High Voltage Circuit Breaker Research Articles

Research on fault identification of high-voltage circuit breakers with characteristics of voiceprint information

High voltage circuit breakers are one of the core equipment in power system operation, and the voiceprint signals generated during operation contain extremely rich information. This paper proposes a fault identification method for high voltage circuit breakers based on voiceprint information data. Firstly, based on the developed voiceprint information data acquisition device, the voiceprint information of a certain high voltage circuit breaker is obtained; Secondly, an improved S-transform is proposed in the article, which generates an amplitude matrix based on the S-transform of voiceprint information; Then, through the matrix Singular value decomposition method, the fault feature quantity of voiceprint information is extracted from the time–frequency angle, and the diagnosis system of the support vector machine model is established, and the system is trained to realize the fault identification of the high-voltage circuit breaker; Finally, through experimental simulation calculations, it was shown that the accuracy of the proposed fault identification method in different operating conditions reached 92.6%, verifying the good accuracy and robustness of the proposed method and equipment.

Methodology for Circuit Breaker Contact Diagnosis through Dynamic Resistance Measurements and Fuzzy-Logic-Based Analysis

This paper presents a methodology capable of measuring, processing, and extracting contact resistance parameters that enable the identification of the contact degradation level inside arc extinction chambers in medium- and high-voltage circuit breakers (CB). To determine the contact degradation level, an algorithm was developed to extract the characteristic parameters from the results of dynamic resistance measurement (DRM). To analyze the dataset parameters, this work employs the combination of different statistical tools, such as boxplots and fuzzy logic, enabling the evaluation to be conducted without the subjectivity inherent in human decision. Therefore, a more objective and reliable diagnosis could be achieved. The DRM tests were performed on two types of minimum-oil CB (MOCB), 800 A/15 kV/12.5 kA (CB-A) and 2000 A/72.5 kV/31.5 kA (CB-B and CB-C). For CB-A and CB-B, three contacts were utilized for each CB, with significant contact degradation. For CB-C, a single contact was employed without significant degradation. The DRM curves of the CB-C contact were used as the reference for evaluation of the CB-B contacts. For this purpose, a new DRM system was developed. The proposed methodology can be defined as a complement for other diagnostic techniques, serving as a non-subjective asset management tool, supporting decisions regarding equipment interventions.

The Methodology for Evaluating the Operating State of SF6 HVCBs Based on IDDA.

To enhance the precision of evaluating the operational status of SF6 high-voltage circuit breakers (HVCBs) and devise judicious maintenance strategies, this study introduces an operational state assessment method for SF6 HVCBs grounded in the integrated data-driven analysis (IDDA) model. The relative degradation weight (RDW) is introduced as a metric for quantifying the relative significance of distinct indicators concerning the operational condition of SF6 HVCBs. A data-driven model, founded on critical factor stability (CFS), is formulated to convert environmental indicators into quantitative computations. Furthermore, an optimized fuzzy inference (OFI) system is devised to streamline the system architecture and enhance the processing speed of continuous indicators. Ultimately, the efficacy of the proposed model is substantiated through validation, and results from instance analyses underscore that the presented approach not only attains heightened accuracy in assessment compared to extant analytical methodologies but also furnishes a dependable foundation for prioritizing maintenance sequences across diverse components.

Intelligent Mechanical Fault Diagnosis Method for High-Voltage Circuit Breakers Based on Grey Wolf Optimization and Multi-Grained Cascade Forest Algorithms

