The planetary gearbox is a critical part of wind turbines, and has great significance for their safety and reliability. Intelligent fault diagnosis methods for these gearboxes have made some achievements based on the availability of large quantities of labeled data. However, the data collected from the diagnosed devices are always unlabeled, and the acquisition of fault data from real gearboxes is time-consuming and laborious. As some gearbox faults can be conveniently simulated by a relatively precise dynamic model, the data from dynamic simulation containing some features are related to those from the actual machines. As a potential tool, transfer learning adapts a network trained in a source domain to its application in a target domain. Therefore, a novel fault diagnosis method combining transfer learning with dynamic model is proposed to identify the health conditions of planetary gearboxes. In the method, a modified lumped-parameter dynamic model of a planetary gear train is established to simulate the resultant vibration signal, while an optimized deep transfer learning network based on a one-dimensional convolutional neural network is built to extract domain-invariant features from different domains to achieve fault classification. Various groups of transfer diagnosis experiments of planetary gearboxes are carried out, and the experimental results demonstrate the effectiveness and the reliability of both the dynamic model and the proposed method.

Based on the nature of wind, wind power fluctuations can cause significant problems in the distribution network. One of the solutions is to integrate an energy storage system with wind farm to mitigate the output power fluctuations. Therefore, an energy storage coordinated control strategy based on model predictive control is proposed to smooth minute-scale fluctuations of wind power. By analysing the limitations of traditional control strategy, four operating modes of battery energy storage system which are determined by the predicted state of charge obtained by model predictive control, are designed to avoid violating the state of charge limitation, and an energy state feedback control is designed to adjust the initial power allocation orders of batteries. Finally, the effectiveness of the proposed control strategy can be verified by the real-time digital simulator. The results indicate that the developed approach reduces the switching times of batteries and improves the ability of the battery energy storage system to smooth wind power fluctuations significantly.

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