554results about "Testing using acoustic measurements" patented technology

The invention discloses a partial discharge detection method and device, and the method comprises the steps: outputting a multi-mode signal matrix after noise reduction through employing a self-adaptive noise reduction algorithm according to a multi-mode sensing signal during the operation of a generator; based on the multi-modal signal matrix, a space-time convolutional neural network is adopted to extract discharge features, meanwhile, a topological relation between signal time-frequency features and sensor space is captured, and a multi-modal feature fusion tensor is output; according to the multi-modal feature fusion tensor, a model is generated through a dynamic map, manifold learning and a particle swarm optimization algorithm are combined, and a dynamic fault map containing discharge intensity, phase and frequency point distribution characteristics is output; and based on the dynamic fault map, calculating a real-time discharge danger coefficient by using a risk prediction model, and outputting a discharge grading early warning instruction and a maintenance priority sequence. According to the embodiment of the invention, high-precision and traceable discharge detection and graded early warning can be realized.

The invention is suitable for the technical field of electrical equipment state monitoring, and provides an electrical equipment state monitoring method based on a non-contact leakage current sensor, and the method comprises the steps: collecting a leakage current signal of the surface of an insulating part of electrical equipment, and obtaining infrared thermal image data and an ultrasonic signal; variational mode decomposition is carried out on the leakage current signal to obtain a plurality of intrinsic mode functions; separating a leakage current effective component and an independent noise source based on a blind source separation algorithm; extracting a time-frequency characteristic of the effective component of the separated leakage current, extracting a local temperature gradient characteristic of the infrared thermal image data and a frequency spectrum energy characteristic of the ultrasonic signal, and generating a multi-modal characteristic vector; analyzing the space-time relevance of the multi-modal feature vector, dynamically distributing each modal weight coefficient for fusion, and generating a comprehensive fault feature; according to the method, the signal-to-noise ratio of the weak leakage current signal is improved, and the misjudgment rate is effectively reduced.

The invention discloses a deep learning-combined power transformer partial discharge signal extraction and diagnosis method. The method comprises the following steps of S1, setting a multi-channel synchronous acquisition system in a power transformer body area to acquire a multi-dimensional original partial discharge data set; s2, preprocessing the acquired multi-dimensional original partial discharge data set; s3, performing time alignment and amplitude matching on the processed signal, and dividing the processed signal into a sliding time window to construct a standard input tensor; s4, constructing an attention enhancement model fused by the convolutional neural network and the bidirectional gating circulation unit; s5, performing supervised training on the attention enhancement model by using the labeled sample; s6, inputting the real-time signal into the training model, and outputting a discharge type label; s7, risk grade evaluation is carried out in combination with statistical characteristics; and S8, generating a structured diagnosis report and uploading the structured diagnosis report to a monitoring platform. According to the invention, multi-source signals and a depth model are fused, and intelligent diagnosis and risk assessment of transformer partial discharge are realized.

The invention relates to the technical field of discharge detection, in particular to a high-precision detection method and device for partial discharge of totally-enclosed GIS equipment. The method comprises the following steps: carrying out metal shell three-dimensional structure modeling on the GIS equipment, and carrying out electromagnetic shielding evaluation so as to obtain a multi-point electromagnetic shielding evaluation value; acquiring an original discharge signal of each sampling point based on the electromagnetic wave sensor, and performing interference suppression processing and back propagation attenuation compensation according to a multi-point electromagnetic shielding evaluation value, so as to obtain an attenuation compensation discharge signal waveform; micro-vibration response parameters are collected based on an MEMS micro-vibration sensor, actual influence waveform point detection marking and physical vibration interference filtering are carried out on attenuation compensation discharge signal waveforms, and therefore pure optimized discharge signal waveforms are obtained. According to the invention, through efficient and accurate partial discharge detection, real-time early warning of the state of the GIS equipment is realized, and the stability and safety of the equipment are improved.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

