4928results 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 relates to the technical field of edge calculation application and partial discharge detection, in particular to a partial discharge multichannel signal real-time synchronous acquisition method based on edge calculation. According to the method, the functions of multi-channel signal acquisition, time synchronization, feature analysis, hierarchical storage and the like are integrated at edge nodes, asynchronous acquisition, unified timestamp marking and data synchronous fusion of multiple types of partial discharge signals are realized, and extraction of multi-dimensional feature parameters such as time domain, frequency domain and energy and intelligent event discrimination are locally completed. For abnormal signals, the system realizes classified storage and remote uploading after encryption and compression processing; and for normal signals, dynamic management is carried out through circular caching. According to the method, the accuracy and efficiency of multichannel signal synchronous acquisition are remarkably improved, the data safety and the system adaptive capacity are enhanced, and the method is suitable for real-time online monitoring and intelligent diagnosis of partial discharge of power equipment.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The invention provides a power distribution network operation risk intelligent identification and early warning method, which comprises the steps of identifying an equipment contact failure probability through an actual wear state, and when the equipment contact failure probability exceeds a safe operation requirement, determining an equipment fault early warning signal through historical fault statistical data, identifying potential equipment failure risk points and extracting risk distribution characteristics; identifying a high-risk equipment node through the equipment fault early warning signal, evaluating whether a cascading fault of adjacent equipment overload is caused after power flow redistribution of a power grid according to the identified high-risk node, extracting a fault propagation path, and determining a system risk level distribution diagram; and carrying out risk area division on the system risk level distribution diagram, identifying key equipment nodes in a high-risk area, extracting a load transfer scheme of the high-risk area, and determining a load distribution path and a power transmission direction.

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 discloses a high-voltage power switch fault diagnosis method based on wavelet threshold correction and noise reduction. Acquiring a voltage signal of the high-voltage power switch by using a sensor, and performing synchronous sampling; a wavelet threshold value correction noise reduction method is adopted to carry out noise reduction processing on the collected signals, wavelet detail coefficients are calculated through multi-scale decomposition, a threshold value is adaptively corrected based on the peak sum ratio, and the noise removal effect is optimized; thirdly, performing normalization processing on the denoised signal, mapping the signal to a polar coordinate system, constructing a two-dimensional Gramer angle field containing an included angle cosine value and amplitude information, and realizing time sequence-space conversion of the signal; and finally, generating two-dimensional image data of the voltage signal of the high-voltage power switch, and providing feature input for subsequent state evaluation and fault detection. According to the method, wavelet transform and two-dimensional feature mapping are combined, noise interference can be effectively reduced, the signal distinguishability and the information retention capacity are improved, and the method is suitable for state monitoring and intelligent diagnosis of a power system.

The invention discloses automatic complete equipment for intelligent predictive maintenance of a primary and secondary fusion circuit breaker. The automatic complete equipment comprises a multi-sensor fusion unit, an edge calculation and analysis module; a parameter interaction module; a predictive maintenance decision unit; the primary and secondary converged communication architecture is used for managing control information and state information on the basis of an IEC61850 (International Electrotechnical Commission 61850) standard; wherein a noise covariance matrix and a feature weight coefficient of the adaptive Kalman filtering health assessment algorithm are dynamically adjusted according to a data quality index and prediction error feedback, and input features of the residual life prediction algorithm based on the LSTM comprise a health index, a change rate and component-level health state information from the health assessment algorithm. Accurate evaluation of the health state of the circuit breaker and accurate prediction of the residual life are achieved, the optimal maintenance strategy is generated, the operation reliability of the circuit breaker is improved, and the maintenance cost is reduced.

A method and system for predicting an effective remaining electrical life of a high-voltage circuit breaker. A circuit breaker electrical life test is carried out and associated data sets of single arcing times and single arcing energy are constructed; a degree of wear of a contact is replaced with accumulated arcing energy to characterize whether a circuit breaker fails; and function fitting is used to construct the relationship between the single arcing energy and the single arcing times; and on this basis, the method for predicting an effective remaining electrical life of a high-voltage circuit breaker is formed. Therefore, the rapid and accurate prediction of a remaining service life of a circuit breaker can be realized only by measuring simple data, thereby providing guarantee for the reliable operation of a power grid.

