2594results about "Circuit interrupters testing" patented technology

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 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 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 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 particularly relates to a leakage current testing method, device and system for an intelligent circuit breaker, and relates to the technical field of electrical safety detection. A composite signal generation and injection module; a broadband signal acquisition and conditioning module; and a line fault positioning and impedance analysis module. According to the invention, the composite signal generation module realizes the non-interference isolation injection of a leakage current signal and a high-frequency positioning signal, and guarantees the high fidelity of data through adaptive filtering, multi-channel synchronous sampling and high-precision timing. The line positioning module integrates TDR and a frequency domain impedance method, and through multi-frequency point deviation correction and topology-GIS association, the fault type is accurately identified, and the test and troubleshooting period is significantly shortened.

The invention discloses a multi-condition simulation capacitance detection relay life prediction method, system, equipment and medium, and relates to the technical field of relays, and the method comprises the steps: applying a plurality of simulation conditions to a relay through a programmable driving power supply, simulating the complex conditions in actual operation, and collecting the multi-dimensional parameters of the relay in real time in the execution process of the simulation conditions, extracting characteristic parameters from the collected multi-dimensional parameters, carrying out data preprocessing on the characteristic parameters, constructing a life prediction model, carrying out prediction calculation and reliability grade evaluation on the residual life of the relay by utilizing the life prediction model, and carrying out reliability grade evaluation on the residual life of the relay by monitoring capacitance change between contacts of the relay. And early performance degradation early warning is carried out in combination with a prediction result. According to the invention, through fusion of multi-working-condition simulation, hybrid modeling and capacitance detection, full-chain optimization from data acquisition, accurate prediction to early warning is realized, and the accuracy, timeliness and engineering application value of relay life prediction are improved.

The invention discloses a method for establishing a high-voltage circuit breaker fault detection model, and the method comprises the following steps: collecting current, voltage, mechanical response, temperature and other multi-dimensional signals of a circuit breaker under different working conditions, and unifying the signals into standardized time sequence data; a nonlinear dynamic sparse identification method is utilized to establish a dynamic model for describing equipment state evolution, and sparse coefficients reflecting physical change rules are extracted from the dynamic model to serve as health features. And the features are fused with current monitoring data to generate a joint feature input vector, and a health prediction model based on a TabPFN architecture is introduced for training and discrimination. And finally, accurate prediction of the current health state or the potential fault of the circuit breaker is realized, and the model self-adaptive updating capability is realized. According to the method, physical modeling and data analysis are combined, so that the accuracy and interpretability of fault prediction are improved.

The invention relates to the technical field of fault diagnosis, in particular to a pole-mounted circuit breaker fault diagnosis method based on multi-information fusion, and the method comprises the steps: firstly obtaining electric quantity modal data such as three-phase current and coil current and mechanical quantity modal data such as mechanism vibration and voiceprint, and constructing a time-frequency domain alignment feature tensor; then, through a graph space-time attention fusion model, deeply mining physical structure association and time sequence evolution laws among heterogeneous data, and generating a dynamic space-time feature matrix; then, respectively constructing a mechanical evidence body for representing the state of the transmission chain of the operating mechanism and an electrical evidence body for representing the working condition of the vacuum arc-extinguishing chamber by adopting a feature level-decision level mixed framework; an improved D-S evidence theory is applied for fusion, when high-conflict evidences are detected, a self-adaptive arbitration mechanism is automatically triggered, conflict weights are dynamically attenuated or distributed to uncertain items, misjudgment is effectively avoided, and high-robustness collaborative diagnosis of the electromechanical state is achieved.

