8453results about "Circuit interrupters testing" patented technology

The invention relates to the technical field of predictive maintenance, in particular to a predictive maintenance method and system for relay protection equipment, and the method comprises the following steps: calculating the change trend of each operation parameter in each time window based on the operation parameters of the relay equipment, and obtaining the change rate of the operation state of the relay equipment; according to the method, the running trend under different time scales is refined, the running state change rate is accurately extracted to quantify the health state of the relay equipment, and the health score attenuation rate is calculated in combination with the equipment aging degree, the contact abrasion degree and the relay action times, so that the health degree decline rate is measured, and accurate judgment of equipment maintenance requirements can be realized; and the maintenance task priority is calculated in combination with the importance weight of the equipment, so that the maintenance resource configuration is more targeted, the load fluctuation condition of the equipment is quantified, and the maintenance period is dynamically adjusted according to the set threshold value to match the load change trend of the equipment, thereby avoiding the problem of excessive maintenance or insufficient maintenance.

The invention discloses an intelligent detection device and detection method for a pole-mounted circuit breaker, and relates to the technical field of intelligent detection, and the device comprises the steps: traversing the pole-mounted circuit breaker to carry out the layout of a plurality of sensors, constructing a data sensing network, and obtaining a plurality of data signals; performing fault diagnosis on the pole-mounted circuit breaker according to the plurality of data signals to generate a fault diagnosis result; performing health assessment to obtain a health score of the pole-mounted circuit breaker; performing fault prediction on the pole-mounted circuit breaker to obtain a multi-modal prediction data set; and performing coverage detection optimization on the operation parameter set, and making a maintenance suggestion. According to the invention, the technical problems of single detection means, low fault diagnosis accuracy and lack of comprehensive evaluation and prospective fault prediction on the health state of equipment in the aspect of pole-mounted circuit breaker detection in the prior art are solved, comprehensive and accurate detection and maintenance of the pole-mounted circuit breaker are realized, and the detection efficiency is improved. And the operation reliability, safety and stability of the pole-mounted circuit breaker are effectively improved.

The invention discloses a multi-agent-based gas insulated switchgear fault diagnosis method and system, and relates to the technical field of intelligent operation and maintenance of power equipment, and the method comprises the steps: obtaining signal data of target equipment, carrying out the feature extraction of the signal data, and constructing a multi-modal feature matrix; time delay features of acoustic and electromagnetic signals are extracted from the multi-modal feature matrix, a GIS propagation model is established, and the space coordinate position of a liberated power source is solved through a wave field inversion algorithm; combining the space coordinate position and the multi-modal feature matrix into a complete fusion feature vector, inputting the fusion feature vector into a dynamic Bayesian model, and outputting a fault type label and a corresponding confidence coefficient; migrating the dynamic Bayesian model based on a migration learning mechanism, and dynamically updating a classification threshold value; inputting the diagnosis history sequence into a time sequence prediction model, and predicting a future operation state; through multi-modal fusion and intelligent reasoning, GIS fault accurate positioning and prediction are realized, and the problems of low precision and poor adaptability of traditional diagnosis are solved.

The invention relates to the technical field of edge calculation application and partial discharge detection, in particular to a partial discharge multichannel signal real-time synchronous acquisition method based on edge calculation. According to the method, the functions of multi-channel signal acquisition, time synchronization, feature analysis, hierarchical storage and the like are integrated at edge nodes, asynchronous acquisition, unified timestamp marking and data synchronous fusion of multiple types of partial discharge signals are realized, and extraction of multi-dimensional feature parameters such as time domain, frequency domain and energy and intelligent event discrimination are locally completed. For abnormal signals, the system realizes classified storage and remote uploading after encryption and compression processing; and for normal signals, dynamic management is carried out through circular caching. According to the method, the accuracy and efficiency of multichannel signal synchronous acquisition are remarkably improved, the data safety and the system adaptive capacity are enhanced, and the method is suitable for real-time online monitoring and intelligent diagnosis of partial discharge of power equipment.

