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.

scholarly journals Pitch control of wind turbine based on deep neural network

2020 ◽  
Vol 619 ◽  
pp. 012034
Author(s):  
Wei Jie ◽  
Chu Jingchun ◽  
Yuan Lin ◽  
Wang Wenliang ◽  
Dong Jian
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Deep Neural Network ◽  
Pitch Control

scholarly journals Active and Reactive Powers Control of DFIG Based WECS Using PI Controller and Artificial Neural Network Based Controller

2020 ◽  
Vol 93 (1-4) ◽  
pp. 31-38
Author(s):  
Bilal Boudjellal ◽  
Tarak Benslimane
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Energy ◽  
Control Strategy ◽  
Energy System ◽  
Maximum Power ◽  
Point Tracking ◽  
Pi Controllers ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this study is to improve the control performance of a Doubly Fed Induction Generator (DFIG) in a Wind Energy Conversion System (WECS) by using both of the conventional Proportional-Integral (PI) controllers and an Artificial Neural Network (ANN) based controllers. The rotor-side converter (RSC) voltages are controlled using a stator flux oriented control (FOC) to achieve an independent control of the active and reactive powers, exchanged between the stator of the DFIG and the power grid. Afterward, the PI controllers of the FOC are replaced with two ANN based controllers. A Maximum Power Point Tracking (MPPT) control strategy is necessary in order to extract the maximum power from the of wind energy system. A simulation model was carried out in MATLAB environment under different scenarios. The obtained results demonstrate the efficiency of the proposed ANN control strategy.

Individual pitch control of wind turbine based on RBF neural network

2016 ◽  
Author(s):  
Zhongpeng Liu ◽  
Feng Huo ◽  
Shuowen Xiao ◽  
Xuesong Zhang ◽  
Shilong Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Rbf Neural Network ◽  
Pitch Control ◽  
Individual Pitch Control

Manipulator Inverse Kinematics using an Adaptive Back-propagation Algorithm and Radial Basis Function with a Lookup Table

Robotica ◽  
1998 ◽  
Vol 16 (4) ◽  
pp. 433-444 ◽  
Author(s):  
A. S. Morris ◽  
M. A. Mansor
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Inverse Kinematics ◽  
Back Propagation ◽  
Lookup Table ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
The Neural Network ◽  
Radial Basis

This is an extension of previous work which used an artificial neural network with a back-propagation algorithm and a lookup table to find the inverse kinematics for a manipulator arm moving along pre-defined trajectories. The work now described shows that the performance of this technique can be improved if the back-propagation is made to be adaptive. Also, further improvement is obtained by using the whole workspace to train the neural network rather than just a pre-defined path. For the inverse kinematics of the whole workspace, a comparison has also been done between the adaptive back-propagation algorithm and radial basis function.

Study on Decreasing Vibration of High-Speed Train Semi-Active Suspension System

2011 ◽  
Vol 230-232 ◽  
pp. 1104-1109
Author(s):  
Zhen Ping Fan ◽  
Heng Zeng ◽  
Jian Wei Yang ◽  
Jie Li
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
High Speed ◽  
Driving Safety ◽  
High Speed Train ◽  
Proportional Valve ◽  
Joint Simulation ◽  
The Neural Network ◽  
Adaptive Control Strategy ◽  
Active Damper

Lateral semi-active damper is designed by author based on the electro-hydraulic proportional valve, from the perspective angle of improving vehicle comfort; its purpose is to ensure vehicle driving safety. At the same time, the neural network adaptive control strategy is used for joint simulation of semi-active damper. The results show that lateral semi-active damper with the train body has significantly improved compared to the traditional passive lateral damper acceleration.

Neural network inverse system decoupling control strategy of BLIM considering stator current dynamics

2018 ◽  
Vol 41 (3) ◽  
pp. 621-630 ◽  
Author(s):  
Wenshao Bu ◽  
Fangzhou He ◽  
Ziyuan Li ◽  
Haitao Zhang ◽  
Jingzhuo Shi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Strategy ◽  
Displacement Component ◽  
Inverse System ◽  
Decoupling Control ◽  
Motor Speed ◽  
State Equations ◽  
Stator Current ◽  
The Neural Network

The bearingless induction motor (BLIM) is a multi-variable, non-linear, strong coupling system. To achieve higher performance control, a novel neural network inverse system decoupling control strategy considering stator current dynamics is proposed. Taking the stator current dynamics of the torque windings into account, the state equations of the BLIM system is established first. Then, the inverse system model of the BLIM is identified by a three-layer neural network; by means of the neural network inverse system method, the BLIM system is decoupled into four independent second-order linear subsystems, include a rotor flux subsystem, a motor speed subsystem and two radial displacement component subsystems. On this basis, the neural network inverse decoupling control system is constructed, the simulation verification and analyses are performed. From the simulation results, it is clear that when the proposed decoupling control strategy is adopted, not only can the dynamic decoupling control between relevant variables be achieved, but the control system has a stronger anti-load disturbance ability, smaller overshoot and better tracking performance.

Neural Network Adaptive Control of Free Floating Space Robot with Actuator Saturation

2014 ◽  
Vol 590 ◽  
pp. 380-385 ◽  
Author(s):  
Guo Liang Zhang ◽  
Ting Lei ◽  
Fan Yang ◽  
Zhuang Cai
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Control Strategy ◽  
Actuator Saturation ◽  
Space Robot ◽  
Global Approximation ◽  
Neural Network Approach ◽  
The Neural Network ◽  
On Line ◽  
The Stability

This paper proposes an adaptive neural network law for trajectory tracking of a class of free-floating space robot with actuator saturation. Using neural network with global approximation, the control strategy design an on-line real time adaptive learning law to approach the uncertain model and the actuator saturation nonlinearity. The neural network approach errors and outside disturbance can be eliminated by a robust controller.The control strategy need not depend on the model, and can be used under actuator saturation.The control strategy can guarantee the stability of system and the asymptotic convergence of tracking errors based on the Lyapunov’s theory. The simulation results indicate that the proposed strategy can effectively work with actuator saturation.

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