The invention relates to a low-voltage circuit breaker state detection method, which comprises the following steps of: synchronously acquiring multi-source state data of a low-voltage circuit breaker in an operation process through a plurality of sensors, the multi-source state data at least comprising a vibration signal, an opening and closing coil current signal, a moving contact displacement signal, a temperature signal and a partial discharge signal; the collected signals are preprocessed, and feature vectors related to the health state of the circuit breaker are extracted; inputting the feature vector into an improved weighted D-S evidence theory fusion diagnosis model; the improved weighted D-S evidence theory fusion diagnosis model is used for fusing evidences from a plurality of sensors in a mode of distributing weights for evidences of different sensors and calculating weighted average evidences; and determining the comprehensive health state grade of the low-voltage circuit breaker according to an output result of the fusion diagnosis model. According to the invention, comprehensive evaluation of the multi-dimensional working condition of the circuit breaker is realized, and the method has the advantages of comprehensively reflecting the multi-dimensional working condition of the circuit breaker, reducing the risk of misjudgment and missed judgment, and improving the performance degradation pre-judgment capability of equipment.

The invention provides a circuit breaker action coil current characteristic diagnosis and analysis system and method. Belongs to the technical field of power system automation and substation state monitoring. The method comprises the following steps: carrying out synchronous wave recording on current of a plurality of circuit breaker actuating coils on a direct current feeder screen side to generate multi-channel synchronous wave recording data; meanwhile, SOE message information is collected, a circuit breaker with specific action is automatically matched and positioned by utilizing a timestamp and action identification information in an SOE message, and action circuit breaker positioning data is generated; the current waveform data of the circuit breaker actuating coil are collected in real time, and a high-precision algorithm is used for feature extraction and analysis, so that abnormal fluctuation in current change can be accurately captured, such as overlarge starting current and unstable holding current, the risk of misoperation or rejection of operation of the circuit breaker caused by abnormal current characteristics is reduced, and the service life of the circuit breaker is prolonged. Electric power system accidents caused by equipment faults are avoided, and the operation reliability of the circuit breaker is improved.

The invention relates to the related technical field of high-voltage switch cabinets, in particular to a high-voltage switch cabinet health state assessment method and system, and the method comprises the steps: obtaining an equipment operation parameter sequence of a high-voltage switch cabinet, combining an operation mechanism, determining a first response data segment and a second response data segment, and constructing a state assessment matrix, and identifying an abnormal operation period and a degradation trend cluster, and performing health risk early warning on the high-voltage switch cabinet by using the health state evaluation model. The technical problems that the sensing precision of key events such as switching-on bounce and partial discharge is insufficient, the generalization ability of health state evaluation under complex working conditions is insufficient, and the accuracy of high-voltage switch cabinet health evaluation is limited are solved, and cross correlation and variation correlation of a signal simulation verification chain are taken as an enhancement mechanism, so that the accuracy of high-voltage switch cabinet health evaluation is improved. The method has the technical effects that the response sensitivity of the model to key events is improved, the health risk of the high-voltage switch cabinet is accurately evaluated in combination with an abnormal period and a degraded cluster, and the accuracy of health evaluation of the high-voltage switch cabinet is improved.

The invention relates to the technical field of substation abnormity identification, and discloses a substation switch cabinet abnormity identification method and system, and the method comprises the steps: obtaining the real-time temperature data of a plurality of monitoring points in a switch cabinet, and uploading the real-time temperature data to a big data analysis platform through a wired or wireless communication mode; and continuous monitoring of the operation state of the equipment is realized. The highest temperature of the monitoring area, the highest temperature difference value of the adjacent monitoring periods and the temperature sudden change value are calculated based on the real-time temperature data, and when the parameters exceed preset threshold values, a corresponding alarm strategy can be triggered in time. And through the measures of generating an infrared thermogram, extracting a temperature distribution difference area, marking high-risk monitoring points and the like, the accuracy and timeliness of anomaly recognition are further improved. Meanwhile, the temperature trend prediction model is used for predicting the future temperature development trend, an early warning signal is generated in advance, a preventive alarm mechanism is started, and a powerful guarantee is provided for safe and stable operation of a power system.

