8431results about "Testing dielectric strength" patented technology

The invention provides an integrated power generation equipment health state supervision system. Comprising the following steps: acquiring operation parameters and environment parameter data of an asynchronous motor precision point inspection diagnosis device, a TLI-X8 battery online monitoring system, a collecting ring intelligent data acquisition system, a transformer oil dissolved gas and micro water online monitoring system, a generator partial discharge online monitoring device and a wireless temperature measurement system; and training prediction is performed to obtain fault prediction data, so that accurate fault diagnosis can be realized on the basis of extracting original data features by deep learning. According to the application, an asynchronous motor precision point inspection diagnosis device, a transformer oil gas analysis system, a partial discharge detection system, a wireless temperature measurement system, a battery online monitoring system and the like are organically combined, and power generation equipment faults are predicted through the mother convolutional neural network model and the sub-machine learning model, so that early fault discrimination is realized; and the accuracy and efficiency of equipment fault identification are greatly improved.

The invention provides a power cable joint insulation state intelligent monitoring method based on a complex environment. The method belongs to the technical field of power system monitoring, and comprises the steps that related data are collected through multiple means, and the related data comprise environment temperature data, partial discharge signals and temperature distribution images; collected environment parameters and partial discharge signals are input to an intelligent analysis system, preprocessing and feature extraction are carried out, an insulation state prediction model is established based on a machine learning algorithm, and the aging degree and potential fault points of a cable joint insulation layer are predicted according to historical data and real-time monitoring data. Environment temperature data, partial discharge signals and temperature distribution images are collected through multiple means, and an intelligent analysis system and a machine learning algorithm are combined, so that the aging degree and potential fault points of the cable joint insulating layer can be predicted more accurately.

The invention relates to the technical field of power supply or power distribution circuit systems, in particular to a photovoltaic module fault monitoring system and method, and the system comprises an acquisition module, a power supply dynamic compensation module, a control optimization module, a power supply module and a digital twin module. The acquisition module acquires multi-source data through the infrared thermal imaging sensor, the electroluminescent detection unit and the current and voltage characteristic curve acquisition module, the power supply dynamic compensation module dynamically adjusts wavelet basis function parameters based on an improved whale optimization algorithm, and the power supply module fuses hot spot distribution, current and voltage abnormity and aging trend data. And driving the dynamic adjustment of the protection threshold. And the digital twin module synchronizes real and virtual system data through transfer learning, rehearses a fault path to generate a maintenance instruction and feeds back the maintenance instruction to an optimization prediction model, so that a monitoring, protection and self-optimization closed-loop system is formed, and the fault diagnosis precision and the system reliability in a complex environment are improved.

The invention discloses an intelligent power distribution network equipment state perception and abnormity diagnosis system, and the system operation process specifically comprises the following steps: collecting the operation state data of power distribution network equipment in real time, carrying out the time-space alignment and feature fusion, and generating an equipment multi-dimensional state vector; inputting a pre-constructed equipment health dynamic baseline model, and outputting a real-time health deviation degree; when the real-time health deviation degree exceeds an early warning deviation threshold value, triggering an abnormal preliminary screening mechanism, and extracting abnormal feature fragments; inputting a multi-stage diagnosis knowledge graph model, and generating an abnormal cause hypothesis set; performing confidence ranking on the abnormal cause hypothesis set, and outputting first # imgabs0 diagnosis results and corresponding confidence weights; and generating an equipment maintenance strategy instruction set according to the diagnosis result. The method has the following advantages and effects: the dynamic baseline is adaptively generated from multi-source data, and a multi-stage diagnosis framework of a physical model, a power grid rule and a historical case is fused, so that the accuracy and timeliness of anomaly diagnosis are finally improved.

The invention relates to the technical field of fault detection, in particular to an intelligent sensing online monitoring system and method for a ring main unit, and the system comprises an energy collection module, a sudden change analysis module, an abnormity locking module, a risk partitioning module and a link synchronization module. According to the method, an energy parameter is formed through 50 millisecond sampling period integration, a short-time energy change characteristic is captured, an abnormal segment is extracted based on a sudden change rate threshold value, an energy change direction and sudden change amplitude are extracted to lock an abnormal starting point, a leakage current instantaneous value is continuously collected, and a maximum amplitude and an average amplitude are extracted to calculate offset quantification insulation fluctuation. And calling time window data to calculate an amplitude fluctuation rate, executing multi-threshold judgment and evaluation of a degradation degree, packaging an energy parameter, an amplitude offset and insulation grade information in combination with an offset, executing synchronization consistency to correct node deviation, ensuring reliable data synchronization, and improving insulation trend identification sensitivity and abnormal state monitoring accuracy.