The invention relates to the technical field of circuit breakers, in particular to a circuit breaker fault diagnosis method and system for switching an alternating current filter in an extra-high voltage converter station, and the method comprises the following steps: S1, synchronously collecting multiple parameters: synchronously collecting vibration acceleration signals, opening and closing coil current waveforms, arc light intensity and environment temperature and humidity parameters; s2, time sequence feature extraction: performing time-frequency domain decomposition on the vibration acceleration signal to obtain a mechanical action time sequence spectrum; s3, operation mode classification: determining the operation mode of the circuit breaker; s4, three-dimensional fault matrix construction: generating a three-dimensional fault matrix including spatial distribution characteristics; s5, dynamic threshold analysis: generating a diagnosis threshold interval dynamically adjusted along with the environmental parameters; and S6, comprehensive diagnosis output: outputting a mechanical jam grade, a contact ablation degree and an insulation deterioration early warning signal. According to the invention, the mechanical, electromagnetic and insulation characteristics of the circuit breaker can be reflected more comprehensively, and reliable guarantee is provided for safe and stable operation of a high-voltage power grid.

The invention relates to the technical field of electrical variable measurement, in particular to a circuit breaker maloperation detection method for a power transformation and distribution system, and the method comprises the steps: constructing a current data sequence, a voltage data sequence, a temperature data sequence and a vibration data sequence of a to-be-detected circuit breaker; based on the current data sequence, the voltage data sequence and the temperature data sequence, constructing a current data sequence abnormal fluctuation coefficient used for representing the possibility that the current of the circuit breaker to be detected is in normal fluctuation; based on the vibration data sequence, constructing a circuit breaker abnormal coefficient used for representing the health condition of the operation state of the to-be-detected circuit breaker; and based on the abnormal fluctuation coefficient of the current data sequence and the abnormal coefficient of the circuit breaker, realizing circuit breaker maloperation detection. According to the scheme, the influence of external and internal factors of the circuit breaker is considered at the same time, and the accuracy of circuit breaker maloperation behavior detection is improved by analyzing and processing the multi-source data.

The invention relates to the technical field of power equipment fault diagnosis, in particular to a pump station circuit breaker fault detection method and system, and the method comprises the following steps: S1, collecting a vibration signal lower than 50 Hz in the operation process of a circuit breaker; s2, preprocessing the vibration data acquired in the S1; s3, analyzing the vibration data preprocessed in the S2, and extracting characteristic parameters related to low-frequency vibration of the circuit breaker; forming a feature vector by the extracted feature parameters; s4, training the feature mode training set to generate a circuit breaker fault mode recognition model; and S5, converting the current vibration data into a real-time feature vector, inputting the real-time feature vector into the fault mode recognition model generated in the step S4, judging whether the circuit breaker is abnormal or not, and generating a fault diagnosis report. According to the method, high-precision real-time detection and reliable diagnosis of the circuit breaker fault are realized through precise extraction of the low-frequency vibration signal and feature vector construction in combination with the optimized support vector machine fault mode recognition model.

The invention provides a test method and device for a motor active short-circuit relay, and a storage medium. The method includes the steps: performing the zero speed detection of a motor after a motor controller completes a low-voltage power-on operation, wherein when the motor controller is powered down, a first relay and a second relay are both switched on; controlling the second relay to be turned on if the motor passes the zero speed detection, and injecting a first excitation signal at the control ends of a first phase winding and a third phase winding through the motor controller; performing injection detection of the first excitation signal to determine whether the first excitation signal is successfully injected or not; controlling the first relay to be turned on if the first excitation signal is successfully injected, and obtaining a voltage across two ends of the first relay; and judging whether the power-on test of the first relay is successful or not according to the voltage across two ends of the first relay. The method can realize the turn-on test of the short-circuit relay, and ensures the reliability of the active short-circuit function of the vehicle motor duringdriving.