The invention relates to the technical field of relay protection loop detection, and discloses a relay protection loop fault detection method and system, comprising data acquisition, data processing, feature extraction, fault diagnosis and early warning and alarming. Multi-dimensional state data of current, voltage, temperature and vibration are collected in real time, wavelet filtering, mean or median filtering and normalization preprocessing are performed, comprehensive characteristic parameters are extracted in combination with time domain and frequency domain analysis and wavelet packet decomposition, a fault is accurately diagnosed and positioned through a fault diagnosis model, early warning and alarming are achieved based on graded thresholds, and the fault diagnosis accuracy is improved. The fault key information is synchronously transmitted to the operation and maintenance terminal, database storage and display terminal display are matched, the problems of poor real-time performance, one-sided feature extraction, no graded early warning and the like of traditional detection are effectively solved, the fault detection efficiency, the diagnosis accuracy and the operation and maintenance response speed are greatly improved, the operation risk of a power system is remarkably reduced, and the safety and stability of the power system are guaranteed.

The invention discloses an intelligent primary and secondary fusion pole-mounted circuit breaker fault monitoring method, relates to the technical field of power equipment monitoring, and is used for solving the problem of insufficient real-time performance of fault monitoring under complex environment interference. According to the invention, electrical, mechanical vibration and environmental data are synchronously acquired through a multi-mode sensor array, and a timestamp synchronization mechanism is applied; performing multi-source interference classification on the original data, separating noise by using wavelet transform, and identifying interference types by clustering; kalman filtering adaptive compensation is applied based on an interference result, and environment feedback is introduced to ensure low delay; extracting multi-dimensional fault features, and forming a robust matrix through time-frequency analysis and principal component dimensionality reduction; inputting a deep learning model to carry out space-time modeling and rapid classification; a response mechanism is triggered to execute isolation or alarm, and closed-loop optimization is formed. The method effectively solves the problem of insufficient real-time performance under the interference of a complex environment, and improves the monitoring precision and the response speed.

The invention discloses an explosion-proof and intrinsic safety type combined feeder switch state monitoring method based on big data, and relates to the technical field of industrial electrical equipment state monitoring and fault diagnosis, and the method is realized based on a three-level architecture constructed by an edge computing node, a region computing node and a cloud platform. Comprising the following steps: S1, collecting multi-source sensing data of a feeder switch in real time by an edge computing node, and performing feature extraction and fusion calculation on the multi-source sensing data; according to the method for monitoring the state of the explosion-proof and intrinsic safety type combined feeder switch based on the big data, the contradiction between the real-time performance and the accuracy in multi-source heterogeneous data fusion processing is effectively solved, abnormity can be found in the first time, early warning can be triggered, and the reliability of a diagnosis conclusion is improved; and the accuracy and foresight of fault diagnosis are ensured. According to the grading processing mechanism, computing resources are reasonably distributed, the network transmission load is greatly reduced, and the system can stably operate in a complex industrial environment.

The invention discloses a method and a system for determining optimal parameters of an X-ray excited extra-high voltage composite apparatus, and relates to the technical field of extra-high voltage electrical equipment detection. The method comprises the following steps: remotely operating the X-ray machine and calculating partial discharge intensity; for the current irradiation position, the tube voltage and the tube current are gradually adjusted, parameters are dynamically optimized based on the quantitative relation between the partial discharge intensity and the radiation dose rate change rate, and the optimal parameters are recorded; and changing position repetition parameter optimization, and constructing a BP neural network model after data set training optimization so as to predict an optimal irradiation position. The optimized BP neural network adopts an improved sparrow search algorithm to optimize an initial weight and a threshold value, and combines Bayesian to optimize a learning rate and a hidden node number. Accurate optimization of excitation parameters and irradiation positions is achieved, detection accuracy and safety are both considered, the optimal irradiation position positioning efficiency is improved, and the method is suitable for ultra-high voltage GIS detection of different voltage grades.