The present invention relates to the technical field of power system equipment and control. The method of the present invention comprises: installing sensors to acquire data for online real-time monitoring, and constructing a comprehensive condition online monitoring model for comprehensive data analysis; comprehensively evaluating the health condition of a generator on the basis of a comprehensive analysis result, and identifying fault causes; and constructing a generator comprehensive data intelligent monitoring and dynamic early warning model to perform insulation degradation trend analysis and prediction on the generator. In the present invention, by comprehensively monitoring the condition of a turbine generator, various key indicators of the generator are captured in real time, and trends and patterns underlying the data are revealed, providing technical support for accurately evaluating the health condition of the generator; and continuous monitoring for the insulation condition of the generator allows for proactive identification of potential risks, thereby providing decision-making support for preventive maintenance, helping operators take measures promptly, preventing faults, improving the reliability and safety of the generator.

The invention discloses a cable insulation aging defect positioning evaluation self-diagnosis method, and belongs to the technical field of power system detection. According to the method, cable multi-dimensional data are obtained through a multi-band broadband dielectric spectrometer, a high-frequency pulse reflection tester and an optical time domain reflectometer, and features are extracted by applying wavelet transform, Fourier transform and a thermal diffusion model after digital processing. A deep convolutional neural network and a long and short term memory network are used for feature fusion and time sequence analysis, and a self-adaptive threshold algorithm and a fuzzy inference system are used for realizing defect accurate positioning and reliability scoring. According to the method, the accuracy and reliability of cable insulation aging defect diagnosis are improved, and intelligent self-diagnosis is realized.

A reactor partial discharge diagnosis method based on multi-source feature fusion and a deep network relates to the technical field of partial discharge detection, and comprises the following steps: S1, data preprocessing and feature extraction; s2, extracting multi-dimensional feature information; s3, processing the high-dimensional feature set through an SWFK-DPC clustering algorithm; s4, constructing a deep multi-source adaptive convolutional neural network (EDMSACNN); s5, introducing cross-domain feature compression and a double-path attention mechanism; s6, introducing a confrontation enhancement and classification module; and S7, model optimization and iterative training. According to the method, non-linear interaction of multi-source features is realized through tensor shrinkage and an attention mechanism, the limitation of traditional linear fusion is broken through, data distribution differences under different working conditions are effectively relieved based on an optimal transmission feature alignment technology, and the adaptive cross-domain capability is remarkably improved.

The invention discloses a power transformer partial discharge positioning method based on multi-sensor array fusion, and the method comprises the following steps: S1, selecting a sensor installation point, and laying a multi-sensor array structure; s2, partial discharge signals of the three types of sensors are collected, and primary signal processing is carried out; s3, calculating propagation time differences between the reference channel and other channels by adopting a generalized cross-correlation weighting algorithm, and generating a time difference matrix; s4, constructing a TDOA model in combination with the layout coordinates and the time difference matrix, and solving three-dimensional initial coordinates of a power supply; s5, establishing a structure correction model, compensating the path deviation, and outputting corrected positioning coordinates; s6, calculating an error and generating a confidence score; s7, mapping a positioning result to the three-dimensional model and generating an image; and S8, writing the positioning information into a database for filing management. According to the method, the multi-frequency sensor and the path correction model are fused, and high-precision three-dimensional positioning of partial discharge of the transformer is realized.

The invention provides a traveling wave cable measurement sensing method and system based on a time-frequency domain combined reflection principle, and relates to the technical field of measurement, and the method comprises the steps: arranging a high-frequency signal transmitting device and a signal receiving device at one end of a traveling wave cable; establishing a time domain reflection function and a frequency domain reflection function of the traveling wave cable; constructing a time-frequency domain joint reflection model; collecting a reflected signal of the traveling wave cable through the signal receiving device to obtain time domain data and frequency domain data of the reflected signal; carrying out conjoint analysis on the time domain data and the frequency domain data by utilizing a time-frequency domain joint reflection model, and extracting characteristic parameters of a reflected signal; calculating impedance change and signal propagation characteristics along the traveling wave cable based on the characteristic parameters in combination with a time-frequency domain joint reflection model; through analysis of impedance change and signal propagation characteristics, intrusion detection, leakage positioning and fault diagnosis along the traveling wave cable are realized.

