5294 results about "Switchgear" patented technology

The invention discloses an intelligent power distribution layered self-healing fault isolation and power supply recovery method, which is suitable for realizing rapid fault isolation and power supply recovery of a non-fault area when a power distribution system has a fault. According to the method, a hierarchical control framework is adopted, and intelligent switch equipment on the lower layer is responsible for collecting the current and voltage states of a line in real time and immediately completing local identification and fault section removal after a preset fault signal is detected; the fault information is then uploaded to a power distribution automation master station on the upper layer, the master station analyzes a recoverable power supply area in combination with the current network topology and generates a network reconstruction scheme, and a non-fault area is switched to a standby power supply path by controlling an interconnection switch; the system supports the backtracking analysis of historical data such as waveform distortion and partial discharge signals before a fault, identifies a potential abnormal trend, optimizes an operation strategy, and achieves a self-adaptive early warning function. The method has the advantages of being rapid in response, clear in structure and high in autonomy, and the reliability and continuity of the power distribution system can be remarkably improved.

The invention discloses an intelligent operation and maintenance system and method for a high-voltage switch cabinet based on digital twinning, relates to the technical field of intelligent power grids, and solves the problems of nonlinear effect modeling distortion, cross-spatio-temporal scale coupling deviation accumulation, time sequence real-time contradiction and insufficient extreme working condition adaptation in the prior art. Hysteresis parameters of the ferromagnetic material are dynamically calibrated through a quantum annealing optimization algorithm, and electromagnetic-thermal field strong coupling synchronous calculation is realized by combining multi-scale mesh generation and an implicit thermal field iterative algorithm; constructing an incremental transfer learning framework to fuse aging features and real-time data, and correcting boundary conditions of the model by adopting four-dimensional variational assimilation; establishing a hybrid verification platform to dynamically feed back extreme working condition parameters, and generating a credible operation and maintenance instruction in combination with a block chain; according to the method, the contact temperature rise prediction precision, the residual life evaluation reliability and the circuit breaker transient response real-time performance are remarkably improved, and active immune type intelligent operation and maintenance of the high-voltage switch cabinet under the extreme working condition are achieved.

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 fault prediction, in particular to a switch equipment fault early warning method based on multi-source data fusion, which comprises the following steps of: acquiring a channel transition time sequence to generate a time interval, marking a behavior label to generate a time sequence, constructing a sliding window to form a combined alarm, analyzing a cross matrix to extract an abnormal time period, and performing early warning. And identifying the leading channel and sending out early warning. According to the method, alignment processing is carried out through the time intervals of multi-channel state transition responses, dynamic linkage recognition among multiple signals is achieved, time sequence correlation among abnormal behaviors is enhanced by means of behavior label combination and a sliding statistical strategy, and the sensitivity and accuracy of abnormal recognition are enhanced through aggregation expression of composite alarm events. In combination with cross-channel behavior label matrix analysis, the continuity of abnormal labels is clear and key channels are identified, so that the accuracy and timeliness of early warning of the switch equipment are improved, the risk of false alarm and missing alarm is reduced, and the actual demand of multi-source sensing data fusion analysis of the switch equipment is met.

The invention discloses a switch cabinet fault diagnosis method and system based on thermal imaging and voiceprint. The method comprises the following steps: acquiring historical operation data, voiceprint data and thermal imaging data of a switch cabinet and an operation site picture of the switch cabinet; carrying out noise reduction processing on the acquired voiceprint data and historical operation data by adopting an infinite Kalman filter; constructing a space-time attention mixed expert model to carry out training prediction on the voiceprint data set and the historical operation data set, and obtaining predicted voiceprint data and historical operation data at the next moment; constructing an improved MaxViT-DF model to classify the collected thermal imaging data and the operation site picture, and training to obtain a fault feature image and a fault-free image; and fusing fault feature data output by the space-time attention hybrid expert model and the MaxViT-DF model by adopting multi-modal orthogonal sequence fusion. According to the invention, the accuracy and efficiency of fault diagnosis of the switch cabinet are improved, operation and maintenance personnel can find and process potential problems, and the reliability and safety of a power system are improved.

