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.

In this paper, we propose a new non-symmetric Gaussian wake model, which allows for different lateral expansions on the two sides of a wake to account for its interaction with neighbouring wakes. The proposed model is formulated following classical speed-deficit assumptions and momentum conservation. Departing from the existing literature, a non-symmetric Gaussian function is used to represent the velocity deficit in the wake. Accordingly, different wake expansions are assumed on the two sides of the wake, each expressed as a function of the locally prevailing turbulence intensity. The model considers that wake-added turbulence changes with downstream distance; hence, the turbulence intensity on a wake-immersed side of the wake is location dependent. The new model is compared to LES-ALM numerical simulations of three turbines in partial wake overlap. The free parameters of the model describing the wake development are tuned based on the CFD results. Results indicate that the new model provides for a very good agreement of the velocity profiles at different downstream positions, generating an improved representation of merging wakes and their downstream development.