The invention relates to a reverse polarity protected electronic circuit with a negative and a positive supply voltage line and with an electrical application circuit. The electrical application circuit has a positive and a negative power supply terminal. The reverse polarity protected electronic circuit comprises a reverse polarity protection circuit (1). The reverse polarity protection circuit (1) has a first and a second N-channel enhancement transistor as part of a reverse polarity protection transistor group (17). The first and second N-channel enhancement transistors are electrically connected in anti-series via a center terminal (86) of the reverse polarity protection circuit (1). The reverse polarity protection circuit (1) includes a control device (4) which is connected to the positive and negative supply voltage lines.Only when connected with the correct polarity does the control device (4) switch the first and second N-channel enhancement transistors of the reverse polarity protection transistor group (17) on. The control device (4) includes its own additional control reverse polarity protection circuit (81) which prevents damage to the control device (4) if connected with incorrect polarity, so that the control device (4) does not switch on the first and second N-channel enhancement transistors if connected with incorrect polarity.

The invention discloses a GIS disconnecting switch multi-state intelligent sensing system, and relates to the field of GIS disconnecting switch state monitoring. A self-calibration multi-mode sensor is deployed for multi-source data acquisition, and novel sensors including terahertz imaging and the like are included; in data preprocessing, deep learning noise reduction is applied, fuzzy entropy is used for dynamic weighted fusion, and abnormal values are processed by an improved algorithm; feature extraction is combined with a plurality of frontier algorithms to process vibration signals, and CRNN is used to analyze acoustic signals; the QPSO evidence theory is adopted for state fusion perception, the node relation is learned by means of GNN, and the weight is adjusted according to the information gain rate; state assessment and early warning are based on transfer learning, GAN and LSTM-attention mechanisms, and early warning priorities are ranked by FAHP. According to the invention, multi-mode accurate acquisition, intelligent data processing, deep feature mining, innovative fusion perception, accurate evaluation and early warning and efficient fault diagnosis and positioning are realized, the state of the GIS isolation switch can be comprehensively and accurately perceived, the system is self-learned and optimized, the equipment safety is guaranteed, the risk of a power system is reduced, and the power operation and maintenance benefits are improved.

The invention relates to the technical field of earth leakage protection monitoring, and discloses an earth leakage circuit breaker remote monitoring system integrated with the Internet of Things. An electric leakage characteristic modeling module of the system obtains real-time monitoring data, extracts current waveform characteristic parameters, identifies an abnormal waveform mode and a first occurrence time sequence, and generates an original electric leakage characteristic template; the network path generation module performs topological sorting on the monitoring nodes based on the template, establishes an equipment connection path and constructs an Internet of Things topological path; a path anomaly analysis module extracts a node sequence and screens abnormal transmission paths to obtain an abnormal path set; the risk level judgment module collects terminal node risk tags and obtains a risk level tag group through matching; and the topological structure output module counts associated nodes, divides abnormal paths, establishes a mapping relation and generates a remote monitoring topological table. The system improves the accuracy and efficiency of electric leakage monitoring, and is suitable for a complex power network environment.

A solid-state circuit breaker comprises an air gap, at least one counter and / or a timer, a memory, a radio and a microprocessor to: retrieve saved health data, alert data and counter, timer data from the memory, determine a status of a heath rating by data and comparing the heath rating to a threshold, if it is poor, send a message via a smart phone application (APP) and start a counter or timer, determine if the counter is at a predefined value and / or the timer is expired, if the counter is at a predefined value and / or the timer is expired, turn OFF the breaker and inhibit from being turned on, send a breaker OFF / disabled alert message using the radio to an end user / a customer via the APP, and if the counter is not at a predefined value and / or the timer is not expired, increment / decrement the counter and / or the timer.

The invention discloses a primary and secondary fusion pole-mounted intelligent circuit breaker. The primary and secondary fusion pole-mounted intelligent circuit breaker comprises a fault detection module, a protection action module, a reclosing module and an information reporting module, the fault detection module collects multi-dimensional electrical quantity through a current transformer and a voltage transformer, and judges a fault and identifies the type in combination with a dynamic normal range and a duration threshold. The protection action module calculates the fault distance based on the correction impedance, judges whether to act or not by combining the current flow direction and the protection range, and selects a protection level according to the fault severity; the reclosing module dynamically sets an observation time window, distinguishes instantaneous or permanent faults through a retest verification mechanism, and adaptively adjusts a reclosing strategy; and the information reporting module uploads fault related information. According to the method, the problems of high misjudgment rate, poor protection selectivity, rigid reclosing logic and the like of a traditional circuit breaker are solved, and the accuracy, quickness and self-healing capability of power distribution network fault processing are remarkably improved.

