The transmission cable and power conversion device need to be buried underground for dynamic wireless charging of an expressway, so cable insulation deterioration caused by aging and corrosion may occur. This paper presents an on-line insulation monitoring method based on BP neural network for dynamic wireless charging network. The sampling signal expression of the injection signal is derived, and the feasibility of this method is verified by experiments, which effectively overcomes the problem of large calculation error of insulation resistance when the cable capacitance to ground is large. The experimental results indicate that the error of the proposed method is less than 9%, which can meet the needs of insulation monitoring.
In the practical application of wireless power transfer (WPT), the identification of the secondary coil and the analysis of the space magnetic field of the coil will affect the matching scheme of the coil, which will further affect the performance of energy transmission. At present, the establishment of the coil space magnetic field model mainly adopts the finite element method (FEM). The accuracy of the results is limited by the computer performance and the specific settings during calculation, which usually takes a long time. Additionally, it can only analyze and establish the space magnetic field of the coil with specific parameters. Especially when the coil structure and parameters change, it is difficult to quickly establish the spatial magnetic field. This paper presents a secondary side coil identification method of a wireless charging system based on the magnetic field cloud image characteristics. The image feature extraction algorithm is used to extract features of a certain height magnetic field cloud image of an unknown structure type coil obtained by FEM. Further, by matching with the characteristics of the magnetic field cloud image of the known coil, the identification of the unknown coil structure type is realized. The effectiveness and accuracy of the proposed method is verified by an example. This algorithm is helpful to extract the characteristics of the coil space magnetic field, and can establish coil space magnetic field models with different structure types and different coil parameters combined with deep learning to guide the matching scheme of the primary and secondary coils, and realize efficient energy transmission.
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