The invention relates to the field of intelligent control of electromagnetic appliances, and provides a control method and system for online fault diagnosis of a magnetic latching relay, and the method comprises the steps: responding to an external control instruction, collecting real-time bus voltage and real-time environment temperature data, constructing environment state characteristics through a magnetothermal coupling algorithm, and generating an initial PWM drive duty ratio; pulse modulation voltage is loaded, a current sampling sequence is collected, a current change rate sequence is obtained through moving average filtering and electromechanical coupling decoupling, extreme value searching is executed to extract counter electromotive force distortion characteristics, and a dynamic current sequential sequence is generated; a fusion input tensor is constructed, armature state probability distribution is output through a gating circulation unit, and a real-time energy compensation instruction is generated in a displacement interval before closing; and according to the real-time energy compensation instruction, respectively generating an enhanced driving waveform, a delayed driving waveform or a vibration driving waveform. According to the method, multi-mode physical characteristic perception and depth time sequence deduction are fused, and online fault diagnosis and active intervention closed loop of the magnetic latching relay are realized.

The invention discloses a high-voltage switch cabinet partial discharge fault identification method and a storage medium. The method comprises the following steps: synchronously acquiring acoustic, electric, magnetic and thermal signals through a multi-physical field sensor array; performing preprocessing and feature extraction on the signals; using a cascade deep learning network to fuse multi-modal features to identify fault types and levels; realizing accurate positioning of the discharge source in the digital twinborn model by adopting a dynamic simulation coherent positioning method; and risk assessment and early warning are carried out by integrating diagnosis and positioning results. According to the method, deep learning and physical mechanism simulation are deeply fused through a data and model dual-drive normal form, so that the defects of shallow fusion level, low positioning precision, lack of interpretability and the like in the prior art are overcome, and intelligent, precise and prospective identification and early warning of the partial discharge fault of the high-voltage switch cabinet are realized.

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 primary and secondary fusion pole-mounted circuit breaker state monitoring system and method, and relates to the technical field of power system automation. The system comprises a native signal acquisition module which is used for multiplexing an inherent electrical loop of a circuit breaker and acquiring a current signal of an opening and closing coil and a capacitive reactance change signal of a parasitic capacitor between a high-voltage electrode and a grounding end; and the edge analysis module is used for processing the signal acquired by the original signal acquisition module, including generating a mechanical health degree through feature extraction and state mapping, calculating an insulation aging index based on capacitive reactance change, and dynamically correcting a monitoring reference in combination with a digital twin model and a filtering algorithm. Non-auxiliary element monitoring is realized by multiplexing the inherent electrical loop of the circuit breaker, so that the service life of the monitoring system and the service life of primary equipment are completely synchronous, and elements do not need to be replaced in the whole life cycle; errors introduced by additional sensors are avoided by using the original electrical signals, and the mechanical fault recognition and insulation state evaluation precision is remarkably improved.

The invention relates to the technical field of power equipment, in particular to an intelligent circuit breaker electric operation mechanism integrating multi-parameter detection and an intelligent monitoring system.The intelligent circuit breaker electric operation mechanism comprises a basic mechanism unit, a detection unit, an intelligent control unit and a communication unit, and the basic mechanism unit comprises a double-layer sliding block compensation system and a transmission mechanism; the double-layer sliding block compensation system is composed of a first sliding block, a second sliding block and a spring set, and a thin film pressure sensor is integrated on the bottom face of the second sliding block. According to the intelligent circuit breaker electric operation mechanism integrated with multi-parameter detection and the intelligent monitoring system, the health state and fault assessment system achieves fault assessment through data receiving, feature fusion, health modeling and fuzzy reasoning, and the intelligent monitoring system comprises a remote monitoring platform. And data display, fault early warning, remote control and reference model optimization are supported, intelligent control and multi-dimensional state monitoring of the circuit breaker are realized, and the fault diagnosis accuracy and the reliability of a power system are improved.

The invention belongs to the technical field of equipment state monitoring and fault diagnosis, and relates to a pole-mounted switch damp defect diagnosis method and system based on multi-source information fusion, and the method comprises the steps: collecting a leakage current signal and a high-frequency pulse current signal flowing through a grounding wire in real time, and environment parameters around a target pole-mounted switch; preprocessing the leakage current signal and the high-frequency pulse current signal, and performing feature extraction based on the preprocessed leakage current signal, the preprocessed high-frequency pulse current signal and environmental parameters to obtain a leakage current feature quantity, a high-frequency discharge feature quantity and an environmental coupling factor; and based on the leakage current characteristic quantity, the high-frequency discharge characteristic quantity and the environment coupling factor, judging a damp fault of the target pole-mounted switch, and combining a D-S evidence theory and fusing multi-source information confidence to complete damp defect diagnosis of the target pole-mounted switch. According to the invention, complementarity between different information sources can be fully utilized, and the damp state of the pole-mounted switch can be accurately judged.

