This paper presents the design and optimization of a wireless power transfer (WPT) charging system based on magnetically coupled resonant technology, applied to an Unmanned Aerial Vehicle (UAV). In this paper, a charging system, including dual active transmitter coils and a single receiver coil, is proposed. The dual transmitting coils adopt a coaxial structure with different radii. This structure simplifies the calculation of the complex mutual inductance between the coils to a function of mutual inductance only related to the value of the radial misalignment. Aiming toward a constant charging power, the optimal transmission efficiency of electric energy is achieved by controlling the input voltages of the active coils, which are solved via a set of equations defined as Lagrange multipliers. The simulation results of the 570 V and 85,000 Hz system verified the validity of the proposed wireless UAV charging scheme.

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Aimed to automatically provide accurate fault diagnosis of data from failed power electronics, various studies have been researched based on different approaches. Recently, data driven methods based on deep learning have required increasing attention because of their automatic feature learning abilities. Nonetheless, one of the challenges using these methods in practice is how to obtain the most representative fault features and ensure the better predication performances at the same time. This paper is in respect of the open-circuit fault diagnosis of the phase-controlled three-phase full-bridge power rectifier using deep convolutional neural network (DCNN) for extracting and further classifying fault features. The process mainly includes four steps. Firstly, a presupposed approach of DCNN which is applied to automatically capture the paramount fault features from the raw data is briefly introduced. Then a structure of DCNN is designed to extract the features from output data. Furthermore, the model and framework of the fault diagnosis system are developed to diagnose the open-circuit fault of the power rectifier. Finally, the effectiveness of the proposed method is validated using simulation results. Experiments illustrate that the DCNN model can achieve high accuracy in different fault cases and present great capability of diagnosing fault types in open-circuit fault of power rectifiers.

Aiming to detect the fault of electrical equipment with infrared thermography, this paper presents a modified mean shift clustering method for finding the region of fault. At the beginning, the whole image is separated by the highest threshold, as to find the coarse fault region. Then the mean shift clustering method is modified by introducing the weight factor associated with the neighboring pixels, in order to cluster the region with similarity. Meanwhile, the original way of mean shift clustering method to iterate in the image is abandoned, and we propose a threshold segmentation mechanism to solve. It thereby promotes the speed of clustering, and extracts the region of fault effectively. Finally, the experiments on real world infrared images show that our method has higher performance than some existing methods, including original mean shift clustering method.