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A method of space charge suppression for high-voltage direct current (HVDC) cable is proposed by the modification of the semiconductive layer. In this work, the magnetic particle of SrFe12O19 is used to modify the semiconductive shielding layer, and the effect of SrFe12O19 orientation on the charge accumulation of the insulation layer has been studied. First, the semiconductive composite with different SrFe12O19 contents is prepared and their physicochemical properties are characterized. Second, the effect of the SrFe12O19 orientation in the semiconductive layer and the doping contents on the charge accumulation in the insulation layer is analyzed by measuring the resistivity and space charge. The experimental results show that a certain amount of SrFe12O19 (≤10 wt%) can effectively reduce the positive temperature coefficient (PTC) effect, and the orientation can more effectively reduce the space charge injection from the metal wire core to the insulation layer, thus inhibiting the charge accumulation in the insulation layer. While when the content of magnetic particles is high, it will promote the second injection of the charges, leading to an increase of the charge accumulation. The work provides a new idea for space charge suppression of HVDC cable insulation layer. [ABSTRACT FROM AUTHOR]

To fully understand the distribution network cable accessories’ moisture fault, the variation in insulation parameters of the cable main insulation of cross-linked polyethylene (XLPE) and the cable accessory reinforced insulation of silicone rubber (SIR) under different moisture conditions are studied experimentally. Furthermore, the effects of typical moisture defect forms and moisture positions on electric field distributions are studied by establishing the interface defect model. The experimental results show that the insulation properties of XLPE after water immersion are more seriously reduced than SIR. The resistivity of XLPE and SIR specimens decreases significantly with the increase in moisture time, decreasing by 90.3% and 48.3% after 720 h, respectively. The relative dielectric constants of the two kinds of specimens increase with different degrees after moisture, increasing by 5.16% and 7.83%, respectively, and the breakdown strength decreases by 18.3% and 13.1% after 720 h, respectively. The simulated results show that the moisture form and position have a great influence on electric field distribution. The electric field distortion caused by water trees’ defect is the most serious, followed by water film defects and water droplet defects. Besides, for different types of moisture defects, the position of the maximum distortion electric field is different. [ABSTRACT FROM AUTHOR]

The state-of-the-art objects discovery approaches can be categorized into two: deep learning methods based on a convolutional neural network with region proposals, and conventional machine learning methods based on topic models, low-rank matrix factorization, or image processing. The deep learning methods for objects discovery are based on sacrificing computational complexity to achieve precision, and the training time can be long without GPU platforms, whereas the conventional methods usually lack detection accuracy. To effectively address the problems of the training complexity and the detection speed, we present a new objects discovery approach by proposing a two-stage (training and verification) method. In the training stage, a topic model with words co-occurrence prior is proposed on the basis of Latent Dirichlet Allocation (LDA) model, in which the co-occurrence information among the features is sufficiently utilized. In the verification stage, we propose an anchor-box polishing algorithm that fine-tunes the detection results corresponding to the pre-trained topic model from some conventional algorithms with fast detection time. The experiments on various datasets demonstrate that the proposed approach can improve the detection performance in terms of efficiency and computing costs. It is also robust to objects different in colors, lightings, scales, and so on. Interestingly, the proposed method can be combined with many fast but inaccurate detection algorithms, in which it enhances the model’s flexibility. [ABSTRACT FROM AUTHOR]