The invention relates to the technical field of nondestructive testing, in particular to a leak detection and partial discharge digital imaging detection method and system based on acousto-optic fusion, and the method comprises the following steps: based on channel microphone array sound wave data in a partial discharge signal suspicious region, extracting a sound wave abnormal section, positioning a sound source, matching image edge features, and synchronously marking; and analyzing the phase change of the multi-frequency signal to judge a sound source concentration area, tracking the moving trend of a disturbance point, and outputting an acousto-optic fusion positioning trend track. According to the method, the partial discharge feature recognition sensitivity is improved through high-frequency peak paragraph screening and dominant frequency recognition, the abnormal region positioning precision is enhanced in combination with image edge extraction and sound source space matching, and acousto-optic synchronous positioning and trend trajectory display are achieved through multi-frequency signal phase analysis and image frame disturbance tracking. Through fusion of frequency domain feature extraction, image recognition, dynamic comparison and other actions, the spatial precision of abnormal source recognition and the multi-source fusion analysis efficiency are improved, and the partial discharge traceability and dynamic monitoring capability are enhanced.

The invention discloses a self-adaptive intelligent power distribution box abnormal discharge hidden danger accurate monitoring system, and relates to the technical field of power equipment monitoring. The system comprises a sensor array used for collecting multi-physical field signals in a distribution box; the decoupling module is used for performing interference decoupling and feature extraction of time, frequency and space dimensions on the multi-physical field signal; and the edge-cloud collaborative computing architecture comprises an edge computing node and a cloud platform, the edge computing node is used for executing signal real-time processing and abnormal early warning, and the cloud platform is used for constructing an equipment health model and outputting long-term trend prediction. According to the invention, through the sensor array, the time-frequency-space three-dimensional signal decoupling technology and the edge-cloud cooperative computing architecture, high-precision acquisition and interference decoupling of multi-physical field signals are realized, and the monitoring precision and reliability of abnormal discharge hidden dangers are fundamentally improved.

The invention discloses a GIS (Gas Insulated Switchgear) tiny defect detection method and system based on X-ray multi-effect fusion, and the method comprises the following steps: firstly, exciting a latent defect in a GIS to generate partial discharge by using an X-ray photoionization effect, detecting a discharge signal by using an ultrahigh frequency method and an ultrasonic method, and after the discharge signal is processed by a noise reduction model, carrying out noise reduction on the discharge signal; and respectively obtaining an ultrahigh-frequency discharge phase spectrogram and an ultrasonic discharge phase spectrogram. Then, the imaging plate is placed on the back face of the GIS, digital DR imaging is carried out through X rays, and an X-ray image is obtained; the ultrahigh frequency discharge phase spectrogram and the ultrasonic discharge phase spectrogram are fused after being denoised, an X-ray image is combined to establish a GIS health state diagnosis model, and reliable defect detection is carried out on the GIS according to the evaluation score of the diagnosis model. According to the method, multiple effects of X-rays are fused, so that smaller GIS latent defects can be found, the detection sensitivity is remarkably improved, and the problem that the traditional method is difficult to identify the tiny latent defects is solved.

The invention discloses a collaborative diagnosis method for partial discharge and overheating faults of a high-voltage switch cabinet, and particularly relates to the technical field of fault diagnosis of the high-voltage switch cabinet. Signals are synchronously acquired through ultrahigh frequency, acoustic emission and infrared sensors, and an abnormal candidate sequence is extracted through adaptive baseline segmentation; through laser calibration and time interpolation calibration, uniform event position information and timestamps are mapped; identifying potential coupling pairs of partial discharge and hot spot events based on dynamic threshold and time difference matching; generating a high-risk linkage region through spatio-temporal clustering, trajectory analysis and region fusion; updating the diagnosis model based on the high-frequency data increment, and generating a collaborative diagnosis report; the method breaks through the limitation of a static threshold value and a single sensor, and remarkably improves the diagnosis precision and adaptability of a composite fault under a complex working condition.

The invention relates to a multi-element microphone array sound source localization method based on multistage signal preprocessing and subspace spectrum optimization, and belongs to the technical field of acoustic detection. Aiming at the problems of poor noise immunity, weak multi-sound-source resolution capability and low calculation efficiency of the existing sound source positioning technology, a triple signal preprocessing and subspace collaborative optimization scheme is provided; firstly, incoherent noise is suppressed through phase coherent filtering, a signal is reconstructed through principal component analysis, and phase deviation is calibrated through fundamental frequency; then constructing a guiding matrix and decomposing a noise subspace, and extracting a coarse positioning result; and finally, high-precision angle optimization is realized based on a chaos initialization differential evolution algorithm, and the efficiency is improved by combining a dynamic search range and an early stop mechanism. According to the method, the anti-interference capability in a low signal-to-noise ratio environment is remarkably enhanced, the problems of missing detection and false detection during dense distribution of multiple sound sources are effectively solved, meanwhile, the positioning precision and the real-time performance are considered, and the method is suitable for acoustic fault detection of complex scenes such as power transmission line inspection.

