1181 results about "Partial discharge" patented technology

The invention provides a partial discharge on-line monitoring method and system based on multi-modal fusion and adaptive noise reduction, and the method comprises the steps: employing an ultrahigh frequency UHF sensor, a miniature ultrasonic sensor, and a miniature detector for gas dissolved in oil, which are disposed on a transformer; synchronously acquiring electric signals, sound signals and characteristic gas concentration data in oil generated in the partial discharge process; performing format unification, abnormal value elimination and time alignment processing on the electric signal, the sound signal and the gas concentration data in an edge calculation unit to obtain aligned multi-source original data; performing adaptive wavelet noise reduction processing on the electric signal to obtain a de-noised UHF signal; respectively extracting time domain, frequency domain and chemical features from the de-noised UHF signal, the aligned sound signal and the gas concentration data to form a multi-dimensional feature vector; and inputting the multi-dimensional feature vector into a pre-trained defect traceability model, and outputting a partial discharge defect type, severity level and development trend prediction result.

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

The invention belongs to the technical field of power equipment state monitoring, and discloses a cable branch box operation state intelligent evaluation system based on multi-sensor fusion, which comprises a state acquisition module used for acquiring a partial discharge signal, a voltage fluctuation signal, a terminal temperature signal and a terminal contact resistance signal of a cable branch box in an operation process, constructing a multi-source operation state parameter set of the cable branch box through time synchronization and physical quantity normalization processing; the mutual coupling analysis module is used for performing transient correlation deduction on the partial discharge signal and the corresponding voltage fluctuation signal based on the multi-source operation state parameter set to obtain a partial discharge-voltage coupling amount; when the partial discharge-voltage coupling amount exceeds a preset partial discharge-voltage coupling amount threshold value, determining that partial discharge abnormity caused by insulation degradation exists in the cable branch box; comprehensive and accurate evaluation of the operation state of the cable branch box is realized, and reliable support is provided for safe operation and maintenance of equipment.

The invention provides a method for extracting electrical characteristic signals of electrical equipment in a complex working condition environment, and belongs to the technical field of electrical equipment detection.The method comprises the steps that a multi-channel ultrasonic sensor array is arranged to collect partial discharge signals, background noise is eliminated through adaptive noise cancellation processing, and an ultra-sparse frequency band energy distribution vector is constructed; the method comprises the following steps: calling a self-adaptive time-frequency analysis model to extract instantaneous frequency, amplitude and phase parameters to form a micro-hour-frequency characteristic matrix, separating independent source signals through independent component analysis, calculating a kurtosis value and a skewness value, fusing multi-domain characteristics to construct a transient stationary comprehensive characteristic vector, matching with a standard discharge characteristic vector library to identify the discharge type and intensity, and calculating the discharge intensity. A corresponding prediction algorithm is selected according to the discharge mode, multi-parameter coupling optimization adjustment is started under a certain condition, an electrical characteristic signal description vector is finally constructed, and the technical problem that the partial discharge signal of the power equipment is difficult to accurately extract under a complex working condition environment is solved.

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

The invention relates to the technical field of fault detection, in particular to an electrical equipment fault diagnosis method and system, and the method comprises the following steps: constructing a ratio group of a trigger incremental sequence quantity and a frequency spectrum cluster set based on a partial discharge pulse interval, deducing impedance frequency boundary migration, analyzing a conductor heating and cooling rate difference, and forming thermal response lag information. According to the method, frequency spectrum energy evolution characteristics are extracted through partial discharge pulse interval dynamic behaviors, a frequency migration path is constructed to reflect an impedance boundary fluctuation process, hysteresis effect characteristics are extracted in combination with thermal inertia response differences in a temperature rise stage, and the impedance boundary fluctuation is determined. And analyzing a voltage and current phase fluctuation rule and deducing a change trend of a combined feature, establishing a layer-by-layer progressive judgment logic between multi-stage dynamic coupling features, realizing evolutionary process tracking from local disturbance to a global abnormal state, and improving a fine-grained identification level of an abnormal mechanism in an electrical equipment operation behavior.

