High Voltage最新文献

Gate drivers for high-voltage silicon carbide (SiC) converters require a reliable power supply with strong isolation and compact size. However, existing gate driver power supplies (GDPS) with the transformer structure make it difficult to enhance the isolation performance while keeping the small coupling capacitance and volume. This paper firstly presents a novel wireless GDPS with an improved Class-E inverter for high-voltage energy harvesting applications. The proposed design requires only one switch and reduces the input inductor significantly. Meanwhile, it solves the issue of harmful surge currents and maintains a zero-voltage-switching state when the load changes. Finally, a compact wireless GDPS prototype is developed and the insulation characteristics are studied. Results demonstrate that the proposed design achieves isolation voltage up to 17.7 kV with the smallest size volume and extra-low coupling capacitance of 1.83 pF.

As power-electronic (PE)-based systems become increasingly common in the electric power grid, the insulation systems used in medium- and high-voltage (HV) applications will be exposed to high-frequency (HF) electric fields. Therefore, the insulation materials must be characterised using HF waveforms. However, generating these waveforms presents a significant challenge due to the large reactive power associated with the d$��v�$/d$��t�$. This paper proposes a resonant test system with a ferrite-based transformer for HF insulation testing. The resonant circuit is formed by the transformer's leakage inductance and the insulation sample capacitance, with an adjustable frequency tuning capacitor. The system can be driven with an inverter or linear power amplifier. Increasing the test voltage level while maintaining the same test frequency presents several challenges: transformer core grounding, high resonant current and implications for bobbin and insulation design. This paper investigates these challenges and proposes an oil-insulated resonant transformer, capable of extending the test voltage to 23 kV_pk for HF insulation tests at around 40 kHz. High-frequency breakdown tests are performed on enamelled copper wire in various insulation media using the prototype resonant test system, highlighting the importance of the dielectric's thermal performance.

Partial discharge (PD) detection is considered one of the most crucial and effective methods for identifying defects in electrical equipment. Consequently, investigating advanced and efficient PD monitoring techniques is essential for the development of gas-insulated equipment (GIE) and the construction of ultra-high-voltage (UHV) networks. This paper first explores the causes and impact characteristics of various defects in GIE based on experimental results and simulation analysis. It then reviews current research on advanced PD measurement techniques, integrating acoustic, chemical and optical methods. The findings preliminarily demonstrate the unique advantages and applicability of the advanced methods for complex detection environments. Finally, this paper addresses the technical challenges and potential breakthroughs associated with these detection techniques. In this regard, this study aims to provide technical insights and research directions for defect detection techniques in UHV GIE.

Frequency domain spectroscopy (FDS) is widely used for assessing the condition of oil-paper insulated electrical equipment. However, in low temperatures, measurement results often deviate from expected values. This study investigates the low-temperature dielectric response characteristics of oil-paper insulation under large temperature differences (−60°C to 30°C), wide frequency ranges (1 mHz–5 kHz) and varying moisture contents (0.41%–3.91%). The mechanisms of low-temperature effects on oil-paper insulation relaxation polarisation are revealed. Further, a novel low-temperature normalisation method and a moisture evaluation method are proposed based on the improved Havriliak–Negami model. Compared to traditional methods, the new approach significantly enhances the accuracy. The goodness of fit R² for temperature normalisation improves from 0.9526 to 0.9757, and the error in moisture content evaluation has reached 0.498%. Finally, the novel approach is applied to diagnose the condition of a damped bushing model, demonstrating high potential for practical applications. This study enables condition diagnosis of oil-impregnated electrical equipment in extremely cold environments, filling a critical gap in the field.

Mechanical vibration defect is the key factor leading to sudden failure of gas-insulated switchgear (GIS) equipment. It is important to realise effective prediction of the mechanical vibration state development trend of GIS equipment in order to improve its active safety protection level. This paper carried out research on the accurate prediction method and experimental validation of the mechanical vibration state and its defect severity development trend for the GIS equipment. Firstly, the deep and shallow vibration feature parameters for different mechanical defect signals were jointly extracted by time-domain features and deep belief network methods. Secondly, a new prediction model, incorporating the attention mechanism and the bidirectional gated recurrent unit (BiGRU), was constructed with the deep and shallow vibration feature parameters as inputs. Finally, the prediction trend effectiveness was verified based on the real-type GIS mechanical simulation platform and the field operation GIS equipment. Results show that the deep and shallow vibration feature extraction method proposed in this paper can characterise the mechanical defect information more comprehensively. The new prediction method of the vibration state trend based on the attention-BiGRU model shows ideal accuracy, and the predicted vibration state development trend is highly consistent with the actual, with an average absolute error of 0.063. The root mean square error (E_RMSE) value of the prediction method is <5%, which reduces the relative error value at least 37% compared with the traditional prediction models. This paper provides a valuable reference for the proactive defence of GIS mechanical failure.