The invention discloses a GIS (Gas Insulated Switchgear) tiny defect detection method and system based on X-ray multi-effect fusion, and the method comprises the following steps: firstly, exciting a latent defect in a GIS to generate partial discharge by using an X-ray photoionization effect, detecting a discharge signal by using an ultrahigh frequency method and an ultrasonic method, and after the discharge signal is processed by a noise reduction model, carrying out noise reduction on the discharge signal; and respectively obtaining an ultrahigh-frequency discharge phase spectrogram and an ultrasonic discharge phase spectrogram. Then, the imaging plate is placed on the back face of the GIS, digital DR imaging is carried out through X rays, and an X-ray image is obtained; the ultrahigh frequency discharge phase spectrogram and the ultrasonic discharge phase spectrogram are fused after being denoised, an X-ray image is combined to establish a GIS health state diagnosis model, and reliable defect detection is carried out on the GIS according to the evaluation score of the diagnosis model. According to the method, multiple effects of X-rays are fused, so that smaller GIS latent defects can be found, the detection sensitivity is remarkably improved, and the problem that the traditional method is difficult to identify the tiny latent defects is solved.

The invention relates to the technical field of electric power system safety early warning, and discloses a fault risk sensing early warning method for a high-low voltage switchgear assembly, and the method carries out the data preprocessing and feature extraction through collecting the state monitoring data, environmental parameters and basic attribute information of a device in historical operation. And based on the feature data sets, constructing a fault risk prediction model to predict the fault risk probability of the equipment in a period of time in the future, obtaining the operation state data and environmental parameters of the equipment in real time, and inputting the prediction model to obtain the fault risk probability. Meanwhile, the equipment is monitored in real time, original monitoring data is cleaned, abnormal values are removed, real-time monitoring data is compared with prediction data, whether the difference exceeds a threshold value or not is judged according to a preset risk threshold value, and risk points are marked. And evaluating the equipment fault risk according to the risk point and the evaluation strategy, and sending a result to related personnel to take prevention measures.

According to the 20KV electric brake switch multi-mode working condition abnormity comprehensive monitoring system and method based on voiceprint recognition, all internal faults of a 20KV generator brake switch are monitored online in real time through the voiceprint recognition technology, and the industry blank of real-time online monitoring of the 20KV generator brake switch is filled up; a voiceprint collector, a temperature sensor, a vibration sensor, an SF6 gas monitoring device, a position node relay and the like are installed in a proper area, and multi-modal data are integrated into the same system, so that comprehensive analysis of the working condition of the 20KV generator brake switch is realized; and deep learning and fault judgment process logic design are carried out on each data parameter, so that the multi-mode working condition abnormity comprehensive diagnosis of the 20KV generator brake switch is realized, and the timeliness of working condition abnormity or fault judgment of the 20KV generator brake switch equipment is greatly improved.

The invention discloses a low-voltage switch cabinet operation data visualization and control system, and relates to the technical field of power equipment intelligent control, and the system comprises a multi-modal sensing module which outputs a multi-dimensional feature vector to an edge unit, and obtains a mechanical looseness level and an arc fault type; the self-adaptive feature engine is used for obtaining an unstructured feature matrix; the dynamic topology reconstruction module is used for obtaining a reconstruction topology and a thermal stress prediction result; the virtual-real mapping visualization engine is used for inputting a real-time topological graph and a fault path and outputting a visual interface containing arc hot spots and cascade early warning; the intelligent decision-making module outputs to the circuit breaker and the energy storage unit; and the man-machine cooperation interface module is used for outputting a tamper-proof log. According to the invention, potential faults can be pre-warned in advance, fault switching can be quickly processed, the operation data of the low-voltage switch cabinet with visual space interaction experience can be provided, and the problems that the potential faults cannot be pre-warned in advance, the fault switching processing is slow, and the space intuition and interactivity are poor in the prior art are solved.