The invention relates to the technical field of partial discharge detection, in particular to a leak detection and partial discharge detection method and system based on artificial intelligence, and the method comprises the following steps: obtaining a partial discharge pulse signal, extracting a phase mutation coordinate, analyzing an incremental feature, reconstructing a propagation waveform, constructing an amplitude gradient matrix, and screening a direction vector; and a backtracking path calculates a change rate to generate a time track, and track mapping and space positioning are completed in combination with image data. According to the method, the accuracy and the response speed of partial discharge detection are improved through multi-stage signal processing, extreme points and phase abrupt change are analyzed, more accurate discharge feature capture and equipment defect positioning are achieved, phase and amplitude data are combined, the discharge mode is recognized, a signal propagation path is reconstructed, the fault source positioning accuracy is improved, and the fault source positioning accuracy is improved. Through dynamic rendering, the intuition of a detection result is enhanced, real-time decision is supported, the method is more intelligent and efficient, the equipment fault risk is greatly reduced, and the safe operation level of power equipment is improved.

The invention relates to the field of high-voltage cable fault diagnosis, and discloses a high-voltage cable insulation fault intelligent diagnosis method and system based on multi-information fusion, and the method comprises the following steps: S1, obtaining the operation multi-source data of a high-voltage cable, and constructing a multi-source original data matrix; s2, preprocessing to obtain a time-space aligned standardized data matrix; s3, performing feature extraction to obtain a multi-dimensional feature vector, and learning internal association between features by using a multi-modal deep network to obtain a joint multi-modal feature; s4, constructing a fault type identification model based on Bayesian reasoning and Monte Carlo sampling, and obtaining a fault type classification result; s5, in combination with deep learning and a physical model, obtaining a fault occurrence interval, positioning information and a fault level; and S6, based on a fault type classification result, a fault occurrence interval and positioning information, obtaining a fault level, and carrying out early warning pushing on a generated diagnosis report. According to the invention, high-precision identification, positioning and risk assessment of high-voltage cable insulation faults are realized.

The invention relates to the technical field of electrical equipment detection, and discloses an electrical equipment real-time state live detection method and system, and the system comprises a non-contact multi-source sensing array, an edge calculation unit, a high-frequency pulse excitation module, a self-adaptive installation structure, an intelligent diagnosis platform, and a self-energy-taking power supply unit. When electrical equipment state live-line detection is carried out, infrared thermodynamic characteristics, ultraviolet corona intensity and ultrasonic discharge signals are fused and collected through a non-contact multi-source sensing array, multi-parameter real-time sensing under the live-line condition is achieved, the problem of signal distortion caused by electromagnetic interference in traditional detection is solved, and the accuracy of state parameter extraction is improved; and meanwhile, the multi-dimensional feature data is subjected to standardization processing in combination with an edge calculation unit, so that the system can eliminate interference of environmental factors on detection results, the consistency of evaluation results under different working conditions is guaranteed, and state diagnosis errors are reduced.

The invention provides a fault positioning method and system based on multi-mode acousto-optic electric signal collaborative diagnosis, and the method comprises the steps: firstly collecting an acoustic signal sequence, an optical signal sequence and an electric signal sequence during the operation of high-voltage electrical equipment, and then carrying out the cross-mode signal level correlation processing of a multi-mode signal, and generating a multi-mode signal correlation level chain; extracting a dynamic association feature set based on the multi-modal signal association hierarchical chain, inputting the dynamic association feature set into the fault feature evolution model to generate a fault feature identification sequence and a feature space diffusion trajectory, and finally determining a fault initial region and a fault influence boundary range of the equipment according to the fault feature identification sequence and the feature space diffusion trajectory. Therefore, the accuracy and reliability of fault positioning of the high-voltage electrical equipment can be effectively improved.

