Vacuum Switching Technology for Future of Power Systems

Even though switching in vacuum is a technology with almost 100 years of history,its recent develop-ments are still changing the future of power transmission and distribution systems.First,current switch-ing in vacuum is an eco-friendly technology compared to switching in SF 6 gas,which is the strongest greenhouse gas according to the Kyoto Protocol.Vacuum,an eco-friendly natural medium,is promising for reducing the usage of SF 6 gas in current switching in transmission voltage.Second,switching in vacuum achieves faster current interruption than existing alternating current(AC)switching technolo-gies.A vacuum circuit breaker(VCB)that uses an electromagnetic repulsion actuator is able to achieve a theoretical limit of AC interruption,which can interrupt a short-circuit current in the first half-cycle of a fault current,compared to the more common three cycles for existing current switching technologies.This can thus greatly enhance the transient stability of power networks in the presence of short-circuit faults,especially for ultra-and extra-high-voltage power transmission lines.Third,based on fast vacuum switching technology,various brilliant applications emerge,which are benefiting the power systems.They include the applications in the fields of direct current(DC)circuit breakers(CBs),fault current lim-iting,power quality improvement,generator CBs,and so forth.Fast vacuum switching technology is promising for controlled switching technology in power systems because it has low variation in terms of opening and closing times.With this controlled switching,vacuum switching technology may change the“gene”of power systems,by which power switching transients will become smoother.

A novel method for small and unbalanced sample pattern recognition of gas insulated switchgear partial discharge using an auxiliary classifier generative adversarial network

Convolutional neural networks(CNNs)have been widely used for gas-insulated switchgear(GIS)partial discharge(PD)pattern recognition due to their powerful feature extraction ability.However,there is commonly a scarcity of fault samples due to low insulation failure rate of GIS equipment,which degrades the diagnostic performance of these CNN networks when directly applied to small and unbalanced datasets.Therefore,we propose a novel auxiliary classifier generative adversarial network for GIS PD pattern recognition for small and unbalanced samples.First,we propose using synchrosqueezed wavelet transform to extract time-frequency characteristics of PD pulses and obtain a time-frequency image with high energy aggregation and timefrequency distribution rate.Then,we propose an improved generative adversarial network with an auxiliary classier and self-attention mechanism,which can generate highquality PD samples for situations with few classes.Experiments show that our proposed method can reach 95.75%recognition accuracy for small datasets,which is the highest among several comparable methods.Furthermore,the proposed method has excellent and stable recognition performance for various unbalanced datasets.

Study on the influencing factors to reduce the recovery time of superconducting tapes and coils for the DC superconducting fault current limiter applications

Superconducting fault current limiters(SFCLs)are important in improving the stability of electrical power systems.However,the existing resistive-type DC SFCL has a long re-covery time,which cannot meet the requirements of power system auto-reclosure.Thus,it is necessary to investigate the methods of reducing recovery time of SFCLs.In the simulations on superconducting tapes,there is a lack of investigation about the effects of design factors of superconducting tapes and coils such as material,thickness,coil radius,coil winding method on recovery characteristics.The objective of this study is to obtain the effects of material and geometric properties of superconducting tapes and coils on recovery characteristics and obtain methods to reduce the recovery time.The recovery times under different conducting currents are also compared by simulations and exper-iments to verify simulation accuracy.The maximum error is below 40%and most of the cases have an error below 10%.Besides,by increasing tape thickness,the recovery time can be reduced by 22%.Tape materials and coil winding methods can be designed ac-cording to the auto-reclosure and current-limiting requirements of the power systems.The research provided directions for the design of superconducting tape and super-conducting coil for resistive-type DC SFCLs.

Breakdown of shield gaps in vacuum interrupters

The objective of this paper is to determine how the high-voltage discharging metal vapour deposited on the nearby ceramic inner surface influences the breakdowns of gaps between centre shield and end shield in vacuum interrupters.Two types of shield materials were selected,namely copper and stainless steel.The end curvature radius of the shield was 3 mm.The distance between the shields could adjust manually from 4 to 8 mm.The distance between the shields and ceramic was 3.5 mm.The negative polarity standard lightning impulse voltage(12/50μs)was repeated for 900 operations based on an up-and-down method.The experimental results illustrated that the metal deposition layer on the inner surface of the ceramic en-velope significantly influenced the breakdown voltage of the shield gaps.At a shield gap distance of d?4 mm,the breakdown voltage of the shield gap increased from an initial lower voltage to the saturation voltage by approximately 200 operations.Then,the breakdown voltage decreased to a lower voltage range during only 50 operations,and the breakdown voltage maintained at this lower voltage range until the end of 900 operations.Furthermore,as the shield gap distance increased from d?4 to 6 mm and 8 mm,the breakdown voltage also maintained at this lower voltage range.A metal deposition layer was formed on the inner surface of ceramic by repetitive application of the lightning impulse voltage.To analyse the influence of the metal deposition layer,the electric field distributions were calculated for the original vacuum interrupter and the vacuum interrupter with the metal deposition layer on the ceramic inner surface.The simulation results suggested that the metal deposition layer took part in the breakdown path between the shield gaps and deteriorated the insulation performance.

Effect of barrier thickness of Polytetrafluoroethylene on the Direct Current breakdown characteristics of liquid nitrogen

When high-temperature superconducting(HTS)tapes in high voltage DC HTS devices are quenched,amounts of thermal bubbles are generated in liquid nitrogen(LN_(2))that will reduce the insulation strength greatly.Especially when resistive-type superconducting fault current limiters meet a short-circuit fault,the insulation strength of the LN_(2)decreased significantly due to the thermal bubbles and electrothermal pressure.Solid insulating barriers can improve the insulation strength of the LN_(2).The objective of this study is to obtain the effect of barrier thickness on the DC dielectric breakdown characteristics of LN_(2).Insulating barrier Polytetrafluoroethylene with three different thicknesses were tested.Three types of electrodes such as rod-plane,needle-plane,and planeplane were applied.The results showed that the barriers can increase the negative and positive breakdown voltage both with and without thermal bubbles in a slightly nonuniform field(SNF)and non-uniform field(NF).For the SNF,the DC breakdown voltage is higher when the barrier is thinner.The effect of the barrier thickness in the NF is lower than that in the SNF field.For the NF,the positive breakdown voltage is higher when the barrier is thicker.

GAN and CNN for imbalanced partial discharge pattern recognition in GIS

The convolutional neural network(CNN)achieves excellent performance in pattern recognition owing to its powerful automatic feature extraction capability and outstanding classification performance.However,the actual samples obtained are unbalanced,and accurate diagnoses are difficult for the existing methods.A classification method for partial discharge(PD)pattern recognition in gas-insulated switchgear(GIS)that uses a generative adversarial network(GAN)and CNN on unbalanced samples is proposed.First,a novel Wasserstein dual discriminator GAN is used to generate data to equalise the unbalanced samples.Second,a decomposed hierarchical search space is used to automatically construct an optimal diagnostic CNN.Finally,PD pattern recognition classification in GIS of the unbalanced samples is realised by the GAN and CNN.The experimental results show that the GAN and CNN methods proposed in this study have a pattern recognition accuracy of 99.15%on unbalanced samples,which is significantly higher than that obtained by other methods.Therefore,the method proposed in this study is more suitable for industrial applications.