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.

The invention discloses a switch cabinet discharge detection method and system, and relates to the technical field of power equipment state monitoring and fault diagnose.The light intensity attenuation rate and the vibration frequency are collected through a photonic crystal fiber, the concentration of decomposed gas is detected in combination with a Raman spectrum, and a space-time joint feature matrix is generated; performing space weight distribution on the space-time joint feature matrix based on the switch cabinet structure, and inputting the space weight to an RBF-SVM (Radial Basis Function-Support Vector Machine) to generate discharge type probability distribution; based on a high-frequency energy parameter and a gas decomposition concentration parameter in the spatio-temporal joint characteristic matrix, inputting the parameters into a multiple linear regression model, calculating an insulation aging index, and judging an insulation degradation grade according to a threshold value; the photonic crystal fiber is used for collecting the light intensity attenuation rate and the vibration frequency, the Raman spectrum is combined for detecting the decomposition gas concentration to generate the space-time joint characteristic matrix, synchronous monitoring of various physical and chemical parameters in the switch cabinet is achieved, and the accuracy and comprehensiveness of recognition of different types of discharge phenomena are improved.

The present teaching relates to method and system for charging a rechargeable battery deployed in an electric apparatus. The system resides in the electric apparatus. The system comprises a motor, an inverter, an output rectifier, a configurator, and a controller. The motor comprises a stator having a plurality of stator teeth and a plurality of stator windings wounded on the plurality of stator teeth. The inverter comprises a plurality of power switch devices. The configurator comprises a plurality of contactors coupled with the plurality of stator windings and the plurality of power switch devices. The controller controls the plurality of power switch devices and the plurality of contactors, so as to configure the system to operate in one of a traction mode and a charging mode.

The invention discloses a circuit breaker opening and closing detection method and system for a gas insulated switchgear, and relates to the technical field of circuit breaker opening and closing detection, and the method comprises the following steps: in a circuit breaker opening detection process, firstly carrying out real-time data collection on all circuits in a power grid, and providing initial conditions for subsequent fault simulation and analysis; a target circuit is randomly selected from all circuits, and short circuit faults of the circuit under different conditions are simulated. Through real-time power grid data acquisition, random short-circuit fault simulation and feature extraction in a dynamic monitoring window, in combination with a convolutional neural network and a fuzzy logic algorithm, the short-circuit fault severity degree is intelligently evaluated, the optimal opening speed range is predicted, the actual opening speed is accurately compared, abnormity is timely recognized, the problem of misjudgment in traditional detection is avoided, and the detection accuracy is improved. The high-efficiency response of the circuit breaker under different fault conditions is ensured, and the detection precision and the safety and stability of a power system are effectively improved.

The invention relates to the technical field of operation and maintenance of complete switch equipment, and discloses a DMC material-based operation and maintenance method for complete switch equipment, which comprises the following steps of: determining key performance parameters such as insulating strength, heat conductivity coefficient and arc resistance of a DMC material through an experiment, and constructing an aging characteristic model; calculating a material health index based on running state data collected in real time, and dynamically generating a degradation trend and residual life in combination with a time sequence prediction algorithm; and generating a maintenance plan through an optimization model, and introducing a risk weight coefficient to realize dynamic balance between maintenance cost and equipment reliability. According to the method, through material performance modeling and multi-source data dynamic fusion, the accuracy and response speed of equipment state monitoring are remarkably improved; through multi-dimensional cooperation of trend prediction and maintenance optimization strategies, full-life-cycle closed-loop management is formed, and intelligent upgrading of operation maintenance under complex working conditions is realized.

