The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data

Abstract. This paper describes a method to identify the heterogenous flow characteristics that develop within a wind farm in its interaction with the atmospheric boundary layer. The whole farm is used as a distributed sensor, which gauges through its wind turbines the flow field developing within its boundaries. The proposed method is based on augmenting an engineering wake model with an unknown correction field, which results in a hybrid (grey-box) model. Operational SCADA data is then used to simultaneously learn the parameters that describe the correction field, and tune the ones of the engineering wake model. The resulting monolithic maximum likelihood estimation is in general ill-conditioned because of collinearity and low observability of the redundant parameters. This problem is solved by a singular value decomposition, which discards parameter combinations that are not identifiable given the informational content of the dataset, and solves only for the identifiable ones.

The farm-as-a-sensor approach is demonstrated on two wind plants with very different characteristics: a relatively small onshore farm at a site with moderate terrain complexity, and a large offshore one in close proximity of the coastline. In both cases, the data-driven correction and tuning of the grey-box model results in much improved prediction capabilities. The identified flow fields reveal the presence of significant terrain-induced effects in the onshore case, and of large direction and ambient-condition dependent intra-plant effects in the offshore one. Analysis of the coordinate transformation and mode shapes generated by the singular value decomposition help explain relevant characteristics of the solution, as well as couplings among modeling parameters. CFD simulations are used for confirming the plausibility of the identified flow fields.