High-voltage circuit breakers (HVCBs) handle the important tasks of controlling and safeguarding electricity networks. In the case of insufficient data samples, improving the accuracy of the traditional HVCB mechanical fault diagnosis method is difficult, so it poses challenges in meeting performance requirements for mechanical fault diagnosis. In this study, a HVCB fault diagnosis method is introduced. It utilizes a combination of grey wolf optimization (GWO) and multi-grained cascade forest (gcForest) algorithms to resolve these issues and improve the accuracy of HVCB mechanical fault diagnosis. To simplify the original vibration signal, the input feature quantity for the fault diagnosis method is obtained by calculating the energy entropy of the wavelet packet decomposition. The GWO algorithm is employed to optimize the parameters of the gcForest model, leading to identification of the optimum parameter configuration. Subsequently, the diagnostic effect in the case of a small sample size was analyzed through a VS1 vacuum circuit breaker example, and the accuracy reached 95.89%. In the case of unbalanced samples, further analysis and comparison with different methods confirm the feasibility and efficiency of the combination of GWO and gcForest algorithms. This study provides an effective solution for the diagnosis of mechanical faults in HVCBs.

Analysis of multiple-faults of high-voltage circuit breakers based on non-negative matrix decomposition

High-voltage circuit breakers are the core equipment in power networks, and to a certain extent, are related to the safe and reliable operation of power systems. However, their core components are prone to mechanical faults. This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring. First, a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers. Subsequently, the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals. Based on this, the recognition of the features of a single-fault voiceprint signal can be achieved. Finally, multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site. The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults, such as spring structures and loose internal components of circuit breakers. In addition, it provides a reference method for the real-time online monitoring of high-voltage circuit breakers.

Graphene-Enhanced CuW Composites for High-Voltage Circuit Breaker Electrical Contacts

To address the issue of over-standard short-circuit currents in a power system, it is imperative to enhance the comprehensive performance of the electrical contacts, which serve as the lynchpin of circuit breakers, so as to improve the breaking capacity of high-voltage circuit breakers. Graphene, as the most prominent two-dimensional carbon material in recent years, has garnered widespread applications across various fields. In this study, graphene-enhanced CuW composites for high-voltage circuit breaker electrical contacts were prepared innovatively using integrated vacuum infiltration technology. The innovative graphene-enhanced CuW composites significantly improved the mechanical, electrical, and ablation resistance properties, and have been successfully applied in the 252 kV/63 kA high-voltage SF6 circuit breakers, achieving 20 times effective consecutive full-capacity short-circuit current breaking. It provides a new route for the development and application of high-performance CuW electrical contacts. Looking ahead, it is planned to extend their application to higher voltage grade high-voltage circuit breakers.

High-temperature properties of dry air used as arc quenching medium and its mixture with vaporized C2F4 : reaction products and critical reduced electric field strength at 300–3000 K

Recently, electric power equipment manufacturers released a joint statement with the aim of developing high-voltage electrical equipment completely free of fluorinated gases such as fluoroketones and fluoronitriles, which are considered as alternatives to SF6 and artificially produced compounds of F. This paper considers air as a typical gas present naturally in the environment to investigate its high-temperature properties, including an air mixture with C2F4 vapor injected from a polytetrafluoroethylene nozzle, for application as an arc-quenching medium in a high-voltage circuit breaker equipped with a nozzle. Chemical composition results for the reaction products at 300–3000 K show that the admixture of C2F4 vapor causes a decrease in the molar number of O2 contained in the air, owing to the production of O-containing compounds such as CO2 and CO . Additionally, the results reveal that the admixture of C2F4 vapor lead to the production of CF4 as the predominant F-containing compound. Furthermore, the boundary condition for O2 production in terms of O2 molar number XO2 and C2F4 molar number XC2F4 is formulated. Such evaluation of the mixture composition enables the determination of a reduced effective collision ionization coefficient αˉ/N and a critical reduced electric field strength Ecr/N , resulting in αˉ/N = 0. The strength Ecr/N for the mixture with 30%–100% C2F4 molar fraction rises to approximately 150 Td (1 Td = 1 ×10−21 V m2) over 300–3000 K, which is 20–50 Td higher than that for high-temperature air unmixed with C2F4 vapor. The variation in Ecr/N is discussed in terms of predominant electron impact processes and the electron energy distribution function in the mixture.