The invention relates to a multi-sensor fault feature fusion diagnosis method for electrical equipment, which comprises the following steps: synchronously acquiring operation data of the electrical equipment through a vibration sensor, a temperature sensor, a current sensor and an ultrasonic sensor, dynamically adjusting the sampling frequency according to the physical characteristics of each sensor, and the sampling rate of the temperature signal is not lower than 1Hz. Through a multi-source sensor data synchronous acquisition and time sequence alignment technology and a signal alignment method combining a dynamic time warping (DTW) algorithm and Hilbert-Huang transformation, the problem of time asynchronization of heterogeneous sensor data such as vibration and temperature is solved, so that the time alignment precision of multi-source data is improved, the feature extraction accuracy is improved, and the accuracy of feature extraction is improved. Through hierarchical feature extraction and graph convolutional network fusion, a feature incidence matrix based on mutual information is constructed, deep correlation between vibration signal TKEO features and cross-modal features such as current harmonics is mined by using GCN, the feature dimension is reduced, and the fault feature separability index is improved.

The invention discloses an on-load tap-changer fault diagnosis method based on lightweight YOLO11, and is used for solving the problem of low mechanical fault diagnosis efficiency of a traditional on-load tap-changer. Comprising the following steps of: converting vibration signals in a motor starting stage and a change-over switch action stage into high-resolution time-frequency images through continuous wavelet transform (CWT) so as to comprehensively reflect time and frequency characteristics of the signals; starNet is adopted to replace a YOLO model backbone network, and the feature extraction capability is enhanced while the network compactness is kept through star operation; and designing an efficient joint convolution detection head EJCD, and realizing model lightweight and multi-scale target detection through grouping normalization and convolution parameter sharing. According to the invention, through combination of the time-frequency image representation capability of continuous wavelet transform and an efficient target detection framework of deep learning, the limitation of a traditional method in non-stationary signal processing is overcome, and the diagnosis efficiency and the model performance are significantly improved.

The invention relates to the technical field of active testing of circuit breakers, discloses an active testing method and system for a circuit breaker, and is used for solving the problem that an existing active testing method is difficult to realize self-adaptive adjustment of a threshold value. Time-frequency analysis is carried out on the contact speed signal to extract time domain and frequency domain features, and heat flow analysis of the thermopile array is combined to obtain a heat engine coupling feature vector; a thermo-electro-mechanical coupling dynamic graph with the sensors and the components as nodes and the reciprocal of thermal conductance as the edge weight is constructed, the structure is updated in real time, and node drift is predicted through a graph convolutional network; fusing the environment derating and the predicted drift amount to adaptively adjust a threshold value; extracting a thermoelectric coupling component of a contact speed fast change component and a heat flow refraction angle change rate, and generating a reversed polarity pulse compensation signal to suppress interference; time synchronization, closed-loop feedback of data and threshold adjustment results are realized through secondary alignment of IEEE1588 and the cloud, graph model node weight and threshold rules are dynamically updated, synchronization with equipment health degree is ensured, and accurate early warning and protection are realized.

The invention discloses a partial discharge positioning method, system and device based on an acoustic-electric combined signal, and relates to the technical field of electrical device detection, and the method comprises the steps: deploying a plurality of sensor nodes based on a target electrical device, and collecting an electromagnetic signal and a sound wave signal generated by partial discharge at the same time; calculating a preliminary positioning result of the partial discharge source based on the collected acoustic-electric combined signal; environment parameter data, historical operation data and electrical equipment structure information in the electrical equipment are collected and input into a preset error correction model, and a corresponding error correction interval is obtained; when a certain preliminary positioning result exceeds the corresponding error correction interval, obtaining a timestamp corresponding to the preliminary positioning result; performing multi-source information fusion positioning based on the environmental parameter data, the historical operation data, the electrical equipment structure information and the corresponding timestamps, and analyzing and determining final positioning coordinates of the partial discharge source; the accuracy of partial discharge signal positioning is improved, and the positioning efficiency is improved.

