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Icing prediction on blade wind turbine using forecast data

From Antoine Amossé, instrumentation and control Engineer

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Summary

1. Context & Challenge

2. Data integration and algorithm

3. Results

4. Conclusion & Further development

5. Nergica

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Cold climate + precipitations

Wind turbine

Icing accretion Context

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Context Icing - Risks for H&S

- Fatigue on structure

- Power loss

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Context

Coating Electrothermal Hot air Solution!!

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Challenge

Forecast Data PI Node/Svr Model/Algorithm Action Notifications

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Forecast data

- Forecast models:

HRDPS High Resolution Deterministic Prediction System

PRIVATE

NAM North American Model

PUBLIC access

+3.5 days +2 days

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Forecast data

FTP site

6h

Station 1

Station 2

Station 3

Updates ~6h

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Integration in PI Server

- Data is imported into PI using Universal File Loader (UFL) interface

Station 1

Station 2

Station 3

PI Server

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Building structure in PI AF

Build an AF Structure to facilitate deployment of analysis and algorithms through multiple sites.

PI Server

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Visualization in PI

Future Now

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Building algorithm

ICING = LOW TEMP. + WATER

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Building algorithm

<TEMP

0degC

>PrecipiationRate

0mm/h

Low Temp.

Water

AND Icing

ICING = LOW TEMP. + WATER

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Building algorithm

Validation in PI AF

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Ice measurement

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Ice measurement

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Data comparison

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Data comparison

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Visualization in PI Vision

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Alerting customer Choose

user/groups Manage format Edit the trigger

12h

1h

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Conclusion • Alerts are sent 12h and 1h before event to users

• Challenges • Data available periodically • Building and optimize algorithm

• PI system allows to • Deployment • Archiving & validation • Review the events

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Future work

• Optimization of algorithms

• Analysis of benefits in $$ or operation time

• Microgrid management based on forecast data

• Renewable production (Wind, solar)

• Storage management (battery, flywheel)

• Load estimation

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Nergica Centre of Applied research that stimulate

innovation in the renewable energy

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Wind Energy Conference

Carleton-sur-Mer, QC, CANADA

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Optional: Click to add a takeaway you wish the audience to leave with.

Thank You

Merci

Grazie

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Conference Theme & Keywords

Icing Forecast

AF structure PI Analytics

Matlab

PI_UFL

Prediction

NAM HRDPS

Cold climate

Québec

Canada

Wind Energy

Solar Energy

Notification

Blade

Wind Turbine

In-site measurement

Icing sensors

Camera system Weather

Nergica

Nergica Nergica

Solar energy

Image Analysis Precipitation

Snow

Freezing rain

Ice pellets

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RESULTS CHALLENGE SOLUTION

COMPANY and GOAL

27 #OSIsoftUC #PIWorld ©2018 OSIsoft, LLC

Icing prediction for blade wind turbine using forecast data

Nergica éolien is a center of expertise that supports the development of the wind industry through research, technology transfer and technical assistance for businesses

Deicing systems installed on wind turbine blades need to be activated prior the meteorological event, in order to prevent icing accretion.

Algorithm based on forecast data are built to detect the beginning of the icing event.

Activating heating system in wind turbine keep the blade hot and avoid ice accretion, up to a given rate

Algorithms are compared with in-site instrumentations

Benefits have not been measure yet

• Integration of forecast data

• Building algorithm to detect meteorological icing

• Send notification prior the event

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• Antoine Amossé

• aamosse@nergica.com

• Instrumentation and Control Analyst

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