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

The invention discloses an electrical safety test data analysis method and system for an electrical cabinet, and relates to the technical field of electrical measurement and test. The method is used for solving the problems of single test dimension, poor anti-interference capability and inaccurate fault positioning in the safety test of the electrical cabinet. Firstly, multi-channel electrical parameter synchronous acquisition is triggered based on a working condition event, real discharge and interference signals are distinguished through time-frequency analysis, and a time sequence incidence matrix of discharge pulses and load currents is established; then, analyzing phase distribution characteristics of partial discharge, calculating a correlation coefficient between a discharge repetition rate and a load current, and dynamically adjusting a weight to generate an insulation state comprehensive test index; further, insulation degradation characteristics are extracted through signal decomposition, and an insulation degradation trend and a fault probability are predicted in combination with energy entropy analysis and discharge mode recognition; and finally, the position of a fault component is analyzed and positioned based on the impedance network, and a maintenance priority sequence is generated according to the fault probability and the degradation rate.

The invention discloses a series ultrahigh frequency sensing GIS three-dimensional positioning and monitoring system and method, and belongs to the technical field of high-voltage equipment state monitoring. The system comprises a sensing layer, a processing layer, a transmission layer and an application layer, wherein the sensing layer synchronously collects ultrahigh-frequency electromagnetic signals, sound wave vibration signals and SF6 gas state parameters through a plurality of serial multi-physical-quantity sensing nodes; the processing layer eliminates interference based on signal homology verification, calls an intelligent response model to dynamically compensate propagation path errors in combination with a GIS three-dimensional structure model and real-time environment data, outputs accurate three-dimensional coordinates of the partial discharge source by adopting a progressive time difference of arrival positioning algorithm, and identifies fault types and risk levels by fusing multi-dimensional features; the transmission layer encrypts and uploads the diagnosis report; and the application layer maps defect positions in the three-dimensional visual model and automatically generates an operation and maintenance work order according to risk levels. According to the invention, high-precision positioning, multi-source fusion diagnosis and closed-loop operation and maintenance are realized, and the accuracy and intelligent level of GIS equipment monitoring are remarkably improved.

The invention discloses an intelligent cable life prediction method and system based on data driving and a cable, and relates to the technical field of intelligent operation and maintenance of power equipment, and the method comprises the steps: collecting the current, voltage, partial discharge, temperature, environment temperature and humidity and other multi-dimensional historical operation time sequence data of the cable; a cable comprehensive state feature sequence is generated through processing of a multi-source heterogeneous data fusion technology; extracting a load fatigue cumulant, an insulation degradation index and a thermal stress damage degree based on an aging mechanism to form a degradation feature vector; inputting the vector into a pre-training model for time sequence evolution deduction, and outputting probability distribution of residual life and a key failure time node; and generating a preventive maintenance decision scheme including a maintenance window, an operation priority and inventory early warning. According to the method, more accurate evaluation and risk quantification of the cable life are realized through mechanism characteristic driving and probabilistic prediction, and a direct decision basis is provided for active operation and maintenance.

The invention discloses a GIS ultrahigh frequency sensor ultrasonic cooperative detection system based on piezoelectric energization, and relates to the field of electric power detection, and the system comprises an excitation module which is used for transmitting a piezoelectric excitation signal with a specific frequency to a preset detection region in GIS equipment, so as to trigger the potential partial discharge acoustoelectric signal radiation of the region; the sensing module is used for receiving ultrahigh-frequency electromagnetic signals and ultrasonic vibration signals radiated by partial discharge in the GIS equipment; the excitation frequency, the filtering bandwidth and the sampling rate are dynamically optimized according to the detection result, the detection accuracy and efficiency are continuously improved, and reliable and effective guarantee is provided for safe operation and defect early warning of GIS equipment.

