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The random fluctuations of wind energy and external grid voltage disturbances can both lead to serious voltage fluctuations and voltage deviations in the wind farm (WF). Voltage/reactive power control is an effective way to improve the voltage stability of WF. The existing research is based on complex physical models with prior parameter information, and the accuracy and calculation speed of the WF model are difficult to guarantee. To address this issue, this paper explores a decentralized optimal voltage control method for WF with deep learning-based (DL) data-driven modeling (DL-DOVC). A hybrid convolutional neural network (CNN)-Transformer architecture is proposed to establish the data-driven model of WF, leveraging its enhanced capabilities for extracting time series correlation, to learn complex patterns and dynamics from time series data. Then, we develop a fully decentralized voltage control method for WF to regulate the terminal bus voltage of wind turbines (WTs) within a feasible range. A DL-based data-driven predictive controller is specially designed to solve the established data-driven voltage optimization problem, it eliminates the necessity for frequent manual model maintenance and is suitable for real-time application. Additionally, the difficulty of WF decentralized optimal voltage control arises from the inability of local controllers to fully consider the impact of all non-local WTs on the local WT states. By designing the DL-based auxiliary state-feedback controller, the effects of non-local WTs are implicitly considered in the auxiliary feedback control law. A WF with 32*6.25 MW WTs is used to test the proposed DL-DOVC method. Simulation results show that the proposed DL-based model can efficiently learn and predict the WF dynamics under different operating scenarios. The near-global optimal voltage control performance is achieved only with local measurements by employing the DL-DOVC method.

In wireless power transfer (WPT) systems using magnetic coupling resonance, when there is a registration deviation between the transmitting and receiving coils, there will be significant fluctuations in the coupling coefficient, output power, and transmission efficiency, which will seriously affect the stability of the WPT system. The coupling coefficient is related to the length and width of the rectangular coil, number of turns, mutual geometric position, and space magnetic medium, making it difficult to maintain a constant value when the coil is offset. A single-emitter two-receiver positive-series rectangular coils structure and a method of calculating the structure are proposed. Then an optimization method for mutual inductance was proposed, and the structural parameters that met the design requirements were obtained using the proposed optimization method. The calculation formula for coupling coefficient was verified through simulation and experiments. The results showed that when the offset distance of the receiving coil along the Y-axis (driving direction) and X-axis reaches half of the length of the transmitting coil and 10 cm, the coupling coefficient, transmission efficiency, and output power remain almost unchanged.