The invention relates to the technical field of power equipment monitoring, and discloses a high-voltage circuit breaker state detection and fault diagnosis method. The method comprises the following steps: acquiring real-time state data of the high-voltage circuit breaker and preprocessing to obtain standardized data; extracting time domain, frequency domain and time frequency features from the standardized data to form feature vectors; analyzing the feature vector by using an anomaly detection algorithm, and identifying an abnormal state mode; based on the abnormal state mode, combined with historical fault records, a causal reasoning method is applied to mine a causal relationship between an abnormal state and a fault, and a key fault index is determined; establishing a health reference model according to the key fault indexes, and calculating the deviation degree between the current state data and the health reference model; evaluating the deviation degree to obtain a fault risk level; and estimating the fault probability of the high-voltage circuit breaker in the future time period by using the prediction model according to the fault risk level and the key fault index, and generating a state detection report.

The invention discloses a handcart type high-voltage circuit breaker operating characteristic evaluation method based on big data analysis, and particularly relates to the field of data analysis, which comprises the steps of monitoring time division, data entry, data analysis and evaluation. All-time multi-dimensional monitoring is achieved through big data analysis, monitoring sub-areas are divided, dynamic data are collected at high frequency, transient changes are captured, the limitation of traditional single-point monitoring is broken through, and comprehensive data support is provided; quantitative indexes are constructed based on a multi-parameter fusion model, the electrical and mechanical system collaboration is comprehensively evaluated, the multi-dimensional performance is reflected, and the evaluation precision is improved; and a health interval judgment rule is established, so that real-time comprehensive evaluation can be realized, faults can be accurately positioned, anomalies can be recognized in advance, a basis is provided for maintenance, and the intelligent level of power operation and maintenance and the fault prevention capability are improved.

The invention discloses a gear calibration method for a 3D Hall type combination switch, and the method comprises the steps: starting an upper computer, notifying a six-axis force control robot and a CAN bus tester to prepare, enabling the robot to insert a test sleeve into a switch driving lever, and calibrating the test sleeve to a switch center; the six-axis force control robot pokes a poking rod to test a first gear, collects a force-angle-time array and X / Y / Z magnetic field data of a 3D Hall sensor, calculates alph and beta angles, constructs a correlation array and sends the correlation array to an upper computer; the upper computer generates a multi-dimensional curve graph, analyzes the maximum operating force F1 and the angle S1, finds out a force control point F3 and an angle S3 which are equivalent to the F1 after the S1, and determines the trigger time T3 and calibration data alpha < ph3 >, beta < 3 >, X3, Y3 and Z3; repeating the steps to obtain calibration data of each gear, and after the upper computer is verified to be qualified, writing a product by the CAN bus tester to complete calibration; and 6, the axial force control robot tests all gears, and the CAN bus tester detects the triggering condition and verifies the calibration effectiveness. Problems of early triggering, delayed triggering and the like of the switch can be avoided, and the gear function is accurately triggered.

The invention relates to the technical field of electrical fault positioning, in particular to an urban rail train relay state online monitoring method and system. The specific implementation process comprises the following steps: collecting coil current, contact voltage, load current and environment vibration signals by using an electrical sensor group; when the coil current is subjected to step change, intercepting an electromechanical characteristic data set; arc energy and contact resistance fluctuation characteristics are calculated, correlation analysis is carried out in combination with environment vibration signals, and passive fluctuation and active fluctuation are distinguished; and establishing a multi-factor health degree evaluation model, dynamically adjusting the arc energy and the weight coefficient of the contact resistance according to the magnitude of the load current and the vibration intensity, monitoring the health state of the relay in real time, and predicting the residual life. Through multi-dimensional data synchronous acquisition and vibration decoupling, the interference of mechanical vibration on electrical performance monitoring is effectively eliminated, the problem that a dynamic working condition cannot be reproduced in an off-line test is improved, and the accuracy of relay state evaluation and service life prediction is remarkably improved.