The method is mainly applied to the technical field of power system fault diagnosis. The invention discloses an intelligent fault diagnosis method, device and equipment for a circuit breaker and a medium, and the method comprises the steps: updating a preset fault recognition model according to a historical data set, so as to enhance the incidence relation between data features and fault features; acquiring real-time operation data of the target circuit breaker, and performing feature extraction on the real-time operation data to obtain a plurality of data features; key data features are screened out from the multiple data features, and fault features corresponding to the key data features are determined based on the association relationship between the enhanced data features and the fault features; outputting a recognition result corresponding to the fault feature through the updated fault recognition model, wherein the recognition result comprises each fault type and a confidence coefficient corresponding to each fault type; and when the confidence coefficient of any fault type is greater than a preset threshold value corresponding to the fault type, sending out an alarm notification. According to the invention, the precision and efficiency of circuit breaker fault detection can be improved.

An internal heating early warning method and system for a GIS device isolation switch. The heating early warning method comprises: acquiring an internal gas temperature, an original absolute pressure, and an ambient temperature of at least one GIS device isolation switch, and a three-phase load current of the isolation switch (S11); on the basis of the original absolute pressure, the ambient temperature, the gas temperature, and the three-phase load current, calculating a plurality of characteristic quantities of the pressure, wherein the plurality of characteristic quantities comprise a month-on-month growth quantity, a month-on-month growth rate, a year-on-year growth quantity, a year-on-year growth rate, an interphase difference quantity, an interphase difference rate, a growth quantity compared with an adjacent gas chamber, and a growth rate compared with the adjacent gas chamber (S21); making a severe heating anomaly determination, comprising: if an anomaly condition is satisfied, sending an early warning signal for a severe heating defect (S31); determining the correlation between the original absolute pressure and the ambient temperature, if the original absolute pressure is correlated to the ambient temperature, calculating the ambient compensation pressure after the ambient temperature is compensated, and using the ambient compensation pressure to recalculate the plurality of characteristic quantities of the pressure, and if the original absolute pressure is not correlated to the ambient temperature, determining the correlation between the pressure and the three-phase load current, and if the pressure is not correlated to the three-phase load current, making a general heating anomaly determination (S41); and making a general heating anomaly determination, comprising: if an anomaly condition is satisfied, sending an early warning signal for a general heating defect (S51).

The invention discloses a sensor anomaly detection method based on a conditional diffusion probability model and a related system, the system comprises a data acquisition processing unit, a detection model unit and an anomaly detection unit, anomaly is detected by using the conditional probability diffusion model, noise is predicted by adding and removing noise and using a U-Net model, and data is reconstructed. Completing the training of a detection model, and obtaining a trained detection model; and taking the distance between the to-be-detected data and the noise reconstruction data obtained by the trained detection model corresponding to the to-be-detected data as an abnormal score, and if the abnormal score exceeds a threshold value, determining that the data is abnormal. According to the invention, the monitoring accuracy is improved, the false alarm is reduced, the anti-noise capability is enhanced, the noise is effectively identified, and the pertinence is high.

The invention discloses a GIS partial discharge on-line monitoring method, and relates to the technical field of power equipment monitoring and fault diagnosis, and the method comprises the following steps: a detection system carries out the real-time collection of an electrical signal caused by a partial discharge phenomenon in the operation process of GIS equipment through a high-frequency sensor disposed on the GIS equipment, therefore, a data basis for subsequent distortion analysis and positioning is obtained. Partial discharge signals are collected in real time through the high-frequency sensor, signal distortion is evaluated in combination with a machine learning model, the time domain and the frequency domain of the signals are accurately corrected by adopting a dynamic time warping algorithm and a frequency compensation means, and the real characteristics of the signals are recovered. According to the method, the signal positioning precision is improved, the GIS equipment fault detection capability is enhanced, potential risks are warned in advance, equipment faults and safety accidents are reduced, and the safety and stability of a power system are remarkably improved.

