A spatial autoregressive approach for wake field prediction across a wind farm

To reduce the operation and maintenance cost for wind farms, turbine operators are actively developing strategies to, among others, reduce service cost, maximise power production, and prolong lifetime of components and super-structure. All of these tasks require a wind farm model that can accurately predict turbine behaviours in response to the changing environment. Recent studies focus on developing data-based methods for predictive maintenance purposes. This paper proposes a data-based model that aims to capture the spatial and temporal wind variations across a wind farm, as a means to predict the interactions between operating turbines and the environment, which can be useful for wind farm performance monitoring. The proposed method is a Gaussian process-based spatial autoregressive model, which reflects our physical understanding of the wake effect while taking the advantage of a stochastic data-driven learner. In the case study of a simulated wind farm, the proposed model (named here a GP-SPARX model) provides the best predictive accuracy in comparison to two other spatial autoregressive regression models, showing its capability of capturing nonlinear correlations and its potential as a low-cost wake field predictor given inputs from weather station measurements.