Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study.

Abstract Intensive condition monitoring of wind generation plant through analysis of routinely collected SCADA data is seen as a viable means of forestalling costly plant failure and optimising maintenance through identification of failure at the earliest possible stage. The challenge to operators is in identifying the signatures of failure within data streams and disambiguating these from other operational factors. The well understood power curve representation of turbine performance offers an intuitive and quantitative means of identifying abnormal operation, but only if noise and artefacts of operating regime change can be excluded. In this paper, a methodology for wind turbine performance monitoring based on the use of high-frequency SCADA data is employed featuring state-of-the-art multivariate non-parametric methods for power curve modelling. The model selection considerations for these are examined together with their sensitivity to several factors, including site specific conditions, seasonality effects, input relevance and data sampling rate. The results, based on operational data from four wind farms, are discussed in a practical context with the use of high frequency data demonstrated to be beneficial for performance monitoring purposes whereas further attention is required in the area of expressing model uncertainty. Highlights • The effectiveness of SCADA-based condition monitoring is negatively influenced by the low resolution of the 10-minute data. • A novel framework for wind turbine performance monitoring is presented, based on the use of high-frequency SCADA data. • The potential of high-frequency data for monitoring purposes is thoroughly investigated. • Wind turbine power curve is modelled using state-of-the-art multivariate nonparametric methods. • Sensitivity of power curve models to site specific conditions, seasonality, input relevance and sampling rate is studied. • The results demonstrate that using high-frequency data is beneficial for performance monitoring purposes. [ABSTRACT FROM AUTHOR]