The embodiment of the invention provides a switch cabinet partial discharge quantum sensing monitoring device. The device is applied to the technical field of power equipment monitoring, and comprises a light path regulation and excitation module which is used for generating and beam-splitting detection laser of a quantum sensor coupled to each Rydberg atom; the quantum electromagnetic induction module acts on Rydberg atoms and a partial discharge electromagnetic field, and fluorescence or absorption spectrum characteristics of atomic energy level transition are changed; extracting a weak discharge signal, and converting the weak discharge signal into an analyzable electric signal; the signal processing and diagnosis module is combined with spatial information acquired by a quantum sensor of doydberg atoms in a distributed manner, and a time-frequency analysis and pattern recognition algorithm is adopted to extract discharge characteristic quantity; partial discharge positioning, type identification and insulation state evaluation are realized based on the parameters, and a real-time monitoring result and early warning information are output. In this way, through deep fusion of quantum sensing and advanced signal processing, a high-precision and high-reliability technical solution is provided for power equipment state monitoring.

The invention discloses a transformer insulation state evaluation method based on artificial intelligence multi-parameter fusion perception, and relates to the technical field of electrical equipment detection, and the method comprises the steps: 1, obtaining the multi-modal monitoring data of a transformer, and constructing a dynamic graph structure; wherein the multi-modal monitoring data comprises oil chromatography data, partial discharge data and thermal image data; 2, encoding the dynamic graph structure through an encoder extraction embedding technology, carrying out space-time dimension feature aggregation and evolution modeling based on the encoded dynamic graph structure by utilizing graph convolution and time convolution technologies, inputting preset transformer insulation positive and negative sample pairs into a constructed model, carrying out InfoNCE loss unsupervised learning, and carrying out infoNCE loss unsupervised learning; obtaining a state manifold of a normal transformer, and carrying out multi-modal data space-time alignment and anomaly distinguishing on the state manifold; and step 3, introducing a Bayesian weight after multi-modal data space-time alignment and anomaly distinguishing, and outputting probability distribution of insulation state embedding.

The invention belongs to the technical field of state monitoring and fault diagnosis of high-voltage power equipment, and provides an environment-friendly GIS partial discharge trigger criterion construction method and system, and the method comprises the steps: collecting the gas concentration change rate [delta] C X (t), the peak voltage amplitude A UHF and the pulse number N p in unit time of a decomposition product of environment-friendly GIS equipment in the discharge process; time sequence synchronization and space fusion are carried out, and a fusion feature vector is constructed; a real-time partial discharge triggering criterion is obtained through calculation; a sliding window statistics and Bayesian probability updating mechanism is adopted to construct a partial discharge trigger dynamic threshold and alarm logic which can be adjusted along with the change of the operation condition and the environment; and outputting the current partial discharge risk level according to the amplitude and variation trend of the real-time partial discharge trigger criterion relative to a preset risk level threshold. According to the method, the sensitivity and the precision of detecting and judging the partial discharge event of the environment-friendly GIS are improved.

The invention discloses a DC GIS partial discharge signal multi-dimensional joint fault diagnosis method and system, and belongs to the technical field of power equipment fault detection. The method comprises the following steps: acquiring a typical defect partial discharge UHF signal of the high-voltage direct-current GIS; extracting time domain and frequency domain characteristics of the signal; extracting time-frequency domain features by using empirical wavelet transform and multi-scale permutation entropy; performing normalization processing on the signal to obtain a time domain sequence and a frequency domain sequence; and constructing a double-input neural network, taking the time domain, frequency domain and time-frequency domain features as first input, taking the time domain sequence and the frequency domain sequence as second input, and performing feature fusion and classification to realize fault diagnosis. According to the method, deep information of the partial discharge signals is fully mined and fused through multi-domain feature combination and a double-input deep learning model, the problems that a direct-current GIS partial discharge fault mode is difficult to recognize and low in precision are solved, and accurate, reliable and rapid diagnosis of typical insulation defects is achieved.

The invention specifically relates to an intelligent circuit breaker health degree online diagnosis system, device and method, and relates to the technical field of power equipment monitoring and diagnosis. An electrical response synchronous monitoring module; a feature extraction and correlation modeling module; and a tolerance degradation evaluation module. According to the invention, a plurality of types of high-precision sensors capture environmental parameters such as dirt, temperature and humidity in real time, electrical signals such as leakage current and partial discharge are synchronously collected by combining a GPS / Beidou dual-mode synchronization technology, and space-time alignment of environment and electrical data is realized; compared with traditional off-line detection, equipment operation does not need to be interrupted, and the insulation degradation process can be dynamically captured.