A Co-Simulation Model for the Operating Mechanism of a High-Voltage Circuit Breaker

A Co-Simulation Model for the Operating Mechanism of a High-Voltage Circuit Breaker

Fluid modeling of CF3I/CO2 arc formation process

The environmentally friendly insulating gas CF3I is a promising replacement for SF6 due to its excellent insulation and arc extinguishing performance. In the arc chamber of high-voltage circuit breakers filled with CF3I/CO2, the near-electrode sheath dominates the current transfer process between contact and arc column during arc formation, which has a significant effect on the transition from glow to arc discharge. The 30%CF3I/70%CO2 was taken as the research object in this research, and a more perfect fluid model was established considering the influence of diffusion current. The arc formation process between the contacts at the preliminary stage of circuit breaker interruption was conducted. The results show that the arc formation process can be divided into three stages: glow discharge, abnormal glow discharge, and arc discharge. As the arc formation process proceeds, the thickness of the near-cathode sheath decreases gradually. When the arc reaches stability with the current density 8×106–1.6×107A·m−2, the thickness of the near-cathode sheath is 7 μm, the steep voltage fall near the cathode sheath is generated within 7 μm away from the cathode surface, and this voltage fall almost does not change with the current density. CF3I− is the dominant negative ion between the contacts, which indicates that CF3I has excellent electron adsorption performance and is beneficial to arc deionization. The computational results of this work were compared with the existing experimental ones, which shows that the model used in this work is accurate enough. Finally, suggestions for model optimization and future work are given.

High Voltage Circuit Breakers:SF6vs. Vacuum

As important as finding new renewable and environmentally friendly sources of energy is the fact of transmitting and distributing this energy in the safest and most efficient way. In this field, High Voltage Circuit Breakers (HV CB), designed for operation of the electric network under normal conditions as well as for the interruption of faulted conditions, have played an important role in power systems over 100 years. However, research, development and improvement of the design of CBs have not stopped in our nowadays expanding grid. The aim of this paper is to make a review of the existing different types of HV CB, comparing the most used one, SF6 CB, with the Vacuum CB, which has been developed and recently applied for HV, and could become the future environmental friendly HV CB.

A novel mechanical fault diagnosis for high-voltage circuit breakers with zero-shot learning

In recent years, data-driven methods have been widely used in the field of high-voltage circuit breakers (HVCBs) fault diagnosis. However, due to the complex mechanical structure of HVCBs and the special operating environment, it is difficult to obtain a large amount of fault samples and exhaust all fault types. The lack of fault samples and fault types typically results in significant degradation of diagnostic performance. To address this problem, we design a novel method named R-MLL for zero-shot HVCB fault diagnosis. R-MLL tries to identify unseen fault types only by training seen fault types. First, to focus on all the details of the HVCB mechanical vibration signal, the wavelet transform is used to multi-scale refine the fault data. Second, a new network (RDSCNN) is designed to extract multidimensional features based on convolutional neural network incorporating residual block and depthwise separable convolution. Third, a multi-label attribute learning network is designed, enabling the fusion of fault features and attributes and allowing attribute labels to assist fault diagnosis tasks. Extensive experiments show that R-MLL gets average accuracy of 86.2% for compound fault diagnosis without the need of using target fault samples for building the diagnostic model. Comparisons with a number of state-of-the-art techniques show the superiority of the proposed method for zero-shot HVCB diagnosis.

