Comparative study of binning and gaussian process based rotor curves of a wind turbine for the purpose of condition monitoring

Research output: Contribution to conferenceProceeding

Abstract

The wind turbines deteriorating performance cause failures, including catastrophic failures that lead to high operation and maintenance (O&M) cost. SCADA based continuous monitoring of wind turbines is a cost-effective approach and plays an essential role as turbine sizes increase, and they are placed in more remote locations, for example, offshore to improve the performance of wind turbines and reduces the O&M cost. Various studies suggest that the internal operation of the wind turbine depends on various variables, especially on rotor speed. The proper analysis of rotor speed can be useful for constructing effective SCADA based models for wind turbine condition monitoring. Gaussian Process (GP) is a nonparametric, stochastic process that's designed to solve regression and probabilistic classification problems. GP models powerful to solve nonlinear systems, however, its application are not much explored in wind turbine condition monitoring.This paper describes a wind turbine condition monitoring Gaussian Process technique that uses the rotor speed to derive a rotor curve for wind turbine condition monitoring. Developed GP model, then compared with the conventional approach based on binned rotor curves together with individual bin probability distributions to identify operational anomalies. The proposed techniques have been validated experimentally using SCADA data sets obtained from operational turbines. Finally, comparative analysis of these techniques described outlining the strength and weakness of individual models.

Conference

Conference3rd International Conference on Offshore Renewable Energy
Abbreviated titleCORE2018
CountryUnited Kingdom
CityGlasgow
Period29/08/1830/08/18
Internet address

Fingerprint

Condition monitoring
Wind turbines
Rotors
Turbines
Costs
Bins
Random processes
Probability distributions
Nonlinear systems
Monitoring

Keywords

  • wind turbine
  • SCADA analysis
  • gaussian process
  • rotor speed
  • condition monitoring
  • Prediction

Cite this

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title = "Comparative study of binning and gaussian process based rotor curves of a wind turbine for the purpose of condition monitoring",
abstract = "The wind turbines deteriorating performance cause failures, including catastrophic failures that lead to high operation and maintenance (O&M) cost. SCADA based continuous monitoring of wind turbines is a cost-effective approach and plays an essential role as turbine sizes increase, and they are placed in more remote locations, for example, offshore to improve the performance of wind turbines and reduces the O&M cost. Various studies suggest that the internal operation of the wind turbine depends on various variables, especially on rotor speed. The proper analysis of rotor speed can be useful for constructing effective SCADA based models for wind turbine condition monitoring. Gaussian Process (GP) is a nonparametric, stochastic process that's designed to solve regression and probabilistic classification problems. GP models powerful to solve nonlinear systems, however, its application are not much explored in wind turbine condition monitoring.This paper describes a wind turbine condition monitoring Gaussian Process technique that uses the rotor speed to derive a rotor curve for wind turbine condition monitoring. Developed GP model, then compared with the conventional approach based on binned rotor curves together with individual bin probability distributions to identify operational anomalies. The proposed techniques have been validated experimentally using SCADA data sets obtained from operational turbines. Finally, comparative analysis of these techniques described outlining the strength and weakness of individual models.",
keywords = "wind turbine, SCADA analysis, gaussian process, rotor speed, condition monitoring , Prediction",
author = "Pandit, {Ravi Kumar} and David Infield",
year = "2018",
month = "8",
day = "30",
language = "English",
note = "3rd International Conference on Offshore Renewable Energy, CORE2018 ; Conference date: 29-08-2018 Through 30-08-2018",
url = "http://www.offshore-renewables.co.uk/",

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Pandit, RK & Infield, D 2018, 'Comparative study of binning and gaussian process based rotor curves of a wind turbine for the purpose of condition monitoring' 3rd International Conference on Offshore Renewable Energy, Glasgow, United Kingdom, 29/08/18 - 30/08/18, .

Comparative study of binning and gaussian process based rotor curves of a wind turbine for the purpose of condition monitoring. / Pandit, Ravi Kumar; Infield, David.

2018. 3rd International Conference on Offshore Renewable Energy, Glasgow, United Kingdom.

Research output: Contribution to conferenceProceeding

TY - CONF

T1 - Comparative study of binning and gaussian process based rotor curves of a wind turbine for the purpose of condition monitoring

AU - Pandit, Ravi Kumar

AU - Infield, David

PY - 2018/8/30

Y1 - 2018/8/30

N2 - The wind turbines deteriorating performance cause failures, including catastrophic failures that lead to high operation and maintenance (O&M) cost. SCADA based continuous monitoring of wind turbines is a cost-effective approach and plays an essential role as turbine sizes increase, and they are placed in more remote locations, for example, offshore to improve the performance of wind turbines and reduces the O&M cost. Various studies suggest that the internal operation of the wind turbine depends on various variables, especially on rotor speed. The proper analysis of rotor speed can be useful for constructing effective SCADA based models for wind turbine condition monitoring. Gaussian Process (GP) is a nonparametric, stochastic process that's designed to solve regression and probabilistic classification problems. GP models powerful to solve nonlinear systems, however, its application are not much explored in wind turbine condition monitoring.This paper describes a wind turbine condition monitoring Gaussian Process technique that uses the rotor speed to derive a rotor curve for wind turbine condition monitoring. Developed GP model, then compared with the conventional approach based on binned rotor curves together with individual bin probability distributions to identify operational anomalies. The proposed techniques have been validated experimentally using SCADA data sets obtained from operational turbines. Finally, comparative analysis of these techniques described outlining the strength and weakness of individual models.

AB - The wind turbines deteriorating performance cause failures, including catastrophic failures that lead to high operation and maintenance (O&M) cost. SCADA based continuous monitoring of wind turbines is a cost-effective approach and plays an essential role as turbine sizes increase, and they are placed in more remote locations, for example, offshore to improve the performance of wind turbines and reduces the O&M cost. Various studies suggest that the internal operation of the wind turbine depends on various variables, especially on rotor speed. The proper analysis of rotor speed can be useful for constructing effective SCADA based models for wind turbine condition monitoring. Gaussian Process (GP) is a nonparametric, stochastic process that's designed to solve regression and probabilistic classification problems. GP models powerful to solve nonlinear systems, however, its application are not much explored in wind turbine condition monitoring.This paper describes a wind turbine condition monitoring Gaussian Process technique that uses the rotor speed to derive a rotor curve for wind turbine condition monitoring. Developed GP model, then compared with the conventional approach based on binned rotor curves together with individual bin probability distributions to identify operational anomalies. The proposed techniques have been validated experimentally using SCADA data sets obtained from operational turbines. Finally, comparative analysis of these techniques described outlining the strength and weakness of individual models.

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KW - SCADA analysis

KW - gaussian process

KW - rotor speed

KW - condition monitoring

KW - Prediction

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M3 - Proceeding

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