Compare with circular coils, DoubleD coils are widely used in the wireless charging system of electric vehicles for its high coupling coefficient and excellent offset tolerance. In order to obtain higher coupling coefficient and reduce the leakage magnetic flux in the central part of the coils, this paper proposes a method that optimizing stray parameters, which can achieve more than 95% of transmitting efficiency. The investigation is performed by joint simulation for the choice of experimental devices concerning current-withstanding characteristic. According to simulations and experiments, more than 11 kW output power can be achieved for a wide range of frequency.
DD (Double D) coils have been researched and utilized due to their excellent misalignment tolerance. Here, a compound DD coupler sets for stationary wireless power chargers, which has significantly better anti-misalignment performance than single DD coupler in all directions, is proposed. The transmitting coils are composed of two parts of DD coils wound in opposite directions. Moreover, to obtain the low-level variation of mutual inductance between compound transmitting coils and receiving coils when offset occurs, a parameter optimization strategy of compensation coils is also proposed. With the properly designed parameters, the mutual inductance between transmitting and receiving coils could remain basically constant when misalignment occurs, which means that the efficiency and power remain relatively constant when offset occurs. Finally, both single DD coils and compound DD coils experimental prototypes are built to compare anti-misalignment ability performance. The results show that the proposed system is basically more stable and has a higher output power and more stable efficiency than that of unoptimized coupler during migration. In particular, with the employment of the antiparallel winding, the efficiency fluctuates from 85.5% to 85% when the 0.1-m offset in the X-axis and Y-axis occurs simultaneously. Moreover, the higher and basically more stable output power is also achieved.
The big data technology has been widely used in power consumption behavior analysis and power user portrait. In this paper, the electricity data is constructed as two-dimensional time-series. Based on the designed data structure, a special kind of Artificial Neural Networks (ANNs) named as text convolutional neural networks (TCNN) is proposed for electricity theft detection. Moreover, considering the imbalance of electricity theft data in realistic datasets, a data augmentation method is proposed. Numerical results obtained on realistic datasets validate the proposed model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.