The invention discloses a photovoltaic cable health state assessment method and system, and the system comprises a signal collection module which is disposed at a key node of a cable, and comprises an ultra-wideband pulse sensor, an infrared thermometer, and an illumination sensor; the edge processing module is used for constructing a state characteristic curve and identifying a target sample time interval; the cloud analysis engine is used for executing defect contribution simulation value extraction and thermal-electric coupling model iterative optimization; according to the scheme, multi-dimensional signals such as current, voltage, partial discharge pulse, temperature and illumination intensity are synchronously collected through a distributed sensor network, a multi-parameter evaluation system including amplitude, phase and temperature is constructed, and the health state level, the defect position distribution and the simulation value change trend are displayed through multi-dimensional signal fusion and multi-parameter analysis. And the detection accuracy and comprehensiveness are improved.

The invention provides an intelligent lightning arrester for a line. The intelligent lightning arrester comprises a lightning arrester body and an intelligent control box, the lightning arrester body is used for providing a low-impedance grounding channel for overcurrent when overvoltage caused by lightning or switching operation occurs on a line, so that the overvoltage is limited below a safety level, subsequent electrical equipment is protected from being damaged, the lightning arrester body recovers to an insulation state after the overvoltage disappears, and normal operation of a power system is guaranteed. The intelligent control box comprises an embedded intelligent sensing module and a diagnosis module, and the embedded intelligent sensing module is used for collecting operation state parameters of the lightning arrester body in real time; and the diagnosis module is used for evaluating the insulation state of the lightning arrester body by adopting a partial discharge mode recognition model according to the operation state parameters of the lightning arrester body, and predicting the aging trend of a valve plate unit of the lightning arrester body by adopting a valve plate aging trend prediction model. The lightning arrester disclosed by the invention has the dual advantages of compact structure and high-performance protection.

The invention relates to the technical field of control systems, and particularly discloses an intelligent control system for an insulation test of a marine dry-type transformer. The system comprises a high-voltage test power supply module, a multi-mode sensing acquisition module, an insulation state evaluation module, a self-adaptive boost decision module and a test process control module, and evaluates an insulation state and dynamically decides a boost rate and a holding strategy by acquiring voltage, leakage current, partial discharge and temperature data in real time. Intelligent control and safety protection in the insulation test process are achieved, potential insulation damage is effectively avoided, and diagnosis precision is improved.

The invention relates to a cable insulation aging state evaluation method based on operation data analysis. The method comprises the following steps: S1, acquiring cable operation data in real time through a heterogeneous sensor array; s2, adopting a layered preprocessing strategy on the edge side; s3, constructing a hybrid deep learning model; s4, outputting by adopting an improved evidence theory fusion model; s5, outputting a fourth-level aging state label and a confidence evidence theory fusion model; electrical parameters and non-electrical parameters of the cable are collected in real time through the heterogeneous sensor array, and multi-dimensional data such as partial discharge signals, three-phase current harmonic distortion rate, distributed optical fiber temperature, mechanical vibration acceleration and environment dew point temperature are covered. The multi-source data fusion mode can comprehensively reflect the operation state of cable insulation, the limitation of single parameter evaluation is avoided, and the accuracy of the evaluation result is greatly improved.

The invention discloses a quantum partial discharge detection system and method based on single photon measurement. The system comprises a photoelectric detection module and an information transmission and processing module which are connected in pairs. A free space light collection unit in the photoelectric detection module is used for converging photons of partial discharge radiation and guiding the collected photons to a classic weak light detection unit and a single photon detection unit respectively; the classic weak light detection unit is used for converting an optical signal into an electric pulse signal based on an internal photoelectric effect; the single-photon detection unit is used for detecting extremely weak light and even single-photon events and outputting discrete electric pulse signals. The classical weak light detection unit and the single photon detection unit operate in parallel, partial discharge optical detection with a wide dynamic range and a high signal-to-noise ratio is realized through a signal fusion and processing algorithm, electromagnetic interference is effectively inhibited, and a more comprehensive and reliable monitoring means is provided for evaluation of the insulation state of power equipment.