The present application relates to a switch cabinet discharge fault detection method, system, equipment and medium, belonging to the technical field of power equipment fault detection, the fault detection method includes: receiving real-time monitoring data of the switch cabinet to be monitored and aligning the timestamp; performing dynamic amplitude compensation on the electric field signal data based on the aligned current humidity data, generating the corrected electric field signal data and performing time-frequency domain analysis, extracting the discharge pulse density and high-frequency energy proportion; performing frequency filtering and waveform analysis on the vibration signal, calculating the impact intensity factor and the vibration energy entropy value; making abnormal judgments on the discharge pulse density and the impact intensity factor, and marking the abnormal discharge according to the abnormal judgment results; based on the synchronous change correlation of the high-frequency energy proportion and the impact intensity factor and the vibration energy entropy value, verifying the abnormal discharge marking result, and obtaining the discharge fault result. The present application can realize efficient detection and accurate diagnosis of switch cabinet discharge faults.

The invention discloses an intelligent cable fault accurate positioning and early warning method and system, and the method comprises the steps: collecting the temperature gradient, strain distribution and partial discharge signals of the whole length of a cable in real time through a distributed optical fiber sensing network, and generating a multi-dimensional feature matrix of the operation state of the cable; based on the multi-dimensional feature matrix, outputting a preliminary fault positioning coordinate; generating corrected fault coordinates according to the topological structure data of the cable laying environment and the electromagnetic interference distribution diagram; historical fault data, real-time operation parameters and the corrected fault coordinates are fused, and a fault risk thermodynamic diagram in a future preset duration is output; and based on the fault risk thermodynamic diagram and real-time monitoring data, generating fault first-aid repair information by using a dynamic priority algorithm, synchronously triggering an early warning signal, and visually displaying a fault positioning result and a risk area in a three-dimensional geographic information system. According to the embodiment of the invention, rapid positioning, accurate early warning and intelligent disposal of the cable fault can be realized.

The invention relates to a cable online operation fault positioning, monitoring and early warning method and system based on a neural network algorithm, and the method comprises the steps: collecting the operation environment and state data of a cable through a multi-source sensor, and generating an original monitoring data set through noise reduction and time sequence alignment; aiming at the problem of periodic distortion caused by seasonal fluctuation of environment temperature and humidity, performing dynamic reference compensation to generate a reference data set without seasonal influence; aiming at a coupling effect of a dynamic load and temperature drift, calculating insulation performance evaluation data of temperature compensation through load-insulation correlation mapping; aiming at the problem of disconnection of waterproof sealing monitoring and fault positioning, a sealing degradation grade is generated based on regional high-humidity detection; aiming at the defect of a fixed threshold strategy, generating a fault coordinate and a risk probability in combination with spatial positioning and probability analysis; and finally, self-adaptive updating of the parameter library is realized through credibility verification.

The invention relates to the technical field of artificial intelligence and data processing, in particular to a mining explosion-proof high-voltage power distribution device-oriented all-insulation state intelligent monitoring method, which specifically comprises the following steps of: acquiring training data to construct a data set; marking the data in the data set; preprocessing the data in the data set; constructing an intelligent monitoring model for the full-insulation state of the mining explosion-proof high-voltage power distribution device based on a deep probabilistic neural network, inputting the preprocessed data set into the model for training, and repeating the iterative training process until a preset iteration stopping condition is met to obtain a trained model; and inputting the preprocessed real-time data into the trained model, generating probability distributions of normal operation, slight degradation and serious degradation and corresponding uncertainty quantized values by an output layer, and obtaining a final diagnosis result according to a preset discrimination condition. According to the invention, the accuracy and anti-interference capability of insulation state diagnosis are significantly improved.