The invention discloses an intelligent dehumidification and heat dissipation type high-low voltage switch cabinet, and belongs to the technical field of switch cabinets, the intelligent dehumidification and heat dissipation type high-low voltage switch cabinet comprises a cabinet body, a sealing strip is fixedly installed on the front side of the cabinet body, a sealing cabinet door is hinged to the front side of the cabinet body, and a temperature sensor, a humidity sensor, a controller and a control assembly are arranged in the cabinet body; a ventilation assembly, a condensation mechanism, a water collection mechanism and a heat exchange mechanism are sequentially arranged on one side of the cabinet body from top to bottom, and a ventilation assembly is arranged at the bottom of the other side of the cabinet body. Forced ventilation can be achieved through the arranged ventilation mechanism, the ventilation speed can be adjusted according to the temperature, the flow direction of air in the switch cabinet can be changed according to needs, uniform and controllable heat dissipation is achieved, heat dissipation is achieved while dehumidification is achieved through the arranged condensation mechanism, heat dissipation work is further conducted through condensate water, and the dehumidification and heat dissipation effect is improved.

The invention discloses a power distribution switchgear remote monitoring and maintenance method based on the Internet of Things, relates to the technical field of power system operation and maintenance, and solves the problems that in an existing power distribution system, the edge anomaly recognition capability is weak, a control feedback chain is not closed, and multi-terminal control conflicts occur frequently. According to the scheme, a structured time sequence index of multi-source data is constructed on edge equipment, and equipment state drift is judged based on residual function included angle change; a control command management mechanism of a unique transaction identifier is introduced, a dual-channel feedback structure and a control mutual exclusion stack are combined, and a remote command consistency control chain is established; a hidden danger propagation chain is constructed on the cloud based on the abnormal Boolean bitmap and the topological relation, and a multi-node state linkage analysis and risk priority intervention strategy is achieved; according to the invention, the abnormity identification accuracy, the remote control stability and the system-level operation and maintenance response efficiency of the power distribution switch equipment are obviously improved.

The invention discloses a clean air switch equipment fault diagnosis method and system, and the method comprises the steps: firstly collecting a gas concentration signal, a partial discharge signal and environment parameters of clean air switch equipment, carrying out the preprocessing, feature extraction and feature fusion, and outputting a multi-modal feature vector; inputting the multi-modal feature vector into a pre-constructed CNN-LSTM-Attention model, and carrying out classification processing on the multi-modal feature vector to obtain a fault type and a fault severity degree of the clean air switch equipment; according to the pre-constructed CNN-LSTM-Attention model, a bidirectional long-short-term memory network is adopted to capture time sequence long-term dependence, a convolutional neural network is used to extract spatial correlation, and key fault features are focused through multi-head self-attention. According to the method, the technical problems that the monitoring precision of a single sensor is insufficient and the modeling capability of a traditional machine learning model on time series data and spatial correlation is weak are effectively solved.

The invention discloses a complete switch equipment online detection method and system based on multi-sensor fusion, and relates to the technical field of equipment state detection.The method comprises the steps that temperature distribution, partial discharge signals, mechanical vibration waveforms and operation current data of switch equipment are collected, key fault features are extracted based on preprocessed multi-source data, and the key fault features are extracted; generating a high-dimensional feature matrix, constructing a graph convolution-long and short-term memory hybrid network model as a fault diagnosis model, and obtaining a defect detection result according to the spatial-temporal features; and constructing an equipment degradation index based on defect characteristics to calculate an equipment health index, performing probabilistic prediction of the remaining service life in combination with a Wiener degradation model, and triggering an early warning signal when defects are detected or the service life is lower than a threshold value. According to the method, the problems of large data limitation and insufficient fault diagnosis precision of a single sensor in the operation state monitoring of the whole set of switch equipment are solved, and fault evaluation and residual life prediction are further realized by combining a hidden defect evolution rule.