The invention discloses a new energy automobile fuse life expectancy prediction system based on current sensing, and belongs to the technical field of power transmission and distribution monitoring. In order to solve the problems that life early warning of a dynamic adjustment mechanism is lacked and variable factors in a complex working environment cannot be handled, the system can recognize different load modes by accurately collecting current, temperature and vibration data related to a fuse and combining multi-dimensional factors such as temperature and vibration, and the service life of the fuse is accurately adjusted. The performance of the fuse under various working conditions is comprehensively evaluated, thermal load and loss conditions of the fuse can be estimated by combining a loss model and temperature influence calculation, the accuracy of life prediction is improved, an early warning threshold value is adjusted in real time through a dynamic threshold value adjusting mechanism, and the service life of the fuse can be predicted by dynamically adjusting residual life threshold values at different stages. According to the utility model, the health state of the fuse can be accurately grasped, the maintenance and replacement time can be reasonably adjusted, unnecessary shutdown or delay can be avoided, the fault risk can be effectively reduced, and the safety and reliability of a new energy automobile system can be enhanced.

The invention discloses a vacuum circuit breaker state online monitoring method and system based on digital twinning, and relates to the field of intelligent operation and maintenance of high-voltage power equipment. According to the method, various sensors are arranged at key parts of the vacuum circuit breaker, breaking current, shielding case potential and vibration signals are collected in real time, and a digital twinborn model is constructed by combining multi-source data preprocessing, three-dimensional modeling, behavior rule definition and fault simulation analysis; and on-line identification and trend prediction of electrical wear, vacuum degree deterioration and mechanical abnormity are realized. The system has the functions of real-time monitoring, three-dimensional visualization, model self-learning and intelligent early warning, can be widely applied to intelligent monitoring and state evaluation of circuit breaker equipment in a power system, and remarkably improves the operation safety and operation and maintenance efficiency of the equipment.

The invention discloses a relay state prediction and fault early warning method and system based on deep learning, and the method comprises the steps: S1, building a constraint condition of a generative adversarial network based on a relay physical model, and forming an enhanced fault waveform signal according with a physical rule through adversarial training; s2, receiving a real-time current and voltage signal and a mechanical vibration signal, and extracting an electric signal feature vector by using a time sequence convolutional network; s3, inputting the combined feature tensor into the lightweight assessment model, and outputting a health degree scoring signal; s4, responding to the meta-learning activation instruction, loading historical data of equipment to construct a parameter optimization set, performing online fine tuning on the early warning model based on a meta-learning framework, and generating a fault determination parameter; and S5, analyzing real-time signal characteristics according to the fine-tuned judgment parameters, and outputting graded early warning signals to a monitoring terminal. According to the method, the problems of early state prediction and accurate early warning of the relay under small sample fault data can be solved.

The embodiment of the invention provides a multi-configuration relay-oriented aircraft electrical box parameter measurement system and method, and belongs to the field of aircraft electrical box test and diagnosis. According to the hardware level, flexible access of a multi-configuration relay electrical box is achieved based on a universal adapter interface, core parameter measurement is completed in combination with an integrated measurement and control circuit, and synchronous acquisition and processing are conducted on multiple paths of signals through a signal acquisition and processing unit; according to the software level, the pull-in / release time, the rebound time and the voltage parameters are accurately calculated through a parameter calculation module on the basis of waveform feature point recognition and dynamic excitation-response theories, and the functions of fault diagnosis, safety protection and data storage are integrated. According to the method, the crossing from on-off judgment to parameterized precise evaluation is realized, the detection efficiency, the measurement precision and the equipment reliability evaluation capability are remarkably improved, and an integrated solution is provided for external field maintenance and state prediction of the aircraft relay.

