Investigation on the Novel High-performance Copper/Graphene Composite Conductor for High Power Density Motor

High-performance Cu/Graphene composite wire synergistically strengthened by nano Cr_(3)C_(2) phase was directly synthesized via hot press sintering followed by severe cold plastic deformation, using liquid paraffin and CuCr alloy powder as the raw materials. Since graphene is in situ formed under the catalysis of copper powder during the sintering process, the problem that graphene is easy to agglomerate and difficult to disperse uniformly in the copper matrix has been solved. The nano Cr_(3)C_(2)-particles nailed at the interface favor to improve the interface bonding. The Cu/Graphene composite possesses high electrical conductivity, hardness, and plasticity. The composite wire exhibits high electrical conductivity of 96.93% IACS, great tensile strength of 488MPa, and excellent resistance to softening. Even after annealing at 400℃ for 1 h, the tensile strength can still reach 268 MPa with a conductivity of about 99.14% IACS.The wire's temperature coefficient of resistance(TCR) is largely reduced to 0.0035/℃ due to the complex structure,which leads the wire to present low resistivity at higher temperatures. Such Cu/Graphene composite wire with excellent comprehensive performance has a good application prospect in high-power density motors.

Faulty Feeder Identification in Resonant Grounding Distribution Networks Based on Deep Learning and Transfer Learning

Identification of faulty feeders in resonant grounding distribution networks remains a significant challenge dueto the weak fault current and complicated working conditions.In this paper, we present a deep learning-based multi-labelclassification framework to reliably distinguish the faulty feeder.Three different neural networks (NNs) including the multilayerperceptron, one-dimensional convolutional neural network (1DCNN), and 2D CNN are built. However, the labeled data maybe difficult to obtain in the actual environment. We use thesimplified simulation model based on a full-scale test field (FSTF)to obtain sufficient labeled source data. Being different frommost learning-based methods, assuming that the distribution ofsource domain and target domain is identical, we propose asamples-based transfer learning method to improve the domainadaptation by using samples in the source domain with properweights. The TrAdaBoost algorithm is adopted to update theweights of each sample. The recorded data obtained in the FSTFare utilized to test the domain adaptability. According to ourvalidation and testing, the validation accuracies are high whenthere is sufficient labeled data for training the proposed NNs.The proposed 2D CNN has the best domain adaptability. TheTrAdaBoost algorithm can help the NNs to train an efficientclassifier that has better domain adaptation. It has been thereforeconcluded that the proposed method, especially the 2D CNN, issuitable for actual distribution networks.

Development and test in grid of 630 kVA three-phase high temperature superconducting transformer

A 630-kVA 10.5 kV/0.4 kV three-phase high temperature superconducting(HTS)power transformer was successfully developed and tested in a live grid.The windings were wound by hermetic stainless steelreinforced multi-filamentary Bi2223/Ag tapes.The structures of primary windings are solenoid with insulation and cooling path among layers,and those of secondary windings consist of double-pancakes connected in parallel.Toroidal cryostat is made from electrical insulating glass fiber reinforced plastics(GFRP)materials with room temperature bore for commercial amorphous alloy core with five limbs.Windings are laid in the toroidal cryostat so that the amorphous core operates at room temperature.An insulation technology of double-half wrapping up the Bi2223/Ag tape with Kapton film is used by a winding machine developed by the authors.Fundamental characteristics of the transformer are obtained by standard short-circuit and no-load tests,and it is shown that the transformer meets operating requirements in a live grid.