Quantification of Extreme Weather on Wind Farm Performance · Quantification of Extreme Weather on...

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Quantification of Extreme Weather on Wind Farm Performance Philippe Cambron CANWEA O&M, January 2020

Transcript of Quantification of Extreme Weather on Wind Farm Performance · Quantification of Extreme Weather on...

Page 1: Quantification of Extreme Weather on Wind Farm Performance · Quantification of Extreme Weather on Wind Farm Performance Philippe Cambron CANWEA O&M, January 2020

Quantification of Extreme Weather on Wind Farm Performance

Philippe Cambron

CANWEA O&M, January 2020

Page 2: Quantification of Extreme Weather on Wind Farm Performance · Quantification of Extreme Weather on Wind Farm Performance Philippe Cambron CANWEA O&M, January 2020

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Context – Climate Change

• Extreme weather events are more likely to occur with climate change

• More storms• More frequent heatwaves

• More frequent extreme cold• More snow and ice storms

Puerto Rico wind farm after Hurricane Maria in 2017 Blade of a WTG stopped several days for to severe icing

Page 3: Quantification of Extreme Weather on Wind Farm Performance · Quantification of Extreme Weather on Wind Farm Performance Philippe Cambron CANWEA O&M, January 2020

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Context – Extreme Weather

• Direct and indirect impact on WTG performance• Icing losses / Derating events

• Storm shut downs

• Unable to access WTG for troubleshooting downtimes due to blades icing

• Impact on Fatigue• Stops at high winds (ex: tower vibration faults or storm shutdowns)

• Impact of turbulence

Page 4: Quantification of Extreme Weather on Wind Farm Performance · Quantification of Extreme Weather on Wind Farm Performance Philippe Cambron CANWEA O&M, January 2020

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Context – Action Plan

• Still hard to quantify impacts on the climate side

• Hard to estimate how much more icing losses would affect a certain wind farm or how many more storm shut downs would occur

• Opportunity for proactiveness in wind farm management by working on a proper quantification of the impact of these extreme events.• Energy Loss Allocation

• Fatigue KPIs

Page 5: Quantification of Extreme Weather on Wind Farm Performance · Quantification of Extreme Weather on Wind Farm Performance Philippe Cambron CANWEA O&M, January 2020

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Tracking Energy Loss – IEC Classification

• Events classification with IEC 61400-26-1

• Information levels 1 to 4 are describe in the standard

• Information level 5 is customizable and allows to configure the tracking of certain type of events

Page 6: Quantification of Extreme Weather on Wind Farm Performance · Quantification of Extreme Weather on Wind Farm Performance Philippe Cambron CANWEA O&M, January 2020

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Tracking Energy Loss – IEC Classification

• Event classification sample for an Ice Detect : Stoppedevent

• Reliable expected energy required for the evaluation of losses

• Energy allocation rules (multiple events at the same time)

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Tracking Energy Loss – “Homebrew” Rules

• Additional rules needed to capture some events

• Example, icing losses events:• No other events

• Condition on ambient temperature

• Condition on relative humidity

• Under-performance observed

Page 8: Quantification of Extreme Weather on Wind Farm Performance · Quantification of Extreme Weather on Wind Farm Performance Philippe Cambron CANWEA O&M, January 2020

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Tracking Energy Loss – Analytics

Page 9: Quantification of Extreme Weather on Wind Farm Performance · Quantification of Extreme Weather on Wind Farm Performance Philippe Cambron CANWEA O&M, January 2020

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Fatigue KPIs – What is Fatigue?

Weakening of a material caused by cyclic loading that results in progressive and localized structural damage and the growth of cracks

More turbulence -> More cycles

More storms -> More extreme cycles

Crack propagation in WTG hubsCracks in foundation in tower base

http://bit.ly/36yaa1O http://bit.ly/36yaa1O

Page 10: Quantification of Extreme Weather on Wind Farm Performance · Quantification of Extreme Weather on Wind Farm Performance Philippe Cambron CANWEA O&M, January 2020

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Fatigue KPIs – Start / Stop Cycles

• Turbine stops at high wind speed are generating high stress on the components

• Case of WTG with improper cut-out settings

• Operator was able to change the SCADA parameters accordingly and therefore limit the WTG fatigue

Page 11: Quantification of Extreme Weather on Wind Farm Performance · Quantification of Extreme Weather on Wind Farm Performance Philippe Cambron CANWEA O&M, January 2020

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Fatigue KPIs – Rainflow Counting

Rainflow – Stress cycle countingS-N Curve – Damage and

Remaining usefull life

Page 12: Quantification of Extreme Weather on Wind Farm Performance · Quantification of Extreme Weather on Wind Farm Performance Philippe Cambron CANWEA O&M, January 2020

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Action Plan

• Evaluation of mitigation approaches / systems• De-icing (passive, active)

• Dust cleaning (better ventilation / cooling)

• Storm management

Source : GE

Source : Aerones

Page 13: Quantification of Extreme Weather on Wind Farm Performance · Quantification of Extreme Weather on Wind Farm Performance Philippe Cambron CANWEA O&M, January 2020

13© 2019 Power Factors LLC. All rights reserved. Commercial in Confidence.Power Factors, LLC

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Conclusion

• Climate change will bring more events of extreme weather

• Still hard to predict how a given wind farm will be affected

• However adopting a structured asset monitoring methodology will allow to quantify the impact of climate change and orient improvement programs

• Take time to properly test and validate any mitigation / improvement technology before deploying all over the fleet

Page 14: Quantification of Extreme Weather on Wind Farm Performance · Quantification of Extreme Weather on Wind Farm Performance Philippe Cambron CANWEA O&M, January 2020

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Thank YouPhilippe Cambron, Ph.DWind R&D [email protected]