The invention discloses a GIS partial discharge intelligent diagnosis system and method based on one-dimensional ultrahigh frequency signal analysis, and relates to the technical field of power electrical equipment intelligent monitoring, and the system comprises a signal collection and preprocessing module which is used for collecting ultrahigh frequency signals of GIS equipment and obtaining preprocessed signal data through a dynamic threshold algorithm; the discharge initial judgment module is used for performing multi-dimensional sequential judgment to eliminate interference discharge data so as to obtain effective discharge signal data; the feature extraction module is used for performing time domain kurtosis and pulse width analysis, frequency domain energy distribution analysis and time-frequency domain wavelet entropy calculation based on the multi-dimensional features of GIS partial discharge, and generating an optimized feature subset; and the type identification module is used for identifying the partial discharge type by using the integrated learning model to obtain a diagnosis result. According to the invention, the problem of unstable recognition accuracy caused by insufficient signal preprocessing, single feature representation and single classification algorithm in the prior art is solved.

The invention discloses a distribution network cable dielectric loss withstand voltage partial discharge intelligent detection and evaluation method and system, and relates to the technical field of power equipment state monitoring, and the method comprises the steps: synchronously collecting a dielectric loss spectrum, a withstand voltage leakage current curve and a partial discharge spectrum of a distribution network cable through a phase resolution measurement device; processing the partial discharge spectrum by using a frequency domain adaptive filter to generate a denoised partial discharge characteristic spectrum; constructing a cable multi-dimensional parameter normal domain hyperspherical model, and calculating a deviation distance value; and inputting the deviation distance value into a fuzzy inference engine, performing nonlinear conversion according to a preset fuzzy rule base, generating an insulation state evaluation index and a fault risk probability distribution diagram of the distribution network cable, and realizing accurate evaluation of the cable insulation state. According to the invention, the accurate evaluation of the cable insulation state and the accurate positioning of the fault position are realized, and the operation reliability and maintenance efficiency of the power distribution network are improved.

The invention discloses a self-adaptive filtering and intelligent separation method for a multi-mode partial discharge signal, and relates to the technical field of insulation state monitoring and signal processing of power equipment, and the method comprises the following steps: S1, collecting the partial discharge signal of the power equipment in real time, and synchronously obtaining an equipment operation parameter and an environment noise signal; according to the adaptive filtering and intelligent separation method for the multi-modal partial discharge signal, the problems of modal aliasing and feature distortion caused by signal and noise time-frequency coupling in a dynamic noise environment in a traditional method are effectively solved through a cooperative mechanism of dynamic coupling degree modeling and dual-channel adversarial decoupling. The dynamic coupling path sensing module quantifies interaction characteristics of noise and signals in real time, and realizes accurate suppression of high-frequency transient interference and low-frequency periodic noise in combination with a double-channel architecture of complex field phase sensitive filtering and vibration trajectory matched filtering.

The invention discloses a method and system for monitoring insulation aging of a distribution cable based on leakage current, and belongs to the technical field of power system monitoring. According to the method, data are collected through a leakage current sensor, abnormal data are screened after preprocessing, time-frequency domain analysis is carried out through multi-scale wavelet transform, and features are extracted and input into a deep learning model to recognize insulation aging features. And calculating an aging index based on the identification result, determining an aging level and generating a maintenance suggestion. And the result is sent to a management center and stored in a database for subsequent analysis and strategy making. According to the method, real-time monitoring, intelligent diagnosis and predictive maintenance of the insulation state of the power distribution cable are realized, the reliability of a power distribution system is improved, and the maintenance cost is reduced.

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 ring main unit monitoring, and discloses a ring main unit online monitoring system based on multi-state quantity sensing. The system comprises an acquisition module used for acquiring a temperature signal and a partial discharge signal and obtaining state quantity data; the calibration module is electrically connected with the acquisition module and is used for preprocessing and calibrating the state quantity data; the analysis module is electrically connected with the calibration module and is used for performing multi-dimensional analysis on temperature and discharge characteristics; the transmission module is electrically connected with the analysis module and is used for data transmission and fusion processing; the early warning module is electrically connected with the transmission module and is used for performing multi-stage early warning analysis; and the adjusting module is electrically connected with the early warning module and is used for dynamically adjusting the monitoring parameters. According to the invention, accurate evaluation and timely early warning of the equipment operation state are realized, and the technical problems of low accuracy and poor adaptability of a single state quantity monitoring method are solved.