The invention discloses a circuit breaker fault positioning method based on a graph neural network, and the method comprises the following steps: S1, collecting operation data, constructing a node set and an edge set, and forming a power grid topological graph; s2, preprocessing the operation data, and embedding the operation data as node attributes into a power grid topological graph; s3, constructing a graph convolutional neural network, and generating a multi-scale graph embedded representation; s4, introducing a residual error coding module, constructing a residual error response characteristic graph, and fusing adjacent node residual error characteristics in a graph convolution mode; s5, constructing a global sensitive node sorting function, and extracting node indexes with activation response amplitudes exceeding a threshold value as a candidate fault node set; and S6, performing confidence calculation on each node, and selecting the node number with the highest confidence value as a positioning result of the circuit breaker fault. The fault circuit breaker node can be accurately positioned in the complex power grid topology, and the fault diagnosis efficiency and the positioning precision 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 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 relates to the technical field of pattern recognition, in particular to a GIS ultrahigh frequency partial discharge signal separation method based on pattern recognition, which comprises the following steps: synchronously acquiring original data by using various sensors, and preliminarily eliminating interference data to obtain data to be analyzed; generating a first characteristic spectrogram and a second characteristic spectrogram based on the to-be-analyzed data, and extracting spectrogram features; according to the spectrogram characteristics, judging judgment conditions which are met by the to-be-analyzed data, wherein the judgment conditions comprise a first mode, a second mode and a third mode; if the to-be-analyzed data meets the corresponding discrimination condition, classifying the to-be-analyzed data through a corresponding data classification path; and fusing classification results obtained through the data classification paths based on confidence, outputting a classification result of the to-be-analyzed data, and separating identified real data from interference data.

The invention discloses a primary and secondary fusion pole-mounted circuit breaker intelligent fault diagnosis and monitoring system, and relates to the technical field of electric appliance fault diagnosis analysis, and the system comprises an information collection module, a sensing monitoring module, a diagnosis prediction module, an evaluation module and an optimization module. Communication barriers among different devices are broken through, the system can be compatible with various devices, comprehensiveness and compatibility of data acquisition are improved, it is ensured that data of various devices can be effectively acquired and integrated, accuracy and reliability of the data are ensured, data transmission quantity is reduced through compression transmission, network bandwidth pressure is reduced, and key fault information can be transmitted in time. Operation and maintenance personnel can quickly respond and process, the fault influence range and time are reduced, key information can be accurately extracted according to actual requirements through the feature extraction module, and the pertinence and accuracy of fault diagnosis are improved.

The invention belongs to the technical field of power monitoring, and discloses a detection method for monitoring the state of a primary and secondary fusion circuit breaker, which comprises the following steps: acquiring state data of the circuit breaker, including electromagnetic waveform data, harmonic response characteristics and temperature distribution gradient; obtaining circuit breaker electric field distribution fingerprints through nonlinear segmented harmonic analysis, constructing a multi-dimensional permeation characteristic matrix, and establishing a micro-scale electrothermal coupling map; performing impedance distortion analysis to obtain a nonlinear region situation table, performing multi-frequency response analysis to obtain a harmonic penetration curve, and constructing a high-frequency harmonic penetration path spectrum; a micro-region diagnosis algorithm is established, nonlinear impedance calibration is carried out, and a multi-level protection instruction sequence is generated; adjusting the operation state of the circuit breaker to obtain a circuit breaker medium steady-state index, and performing risk analysis to obtain a harmonic penetration early warning report; according to the invention, accurate identification and early warning of the abnormal-state area in the circuit breaker are realized, and the safe and stable operation capability of the primary and secondary fusion circuit breaker is improved.