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Advanced integrated control of large-scale wind power plants and wind turbines

Giebel, Gregor; Larsen, Gunner; Natarajan, Anand; Meyers, Johan; Bossanyi, Ervin; Merz, Karl

Publication date:2019

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Citation (APA):Giebel, G., Larsen, G., Natarajan, A., Meyers, J., Bossanyi, E., & Merz, K. (2019). Advanced integrated controlof large-scale wind power plants and wind turbines. Poster session presented at WindEurope Conference andExhibition 2019, Bilbao, Spain.

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MEET US AT 1-F41 (DTU), 1-E40 (Siemens Gamesa), or 3-D54 (DNV-GL)!

TotalControlAdvanced integrated control of large-scale wind power plants and wind turbines

Gregor Giebel, Gunner Larsen, Anand Natarajan, Johan Meyers, Ervin Bossanyi, Karl MerzDTU Wind Energy, KU Leuven, DNV GL, SINTEF

Current state of the art wind power plant (WPP) controllers operate wind turbines (WTs)independently as individual machines, thus dispatching the WTs’ set points in an equal manner to all ofthem. To achieve optimal WPP control, three aspects need to be addressed:• Maximizing the yield (power production) balanced against turbine mechanical loading and

electricity price• Enhancing WPP capability to provide ancillary services (primary, secondary, and tertiary reserves),

and• Reducing operating costs (i.e. reduced fatigue load degradation of WTs and O&M requirement)

over the lifetime of the WPP.

The goal of TotalControl is to move the WPP controller design philosophy from individual optimizationof WT operation to a coordinated optimization of the overall WPP performance. The TotalControl projectaims to achieve this by developing and validating advanced integrated WPP/WT control schemesconditioned on grid demands and wind turbine fatigue damage limits. For developing and testing of thedifferent WPP controllers, a range of high-fidelity and medium-fidelity simulation models are used.These models are already available in the consortium, but will be thoroughly validated against full-scalemeasurements in the Lillgrund WPP. Due to the complexity and multi-scale nature of WPP flowdynamics, the high-fidelity CFD-based models are very expensive in simulation time, e.g. requiringsupercomputing, and therefore not well suited as control design models.

www.TotalControlProject.eu

Lillgrund tests Levenmouth tests

References

TotalControl is built on a hierarchy of controllers,each reacting at different time scales and controltime steps. At the slowest control level the WPP isquasi-statically adapting its WT active and reactivepower set points and WT yaw angles, adapting toslowly changing environmental conditions andmarket elements. A second control level is the WTcontroller, accepting power set points from thequasi-steady control levels.Finally, a fast WPP controller is considered whichresponds dynamically to faster events (turbulentgusts, requests for ancillary services, etc.) and usesfeedback from the WTs. This controller uses model-predictive control for prediction of dynamic wakebehavior and impacts on turbine loads. Thedynamic WPP controller also contains a directcontrol level related to the WPP internal power grid.

ORE Catapult’sSamsung 7MWprototype turbine atLevenmouth, to be usedfor turbine control trials.

Vattenfalls Lillgrund windfarm, where a full-scale testwill be run, monitored withtwo synchronized lidars.Lillgrund image © www.siemens.com/press,Lidar scan pattern from Windscanner.eu.

WP1: WPP design and control modelsGoal – Development of appropriate control models for other WPs– Set up of virtual testing environment that can be used in other WPs

To that end: – Measurement campaign in Lillgrund– Use of high-fidelity numerical simulation models (SP-Wind, SOWFA, Ellipsys)

WP2: quasi-static open-loop WPP controlGoal Wind-farm control with control time steps of 10-20 min

To: – Improve power extraction by yawing– Reduce loads (steered by lifetime and O&M costs/timing) by yawing, or induction control– Decide on WPP downrating in response to market, tertiary ancillary services– …

– Experiments @ Lillgrund– Use virtual simulation environment

WP4: feedback WPP controlGoal Wind-farm control with control time steps < 1 min

To: – Dynamic optimization– Reduce loads (subject to turbulent gusts)– Provide primary and/or secondary ancillary services (e.g. power signal tracking) – Increase energy extraction (subject to turbulence)– Deep understanding of WPP system dynamics

– Laboratory experiments– Use virtual simulation environment (validated in WP1)

WP3: WT controlGoal Enhance WT control control time step: < 1s

To: – Further load reduction– Turbulence or market based derating– Primary ancillary services (FFR, Voltage support)– Allow other and dynamically changing set-points – widen available operational range for controllers in WP2 and WP4– LIDAR assisted control– ….

– Experiments on the Samsung 7MW turbine

VERIFICATION/VALIDATION

WP5

: D

ISSE

MIN

ATIO

N

WP6

: MA

NA

GE

ME

NT

This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 727680

Abstract First video released

Wind farm control time scales Project set-up

http://www.totalcontrolproject.eu/dissemination-activities/videos