The invention discloses a transformer partial discharge intelligent diagnosis method based on an improved transformation network, and relates to the technical field of transformer fault diagnosis, and the method comprises the steps: collecting a transformer partial discharge signal, and carrying out the preprocessing of the partial discharge signal, and obtaining a time sequence; respectively converting the time sequence into feature images by adopting a Markov conversion field (MTF) algorithm, a GASF algorithm and a GADF algorithm; processing the feature image through an improved transformation network model to obtain a fusion feature vector; and inputting the fusion feature vector into a fault diagnosis model, and outputting a transformer partial discharge fault diagnosis result. According to the method, the oil gap discharge recognition rate is improved in the test by combining an interpretable diagram reasoning mechanism of environmental parameters and signal characteristics, so that fault diagnosis spans from a traditional judgment mode based on a threshold value to an intelligent mode based on causal logic reasoning.

The invention discloses a multi-parameter measurement system and method for a power frequency non-partial discharge test transformer, and relates to the technical field of state monitoring of high-voltage test equipment and power equipment, and the system comprises a synchronous trigger unit which is connected to a power frequency voltage signal collection end and is used for detecting a zero crossing point of a power frequency voltage signal and generating a synchronous trigger signal, a power frequency period is divided into a plurality of sub-windows with equal phase angles. According to the multi-parameter measurement system and method for the power frequency non-partial discharge test transformer, the problem of phase mismatch of a power frequency signal and a high-frequency partial discharge signal in traditional multi-parameter measurement is effectively solved through a power frequency phase locked synchronous trigger mechanism and a dynamic noise suppression technology; and the positioning precision of the partial discharge source and the insulation defect diagnosis reliability are improved. The nanosecond-level time sequence error control is realized, and the power frequency coupling interference is inhibited while the details of the high-frequency pulse are kept by combining a phase correlation dynamic filtering strategy, so that the signal-to-noise ratio of the weak discharge signal is improved.

The invention discloses a multi-modal data fusion power equipment health state diagnosis method and system, and belongs to the field of power equipment state monitoring, and the method comprises the steps: S100, collecting infrared thermal imaging data, vibration signals, current harmonic data and partial discharge signals of power equipment, and carrying out the time synchronization and space registration; and S200, acquiring infrared thermal imaging data, vibration signals, current harmonic data and partial discharge signals, and inputting the infrared thermal imaging data, the vibration signals, the current harmonic data and the partial discharge signals into the dynamic weight fusion model to obtain an equipment health state score and a fault type. And S300, according to the equipment health state score and the fault type, triggering grading alarm. According to the invention, timely early warning can be carried out on potential fault hidden dangers of power equipment.

The invention relates to the technical field of industrial safety, and discloses an electrolytic aluminum short circuit port operation safety early warning system based on multi-parameter collaborative awareness and intelligent diagnosis, and the system comprises a parameter collaborative awareness module, a dynamic diagnosis module, an early warning decision module, and an execution feedback module. By constructing a multi-dimensional parameter collaborative sensing mechanism, fusing temperature field distribution, current balance degree and insulation state multi-source data in real time and dynamically capturing early abnormal symptoms of a short circuit port, the hysteresis problem of traditional single-parameter threshold monitoring is solved, conversion from passive response to active defense is achieved, and the comprehensiveness and timeliness of operation state monitoring are improved; and meanwhile, based on a historical fault database and a real-time evolution model, a health index is generated and a fault path is predicted, so that maintenance personnel can pre-judge a development trend and a time window of potential risks in advance, and sudden equipment accidents are avoided.

The invention relates to the technical field of line arc abnormity monitoring, in particular to a data center operation and maintenance service environment monitoring system which comprises a multi-mode sensing unit, a dynamic baseline modeling unit, a transition state abnormity extraction unit and a grading early warning unit. The dynamic baseline modeling unit builds a three-layer safety baseline model, the base layer builds harmonic references in a segmented mode according to the load rate, the environment layer generates voiceprint feature template libraries of different temperature and humidity intervals, the time layer fits a 24-hour change trend envelope line, and the transition state anomaly extraction unit separates and decouples an arc voiceprint feature frequency band through a blind source and calculates the energy ratio. The harmonic abrupt change phase deviation amplitude is analyzed in combination with window sliding correlation, the abnormal evolution index is generated through fusion, the graded early warning unit triggers response according to the index, transition state abnormity is accurately recognized, the perspectiveness and reliability of operation and maintenance early warning are improved, and the method is suitable for safe operation and maintenance of data center equipment.

