Abstract
Abstract. Wind farm control often relies on computationally inexpensive surrogate models to predict the dynamics inside a farm. However, the reliability of these models over the spectrum of wind farm operation remains questionable due to the many uncertainties in the atmospheric conditions and tough-to-model dynamics at a range of spatial and temporal scales relevant for control. A closed-loop control framework is proposed in which a simplified model is calibrated and used for optimization in real time. This paper presents a joint state-parameter estimation solution with an ensemble Kalman filter at its core, which calibrates the surrogate model to the actual atmospheric conditions. The estimator is tested in high-fidelity simulations of a nine-turbine wind farm. Exclusively using measurements of each turbine's generated power, the adaptability to modeling errors and mismatches in atmospheric conditions is shown. Convergence is reached within 400 s of operation, after which the estimation error in flow fields is negligible. At a low computational cost of 1.2 s on an 8-core CPU, this algorithm shows comparable accuracy to the state of the art from the literature while being approximately 2 orders of magnitude faster.
Highlights
Over the past decades, global awakening on climate change and the environmental, political and financial issues concerning fossil fuels have been catalysts for the growth of the renewable energy industry
As the current grid-connected fossil fuel plants are replaced by non-synchronous renewable energy plants, the inertia of the electricity grid will decrease, making it less stable and more prone to machine damage and blackouts (Ela et al, 2014)
Note that the simulations presented are not conclusive on the feasibility of the solution under all relevant conditions experienced in an operational wind farm
Summary
Global awakening on climate change and the environmental, political and financial issues concerning fossil fuels have been catalysts for the growth of the renewable energy industry. About 80 % of all new capacity added is projected to come from renewable energy sources, enabled by a favorable political climate. While these developments have clear benefits, an important problem with wind energy is that the rotational speed of most commercial turbines is decoupled from the electricity grid frequency via each turbine’s power electronics (Aho et al, 2012). In APC, the power production of a wind farm is regulated to meet the power demand of the electricity grid, which may change from second to second
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