A novel online training neural network-based algorithm for wind speed estimation and adaptive control of PMSG wind turbine system for maximum power extraction

2016 ◽  
Vol 86 ◽  
pp. 38-48 ◽  
Author(s):  
Fernando Jaramillo-Lopez ◽  
Godpromesse Kenne ◽  
Francoise Lamnabhi-Lagarrigue
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Wind Speed ◽  
Wind Turbine ◽  
Online Training ◽  
Maximum Power ◽  
Speed Estimation ◽  
Power Extraction ◽  
Wind Turbine System ◽  
Training Neural Network

scholarly journals Design of a MPPT controller for permanent magnet synchronous generator driven wind turbine

2020 ◽  
Vol 2 (4) ◽  
pp. 251-257
Author(s):  
Việt Anh Trương ◽  
Quang Minh Huỳnh ◽  
Hoài Thương Võ
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Synchronous Generator ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Wind Turbine System ◽  
Mppt Controller ◽  
Power Point ◽  
Working Point

Wind and other renewable energies are more and more developed all over the world, especially in countries with high wind potential such as Vietnam, to replace fossil energy, which would be exhausted in the near future. One important characteristic of wind turbines is that at each different wind speed, there exists a working point, represented by the rotation speed and the mechanical power at the crankshaft of the wind turbine, at which the maximum mechanical power is obtained, called maximum power point (MPP). Therefore, when the wind speed changes, this working point must be changed to be able to extract the maximum power from the wind to improve the total efficiency of the wind turbine system. This, in a wind energy conversion system (WECS), is assigned to the maximum power point tracking (MPPT) controller. In this paper, a MPPT controller is proposed, based on an improved Perturb and Observe (P&O) algorithm, for wind turbines using permanent magnet synchronous generator (PMSG), to maximize energy without measuring the wind speed and power characteristics of the wind turbine. An experimental model is also designed and tested in laboratory conditions, in which two coefficients K1 and K2 are used in turn when the working point is far or close to the maximum power point. The experimental results show that the proposed MPPT controller allows the extraction of maximum power from wind turbines under variable wind speed without determining the wind speed and characteristics of the wind turbine system.

scholarly journals Maximum Power Extraction Strategy for Variable Speed Wind Turbine System via Neuro-Adaptive Generalized Global Sliding Mode Controller

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 128536-128547 ◽  
Author(s):  
Izhar Ul Haq ◽  
Qudrat Khan ◽  
Ilyas Khan ◽  
Rini Akmeliawati ◽  
Kottakkaran Soopy Nisar ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Sliding Mode ◽  
Maximum Power ◽  
Sliding Mode Controller ◽  
Variable Speed ◽  
Power Extraction ◽  
Wind Turbine System ◽  
Variable Speed Wind Turbine

scholarly journals Wake Management in Wind Farms: An Adaptive Control Approach

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1247 ◽  
Author(s):  
Harsh Dhiman ◽  
Dipankar Deb ◽  
Vlad Muresan ◽  
Valentina Balas
Keyword(s):  
Adaptive Control ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Transfer Functions ◽  
Wind Farms ◽  
Control Approach ◽  
Wind Turbine System ◽  
Effective Assessment ◽  
Yaw Angle

Advanced wind measuring systems like Light Detection and Ranging (LiDAR) is useful for wake management in wind farms. However, due to uncertainty in estimating the parameters involved, adaptive control of wake center is needed for a wind farm layout. LiDAR is used to track the wake center trajectory so as to perform wake control simulations, and the estimated effective wind speed is used to model wind farms in the form of transfer functions. A wake management strategy is proposed for multi-wind turbine system where the effect of upstream turbines is modeled in form of effective wind speed deficit on a downstream wind turbine. The uncertainties in the wake center model are handled by an adaptive PI controller which steers wake center to desired value. Yaw angle of upstream wind turbines is varied in order to redirect the wake and several performance parameters such as effective wind speed, velocity deficit and effective turbulence are evaluated for an effective assessment of the approach. The major contributions of this manuscript include transfer function based methodology where the wake center is estimated and controlled using LiDAR simulations at the downwind turbine and are validated for a 2-turbine and 5-turbine wind farm layouts.

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