Individual pitch control strategy for a low wind speed wind turbine

2019 ◽  
Author(s):  
P. Jiang ◽  
Z.Q. Gao ◽  
Y.Z. Wang ◽  
Q.L. Li ◽  
B.F. Xu
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Control Strategy ◽  
Pitch Control ◽  
Low Wind Speed ◽  
Individual Pitch Control

The Research of Model-Free Adaptive Control for Large Scale Wind Turbine

2013 ◽  
Vol 433-435 ◽  
pp. 1293-1297
Author(s):  
Xing Jia Yao ◽  
Jiang Sheng Zhu ◽  
Kui Chao Ma ◽  
Qing Ding Guo
Keyword(s):  
Wind Turbine ◽  
Control Strategy ◽  
Large Scale ◽  
External Disturbances ◽  
Pitch Control ◽  
Model Free ◽  
Variable Speed Wind Turbine ◽  
Turbine Design ◽  
Individual Pitch Control ◽  
Large Wind

Dynamic load is a key consideration in large scale wind turbine design. It is approved that the performance of controller can distinguish impact wind turbine loads. For strong external disturbances and inaccurately modeled of large wind turbines, In this paper, we propose model-free adaptive (Model Free Adapt, MFA) individual pitch control algorithms. The controller was developed in to mitigate the rotor unbalance structural load for variable speed wind turbine. The controller is designed from a nonlinear model of the system which takes into account the blades, shaft and tower flexibilities. Bladed software was used for the control strategy and traditional PID control strategy simulation comparison. The results show that the new control strategy can effectively stabilize wind turbine power output and reduce aerodynamic loads.

scholarly journals Implementation and Validation of an Advanced Wind Energy Controller in Aero-Servo-Elastic Simulations Using the Lifting Line Free Vortex Wake Model

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 783 ◽  
Author(s):  
Sebastian Perez-Becker ◽  
David Marten ◽  
Christian Navid Nayeri ◽  
Christian Oliver Paschereit
Keyword(s):  
Wind Turbine ◽  
Open Source ◽  
Control Strategy ◽  
Vortex Wake ◽  
Load Reduction ◽  
Free Vortex ◽  
Pitch Control ◽  
The Individual ◽  
Individual Pitch Control ◽  
Lifting Line

Accurate and reproducible aeroelastic load calculations are indispensable for designing modern multi-MW wind turbines. They are also essential for assessing the load reduction capabilities of advanced wind turbine control strategies. In this paper, we contribute to this topic by introducing the TUB Controller, an advanced open-source wind turbine controller capable of performing full load calculations. It is compatible with the aeroelastic software QBlade, which features a lifting line free vortex wake aerodynamic model. The paper describes in detail the controller and includes a validation study against an established open-source controller from the literature. Both controllers show comparable performance with our chosen metrics. Furthermore, we analyze the advanced load reduction capabilities of the individual pitch control strategy included in the TUB Controller. Turbulent wind simulations with the DTU 10 MW Reference Wind Turbine featuring the individual pitch control strategy show a decrease in the out-of-plane and torsional blade root bending moment fatigue loads of 14% and 9.4% respectively compared to a baseline controller.

Torque Control and Pitch Control Strategy in VSCF Wind Turbine above Rated Wind Speed

2012 ◽  
Vol 463-464 ◽  
pp. 1715-1720
Author(s):  
Rui Ma ◽  
Shu Ju Hu ◽  
Xun Bo Fu ◽  
Hong Hua Xu ◽  
Nian Hong Li
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Output Power ◽  
Control Strategy ◽  
Pid Control ◽  
Torque Control ◽  
Control Mode ◽  
Pitch Control ◽  
Advantages And Disadvantages ◽  
Power Simulation

Above rated wind speed, the wind turbine speed and output power are maintained near the rated values through the coordinative control of torque and pitch angle. Due to the non-linear behavior of the wind turbine, the traditional PID control is not effective in the pitch control. And accurate mathematical model of wind turbine is very difficult to get. In order to solve the problem, the fuzzy adaptive tuning PID control algorithm is proposed in the paper. About torque control strategy, constant torque control mode and constant power control mode are simulated respectively. Based on the analysis and comparison of the advantages and disadvantages of both modes, a mix control mode is proposed in order to give consideration to both torque and power. Simulation was carried out with the proposed torque control and pitch control strategy in MATLAB and GH Bladed software. The results proved that output power is optimized and the response of the wind turbine is good

Optimal tuning of multivariable disturbance‐observer‐based control for flicker mitigation using individual pitch control of wind turbine

2016 ◽  
Vol 11 (8) ◽  
pp. 1121-1128 ◽  
Author(s):  
Raja Muhammad Imran ◽  
Dil Muhammad Akbar Hussain ◽  
Mohsen Soltani ◽  
Raja Muhammad Rafaq
Keyword(s):  
Wind Turbine ◽  
Disturbance Observer ◽  
Pitch Control ◽  
Optimal Tuning ◽  
Individual Pitch Control

Field testing of multi-variable individual pitch control on a utility-scale wind turbine

2021 ◽  
Vol 170 ◽  
pp. 1245-1256
Author(s):  
Daniel Ossmann ◽  
Peter Seiler ◽  
Christopher Milliren ◽  
Alan Danker
Keyword(s):  
Wind Turbine ◽  
Field Testing ◽  
Pitch Control ◽  
Utility Scale ◽  
Individual Pitch Control

scholarly journals Lookup Table and Neural Network Hybrid Strategy for Wind Turbine Pitch Control

2021 ◽  
Vol 13 (6) ◽  
pp. 3235
Author(s):  
J. Enrique Sierra-García ◽  
Matilde Santos
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Control Strategy ◽  
Neural Control ◽  
Mapping Function ◽  
Energy System ◽  
Lookup Table ◽  
Hybrid Strategy ◽  
Pitch Control ◽  
The Neural Network

Wind energy plays a key role in the sustainability of the worldwide energy system. It is forecasted to be the main source of energy supply by 2050. However, for this prediction to become reality, there are still technological challenges to be addressed. One of them is the control of the wind turbine in order to improve its energy efficiency. In this work, a new hybrid pitch-control strategy is proposed that combines a lookup table and a neural network. The table and the RBF neural network complement each other. The neural network learns to compensate for the errors in the mapping function implemented by the lookup table, and in turn, the table facilitates the learning of the neural network. This synergy of techniques provides better results than if the techniques were applied individually. Furthermore, it is shown how the neural network is able to control the pitch even if the lookup table is poorly designed. The operation of the proposed control strategy is compared with the neural control without the table, with a PID regulator, and with the combination of the PID and the lookup table. In all cases, the proposed hybrid control strategy achieves better results in terms of output power error.

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