Performance assessment of a wind turbine using SCADA based Gaussian Process model

Research output: Contribution to journalArticle

Abstract

Loss of wind turbine power production identified through performance assessment is a useful tool for effective condition monitoring of a wind turbine. Power curves describe the nonlinear relationship between power generation and hub height wind speed and play a significant role in analyzing the performance of a turbine. Performance assessment using nonparametric models is gaining popularity. A Gaussian Process is a nonlinear, non-parametric probabilistic approach widely used for fitting models and forecasting applications due to its flexibility and mathematical simplicity. Its applications extended to both classification and regression related problems. Despite promising results, Gaussian Process application in wind turbine condition monitoring is limited.
In this paper, a model based on a Gaussian Process is constructed for assessing the performance of a turbine. Here, a reference power curve using SCADA datasets from a healthy turbine is developed using a Gaussian Process and then is compared with a power curve from an unhealthy turbine. Error due to yaw misalignment is a common issue with wind turbine which causes underperformance, hence it is used as case study to test and validate the algorithm effectiveness.
LanguageEnglish
Article number023
Number of pages8
JournalInternational Journal of Prognostics and Health Management
Volume9
Issue number1
Publication statusPublished - 20 Jun 2018

Fingerprint

Wind turbines
Turbines
Condition monitoring
Power generation

Keywords

  • condition monitoring
  • Gaussian Process models
  • wind turbine anomaly detection
  • wind turbine
  • SCADA data
  • SCADA analysis

Cite this

@article{176f225bbc094ee9a0ead4283ee3c20d,
title = "Performance assessment of a wind turbine using SCADA based Gaussian Process model",
abstract = "Loss of wind turbine power production identified through performance assessment is a useful tool for effective condition monitoring of a wind turbine. Power curves describe the nonlinear relationship between power generation and hub height wind speed and play a significant role in analyzing the performance of a turbine. Performance assessment using nonparametric models is gaining popularity. A Gaussian Process is a nonlinear, non-parametric probabilistic approach widely used for fitting models and forecasting applications due to its flexibility and mathematical simplicity. Its applications extended to both classification and regression related problems. Despite promising results, Gaussian Process application in wind turbine condition monitoring is limited.In this paper, a model based on a Gaussian Process is constructed for assessing the performance of a turbine. Here, a reference power curve using SCADA datasets from a healthy turbine is developed using a Gaussian Process and then is compared with a power curve from an unhealthy turbine. Error due to yaw misalignment is a common issue with wind turbine which causes underperformance, hence it is used as case study to test and validate the algorithm effectiveness.",
keywords = "condition monitoring, Gaussian Process models, wind turbine anomaly detection, wind turbine, SCADA data, SCADA analysis",
author = "Pandit, {Ravi Kumar} and David Infield",
year = "2018",
month = "6",
day = "20",
language = "English",
volume = "9",
journal = "International Journal of Prognostics and Health Management",
issn = "2153-2648",
number = "1",

}

TY - JOUR

T1 - Performance assessment of a wind turbine using SCADA based Gaussian Process model

AU - Pandit, Ravi Kumar

AU - Infield, David

PY - 2018/6/20

Y1 - 2018/6/20

N2 - Loss of wind turbine power production identified through performance assessment is a useful tool for effective condition monitoring of a wind turbine. Power curves describe the nonlinear relationship between power generation and hub height wind speed and play a significant role in analyzing the performance of a turbine. Performance assessment using nonparametric models is gaining popularity. A Gaussian Process is a nonlinear, non-parametric probabilistic approach widely used for fitting models and forecasting applications due to its flexibility and mathematical simplicity. Its applications extended to both classification and regression related problems. Despite promising results, Gaussian Process application in wind turbine condition monitoring is limited.In this paper, a model based on a Gaussian Process is constructed for assessing the performance of a turbine. Here, a reference power curve using SCADA datasets from a healthy turbine is developed using a Gaussian Process and then is compared with a power curve from an unhealthy turbine. Error due to yaw misalignment is a common issue with wind turbine which causes underperformance, hence it is used as case study to test and validate the algorithm effectiveness.

AB - Loss of wind turbine power production identified through performance assessment is a useful tool for effective condition monitoring of a wind turbine. Power curves describe the nonlinear relationship between power generation and hub height wind speed and play a significant role in analyzing the performance of a turbine. Performance assessment using nonparametric models is gaining popularity. A Gaussian Process is a nonlinear, non-parametric probabilistic approach widely used for fitting models and forecasting applications due to its flexibility and mathematical simplicity. Its applications extended to both classification and regression related problems. Despite promising results, Gaussian Process application in wind turbine condition monitoring is limited.In this paper, a model based on a Gaussian Process is constructed for assessing the performance of a turbine. Here, a reference power curve using SCADA datasets from a healthy turbine is developed using a Gaussian Process and then is compared with a power curve from an unhealthy turbine. Error due to yaw misalignment is a common issue with wind turbine which causes underperformance, hence it is used as case study to test and validate the algorithm effectiveness.

KW - condition monitoring

KW - Gaussian Process models

KW - wind turbine anomaly detection

KW - wind turbine

KW - SCADA data

KW - SCADA analysis

UR - http://www.phmsociety.org/node/2492

M3 - Article

VL - 9

JO - International Journal of Prognostics and Health Management

T2 - International Journal of Prognostics and Health Management

JF - International Journal of Prognostics and Health Management

SN - 2153-2648

IS - 1

M1 - 023

ER -