Hybrid Model of Multiple Echo State Network Integrated by Evidence Fusion for Fault Diagnosis of a High-Voltage Circuit Breaker

Hybrid Model of Multiple Echo State Network Integrated by Evidence Fusion for Fault Diagnosis of a High-Voltage Circuit Breaker

Study on the influence of spring helix angle increase on stress distribution and fatigue life of the operating mechanism

The spring operating mechanism is the key component of the high-voltage circuit breaker, and its split-closing power comes from the self-energy storage of the split-closing spring, so the performance mechanism and fatigue state of the split-closing spring has a great impact on the operation process and dynamic response of the operating mechanism. In this paper, by increasing the coil angle of the spring, changing the spring design material, classifying and modeling the spring, and applying finite element analysis aim to analyze the static structure. The influence of the change of different spring coil angles and materials on the stress distribution of the spring is explored, and the finite element calculation results are imported into the life analysis module to evaluate the life of springs with different helix angles and materials. The results show that under the action of 9 kN working load, after the helix angle is increased by 0.6°, the spring life decreases by 7.23%, the spring life continues to increase by 0.7°, and the overall life of the spring decreases by 12.3%, compared with the initial state. The life decline curve shows an inverse proportional trend.

Research on Circuit Breaker Operating Mechanism Fault Diagnosis Method Combining Global-Local Feature Extraction and KELM.

In response to the lack of generality in feature extraction using modal decomposition methods and the susceptibility of diagnostic performance to parameter selection in traditional mechanical fault diagnosis of high-voltage circuit breaker operating mechanisms, this paper proposes a Global-Local feature extraction method based on Generalized S-Transform (S-Translate) combined with Gray Level Co-Occurrence Matrix (GLCM) and complemented by Maximum Relevance and Minimum Redundancy (mRMR) feature selection. The GL (Global-Local)-mRMR-KELM fault diagnosis model is proposed, which employs the Kernel Extreme Learning Machine (KELM). In this model, the original time-frequency domain features and the time-frequency features of the Generalized S-Transform matrix of vibration signals under different states of the circuit breaker are first extracted as global features. Then, the GLCM is obtained to extract texture features as local features. Finally, the mRMR and KELM are comprehensively applied to perform feature selection and classification on the dataset, thereby accomplishing the fault diagnosis of the circuit breaker's operating mechanism. In this study, the 72.5 kV SF6 circuit breaker operating mechanism is taken as the research object, and three types of mechanical faults are simulated to obtain a vibration signal. Experimental results verify the effectiveness of the proposed GL-mRMR-KELM model, achieving a diagnostic accuracy of 96%. This research provides a feasible approach for the fault diagnosis of circuit breaker operating mechanisms.

The energy dissipation topology and characteristics of GaInSn liquid metal and ZnO resistors based on high-voltage DC circuit breakers

High voltage direct current (HVDC) circuit breakers play a very crucial role in HVDC transmission systems. Energy dissipation circuit branches are the main part of the HVDC breaker. The volume and size of energy dissipation branches are the bottlenecks of the HVDC breaker, and the existing energy dissipation schemes can not meet the lightweight requirements. In this work, a combined energy dissipation scheme based on gallium indium tin (GaInSn) liquid metal and ZnO resistors is proposed for the energy dissipation branch of a circuit breaker. Firstly, the self-shrinking of GaInSn liquid metal mechanism is analyzed and the energy dissipation characteristics at different stages are investigated. The variation patterns of short circuit current, arc voltage, resistance and other parameters in the process of liquid metal arcing and energy dissipation are obtained. Secondly, the circuit model of liquid metal in arcing and energy dissipation process is established. The equivalent nonlinear time-varying conductance of the arcing process of the liquid metal dissipative element is simulated by combining the volt-ampere characteristic curves of the liquid metal dissipative module. Finally, the topology of energy dissipation circuit branches combined by GaInSn liquid metal and ZnO resistors is proposed. At the same time, the HVDC breaker model combined by a hybrid DC circuit breaker and energy dissipation circuit branches is constructed. The energy dissipation ratio and distribution law of GaInSn liquid metal and ZnO resistors in the composite type of circuit breaker are verified. The composite dissipation improves the overall energy dissipation density of the circuit breaker compared to the conventional ZnO resistors energy dissipation.

