Picture of person typing on laptop with programming code visible on the laptop screen

World class computing and information science research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.


Copula based model for wind turbine power curve outlier rejection

Wang, Yue and Infield, David G. and Stephen, Bruce and Galloway, Stuart J. (2014) Copula based model for wind turbine power curve outlier rejection. Wind Energy, 17 (11). 1677–1688. ISSN 1095-4244

[img] Text (Wang-etal-WE-2014-copula-based-model-for-wind-turbine-power-curve-outlier-rejection)
Wang_etal_WE_2014_copula_based_model_for_wind_turbine_power_curve_outlier_rejection.pdf - Accepted Author Manuscript

Download (2MB)


Power curve measurements provide a conventional and effective means of assessing the performance of a wind turbine, both commercially and technically. Increasingly high wind penetration in power systems and offshore accessibility issues make it even more important to monitor the condition and performance of wind turbines based on timely and accurate wind speed and power measurements. Power curve data from Supervisory Control and Data Acquisition (SCADA) system records, however, often contains significant measurement deviations, which are commonly produced as a consequence of wind turbine operational transitions rather than stemming from physical degradation of the plant. To use this raw data for wind turbine condition monitoring purposes is thus likely to lead to high false alarm rates which would make the actual fault detection unreliable and potentially add unnecessarily to the costs of maintenance. To this end, this paper proposes a probabilistic outlier exclusion method developed around a Copula based joint probability model. This approach has the capability of capturing the complex multivariate nonlinear relation between parameters based on their univariate marginal distributions through the use of Copula; data points that deviate significantly from the consolidated power curve can then be removed depending on this derived joint probability distribution. After data filtering in this manner, it is shown how the resulting power curves are better defined and less subject to uncertainty, whilst broadly retaining the same dominant statistical characteristics. These improved power curves make subsequent condition monitoring more effective in the reliable detection of faults.