A Pitch Angle Controller Based on Novel Fuzzy-PI Control for Wind Turbine Load Reduction

A novel fuzzy rule is proposed to adopt a positive pitch strategy when the error between the measured and rated generator speed becomes large and continues to increase, and to adopt a negative pitch strategy when the error is small. The improved approach is introduced into the normal Fuzzy-Proportional-Integral (Fuzzy-PI) control strategy by dividing the fuzzy rules into four areas and analyzing the design method for each area. Furthermore, a low pass filter is used to reduce the ultimate loads of the pitch driver caused by the novel fuzzy rules. The modeling of the wind turbine load under turbulent wind conditions is conducted in GH Bladed, and MATLAB/Simulink is used to interact with the modeling to verify the novel Fuzzy-PI control. The results show that, compared with normal Fuzzy-PI control, the novel Fuzzy-PI control can greatly reduce the ultimate loads and fatigue loads of the pitch driver. The novel Fuzzy-PI control not only reduces the extremum of power deviation, but also decreases some ultimate loads and fatigue loads of the tower base and the blade root. It can reduce these loads by up to 21.53% under the normal turbulent wind condition and by up to 18.14% under the extreme turbulent wind condition.

[1]  Bekhada Hamane,et al.  Control for Variable Speed Wind Turbine Driving a Doubly Fed Induction Generator using Fuzzy-PI Control , 2012 .

[2]  Ligang Wu,et al.  Nonlinear Control of Variable Speed Wind Turbines via Fuzzy Techniques , 2017, IEEE Access.

[3]  M. Safaei,et al.  Nanofluids as secondary fluid in the refrigeration system: Experimental data, regression, ANFIS, and NN modeling , 2019 .

[4]  Ali Jamali,et al.  Performance and robustness of optimal fractional fuzzy PID controllers for pitch control of a wind turbine using chaotic optimization algorithms. , 2018, ISA transactions.

[5]  Nan Zhang,et al.  Select N agents with better fitness values from X all to replace the current population X Evaluate and sort the fitness of X all End of iteration ? Return best solution End Mass weighting Cauchy mutation , 2016 .

[6]  M. Mehrpooya,et al.  Design and Implementation of Optimized Fuzzy Logic Controller for a Nonlinear Dynamic Industrial Plant Using Hysys and Matlab Simulation Packages , 2015 .

[7]  Shuzhi Sam Ge,et al.  Vibration Control of a Nonuniform Wind Turbine Tower via Disturbance Observer , 2015, IEEE/ASME Transactions on Mechatronics.

[8]  J. Kaimal,et al.  Spectral Characteristics of Surface-Layer Turbulence , 1972 .

[9]  Murat Lüy,et al.  Control of Pitch Angle of Wind Turbine by Fuzzy Pid Controller , 2016, Intell. Autom. Soft Comput..

[10]  Yun Zou,et al.  Inertia compensation scheme of WTS considering time delay for emulating large-inertia turbines , 2017 .

[11]  Silvio Simani,et al.  Active actuator fault‐tolerant control of a wind turbine benchmark model , 2014 .

[12]  Adel Akbarimajd,et al.  Nonlinear Multi-Agent Optimal Load Frequency Control Based on Feedback Linearization of Wind Turbines , 2019, IEEE Transactions on Sustainable Energy.

[13]  Hongwei Liu,et al.  Fault analysis of wind turbines in China , 2016 .

[14]  Qingjin Meng,et al.  The Application of Fuzzy PID Control in Pitch Wind Turbine , 2012 .

[15]  Padmanaban Sanjeevikumar,et al.  Fuzzy Logic-Based Pitch Angle Controller for PMSG-Based Wind Energy Conversion System , 2018 .

[16]  Haoran Zhao,et al.  Fatigue Load Sensitivity-Based Optimal Active Power Dispatch For Wind Farms , 2017, IEEE Transactions on Sustainable Energy.

[17]  Kim-Fung Man,et al.  An optimal fuzzy PID controller , 2001, IEEE Trans. Ind. Electron..

[18]  Abdolrahim Rezaeiha,et al.  Fluctuations of angle of attack and lift coefficient and the resultant fatigue loads for a large Horizontal Axis Wind turbine , 2017 .

[19]  M. Goodarzi,et al.  Experimental investigation and performance optimisation of a catalytic reforming micro-reactor using response surface methodology , 2019, Energy Conversion and Management.

[20]  Dong-Choon Lee,et al.  Advanced Pitch Angle Control Based on Fuzzy Logic for Variable-Speed Wind Turbine Systems , 2015, IEEE Transactions on Energy Conversion.

[21]  T. Kármán.  Progress in the Statistical Theory of Turbulence , 1948 .

[22]  Mohsen A. Bakouri,et al.  Smart optimization of a thermosyphon heat pipe for an evacuated tube solar collector using response surface methodology (RSM) , 2019, Physica A: Statistical Mechanics and its Applications.

[23]  Murat Lüy,et al.  A new fuzzy logic proportional controller approach applied to individual pitch angle for wind turbine load mitigation , 2017 .

[24]  Yun Li,et al.  PID control system analysis, design, and technology , 2005, IEEE Transactions on Control Systems Technology.

[25]  Silvio Simani,et al.  Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review , 2019, Renewable Energy.

[26]  Fan Yang,et al.  Individual pitch controller based on fuzzy logic control for wind turbine load mitigation , 2016 .

[27]  Anand Natarajan,et al.  Gaussian vs non‐Gaussian turbulence: impact on wind turbine loads , 2014 .

[28]  Oscar Castillo,et al.  Type-2 fuzzy logic aggregation of multiple fuzzy controllers for airplane flight control , 2015, Inf. Sci..

[29]  Youxian Sun,et al.  Adaptive Power Capture Control of Variable-Speed Wind Energy Conversion Systems With Guaranteed Transient and Steady-State Performance , 2013, IEEE Transactions on Energy Conversion.

[30]  Hamed Habibi,et al.  Adaptive PID Control of Wind Turbines for Power Regulation With Unknown Control Direction and Actuator Faults , 2018, IEEE Access.