Application of DE-ELM Algorithm in SF6 Circuit Breaker Contact Ablation Evaluation

When assessing the ablation status of circuit breaker contacts, the average dynamic contact resistance of the arc contacts of high-voltage SF6 circuit breakers is crucial. The average dynamic contact resistance of arc contacts on circuit breakers under various current levels may be accurately predicted using a method based on a differential evolution algorithm and extreme learning machine (DE-ELM), which is suggested in this research. Combining optimization algorithms yields the ideal input weight and hidden layer bias of the ELM method. The DE-ELM approach has outstanding anticipation performance for anticipation data under various current levels when compared to other anticipation methods. Finally, an expert system for assessing the ablation state of contacts based on the DE-ELM algorithm is created using the average dynamic contact resistance data of arc contacts predicted by DE-ELM. As a guide for the upkeep of high-voltage SF6 circuit breakers, the ablation status of contacts is categorized into four grades: A-level ablation, B-level ablation, C-level ablation, and D-level ablation.

Research on mechanical fault diagnosis method of high voltage circuit breaker based on new measurement

Aiming at the problem that the detection of a single signal is difficult to accurately and comprehensively reflect the mechanical failure of the circuit breaker operating mechanism, the VS1 (VBM7) -12 high-voltage circuit breaker is selected as the experimental object in this paper. The coil current signal is collected by a sensor based on new measurements, and the fault diagnosis method of high-voltage circuit breaker is studied from the acquisition, preprocessing, and feature extraction of vibration signal and sound signal, combined with artificial intelligence algorithm. The research shows that the application of electro-acoustic joint features can effectively enhance the precision of fault diagnosis of high-voltage circuit breakers, and verify the feasibility and superiority of the mechanical fault diagnosis method of high-voltage circuit breakers based on new measurements.

Recent Technical Trend on the High Voltage Circuit Breakers Required in DC and AC Systems

Recent Technical Trend on the High Voltage Circuit Breakers Required in DC and AC Systems

Investigation on the fault monitoring of high-voltage circuit breaker using improved deep learning.

Mechanical faults are the main causes of abnormal opening, refusal operation, or malfunction of high-voltage circuit breakers. Accurately assessing the operational condition of high-voltage circuit breakers and delivering fault evaluations is essential for the power grid's safety and reliability. This article develops a circuit breaker fault monitoring device, which diagnoses the mechanical faults of the circuit breaker by monitoring the vibration information data. At the same time, the article adopts an improved deep learning method to train vibration information of high-voltage circuit breakers, and based on this, a systematic research method is employed to identify circuit breaker faults. Firstly, vibration information data of high-voltage circuit breakers is obtained through monitoring devices, this vibration data is then trained using deep learning methods to extract features corresponding to various fault types. Secondly, using the extracted features, circuit breaker faults are classified and recognized with a systematic analysis of the progression traits across various fault categories. Finally, the circuit breaker's fault type is ascertained by comparing the test set's characteristics with those of the training set, using the vibration data. The experimental results show that for the same type of circuit breaker, the accuracy of this method is over 95%, providing a more efficient, intuitive, and practical method for online diagnosis and fault warning of high-voltage circuit breakers.

Two-dimensional Axisymmetric Modeling of the Magnetic Field for High-voltage Gas Circuit Breakers

High fidelity numerical analyses of high-voltage gas circuit breakers have been conducted at Hitachi Energy Research with an in-house CFD-based arc simulation tool. The tool extends the capability of a commercial flow solver (ANSYS Fluent) to represent physical phenomena at play in a high voltage circuit breaker during a breaking operation, such as magnetostatics, polymeric and metal evaporation, and arc-network interaction. This work describes the implementation of the Biot-Savart law for computing the magnetic field generated by an electric arc under the magnetostatic approximation and in two--dimensional axisymmetric conditions. The implementation is compared to the reference one based on the magnetic vector potential formulation of the Amp`ere’s law in the Coulomb gauge. The limitations of the two formulations are discussed and their numerical accuracy compared.