The invention discloses a gas-insulated switchgear gas leakage repairing method, system, equipment and medium, and relates to the field of equipment repairing. The method comprises the following steps: scanning the gas insulated switchgear to position a gas leakage part, and preprocessing the gas leakage part; determining a scheme for blending a target interpenetrating network coating material according to the characteristics of the air leakage part and the working environment, and coating the pretreated air leakage part with the blended target interpenetrating network coating material to form a target coating; curing parameters are determined according to the type and thickness of the interpenetrating network coating material and the working environment, the power of a heating source is determined through the curing parameters, the target coating is cured through the heating source based on the power of the heating source, and the curing parameters comprise curing time, temperature, humidity and air pressure; and performing air leakage detection on the cured air leakage part to evaluate the repair effect. By implementing the technical scheme provided by the invention, the air leakage problem can be effectively and durably repaired.

The invention belongs to the technical field of simulation analysis, and particularly relates to a medium-high voltage switch cabinet internal humidity inversion calculation method based on simulation analysis, which comprises the following steps: establishing a geometric model of a switch cabinet, importing the geometric model into finite element simulation software to obtain a simulation analysis model, adding each physical field and computational domain and boundary conditions thereof, and calculating the internal humidity of a medium-high voltage switch cabinet. All the physical fields are coupled, and mesh generation is carried out; performing numerical solution on the simulation analysis model by using a transient solver to obtain a temperature and humidity distribution condition in the switch cabinet; the simulation analysis model is optimized, a simulation data set is constructed, and a PINN is trained; based on the environment temperature and humidity and the operation current collected in the actual operation of the switch cabinet, the trained PINN is used for prediction, and the steady-state relative humidity of the current transformer is obtained. According to the invention, based on the limited boundary monitoring data, efficient and accurate reconstruction of the humidity field of the key area in the switch cabinet is realized, and a feasible path is provided for equipment state sensing and condensation risk early warning.

A functional module F is presented for coupling to an electrical / electronic switching device S, which functional module F can be influenced by means of an actuating element B, the electrical / electronic switching device S and a radio transmitter, wherein the functional module F has components necessary for its function on at least one electrical circuit board, and wherein the functional module F has at least one functional module-side interface CF connected to the electrical circuit board, which is suitable for interaction with a switching device-side interface CS, via which interfaces CF, CS actuators, for example lights, blinds or sockets, can be controlled, or sensors, for example touch sensors, can be evaluated, and whereby a control signal is transmitted between the functional module F and the electrical / electronic switching device S, wherein the functional module-side interface CF has an output channel for providing the control signal,which can be influenced both via the radio transmitter and via the actuating element OA, , What is special is that the function module F with its function module-side interface CF can be inserted into the switchgear-side interface CS in the manner of a plug-in connector, and that the function module F has a defined cabling so that the function module F can be influenced by means of the actuating element OA and radio transmitter for influencing actuators, wherein the function module F has recesses that can be engaged with projections HV of the electrical / electronic switchgear S.

The invention discloses a partial discharge on-line monitoring method of a switch cabinet and a storage medium, and relates to the technical field of power equipment monitoring. According to the method, multi-dimensional state parameters such as vibration amplitude and pulse density are obtained in real time by constructing a local, edge and cloud three-level processing architecture, and self-adaptive distribution of calculation tasks is achieved based on a dynamic unloading scoring formula. The local nodes execute time domain feature extraction, the edge nodes perform frequency domain enhancement and confidence evaluation, and the cloud completes time-space depth analysis. A multi-domain feature weighted fusion mechanism is innovatively adopted, the signal quality is improved in combination with a wavelet dual-threshold noise reduction algorithm, and quantization errors are corrected through a residual compensation module. When the edge node loads gt; and when 80%, dynamic resource scheduling is triggered, and resource allocation is optimized and calculated.