The invention relates to the technical field of electrical performance testing, and particularly discloses a method and system for evaluating the electric shock life of an arc extinguish chamber of a low-voltage circuit breaker, and the method comprises the steps: S1, collecting real-time arc extinguish performance index parameters in the arc extinguish chamber of different low-voltage strength grades in a preset time period and the cumulative number of closing operations of the circuit breaker of different low-voltage strength grades; s2, simulating an arcing process change image of each closing operation of the circuit breaker based on real-time arc extinguishing performance index parameters in the arc extinguishing chamber with different low-voltage strength grades, establishing an arc extinguishing three-dimensional model, and performing feature extraction analysis on the arcing process change image to obtain a standard arc state coefficient; s3, inputting the real-time arc extinguishing performance index parameter and the standard arc state coefficient into a preset analysis model for prediction analysis, and obtaining an arc extinguishing prediction value; and S4, inputting the predicted arc extinguishing value, the cumulative number of closing operations and a historical evaluation factor into the predicted analysis model for training to construct an arc extinguish chamber electric shock life evaluation model.

The invention discloses an intelligent response method for an arc fault of a high-altitude switch cabinet, relates to the technical field of safety protection of power equipment in a high-altitude area, and aims to solve the problems that the arc time of the arc fault is prolonged, the pressure in the cabinet is sharply increased, a traditional disposable pressure relief device cannot be reset and is lack of insulation state evaluation, and the reliability is poor. The invention aims to provide a response method which combines optical signal and pressure double-criterion detection, resettable pressure relief, gas component analysis and intelligent decision so as to improve the safety and reliability of the switch cabinet.

The invention relates to the technical field of membrane switch detection, in particular to membrane switch performance detection equipment which comprises a limiting placement table, an elastic pushing and pressing device, a butt joint sealing tool, a continuous touch device, a simulation water conveying device and a simulation heating device. The elastic pushing and pressing device is installed at the top of the limiting placement table, the continuous touch device is installed at the movable end of the elastic pushing and pressing device and used for continuously triggering control keys of the membrane switch, and the butt-joint sealing tool is installed at the bottom of the continuous touch device and used for being attached to the surface of the membrane switch in a sealed mode. The simulation water conveying device is installed on the elastic pushing and pressing device, the output end of the simulation water conveying device is connected with the butt joint sealing tool, the simulation heating device is installed in the continuous contact device, and the reliability and stability of product production are effectively ensured.

The invention discloses a high-voltage circuit breaker arcing monitoring system and method, and relates to the technical field of power equipment operation state detection, and the method comprises the following steps: S1, synchronously collecting arcing spectral signals and electromagnetic interference vector data under a unified time reference, and carrying out primary differential processing to generate stable base plane signals; s2, inputting the stable base plane signal into the interference identification model, generating a multi-dimensional spectrogram through time-frequency mapping, extracting an interference time sequence boundary, and outputting an interference weight matrix; the arcing monitoring method based on spectrum and electromagnetic signal synchronous acquisition is constructed, interference identification, signal purification, energy analysis and control instruction generation are fused, parameter self-optimization is realized through closed-loop feedback, the arcing identification accuracy and the arc quenching response efficiency are effectively improved, and the arcing monitoring method is suitable for large-scale popularization and application. And the operation safety and the intelligent level of the circuit breaker in a strong interference environment are enhanced.

The invention discloses a method and system for measuring earth differential leakage current of a GIS shell, and relates to the technical field of leakage current detection, and the method comprises the steps: arranging a point position optimization scheme based on a sensor, arranging a current sensor, and synchronously obtaining a current signal collected by the current sensor; filtering the collected current signal by using a self-adaptive differential filter to obtain a real leakage current signal result; and performing real-time detection and analysis on signals of different frequency bands of a real leakage current signal result by using a multi-stage frequency domain filter. Based on a method of combining adaptive differential filtering, multi-sensor fusion, multi-frequency-domain analysis and intelligent threshold detection, the leakage current signal can still be accurately identified and extracted in a complex noise environment through the test of an analog signal, and the reliability and effectiveness of GIS equipment monitoring are guaranteed.