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Literary linguistics: Open Access research in English language

Strathprints makes available Open Access scholarly outputs by English Studies at Strathclyde. Particular research specialisms include literary linguistics, the study of literary texts using techniques drawn from linguistics and cognitive science.

The team also demonstrates research expertise in Renaissance studies, researching Renaissance literature, the history of ideas and language and cultural history. English hosts the Centre for Literature, Culture & Place which explores literature and its relationships with geography, space, landscape, travel, architecture, and the environment.

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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

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Wang_etal_WE_2014_copula_based_model_for_wind_turbine_power_curve_outlier_rejection.pdf - Accepted Author Manuscript

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Abstract

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.