3981 results about "Switchgear" patented technology

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 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 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 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 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 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.

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 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 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.

The invention relates to the field of electrical parameter measurement of power distribution switch equipment, in particular to an arc fault measurement method, system and device in the power distribution switch equipment and a medium, and the method comprises the steps: collecting a current signal in a power distribution loop in real time; separating out target frequency band components of which the frequency is greater than a preset frequency threshold in the current signal; performing envelope extraction on the target frequency band component to obtain an envelope signal; performing time domain analysis on the envelope signal to obtain the regularity intensity of the pulse of the envelope signal; performing short-time Fourier transform on the target frequency band component, and calculating a frequency spectrum variance; taking the regularity intensity and the spectrum variance as feature vectors, and inputting the feature vectors into a pre-trained artificial intelligence model; outputting a first fault diagnosis result by the artificial intelligence model; and generating a control instruction based on the first fault diagnosis result to trigger the protection device to act. The method has the advantage that the dangerous electric arc in the power distribution loop can be accurately identified and quickly responded.

The invention discloses a live detection method for insulation defects of high-voltage power equipment. The method comprises the following steps: firstly, synchronously arranging ultrahigh frequency sensors and acoustic emission sensors on a plurality of monitoring points on the surface of a gas insulated switchgear shell to form an array, and synchronously acquiring signals for filtering pretreatment; then, pulse events are extracted from the two types of signals respectively, and associated pulses in a time window are matched into matched pulse pairs representing the same discharge source; then, carrying out time-frequency transformation on the ultrahigh frequency and acoustic emission pulse waveform in each matched pulse pair, carrying out joint noise reduction by calculating a coherence coefficient between time-frequency distribution matrixes, and extracting a joint feature vector containing an energy ratio, a time parameter ratio and a frequency difference from the denoised time-frequency matrix; according to the invention, multi-source signals are fused, and high-sensitivity detection, high-precision positioning and high-accuracy identification of insulation defects are realized.

The invention discloses a switch equipment performance aging intelligent diagnosis method and device based on multi-parameter fusion, relates to the technical field of power equipment abnormal defect diagnosis, and solves the problem of low precision of aging judgment of switch equipment depending on a single temperature and humidity threshold value or a single gas parameter in the prior art. The method comprises the following steps: acquiring internal and external humiture and internal gas characteristic parameters of equipment; constructing multi-channel standardized monitoring data through time synchronization, filtering and de-noising and exception elimination processing; calculating an absolute humidity difference inside and outside the cabinet, constructing a humidity expectation model, and obtaining a standardized residual error; performing offset detection and trend smoothing processing on the standardized residual error to obtain a cumulant and a smoothing trend quantity, and fusing the cumulant and the smoothing trend quantity to calculate an offset index; outputting a moisture seepage risk score, and triggering early warning when the moisture seepage risk score and the characteristic gas concentration exceed a threshold value. According to the invention, real-time high-precision evaluation of the aging state of the equipment is realized, early hidden dangers are effectively identified, and the safety and reliability of a power system are improved.

The invention relates to the technical field of intelligent power grids, in particular to a low-voltage draw-out type switch cabinet which comprises a cabinet body, a power grid arranged on the inner side wall of the cabinet body, a heat dissipation plate arranged on the inner side wall of the cabinet body, a plurality of static contacts arranged on the inner side wall of the cabinet body and a cleaning mechanism arranged on the inner side wall of the cabinet body and used for cleaning the power grid. The cleaning mechanism comprises two first electric sliding rails arranged on the inner side wall of the cabinet body, according to the low-voltage draw-out type switch cabinet, the overturning plate is driven by the first motor to rotate at multiple angles, the spraying head can evenly cover the outer corners of the cabinet body after the insect repellent is atomized, reptiles are dispersed from the source, and the low-voltage draw-out type switch cabinet is convenient to use. The cleaning mechanism can be quickly started, heat dissipation blocking and equipment faults caused by accumulation of dead insects are prevented, and it is guaranteed that the low-voltage draw-out type switch cabinet is always kept in a safe and stable working state in long-term operation.

