Wednesday, 28 September 2016
14:30 - 16:00 Advanced control strategies for wind plants
Turbine technology  
          

Modern control design methods like non-linear model predictive control can take account of complex dynamics and actuator constraints, while making use of richer information input from advanced sensors such as LIDARs. They contribute to the coordinated control of entire wind farms while meeting new operational demands from the electricity system. The session addresses advanced state estimation methods, an important component of such controllers. You will hear about field test results from a controller using wind preview from a LIDAR sensor as well as changes in design and certification guidelines needed to account for the use of LIDARs.

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Learning objectives

• Advanced non-linear state estimation techniques for wind turbines that run in real time;
• Non-linear model predictive control that rung in real time and improve performances;
• A LIDAR-assisted flatness-based controller validated in field tests;
• Requirements for design and certification of wind turbines with LIDAR assisted control;
• A wind farm control framework that can mitigate wake effects and provide grid balancing services.

Co-chair(s):

Jonas Kazda Technical University of Denmark, Denmark
Co-authors:
Jonas Kazda (1) ^F Tuhfe Gogmen (1) Gregor Giebel (1) Michael Courtney (1) Nicolaos Antonio Cutululis (1)
(1) Technical University of Denmark, Roskilde, Denmark

Framework of multi-objective wind farm controller applicable to real wind farms

Introduction

The increasing amount of electricity production from wind requires the electricity system to expand its capability to accommodate intermittent power generation. Grid balancing services such as regulative power or ancillary services can be provided by wind farms using global wind farm control strategies. Such strategies coordinate the operating point of wind turbines in a wind farm in order to achieve a given objective. In addition to balancing services, wind farm control can be used to reduce adverse wake effects in wind farms. Such wake effects can result in power losses of up to 30-40% and up to 80% higher fatigue loads.

Approach

In the present work the investigated objectives of wind farm control are thus (i) to maximise the total wind farm power output and (ii) to follow a specified power for the wind farm’s total power output while minimising the fatigue loads for the wind turbines in the wind farm. The latter control strategy can be used for reducing wind turbine loading above rated wind speed and for providing balancing services to electricity system operators. As regards power maximisation, there are several wind farm control techniques reported in literature. In contrast to these techniques, the controller proposed in the present work is applicable to real wind farms, since the controllers’ input and output match the control framework of present wind farms. Wind farm control strategies developed for the latter objective, that is power reference following, derate upstream turbines in order to reduce the turbulence levels in the wake and consequently the fatigue loads on downstream turbines, while still operating the wind farm at a specified power. Controller designs for this approach can be split into feedforward methods and model-based optimal control design methods. A feedforward approach is used in the present work, since such approach can use higher fidelity wind farm models than approaches using optimal control, and thus the present approach is expected to achieve a better overall performance.

Main body of abstract

A framework of a wind farm controller with the objectives of either maximisation of total wind farm power or wind farm power reference following with load minimization is presented in this work. For both objectives the optimised power set-points are introduced to the wind farm using a feedforward controller. For the power reference following controller a feedback controller additionally ensures the operation of the wind farm at its reference power. The optimal distribution of turbine power set-points is derived using a numerical optimisation tool. The optimisation tool uses the PossPOW [1] algorithm for wind farm power estimation and a real-time load calculation procedure for wind turbine load estimation. PossPOW is an experimentally verified wind farm simulation tool for the real-time estimation of total wind farm power using wind turbine SCADA data as input. The wind farm controller uses commonly available SCADA data as input and a turbine power reference as output, and therefore is applicable to real wind farms. First controller tests using numerical simulation tools are possibly presented in the end of this work. The controller is being developed as part of the national DK CONCERT project, which aims on finally testing it in a real wind farm.

Conclusion

This work presents a wind farm controller framework that can mitigate adverse wake effects and allows wind farms to provide balancing services to transmission system operators. An advantage of the proposed controller is its applicability to real wind farms. Balancing services from wind farms are gaining more importance as the total installed wind power capacity increases. In Germany, for instance, wind farms can currently participate in a test phase for providing balancing power.


Learning objectives
Framework of a multi-objective wind farm controller, which is applicable to real wind farms.

[1] PossPOW: Possible Power of Offshore Wind power plants, http://www.posspow.vindenergi.dtu.dk/