The embodiment of the invention provides an operation scene monitoring method and system based on panoramic stitching and holder linkage, and belongs to the technical field of video monitoring. The method comprises the following steps: respectively acquiring real-time panoramic video data and historical video data of an operation scene and a partial discharge signal of high-voltage electrical equipment; simulating electrical disease characteristics of the high-voltage electrical equipment based on the partial discharge signals and the environmental parameters of the real-time panoramic video data; obtaining work prediction information of the real-time panoramic video based on the historical video data and the electrical disease features; calculating deviation information of a real-time panoramic video according to the operation prediction information and the real-time panoramic video data; positioning a risk target area of the operation scene based on the deviation information, and determining a risk early warning signal of the operation scene for the deviation information based on a preset risk rule base; and issuing the risk early warning signal to a pan-tilt camera of the operation scene, and monitoring a close-up monitoring picture of the risk target area in real time. According to the invention, the risk handling efficiency of operation scene monitoring can be improved.

The invention provides a partial discharge monitoring and early warning method for a switch cabinet. The method comprises the following steps: acquiring ultrasonic sensing data and transient grounding voltage data through configuration acquisition software, and preprocessing the ultrasonic sensing data and the transient grounding voltage data; inputting the real-time signal peak value into a pre-constructed CNN and LSTM-based partial discharge prediction model, and outputting a combined sequence containing original data and a predicted value; and performing M-K detection on the joint sequence for trend analysis, and performing early warning analysis in combination with the predicted value and an alarm threshold. According to the technical scheme, the CNN-LSTM is utilized to predict the partial discharge data sequence, and then trend analysis is carried out through the M-K inspection method to comprehensively consider two indexes of the amplitude of the partial discharge sequence and the trend parameter so as to realize early warning of the partial discharge of the cable joint. The CNN-LSTM combined method is adopted to carry out partial discharge data sequence prediction, the feature information extraction capability of the CNN and the sensitivity of the LSTM to the time sequence can be effectively integrated, and the trend data prediction effect is improved.

The embodiment of the invention provides a GIS direct current partial discharge positioning defect diagnosis method and device and a medium, and the method comprises the steps: detecting a partial discharge position according to a first sensor and a second sensor on an air detection model under the condition that the first sensor detects a partial discharge signal; acquiring gas detection data of a partial discharge position; and determining the defect type of the partial discharge position according to the gas detection data and the partial discharge signal. Location of the partial discharge position is completed through cooperation of the partial discharge sensor arranged on the GIS and the partial discharge sensor arranged on the unmanned aerial vehicle, various discharge conditions can be adapted, and the installation cost and the detection cost are reduced. The defect type of the DC partial discharge position can be quickly diagnosed through the gas detection data and the partial discharge signal of the partial discharge position, the GIS defect diagnosis efficiency is high, and the detection cost is low.

The invention discloses a cable warning pile and method for monitoring the state of an intermediate joint of a directly-buried power cable, relates to the field of state monitoring of power equipment in power engineering, and aims to solve the problems of single-parameter acquisition, large interference influence, non-objective evaluation and non-visual early warning in the existing monitoring technology. The warning pile is vertically buried at the position 0.3-0.8 m above a connector, temperature, partial discharge, vibration and environment data are synchronously collected through multiple sensors such as an optical fiber temperature sensor and a high-frequency current transformer, after data alignment, abnormal value elimination, filtering and integral preprocessing, the real temperature of the connector is restored through an environment and vibration coupling correction model, and the real temperature of the connector is obtained. And extracting a partial discharge peak factor and an average discharge capacity core characteristic. An index weight is determined based on an entropy weight method to construct a health degree evaluation model, operation states are divided into normal, attention, early warning and fault classes, hierarchical visual warning is realized through a three-color indicating lamp group and a buzzer, and the model is dynamically updated in combination with maintenance data every three months.

