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Complex Flow Research and Its Relation to Reliability

Pat Moriarty

Matt Churchfield

Sang Lee

Paul Fleming

Julie Lundquist

John Michalakes

Avi Purkayastha

Mike Sprague

2

Motivation

• Development of validated simulation tools for wind plant operation within complex flow environment

• Complex flow affects: o Power production

o Fatigue loads

o Both influence revenue

generated

• Current models lack

physics

Wind speed contours through the Lillgrund wind plant

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Underperformance and Uncertainty

Industry average bias ~ -9%

Uncertainty overestimated

Johnson, AWEA WRC 2012

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Structural Loads

Atmospheric state (stable vs. unstable)

Kelley, 2011

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Wind Plant Modeling Research

• Simulator fOr Wind Farm Applications (SOWFA)

• High performance computing and engineering modules

• Outcome – Improve industry tools

– Guide experimentalists and understand physics

Turbine Farm Array Mesoscale

Tools:

Scale:

WRF OpenFOAM FAST

SOWFA

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7

Results – Power

Reasonable agreement with field data

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Results – Structural Loads

Row D

No field data

9

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• SCADA data analysis

• Detailed flow measurements o Turbine Wind and Inflow

Characterization Study (TWICS)

• Wake and inflow measurements of 2.3 MW turbine at NWTC

• Influence of complex terrain and stability

o Crop Wind Energy Experiment (CWEX)

• Impact of changing roughness (i.e. corn) on wind farm performance

• Impact of wind farm on crops

• IEA Task 31: Wakebench

Complex Flow Observation Research

Figure courtesy A. Brewer

Figure courtesy J. Lundquist

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SCADA Data Analysis

Gaumond et al. (2011)

Data is often highly averaged - physics are lost and model validation is suspect

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Complex Flow Field Campaigns

• Full scale measurements

o Higher resolution but expensive – New instrumentation developing quickly

o Some physics only at full scale

o Smaller scale testing

o Combined observation and simulation

Banta et al. 2013

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Wake Model Validation

Higher fidelity models often do better, but not always!

decreasing simulation fidelity

Simulation Type

From IEA Task 31: Sexbierum Single Wake Benchmark

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What can we do about it?

• Complex flow will continue to exist

• Improved plant layout and turbine design using better tools and physical understanding

• Improved control systems

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Controls Research

• Reacts to existing flow

• Greedy turbines

• Modify complex flow between turbines

• Good neighbors

Turbine Control Plant Control

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Wind Plant Control

• Heat and flux

• Wake steering – yaw, tilt, turbine motion

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Power

Best case: Yaw: 4.6% Tilt: 7.1% Pos: 41%

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Structural Loading

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Conclusions

• Complex flow is complex o Better understanding required to improve reliability o Many flow scales interact o Terrain and vegetation make things more complex o Combined observation and simulation required

• Improvements possible using design and control • Future needs

o Simulation tools now outpacing observations o Need data for validation

– Higher fidelity flow and SCADA data – Data mining techniques – QA/QC – Good meteorological data on operating wind plants – Structural loading data

o Technology transfer to industry tools