The invention relates to the technical field of remote monitoring, and discloses an intelligent AI-based fault detection method and system for a high-low voltage power distribution cabinet, and the method comprises the steps: constructing an LSTM-Attention hybrid network of a multi-modal data flow, extracting the time sequence characteristics of the multi-modal data flow, extracting the non-stationary signal frequency domain characteristics of the multi-modal data flow, and carrying out the fault detection of the multi-modal data flow. The method comprises the following steps: constructing a fault knowledge graph of a high-low voltage power distribution cabinet, analyzing a topological correlation feature vector of the high-low voltage power distribution cabinet, training a pre-constructed power distribution cabinet fault global model to obtain a trained power distribution cabinet fault global model, and analyzing a fault probability matrix of the high-low voltage power distribution cabinet by using the trained power distribution cabinet fault global model. And establishing a digital twinborn body of the high-low voltage power distribution cabinet, analyzing a fault evolution path of the high-low voltage power distribution cabinet by using the digital twinborn body, and analyzing a fault detection report of the high-low voltage power distribution cabinet. The maintenance efficiency of the high-low voltage power distribution cabinet can be improved.

The invention discloses an electrical equipment overload protection method and system based on multistage load monitoring, and relates to the technical field of electrical equipment overload protection, and the method comprises the steps: setting an equipment-level monitoring node, a line-level monitoring node and a system-level monitoring node, and obtaining a three-level load data set; constructing a load characteristic matrix; setting an initial overload threshold conforming to the industrial standard of the switch equipment; a correction coefficient is configured, and the initial overload threshold value is synchronously corrected; drawing up a grading threshold comparison table; meanwhile, a load dynamic constraint mechanism and an equipment health intervention mechanism are set, and electrical equipment overload protection parameter setting is carried out. The technical problems that in the prior art, the overload protection threshold value of the electrical equipment is fixed, dynamic adjustment is lacked, and the adaptability to the health state and load change of the equipment is insufficient, so that protection pertinence and effectiveness are low are solved, accurate overload protection of the electrical equipment is achieved, and the safety of the electrical equipment is improved. And the technical effects of pertinence and effectiveness of overload protection are improved.

The invention discloses a switch cabinet latent fault remote diagnosis system, method, device and medium, and belongs to the technical field of fault diagnosis, and the system comprises a multi-dimensional sensor sensing module, a data transmission module, a data fusion analysis module, and a data storage and fault detection module. The multi-dimensional sensor sensing module is used for acquiring multi-dimensional data of the operation state of the switch cabinet; the data transmission module is used for transmitting the multi-dimensional data to the data fusion analysis module; the data fusion analysis module is used for performing data preprocessing on the multi-dimensional data to obtain preprocessed multi-dimensional data, and determining a fault classification result according to the preprocessed multi-dimensional data; and the data storage and fault detection module is used for performing fault identification based on the real-time monitoring data and the fault classification result. According to the invention, early recognition and accurate diagnosis of the latent fault of the switch cabinet are realized, and the downtime and maintenance cost of equipment are reduced.

The invention relates to the technical field of rotary mounting tools, in particular to a rotary mounting tool for a gas tank of a gas-insulated switchgear, which comprises a bottom fixing frame body and a left fixing frame body fixed above the left side of the bottom fixing frame body, a right moving frame body is connected to the upper portion of the right side of the bottom fixing frame body. Ball screws are mounted in the middle parts of the left fixed frame body and the right movable frame body; one side surface of the rotary support plate is connected with a main clamp mechanism for clamping the left side surface and the right side surface of the gas tank of the gas-insulated switchgear; two sets of bottom supporting plates used for supporting the bottom face of the gas tank of the gas-insulated switchgear are symmetrically installed below the rotary carrying plate through self-adjusting frames, and side supporting plates used for clamping the front side face and the rear side face of the gas tank of the gas-insulated switchgear are fixed to the upper portions of the horizontal planes of the bottom supporting plates. According to the rotary mounting tool for the gas tank of the gas-insulated switchgear, clamping force can be applied to multiple side faces of the gas tank of the gas-insulated switchgear, the clamping stability of the gas tank of the gas-insulated switchgear during rotation in the later period can be guaranteed, and the rotary mounting tool can conveniently and well drive the gas tank of the gas-insulated switchgear to rotate.