The invention discloses a partial discharge monitoring system for a switch cabinet. The partial discharge monitoring system comprises a microphone array module used for collecting voiceprint signals in the switch cabinet; the voiceprint gene bank is used for storing the partial discharge voiceprint signals according to the types of the partial discharge voiceprint signals in the switch cabinet; the recognition module is used for comparing the collected voiceprint signals in the switch cabinet with the partial discharge voiceprint signals stored in the voiceprint gene pool so as to recognize whether the collected voiceprint signals in the switch cabinet are the partial discharge voiceprint signals or not; the output module is used for outputting the type and pointing information of the partial discharge voiceprint signal when the recognition result of the recognition module is that the voiceprint signal in the switch cabinet is the partial discharge voiceprint signal; and the indication module is used for indicating a partial discharge voiceprint signal source area according to the pointing information output by the output module. According to the invention, the type of the partial discharge in the switch cabinet can be accurately identified, the position where the partial discharge occurs can be accurately indicated, and the identification accuracy and maintenance efficiency of the partial discharge are improved.

The invention relates to the technical field of digital model transformer health assessment, and discloses a transformer health assessment method and system based on digital twinning and electrical characteristics, and the method comprises the steps: collecting the real-time electrical characteristic data and basic ledger information of a target transformer, and calling an initial digital twinning model; when the data meets updating conditions, generating model correction parameters or evaluation guide data by using multi-dimensional characteristics such as oil chromatography, partial discharge or winding deformation, and dynamically updating a digital twin model and a preset health evaluation process; and accurate health state assessment guidance is provided for operation and maintenance personnel through the assessment terminal. According to the method, personalized dynamic adaptation of the evaluation strategy is realized, and the intelligent level of transformer monitoring evaluation is remarkably improved.

According to the fault early warning system for the wind power booster station provided by the invention, the limitation of traditional single-point monitoring is broken through by fusing the infrared ultrasonic technology and the ultrahigh frequency technology through the non-contact sensor, so that the early fault detection rate is greatly improved; the composite heat dissipation system suppresses the temperature rise of a transformer hot spot based on a dynamic heat management mechanism of a phase change material and a heat pipe, and the energy-saving efficiency is improved; the edge calculation early warning center converts temperature, partial discharge, strain and other multi-source heterogeneous data into a unified health degree index through a weighted fusion algorithm, and in combination with an equipment topology correlation analysis model, the positioning accuracy of a fault source is greatly improved, and the early warning response time delay is greatly reduced.

The invention provides a multi-mode fusion switch cabinet partial discharge on-line monitoring system and method, and the system comprises a space setting module which employs a preset space analysis algorithm, obtains an installation point position set according to the space structure of a switch cabinet, and carries out the installation of the installation point position set; the data acquisition module is used for acquiring data acquired by the ultrahigh frequency sensor, the ultrasonic sensor and the temperature sensor, preprocessing the data and then sending the data to the signal processing module; carrying out power frequency noise suppression on the ultrahigh frequency signal and the ultrasonic signal through a self-adaptive filter circuit, and extracting partial discharge characteristics from the filtered signal by adopting a wavelet transform algorithm; and the AI model fusing the convolutional neural network and the long-short-term memory network performs fusion analysis on the partial discharge characteristics and the temperature sensor data, realizes partial discharge mode identification and insulation degradation trend prediction, and outputs early warning information. According to the technical scheme provided by the invention, high-precision and anti-interference discharge detection can be realized.

The invention discloses a partial discharge signal noise suppression method, system and device and a medium, and the method comprises the following steps: receiving an original partial discharge signal, calculating the kurtosis value of the original partial discharge signal, and constructing a kurtosis criterion; constructing a variational mode decomposition model, and optimizing parameters of the variational mode decomposition model by using a northern eagle algorithm; variational mode decomposition is performed on the original partial discharge signal based on the optimized parameters, the kurtosis value of each mode component is calculated, and an effective mode component and a noise mode component are divided according to the kurtosis criterion; reconstructing the effective modal component to obtain an intermediate signal; and performing signal enhancement on the intermediate signal by using a wavelet denoising method based on an improved threshold to obtain a denoised partial discharge signal. According to the method, the noise suppression precision, the algorithm stability, the operation efficiency and the signal integrity of partial discharge signal processing can be effectively improved.