The invention discloses a high-low voltage switch cabinet feeder fault positioning method and system based on transient traveling waves, and belongs to the technical field of power system fault detection and positioning, and the method comprises the steps: synchronously collecting electric and acoustic multi-mode signals, and generating a weighted transient synchronization feature matrix; performing time-frequency transformation and feedback optimization on the matrix, and outputting a multi-scale time-frequency feature set; analyzing the feature set by using an integrated learning network, and outputting a layered preliminary positioning result; convergence to an accurate fault section is carried out through iteration calibration; and finally, fusing multi-model calculation and outputting a comprehensive fault positioning report. According to the method, a technical path of combining multi-modal signal fusion and a physical model is adopted, convergence from fuzzy region division to an accurate position can be realized in stages, and the accuracy, the speed and the anti-interference capability of switch cabinet feeder line fault positioning are remarkably improved.

The invention relates to the technical field of partial discharge of power equipment, and particularly discloses a multi-modal sensing-based partial discharge monitoring and positioning system for power equipment, and the system comprises a multi-modal sensing unit which is configured to be arranged around to-be-monitored power equipment and is used for synchronously collecting various physical signals generated by partial discharge, the multi-mode sensing unit at least comprises an ultrahigh frequency sensing array used for receiving ultrahigh frequency electromagnetic wave signals generated by partial discharge; the ultrasonic sensing array is used for receiving ultrasonic signals generated by partial discharge; and the data acquisition and synchronization unit is connected with the multi-mode sensing unit. According to the power equipment partial discharge monitoring and positioning system based on multi-modal sensing, an integrated framework of multi-modal sensing cooperation and high-precision time synchronization is constructed, so that the problems of insufficient multi-modal cooperation, low time synchronization precision and no closed-loop optimization mechanism in existing partial discharge monitoring are effectively solved.

The invention relates to the technical field of power equipment inspection, in particular to a high-voltage electrical equipment inspection method and system based on microphone array measurement. The method comprises the following steps: acquiring array sound signal data, analyzing the array sound signal data, and determining sound signal feature information; obtaining a layered structure parameter set and an insulating medium parameter set of the target electrical equipment, performing targeted compensation and correction on the sound signal feature information according to the layered structure parameter set and the insulating medium parameter set, and determining abnormal voiceprint feature information; and generating abnormal component information according to the abnormal voiceprint feature information, and determining and outputting an abnormal detection report. According to the invention, non-contact real-time detection of the high-voltage electrical equipment in operation is realized, the adaptability of the detection process to the high-voltage electrical equipment which is internally provided with a multi-layer composite structure and is filled with an insulating medium is enhanced, and the positioning accuracy of a fault position in the equipment is improved.

The invention relates to the field of capacitor state evaluation, in particular to an aluminum electrolytic capacitor state evaluation method and device based on multi-dimensional data analysis. The method comprises the following steps: performing multi-band excitation on an aluminum electrolytic capacitor, and performing three-dimensional fault distribution reconstruction and structure degradation trend prediction so as to construct an impedance tomography degradation trend prediction map; collecting audio signals in the charging and discharging process of the capacitor, carrying out acoustic texture analysis, carrying out voiceprint-structure degradation correlation mining according to the impedance tomography degradation trend prediction map, and constructing an acoustic texture damage evolution evaluation model; obtaining a thermal imaging image of the capacitor, carrying out local overheating diffusion mining, and constructing a three-dimensional thermal anomaly detection model; performing electrolyte chemical component change tracking and electrolyte degradation situation analysis on the electrolyte of the capacitor, and constructing an electrolyte degradation situation curve; according to the invention, efficient and accurate capacitor state evaluation is realized. The operation reliability and stability of capacitor equipment are improved, and the service life of the equipment is effectively prolonged.

