analysis. Power&Energy Society General Meeting. 2009, pp.1-7.

DOI: 10.1109/pes.2009.5275517

Google Scholar

[2] Mingjin Yang, Jie Wu, Junhua Yang. Based on Active Disturbance Rejection Controller of Wind Power Generation System for Maximum Wind Energy Capture Control. Journal of Solar Energy, 4(2004) 525-529.

Google Scholar

[3] Mingfu Liao,R. Gasch,J. Twele. Wind power generation technology. Publishing House of Northwestern University, (2009).

Google Scholar

[4] Slootweg J G, Kling W L. Aggregated Modelling of Wind Parks in Power System Dynamics Simulations. Power Tech Conference Proceedings, 3(2003)23-26.

DOI: 10.1109/ptc.2003.1304458

Google Scholar

[5] Hui Li, Shi K L, McLaren P G. Neural-network-based Sensorless Maximum Wind Energy Capture with Compensated Power Coefficient Industry Applications. IEEE Trans, 41(2005)1548-1556.

DOI: 10.1109/tia.2005.858282

Google Scholar

[6] Lei Zhang. Sliding Mode Variable Structure Control in Wind Turbine Pitch Control Application . Electrical Application, 28(2009) 78-81.

Google Scholar

[7] Xinfang Zhang, Daping Xu, Yuegang Lv, etc. For Large-scale Variable Speed Wind Turbines Based Adaptive Fuzzy Control. Journal of System Simulation, 16(2004) 573-577.

DOI: 10.1109/wcica.2008.4593277

Google Scholar

[8] Bati A F, Leabi S K. NN Self-tuning Pitch Angle Controller of Wind Power Generation Unit. Power Systems Conference and Exposition, 29(2006)2019-(2029).

DOI: 10.1109/psce.2006.296236

Google Scholar

[9] Jun Zhang, Zhengyou He, Xijing Tan. A Kind of ANFIS Based Distribution Network Fault Classification Method and Its Adaptability Analysis. Power System Protection and Control, 39(2011) 23-29.

Google Scholar

[10] Heng Guo, Ke Luo. ANFIS Application in Short Term Load Forecasting. High Voltage Technology, 32(2006) 105-107.

Google Scholar