7736results 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 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 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 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 relates to the technical field of cable asset management and fault prediction, and discloses a cable fault positioning method based on deep learning clustering analysis test waveform characteristics, and the method comprises the steps: collecting waveform and environment data in a cable operation period, and constructing a historical feature library comprising waveform, environment and position features; a self-adaptive detection model is adopted, and parameters are dynamically adjusted to adapt to different working conditions; multi-dimensional feature fusion and matching analysis are combined; a fault point distance is calculated through a signal propagation model and a time difference positioning algorithm, precise positioning is realized by fusing environment compensation and multi-point cross validation, and a three-dimensional geographic coordinate is generated by combining a laying path; and after multiple verifications, a structured report containing a fault type, a risk level, a prediction position, confidence and operation and maintenance suggestions is generated. According to the system, intelligent monitoring, fault risk prediction, asset optimization management and operation and maintenance decision support of a cable operation state are realized, and scientificity and economy of cable management in a complex environment are improved.

The invention discloses a GIS ultrahigh frequency partial discharge abnormity early warning method and system, and relates to the technical field of power equipment fault diagnosis, and the method comprises the steps: collecting a pulse signal, carrying out the time domain normalization and leading edge detection, constructing a four-dimensional spatial-temporal feature matrix based on a detection result, and generating a structured spatial-temporal feature tensor through a coding rule; dividing the structured spatial-temporal characteristic tensor into a plurality of sub-matrixes according to a time window, reconstructing a six-dimensional phase space through an optimal embedding dimension and a time delay algorithm, and deeply fusing the reconstructed six-dimensional phase space with chaotic dynamic characteristics through a coupling equation to generate chaotic trajectory data; extracting kinetic parameters based on the chaotic trajectory data, and fusing the kinetic parameters through a deep learning model to generate a discharge anomaly risk score; according to the method, through phase-space reconstruction and chaos dynamics modeling, the nonlinear dynamic characteristics of partial discharge are accurately captured, and the prediction precision of the abnormal state is remarkably improved.

The invention discloses a comprehensive online monitoring method and system based on GIS partial discharge, and relates to the technical field of power monitoring, and the method comprises the steps: obtaining the operation state information of GIS equipment, and constructing a partial discharge monitoring model; acquiring a multi-modal sensing signal through the partial discharge monitoring model to obtain fused monitoring data; calculating a filter coefficient based on environment characteristic parameters and equipment operation characteristics, performing interference suppression processing on the fused monitoring data, and outputting filtered monitoring data; performing hierarchical processing on the filtered monitoring data by adopting a cloud edge collaborative architecture, extracting basic feature parameters through edge nodes, and transmitting the basic feature parameters to cloud nodes for deep learning analysis; and a deep learning algorithm is combined to identify a partial discharge fault feature mode, and a GIS equipment comprehensive online monitoring result is generated based on multi-dimensional feature fusion and a consistency verification mechanism. According to the invention, intelligent identification and credibility evaluation of the fault mode are realized, the misjudgment rate is obviously reduced, and the accuracy of maintenance decision is improved.

The invention relates to the technical field of bushing detection, and discloses a bushing insulation state detection method based on multi-parameter real-time monitoring. The method comprises the following steps: acquiring multi-dimensional monitoring data such as a partial discharge pulse sequence, dielectric loss angle tangent value fluctuation data and surface leakage current density distribution in a bushing operation environment in real time; carrying out insulation defect feature extraction on the data set, and generating an insulation defect distribution map containing a creeping discharge track, an internal air gap position mark and a carbonization channel density index; high-risk and potential defect areas are divided by adopting a dynamic threshold segmentation algorithm, boundary coordinates are determined, and an insulation state evaluation vector containing parameters such as a surface discharge intensity index and the like is constructed according to the boundary coordinates; and inputting the vector into a pre-trained insulation aging prediction model, and outputting a bushing residual insulation life estimated value and a defect development priority sequence. According to the method, all-around accurate monitoring and pre-judgment of the insulation state of the sleeve are achieved, and scientific support is provided for operation and maintenance decision making of the sleeve.

The invention discloses a power distribution network cable leakage current and insulation state on-line monitoring system and method, and belongs to the technical field of power transmission and distribution of power systems. In order to solve the problems of poor accuracy of fault prediction and low relevance of multi-source data, key features capable of representing the health state of a cable are extracted, and a more comprehensive feature set is formed by combining the multi-source data, so that information among different data sources can be matched and associated with each other; according to the method, the relevance between leakage current and temperature and humidity and the relevance between partial discharge and cable load are analyzed, multi-parameter fusion monitoring is achieved, the future leakage current data change trend is predicted, potential fault omen are rapidly found, an early warning mechanism is triggered when potential fault risks are predicted, and therefore the system can give an early warning in advance before faults occur; and the characterization characteristics of the insulation state of the cable are extracted from the fused data, so that the insulation state of the cable can be accurately evaluated in real time, and remote monitoring and early warning of the leakage current and the insulation state of the cable are realized.

The invention relates to the technical field of cable partial discharge detection and diagnosis, provides a high-voltage cable multi-parameter intelligent diagnosis method, system and equipment and a storage medium, and aims to solve the problems of low accuracy and poor reliability of high-voltage cable multi-parameter diagnosis caused by multiple factors. The method comprises the following steps: decomposing a partial discharge signal after interference filtering, extracting a time domain amplitude change rate and an energy distribution parameter, and generating a multi-dimensional time-frequency domain feature vector; calibrating a multi-dimensional time-frequency domain feature vector, carrying out nonlinear manifold mapping, generating a low-dimensional feature vector matched with dielectric spectrum characteristics, matching a partial discharge type with the highest similarity with an identification result, judging an insulation degradation level in combination with a final loss threshold interval, and generating a comprehensive diagnosis report. According to the invention, the ultrahigh-frequency sensor array captures the partial discharge signal matched with the dielectric spectrum, precise identification of the discharge type and dynamic evaluation of the insulation degradation level are realized through multi-scale analysis and deep learning fusion, and the reliability of high-voltage cable diagnosis is improved.