The embodiment of the invention provides a GIS defect diagnosis method and system for intelligently selecting sensor data and a medium, and the method comprises the steps: obtaining first operation information of a digital twinborn model of a suspected abnormal GIS, and generating defect diagnosis result information in the digital twinborn model of the GIS according to the first operation information; the defect diagnosis result information generation method comprises the following steps: acquiring type and model information and equipment position information of a plurality of high-voltage equipment in an area preset according to a GIS (Geographic Information System), and determining radiation area data according to the type and model information and the equipment position information; determining partial discharge reason information and partial discharge position information according to a phase distribution diagram corresponding to the GIS, the amplitude / time trend data, the radiation area data and the position information of the partial discharge sensor; and determining defect diagnosis result information according to the partial discharge reason information and the partial discharge position information. Through visual man-machine operation, defect diagnosis result information can be accurately obtained through a background only by clicking an abnormal GIS once.

The invention discloses a transformer fault ultrasonic detection system, which comprises an ultrasonic sensor module, a signal conditioning module, a data acquisition and processing module, a data fusion module, a learning module, a diagnosis decision module and a human-computer interaction interface, according to the ultrasonic sensor module, 6-8 ultrasonic sensors are arranged in a transformer shell in an array mode, piezoelectric ceramic sensors with the resonant frequency of 40 kHZ are adopted as the ultrasonic sensors, the sensitivity of the ultrasonic sensors is-65 dBV / uBar, and effective capture is weak. According to the ultrasonic detection system for the transformer fault, single ultrasonic detection is easily influenced by accidental interference, the system is synchronously connected into equipment such as an infrared thermal imager and a vibration accelerometer, and a multi-physical-quantity correlation analysis model is established. When high-frequency ultrasonic pulses are detected, the system automatically calls temperature data of the corresponding position, and if the temperature gradient exceeds 2 DEG C / cm, it is confirmed that discharging heating is conducted; and if the temperature rise does not exist and the low-frequency vibration of 10-200Hz is accompanied, the mechanical looseness is judged.

The invention relates to a partial discharge positioning method, equipment and device and a readable storage medium. The method comprises the following steps: collecting a visible light image and an infrared image of a to-be-detected area containing a partial discharge phenomenon, fusing the visible light image and the infrared image to generate a double-spectrum positioning map, collecting a sound wave signal of the to-be-detected area through a spiral microphone array, carrying out fusion processing on the double-spectrum positioning map and a sound source thermodynamic diagram of the sound wave signal, and obtaining a sound source thermodynamic diagram of the sound wave signal. Generating a comprehensive discharge positioning map, adopting a clustering algorithm to identify a plurality of features of the visible light image, the infrared image and the sound wave signal, determining the discharge position and the discharge type of the partial discharge phenomenon in the comprehensive discharge positioning map based on the plurality of features, and outputting the comprehensive discharge positioning map. Marking the discharge position and the discharge type of the partial discharge phenomenon in the comprehensive discharge positioning map; the optical, thermal and acoustic multi-mode information of the discharge is integrated, the positioning precision of the partial discharge is greatly improved, the fault condition can be conveniently and quickly judged, and the quick inspection requirement is met.

The invention discloses a cable accessory fault accurate positioning method and system based on multi-physics field cooperation, and belongs to the technical field of cable fault detection, and the method comprises the steps: installing and deploying a plurality of signal monitoring sensors in a key region of a cable accessory, and forming a multi-physics field sensing matrix; the method comprises the following steps: monitoring a cable accessory in real time by adopting heterogeneous sampling frequency, collecting a multi-physics signal, extracting a multi-physics signal feature in the multi-physics signal based on a time-frequency domain joint feature algorithm, performing signal analysis on the multi-physics signal feature, and obtaining a multi-physics signal analysis result; a multi-physical field fault model is constructed based on a multi-field coupling positioning algorithm, and a fault point in the cable accessory is positioned according to a multi-physical field signal analysis result, so that the fault of the cable accessory can be quickly and accurately identified and positioned, and the reliability and stability of a power system are improved.

The invention relates to the technical field of power equipment state monitoring, in particular to an online monitoring method for partial discharge of a high-voltage switch cabinet, and aims to solve the problem of insufficient early warning accuracy due to the fact that most life prediction methods in the prior art are based on a single physical quantity and cannot fully consider the synergistic effect of multi-modal signals. The multi-source signal collaborative analysis is realized through the space-time fusion signal processing and transfer learning model, the dynamic scanning strategy and the multi-scale degradation evaluation are combined, the problems of poor real-time performance and incomplete coverage of the traditional monitoring method are effectively solved, and the method has the advantages of improving the monitoring precision, shortening the model training period and realizing dynamic early warning. The multi-scale signal processing can capture high-frequency discharge details and low-frequency degradation trend at the same time, the dynamic threshold setting enhances the adaptability of signals with different intensities, and the weighted fusion strategy effectively integrates multi-dimensional features, so that the state change of equipment is comprehensively reflected, and a reliable basis is provided for early fault early warning.