The tri-post insulator is a core component within the gas-insulated transmission lines (GIL), providing both electrical insulation and mechanical support. Typically, it is high-temperature cured through vacuum casting of a mixture of epoxy resin, curing agent, and alumina fillers. In recent years, frequent incidents of mechanical cracking and breakdown of tri-post insulators have been reported, which are attributed to residual stress concentration. However, the formation mechanism and distribution characteristics of the residual stress remain unclear. This study focuses on the curing kinetics and residual stress modelling of GIL tri-post insulators. It is verified that the epoxy resin/alumina reaction system follows the autocatalytic curing kinetic model by differential scanning calorimetry tests, and the model fitted by Malek's method corresponds well with the experimental results. Based on the Cure Hardening Instantaneously Linear Elastic model and the density inhomogeneity, it is found that a tensile stress concentration with a maximum value of 58.9 MPa at the edge of the insulator/sleeve interface, due to the mismatch of chemical and thermal shrinkage effects. Besides, the filler sedimentation can decrease the coefficient of thermal expansion and suppress the residual stress concentration. The investigation would help with the visualisation of the residual stress distribution in GIL tri-post insulators and provide some guidance for their processing treatments.

Traditional mineral oil has long served as a liquid insulating medium in power transformers. However, its low fire resistance, poor biodegradability and dependence on finite fossil fuel resources, along with its low flash point, contribute to transformer explosions and fires. To overcome these limitations, developing insulating liquids with high flash points and self-extinguishing properties is essential. Although natural ester-based insulating oils and silicone oils have been proposed as alternatives, their performance requires further optimisation, and their application in transformers remains challenging. This study examines the combustion characteristics of three novel self-extinguishing fluorinated silicone oils using combustion experiments and reactive molecular dynamics simulations. The results demonstrate that methylfluorinated and hydrofluorinated silicone oils exhibit superior self-extinguishing performance compared to mineral oil and natural ester-based insulating oils. Simulation analyses indicate that fluorinated silicone oils generate silicon-oxygen polymers upon ignition, which influence subsequent chain reactions. However, hydroxyfluorinated silicone oil releases a higher concentration of key free radicals, intensifying chain reactions and diminishing its self-extinguishing capability. As a result, its self-extinguishing performance is significantly weaker than that of methylfluorinated and hydrofluorinated silicone oils. These findings provide valuable insights for the development of advanced liquid insulating media as potential replacements for mineral oil.

Gas-insulated switchgear (GIS) plays a critical role in ensuring the reliability of power systems, but partial discharge (PD) is a primary cause of failures within GIS equipment. Traditional PD diagnostic methods rely heavily on laboratory data, which differ significantly from that under the complex conditions of field data, leading to a marked drop in recognition accuracy when they are applied to field PD diagnosis. This study addresses the challenge by integrating field data into the training process, utilising a deep transfer learning approach that combines laboratory and field data to improve diagnostic accuracy for GIS PD. The research collected PD data from laboratory models representing five defect types and field data gathered from operational GIS equipment. A deep residual network (ResNet50) was pretrained using laboratory data and fine-tuned with field data through deep transfer learning to optimise the recognition of PD in field conditions. The results show that the proposed model achieves a significantly higher recognition accuracy (93.7%) for field data compared to traditional methods (60%–70%). The integration of deep transfer learning ensures that both low-dimensional general features from laboratory data and high-dimensional specific features from field data are effectively utilised. This research significantly contributes to improving the diagnostic accuracy of PD in GIS under field conditions, providing a robust method for defect detection in operational equipment.

The integration of numerous distributed energy resources and power electronic devices introduces a wide spectrum of frequency disturbances, which significantly challenge the stability of modern power systems. Therefore, there is an urgent need to enhance the current monitoring level of modern power systems. This paper proposes a novel busbar current inversion scheme based on an elliptical magnetic sensor array. By establishing a simulation model, the effect of structural parameters of the elliptical array on its current measurement accuracy was analysed. The anti-interference capability of the elliptical array in complex environments such as busbar displacement and crosstalk was studied, and principles for designing array parameters under different current sensor standards were established. Experiments conducted on the proposed current sensing scheme demonstrated that the designed current array has a range of 0–150 A, with a current measurement error below 0.1% without external interference and not exceeding 1% during busbar displacement. Under conditions of crosstalk, the measurement accuracy achieved was class 0.5. The sensor array possesses high measurement accuracy, robust anti-interference capability, low power consumption, compact size and a noncontact nature. It exhibits significant potential for extensive application in novel business scenarios within the power system.

Moisture ingression accelerates the ageing and degradation of the oil-paper insulation. Rapid and accurate moisture assessment of power apparatus in the field is thus essential. In this paper, an innovative design of a microstrip petal-like complementary split ring resonator (MPCSRR) is proposed for the moisture assessment of the oil-paper insulation in the GHz range. The MPCSRR has advantages in concentrating the electromagnetic field, significantly increasing the number of resonance peaks, and achieving a sensitivity of approximately 3–4 times higher than the traditional microstrip ring resonators (MRRs). A mathematical model for analysing the dielectric response is established, combining the theoretical analysis and equivalent circuit model. The effects of microstrip width and substrate thickness on impedance matching, as well as the incidence depth and test sensitivity of the resonator, are explored using simulation. The experimental results demonstrate that the MPCSRR achieves rapid moisture detection within tens of seconds and accurately characterises the wetness of the oil-paper insulation. A moisture assessment model based on Lasso regression achieves an accuracy of 4%, confirming the reliability and validity of the proposed method. The results also verify that the sensitivity of the MPCSRR is also higher than that of the traditional MRR, showing its potential for precise and efficient moisture detection in oil-paper insulation.