Imputation of missing data from offshore wind farms using spatio-temporal correlation and feature correlation

Many industrial applications, such as fault diagnosis and remaining useful life prediction, require high-dimensional inputs to predict a reliable output. For offshore wind farm supervisory control and data acquisition (SCADA) systems, unfortunately, signal inputs are often missing due to harsh weather, resulting in the failure of network/sensors. It limits the accuracy of subsequent diagnostic or prognostic tasks. Although many methods have been proposed for imputing missing data, their applicability in offshore wind farms is still problematic because wind turbines (WTs) are time-varying systems, and conventional learning methods require high computational cost. To address this problem, we propose a learning framework containing two learning models, corresponding to two missing-data conditions. The framework imputes missing data by designing a spatio-temporal correlation method for entire feature-missing conditions and a feature-correlation method for partial feature-missing conditions, respectively. A real-world offshore wind farm dataset of a SCADA system with 33 WTs and 68 features, which was recorded over a one-month period, is used for experimental validation. We demonstrate that the proposed framework imputes the missing data with much smaller mean absolute error (MAE) and mean squared error (MSE) and requires less computational time, compared to the existing machine-learning methods for both imputation conditions.