Summary For large‐scale commercialized wind turbines, using the speed‐torque lookup table is a traditional strategy to achieve optimum tip‐speed ratio tracking. Due to the constraint of the minimum grid‐connected generator speed and the rated generator speed, the strategy can only use the slashes to describe these 2 transition zones. This affects the smoothness of the output power, shrinks the optimum tip‐speed range, and loses the generating energy. Based on an analysis of the nonlinear aerodynamics of the wind turbine, this paper presents a strategy of realizing optimal power generation control that dynamically updates the output torque limit value of the controller. The strategy solves the problems of unmeasurable wind speed and inability to track wind speed and achieves the target of maximum wind energy capture below rated wind speed. Simulation and field test results proved the correctness of the algorithm.

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Wind farms are usually located in high altitude areas with a high probability of ice occurrence. Blade icing has the potential to result in unexpected mechanical failures and downtimes. In order to avoid these problems, the priority we need to do is to detect blade icing accurately. For this purpose, a novel icing detection method based on multi-feature and multi-classifier fusion is proposed in this paper. Firstly, multi-feature composed of basic features and statistical features are extracted from the operational data. Significant features are then extracted by utilizing Light Gradient Boosting Machine. Secondly, a multi-classifier fusion approach is employed to build an fusion model, which aims to obtain a much more accurate estimation for the icing state. Overall, the proposed method in this paper can achieve more accurate detection on blade icing, compared with other models. This will minimize false alarms, helping wind farms manage the operations more efficiently.