The invention provides an electromagnetic safety test system and method for switch integrated equipment, and relates to the technical field of power system switch equipment. The system is used for solving the problems that multi-parameter dynamic coupling analysis of a traditional test is insufficient, transient and gradual interference is difficult to capture by a fixed window, and comprehensive quantitative evaluation is lacked, and comprises a test module, an interference analysis module and a test evaluation module, calculating a total coupling interference variable; the interference analysis module draws a change curve based on the total coupling interference variable, analyzes a threshold over-area, a threshold duration and a peak value, and generates an interference risk index; and the test evaluation module calculates a total evaluation value and a threshold super-window proportion by combining the risk indexes of the windows, and divides safety levels. According to the invention, multi-parameter dynamic coupling simulation is realized, different types of interferences are accurately captured, a full-period dynamic evaluation system is constructed, and the accuracy of electromagnetic safety performance judgment of equipment is improved.

The invention discloses a switch cabinet operation reliability evaluation method, relates to the field of switch cabinets, and solves the problem of low accuracy of switch cabinet operation reliability evaluation caused by non-targeted test, and the method comprises the following steps: collecting rated voltage values and rated current values of multiple groups of driving plug-ins and power modules in a target switch cabinet; performing an electrical variable protection test on each group of driving plug-ins in the target switch cabinet to obtain an electrical variable protection test result of the corresponding driving plug-ins; performing an over-temperature protection test on each group of power modules in the target switch cabinet to obtain an over-temperature test result of the corresponding power module; performing an open-phase protection test on the target switch cabinet to obtain an open-phase test result corresponding to the target switch cabinet; and integrating all test results corresponding to the target switch cabinet, and evaluating the reliability of the target switch cabinet based on the test results. According to the invention, through targeted testing of different accessories in the switch cabinet, accurate evaluation of the operation reliability of the switch cabinet is realized.

The invention relates to the technical field of power transmission and distribution switches, and discloses a vacuum bypass switch, which comprises an insulating shell, a static end copper bar, a movable end copper bar, a vacuum switch tube, an insulator, a pull rod and a permanent magnetic mechanism, the pull rod is driven by a magnetic field generated by the permanent magnet assembly to drive the moving end copper bar to move in the direction away from the vacuum switch tube so as to realize opening. The first coil and / or the second coil generate / generates a target magnetic field opposite to the magnetic field generated by the permanent magnet assembly in direction under the power-on effect so as to drive the pull rod to drive the movable end copper bar to move towards the vacuum switch tube, and closing is achieved. Closing is achieved through the first coil and / or the second coil, the problem that closing cannot be achieved due to single-coil faults is avoided, and the problem of thermal runaway caused by continuous energization of the single coil is solved. The invention also discloses a bypass switch control system, which controls the energization of the first coil and the second coil through the first energizing loop and the second energizing loop respectively to ensure the reliable closing of the vacuum bypass switch.

The invention discloses an FTU-based power distribution network fault detection method and system, and relates to the field of power distribution network fault detection, and the method comprises the steps: employing a wavelet transform technology to analyze electrical parameters, recognizing a fault line of a power distribution network, transmitting a traveling wave to an end point of the fault line through a traveling wave transmitter, and positioning a fault position point of the fault line of the power distribution network; extracting influence parameters based on a fault reason matching result, judging a probability coefficient of a power failure condition in the power supply area of the power distribution network in the target time period, and evaluating the power supply reliability of the power distribution network according to the probability coefficient; and based on the power supply reliability, using the fault terminal device to control the switch device to perform circuit breaking operation, isolating the power distribution network fault line, generating a personnel scheduling instruction, and implementing power distribution network fault line emergency repair processing. According to the method, a highly fine recording data analysis and multi-dimensional dynamic feature extraction mechanism is introduced, so that deep reasoning from a fault phenomenon to a fault cause is realized.

The invention discloses a longitudinal rotation indoor alternating current metal-enclosed switchgear, which comprises a cabinet body, and a contact box device, a valve device, a longitudinal rotation isolation vacuum circuit breaker and an inductance device which are arranged in the cabinet body from top to bottom, and a driving assembly respectively acts on the longitudinal rotation isolation vacuum circuit breaker and the valve device; through partial strokes of the longitudinal rotation isolation vacuum circuit breaker and the valve, namely, the longitudinal rotation isolation vacuum circuit breaker and the valve device share one driving assembly, but the time point when the valve is driven by the driving assembly is ended earlier than the longitudinal rotation isolation vacuum circuit breaker or executed later than the longitudinal rotation isolation vacuum circuit breaker. Therefore, the motion stroke of the valve is shortened, the space requirement of the valve in the cabinet body is reduced, and the miniaturization of the longitudinal rotation indoor alternating-current metal-enclosed switchgear is realized.