The utility model belongs to the technical field of medium-voltage power distribution systems, and discloses an isolating switch device and a switch cabinet, the isolating switch device comprises an insulating rod, a conductive block, a conductive sheet and an operating mechanism, the conductive block is sleeved on the insulating rod, the conductive sheet is fixedly connected with the conductive block, and the operating mechanism drives the conductive sheet and the conductive block to rotate through the insulating rod. Therefore, the conducting strip is selectively connected or disconnected with the circuit breaker connector bar, and the conducting block is electrically connected with the voltage transformer in the rotating process. An isolating switch device is additionally arranged between a circuit breaker and a voltage transformer in a switch cabinet, an operating mechanism is utilized to drive a conducting strip and a conducting block to rotate through an insulating rod, the conducting block and the voltage transformer keep electrical connection in the rotating process, the position of the conducting strip is changed in the rotating process, and then the conducting strip is selectively connected with or disconnected from a circuit breaker connector bar. The structure is simplified, the number of elements is reduced, flexible operation can be achieved, and the stability and reliability of the disconnecting switch device can be guaranteed.

The invention discloses a mechanical defect vibration sound feature extraction method for GIS equipment, and belongs to the technical field of equipment detection. Comprising a data acquisition unit, a data transmission unit, a vibration sound processing unit, a data storage unit, a data processing unit and a display reminding unit, and the data acquisition unit is used for acquiring various data required in the use process of the mechanical defect vibration sound feature extraction system of the GIS equipment. The invention provides an optimal correction logarithmic spectrum estimation and Wiener filtering fusion algorithm, noise signals are estimated, the amplitude part in the noisy signals is processed and updated to obtain enhanced sound, the OM-LSA is used for estimating and filtering transient noise, the Wiener filtering is used for filtering steady-state noise, and the noise is filtered. According to the method, original vibration signals and acoustic signals of each operation state of the GIS are fused, a series of characteristic parameters are extracted from a multi-dimensional domain, and effective characterization of the state of the GIS equipment is realized.

The invention relates to a multifunctional draw-out type low-voltage switch device, which relates to the technical field of low-voltage switch cabinets and comprises eight mutually matched instrument rooms, a digital detection module compartment, a transformer power supply wire inlet room, a dual-channel distribution bus room, an interbus wire outlet room, a module unit room, a feeder cable room and an emergency standby cable room. According to the invention, a power distribution mode of split-phase management and layered cutoff protection is adopted, so that maintenance personnel can quickly lock fault loops and fault current characteristics, split the fault loops and replace damaged parts, the maintenance process is efficient and convenient, and the downtime of equipment is shortened.

The invention relates to the technical field of switch cabinet temperature measurement, and discloses a method for measuring the temperature of a switch cabinet in real time through an infrared matrix based on multi-modal sensing. The method comprises the following steps: synchronously acquiring an infrared image sequence, an acoustic vibration signal and current waveform data of the switch cabinet through a multi-mode sensor array, and capturing equipment operation and temperature associated information; constructing a three-dimensional temperature field reconstruction mechanism model based on a heat conduction physical equation, outputting a theoretical temperature distribution value fitting a physical rule, processing multi-source data by using a spatial-temporal feature extraction network, and outputting a temperature prediction value reflecting a real-time working condition; inputting the two into a dynamic fusion module based on a Markov decision process to generate a fusion temperature distribution value, and generating a temperature anomaly positioning instruction through a rolling time domain optimization strategy according to the fusion temperature distribution value; when the deviation between the actual temperature sampling value and the fused temperature distribution value exceeds a preset threshold value, calibrating a network output layer parameter; and triggering a linkage control protocol of the switch cabinet protection system according to the abnormal positioning instruction.