The invention relates to the technical field of electrical equipment state monitoring, in particular to an intelligent circuit breaker electrical life detection method and system based on data analysis. The method comprises the following steps: synchronously acquiring opening coil voltage and current sequences, a main loop current sequence and environment temperature data when a circuit breaker acts, extracting actually measured action time based on the coil data, and calculating a mechanical health degree factor by combining the environment temperature and the actually measured action time; after decoupling correction is carried out on an electrodynamic force interference factor derived by a peak current of a main loop, a mechanism wear state index is obtained; a nonlinear damage model containing basic electric heat loss and mechanical degradation acceleration factors is constructed, a single-time electric life loss value is calculated, historical loss values are accumulated, a dynamic threshold value is combined to realize full-life-cycle prediction, and evaluation interference of environmental factors and large-current electrodynamic force is eliminated through deep data analysis. The nonlinear acceleration effect of mechanism hysteresis on contact ablation is quantified, and the detection precision is remarkably improved.

The invention provides a double-path causal fusion circuit breaker fault diagnosis model optimization method and system, and the method comprises the steps: carrying out the staged signal index extraction of obtained working condition data in the switching-on and switching-off process of a circuit breaker, obtaining an original signal segment, and taking the original signal segment as an input; taking a fault classification result corresponding to the original signal segment as output, training the fault diagnosis model, and obtaining a trained to-be-optimized fault diagnosis model, the genetic annealing algorithm is utilized to dynamically optimize the to-be-optimized hyper-parameters of the obtained to-be-optimized fault diagnosis model by adopting the preset composite fitness function to obtain optimized hyper-parameters, and the optimized hyper-parameters are substituted into the to-be-optimized fault diagnosis model to obtain an optimized fault diagnosis model, so that the limitation of a single optimization algorithm is overcome, and the optimization efficiency is improved. Effective improvement of the model is realized, and the diagnosis capability of circuit breaker faults and causal chain fracture faults is further improved.

The invention relates to the technical field of intelligent circuit breakers, in particular to a holographic monitoring method based on a circuit breaker intelligent algorithm. The method comprises the steps of collecting operation data of a line where the circuit breaker is located; performing data preprocessing on the operation data; performing data processing on the preprocessed operation data to obtain monitoring parameters; holographic monitoring is carried out on an electrical fault according to the monitoring parameters; wherein when the operation data is preprocessed, a dual-frequency injection method is adopted to realize the separation of resistive residual current, a 2kHz high-frequency test signal is superposed on the basis of a fundamental wave frequency, and the resistive residual current is separated; meanwhile, an intelligent algorithm is adopted to carry out de-noising processing on the signal, that is, a dynamic threshold value is adopted to carry out wavelet de-noising processing, and an interlayer correlation coefficient is fused into wavelet threshold value calculation, so that self-adaptive protection of fault features is realized, and subsequent accurate power grid holographic monitoring is facilitated.

The invention relates to the technical field of intelligent monitoring of drop-out fuses, in particular to an integrated intelligent monitoring device and method for a drop-out fuse, and the device comprises a measurement module which is used for measuring the voltage and current analog signals of the incoming and outgoing line ends of the drop-out fuse; the peripheral controller is connected with the measuring module and is integrated with an AD acquisition unit, a data processing unit, a GPS positioning unit and a wireless communication unit; and the power supply module is connected with the peripheral controller and is used for supplying power to the device. The system has the beneficial effects that a plurality of key functions such as voltage and current acquisition, data processing, GPS positioning and wireless communication are highly integrated in a compact device, so that the size and installation complexity of equipment can be greatly reduced, the overall reliability and stability of the system can be effectively improved, and meanwhile, the failure rate of the equipment is reduced.