The invention provides an arc fault detection method and device based on lightweight, and a storage medium, and the method comprises the steps: obtaining an arc current signal of a power distribution network load in a preset time period, determining a signal-to-noise ratio parameter, determining a dynamic window length according to the signal-to-noise ratio parameter, carrying out the Hilbert transformation of the arc current signal according to the dynamic window length, and obtaining an arc fault detection result. The method comprises the steps of obtaining m amplitude envelope data, extracting features from the m amplitude envelope data to obtain p time domain statistical features, calculating arc current signals by adopting a preset Fourier transform method to obtain arc current frequency spectrum data, processing the arc current frequency spectrum data to obtain q frequency domain features, and fusing the p time domain statistical features and the q frequency domain features based on a preset feature fusion algorithm to obtain a target arc current feature, and inputting the target arc current feature into a preset lightweight arc fault detection model to obtain an arc fault detection result. Therefore, the accuracy of arc fault detection is improved.

The invention discloses a direct-current arc fault diagnosis method and device with adaptive capacity. The method comprises the following steps: 1) collecting a current alternating-current component when a current direct-current system normally works; 2) extracting time-frequency domain characteristics of a current AC component, wherein the time-frequency domain characteristics comprise a current peak-to-peak value, a standard deviation and an amplitude reconstruction harmonic power sum; 3) performing multi-time-frequency-domain feature fusion based on mahalanobis distance to obtain arc detection features; 4) calculating an arc fault judgment threshold value based on a box plot method; 5) collecting a current AC component in real time to calculate a Mahalanobis distance, comparing the Mahalanobis distance with an arc fault determination threshold, and recording an arc event; the method has the advantages that the system adaptability is high, the detection rate is high, the false alarm rate is low, and the method can be widely applied to high-voltage and low-voltage direct-current power distribution systems such as airplanes, vehicles and ships.

The invention relates to the technical field of data management, in particular to an electrical equipment fault prediction system and method based on data fusion, and the system intelligently adjusts a data collection strategy by accurately capturing electromagnetic signal characteristic parameters of an electrical equipment operation environment and scientifically evaluating an electromagnetic interference index. The accuracy and efficiency of data acquisition are effectively improved, meanwhile, by monitoring the operation state parameters of the electrical equipment, the characteristics of the electrical equipment can be deeply extracted, the data acquisition compliance index is calculated, a powerful basis is provided for data preprocessing, finally, the multi-dimensional operation state parameters of the electrical equipment are highly fused, operation degradation factors can be accurately identified, and the accuracy and efficiency of data acquisition are improved. According to the invention, the method achieves the precise prediction of the fault level of the electrical equipment, gives out an early warning in time, remarkably improves the accuracy and timeliness of fault prediction, effectively reduces the operation and maintenance cost, enhances the stability and safety of a power system, and provides a powerful technical guarantee for the intelligent management and maintenance of the electrical equipment.

The invention relates to the technical field of GIS equipment, and discloses a partial discharge detection method and device for totally-enclosed vacuum environment-friendly GIS equipment, and the method comprises the steps: constructing a dual detection circuit system of the GIS equipment, and building an equipment impedance characteristic model which comprises housing distribution inductance and internal conductor distribution inductance; acquiring a first multi-mode discharge signal in a vacuum insulation environment, performing empirical mode decomposition to obtain a plurality of eigenmode function components, and synchronously recording environment characteristic parameters; constructing a discharge signal identification model; and identifying the second multi-mode discharge signal of the GIS equipment through the discharge signal identification model, and determining the internal defect three-dimensional coordinate and the hazard level of the GIS equipment, so that intelligent identification of discharge characteristics is realized, the calculation efficiency is improved, the anti-interference capability is enhanced, the three-dimensional coordinate of the internal defect of the GIS equipment can be accurately calculated, and the reliability of the GIS equipment is improved. And comprehensive evaluation of defect types and hazard levels is realized.