Enhancing the net energy of wind turbine using wind prediction and economic NMPC with high-accuracy nonlinear WT models
Journal article, 2020

Economic nonlinear model predictive control (ENMPC) is a strong candidate for controlling wind turbines (WTs). In the model predictive control (MPC) group, the model is the crucial component for the true controller performance. It is common to use simplified models to reduce the problem complexity. These models neglect some of the underlying dynamic responses of real wind turbines. This paper simulates the case in which high accuracy nonlinear models describe both the plant and the controller. The results will be compared to reduced-order models in order to extract conclusions and decide the most appropriate model for WT control. On the other hand, one of the main features of MPC and ENMPC is the concept of receding prediction horizon, which considers the future evolution of the plant to compute the control action. The error of prediction will drastically reduce MPC performance. Also, rapid variation in wind speed can cause problems since wind turbines cannot easily follow these sudden variations due to their high inertia and aerodynamic characteristics. This paper provides an advanced control approach to improve the energy extraction from turbulent wind and enhance wind turbine durability. By implementing this method, the wind speed forecasting is done with a combination of artificial neural networks (ANN) and dynamic equations applied in ENMPC. The results show a significant enhancement of the control performance.

ENMPC WT control High accuracy nonlinear WT model Wind speed

Author

Ali Roghani Araghi

Amirkabir University of Technology

G. H. Riahy

Amirkabir University of Technology

Ola Carlson

Swedish Wind Power Technology Center (SWPTC)

Chalmers, Electrical Engineering, Electric Power Engineering, Power grids and Components

Sebastien Gros

Chalmers, Electrical Engineering, Systems and control, Automatic Control

Renewable Energy

0960-1481 (ISSN)

Vol. 151 May 750-763

Vindkraftteknik - kunskapsuppbyggnad och utveckling

Region Västra Götaland, 2015-01-01 -- 2018-12-31.

Driving Forces

Sustainable development

Subject Categories

Energy Engineering

Electrical Engineering, Electronic Engineering, Information Engineering

Areas of Advance

Energy

DOI

10.1016/j.renene.2019.11.070

More information

Latest update

7/30/2020