Torque and pitch angle control of a wind turbine using multiple adaptive neuro-fuzzy control

2019 ◽  
Vol 44 (2) ◽  
pp. 125-141
Author(s):  
Satyabrata Sahoo ◽  
Bidyadhar Subudhi ◽  
Gayadhar Panda
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Fuzzy Inference System ◽  
Pitch Angle ◽  
Fuzzy Inference ◽  
Adaptive Controller ◽  
Inference System ◽  
Proportional Integral ◽  
Simulink Model ◽  
Neuro Fuzzy

This article presents a multiple adaptive neuro-fuzzy inference system-based control scheme for operation of the wind energy conversion system above the rated wind speed. By controlling the pitch angle and generator torque concurrently, the generator power and speed fluctuation can be reduced and also turbine blade stress can be minimized. The proposed neuro-fuzzy-based adaptive controller is composed of both the Takagi–Sugeno fuzzy inference system and neural network. First, a step change in wind speed and then a simulated wind speed are considered in the proposed adaptive control design. A MATLAB/Simulink model of the wind turbine system is prepared, and simulations are carried out by applying the proportional integral, fuzzy-proportional integral and the proposed adaptive controller. From the obtained results, the effectiveness of the proposed adaptive controller approach is confirmed.

Smoothing the output power of a wind energy conversion system using a hybrid nonlinear pitch angle controller

2021 ◽  
pp. 014459872110417
Author(s):  
Ya-Jun Fan ◽  
Hai-tong Xu ◽  
Zhao-Yu He
Keyword(s):  
Fuzzy Logic ◽  
Wind Energy ◽  
Output Power ◽  
Wind Turbines ◽  
Fuzzy Inference System ◽  
Pitch Angle ◽  
Fuzzy Inference ◽  
Hybrid Control ◽  
Inference System ◽  
Neuro Fuzzy

Wind energy has been developed and is widely used as a clean and renewable form of energy. Among the existing variety of wind turbines, variable-speed variable-pitch wind turbines have become popular owing to their variable output power capability. In this study, a hybrid control strategy is proposed to implement pitch angle control. A new nonlinear hybrid control approach based on the Adaptive Neuro-Fuzzy Inference System and fuzzy logic control is proposed to regulate the pitch angle and maintain the captured mechanical energy at the rated value. In the controller, the reference value of the pitch angle is predicted by the Adaptive Neuro-Fuzzy Inference System according to the wind speed and the blade tip speed ratio. A proposed fuzzy logic controller provides feedback based on the captured power to modify the pitch angle in real time. The effectiveness of the proposed hybrid pitch angle control method was verified on a 5 MW offshore wind turbine under two different wind conditions using MATLAB/Simulink. The simulation results showed that fluctuations in rotor speed were dramatically mitigated, and the captured mechanical power was always near the rated value as compared with the performance when using the Adaptive Neuro-Fuzzy Inference System alone. The variation rate of power was 0.18% when the proposed controller was employed, whereas it was 2.93% when only an Adaptive Neuro-Fuzzy Inference System was used.

Wind Turbine Pitch Angle Control Based on ANFIS

2013 ◽  
Vol 291-294 ◽  
pp. 507-512
Author(s):  
Jun Xiao ◽  
Jiang Xiao ◽  
Wei Chen
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Simulation Analysis ◽  
Dynamic Performance ◽  
System Control ◽  
Inference System ◽  
Pitch Control ◽  
Neural Fuzzy

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.

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