| Due to the wireless power transfer(WPT)technology’s features such as no electrical contact need,safety,and flexibility,the working efficiency of underwater equipment such as unmanned submersibles,underwater self-service robots,and underwater wireless sensors can be effectively improved by using WPT technology.However,because of the influence of different transmission media,the underwater WPT system shows a different pattern from that in the air.At the same time,the system will always face the working conditions of different receiving coils and loads,resulting in the system not being at the maximum power opreating point in practical applications.This dissertation mainly focuses on the analysis of the underwater wireless power transmission system and proposes to identify the mutual inductance of the system and the load of the receiving coil by analyzing the distribution of the spatial magnetic field,so that the system works at the maximum power operating point.First,based on the principle of magnetic coupling wireless power transmission,the the establishment of mutual inductance model and spatial magnetic field distribution of two-coil WPT system with SS topology under different transmission media has been completed,the law of the maximum power operating point of the system at a fixed transmission distance is studied,and the conclusion that the maximum power transmission can be achieved by adjusting the mutual inductance and load is drawn.Secondly,a wireless charging system coil recognition algorithm based on the characteristics of magnetic field cloud image is proposed,the concept of contour moment is introduced,and the finite element simulation is used to obtain the magnetic field cloud image of different structure coils at different positions in space.Realize the recognition of coil polarity and type by extracting the features of the contour moment from the magnetic field cloud image.In order to improve the generalization ability of the coil recognition algorithm,a coil recognition algorithm based on convolutional neural network is proposed,which achieves a recognition accuracy of more than 99% for the coil type.The feature map of the magnetic field cloud image extracted by convolution is briefly analyzed and the possibility of realizing coil type recognition by collecting the magnetic field intensity value of some areas near the coil instead of the magnetic field cloud image feature in practical applications is discussed.Thirdly,the influence of coil mutual inductance and load on the transmission power of the system is further analyzed.Based on fixing the transmitting coil,the relationship between the mutual inductance of the system and the magnetic field distribution generated by different receiving coils is discussed,a mutual inductance recognition algorithm for wireless charging systems based on improved convolutional neural networks is established.When not touching the secondary side,the load prediction of the system is realized by detecting the current of the transmitting coil.It further summarizes the method to make the system work at the maximum power operating point by adjusting the number of turns of the transmitting coil for mutual inductance compensation.Finally,the simulation and experimental verification of the underwater wireless charging system have been completed.The changing law of the transmission power of the system under different media and the method of predicting the load have been verified by simulations and experiments.The experimental results show that,compared with the actual resistance,the error of the predicted value of the system resistance is 9.1%,The maximum power of the system can be achieved by adjusting the number of turns of the transmitting coil,which verifies the method proposed in this dissertation to identify the mutual inductance and load of the system through the magnetic field distribution and further adjust the mutual inductance to achieve the maximum power transmission of the system. |