The invention relates to a high-service-life low-voltage complete switch cabinet applied to the field of power distribution, a feedback assembly is arranged on a movable mounting plate, a plurality of butt joint grooves with different diameters corresponding to the feedback assembly are formed in a static mounting plate, and in the closing process of a drawer body, the position accuracy of the drawer body can be monitored and fed back in real time through the feedback assembly; when the position of the drawer body deviates, movement of the drawer body can be stopped immediately and early warning can be carried out, so that the movable contact and the static contact are not prone to butt joint dislocation, damage of the movable contact and the static contact is reduced, the service life of the device is prolonged, meanwhile, the position deviation degree of the drawer body can be effectively judged through comparison of actual monitoring data and initial data of the feedback assembly, and the accuracy of the device is improved. Besides, through the arrangement of the image acquisition assembly, image acquisition is performed on the state of the feedback assembly, the position condition of each long rod is obtained, the position deviation degree of the drawer body is further visually and accurately judged, and a more effective data supporting effect is achieved for subsequent inspection of the drawer body.

The utility model discloses a punching machine for power distribution cabinet production, which comprises a workbench, one side of the workbench is provided with a lubricating mechanism, the lubricating mechanism comprises a material box, the bottom of the left side of the material box is provided with a fixing pipe, one side of the fixing pipe is communicated with a frame body, one side of the frame body is movably connected with a crank shaft, and the crank shaft is movably connected with the workbench. The right side of the bottom of the workbench is fixedly connected with a double-shaft motor. According to the punching machine for production of the power distribution cabinet, the lubricating mechanism is arranged, an electric telescopic rod drives an adjusting plate to move, the adjusting plate drives a spray head to move, so that the spray head moves to the position between an upper die and a lower die, a double-shaft motor drives a crankshaft to rotate, the crankshaft drives an adjusting rod to move, the adjusting rod drives a fixing rod to move, and the fixing rod drives a sealing plate to move; when the sealing plate moves towards the inner side, the lubricating liquid can be pumped out through a fixing pipe, and when the sealing plate moves towards the outer side, the lubricating liquid can be discharged into a discharging pipe and then sprayed out through a spray head, so that the lubricating liquid makes contact with the upper mold and the lower mold.

The invention provides an insulating material surface arc ablation resistance simulation method based on magnetohydrodynamics, and belongs to the field of insulating material performance detection. The method comprises the following steps: establishing an insulating material surface arc ablation resistance model; constructing a magnetohydrodynamic control equation set; setting boundary conditions of the model; and iteratively solving the magnetohydrodynamic control equation set through finite element simulation software to obtain a temperature distribution diagram of the insulating material under different arc ablation times, and comparing the temperature distribution diagram with a TGA curve of the insulating material to judge the arc ablation resistance of the insulating material. According to the invention, the power-frequency alternating-current high-voltage arc and the insulating material are used as simulation objects, engineering practice is better met, the surface arc ablation resistance process of any insulating material can be simulated by changing the physical characteristic parameters of the insulating material, and the TGA curve of the material is introduced to judge the arc ablation resistance of the insulating material. And technical support is provided for high-voltage switch equipment insulating material type selection.

The invention provides a method and a system for improving the self-healing capability of a highway micro-grid in extreme weather, and relates to the technical field of power system automation. The method comprises the following steps: deploying a sensor to collect real-time operation data, and combining meteorological data to generate an integrated system state data set through fusion processing; the digital twin engine generates system state prediction data through state estimation and dynamic simulation; the random multi-objective optimization model solves and generates a preventive scheduling instruction according to the above, and controls a power generation unit, an energy storage unit and a load unit to adjust operation points; fault detection and identification are carried out through residual analysis, a self-healing control instruction sequence is generated by using a sequence decision model, the switch equipment is controlled to isolate faults and restore power supply, closed-loop management from prediction and scheduling to self-healing is realized, and the power supply reliability and rapid recovery capability of the highway microgrid in extreme climate are improved.