The invention provides a transformer substation switch cabinet partial discharge detection method and system based on a sensor, and relates to the technical field of discharge detection, and the method comprises the steps: deploying an ultrasonic sensor according to a sensing collection channel, accessing a signal processing terminal, and configuring a sensor sensitivity threshold value and a signal sampling frequency; adjusting the signal gain of each monitoring node coordinate corresponding to the sensing acquisition channel, synchronously recording the monitoring time, the monitoring node coordinates and cabinet body temperature parameters, and setting a monitoring association control matrix in combination with a sensor sensitivity threshold and a signal sampling frequency; determining a partial discharge type and discharge intensity based on the signal processing terminal; and generating a discharge characteristic distribution map, and generating a partial discharge detection report in combination with the insulation performance index of the substation switch cabinet, a safe discharge threshold and a pulse frequency threshold. The technical problems that in the prior art, a transformer substation switch cabinet partial discharge detection method is poor in anti-interference capacity and low in recognition precision are solved.

The invention discloses a live detection method for insulation defects of high-voltage power equipment. The method comprises the following steps: firstly, synchronously arranging ultrahigh frequency sensors and acoustic emission sensors on a plurality of monitoring points on the surface of a gas insulated switchgear shell to form an array, and synchronously acquiring signals for filtering pretreatment; then, pulse events are extracted from the two types of signals respectively, and associated pulses in a time window are matched into matched pulse pairs representing the same discharge source; then, carrying out time-frequency transformation on the ultrahigh frequency and acoustic emission pulse waveform in each matched pulse pair, carrying out joint noise reduction by calculating a coherence coefficient between time-frequency distribution matrixes, and extracting a joint feature vector containing an energy ratio, a time parameter ratio and a frequency difference from the denoised time-frequency matrix; according to the invention, multi-source signals are fused, and high-sensitivity detection, high-precision positioning and high-accuracy identification of insulation defects are realized.

The invention discloses a patrol detection system of an intelligent substation circuit device, and relates to the field of patrol detection of the intelligent substation circuit device, the patrol detection system comprises a patrol detection frame, the patrol detection frame comprises a detection unit, a patrol unit and an analysis unit, the detection unit is used for carrying out defect identification and voiceprint detection on an intelligent substation circuit, and the patrol unit is used for carrying out patrol detection; the detection unit comprises a voiceprint detection module and a voiceprint positioning module. The voiceprint detection module is used for obtaining voiceprint signals of the intelligent substation and the circuit in real time. Through the arrangement mode that the voiceprint detection module, the acousto-optic fusion module and the analysis unit are matched with one another and the voiceprint detection technology, the problems that abnormal voiceprint features of potential faults cannot be found in time when transformer substation equipment is manually inspected and heard are solved, meanwhile, fusion of multiple physical signals such as voiceprint, vibration and an electromagnetic field is facilitated, and the safety of transformer substation equipment is improved. And a cross-modal correlation model is constructed in combination with the graph neural network, so that compound faults can be distinguished accurately.

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 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 relates to the technical field of discharge monitoring and early warning, in particular to a partial discharge real-time monitoring and early warning system based on multi-source data fusion, which is characterized in that decision tree classification is carried out on a mixed transient signal flow through a signal physical separation module, and pure electromagnetic wave speed type and mechanical wave speed type signal sources are separated; the feature marking module extracts feature points with a unified time reference and marks physical attribute categories; the double-path credibility fusion module executes time difference logic verification on the two types of feature points, a credible discharge event is marked only when an electromagnetic wave verification result and a mechanical wave verification result are logic values 1 at the same time, event confidence parameters are generated through dynamic confidence fusion, and physical interlocking verification and uniqueness locking are achieved; the anti-interference response module dynamically adjusts a response threshold value based on the event confidence parameter, and only triggers a targeted response to the threat which is physically and essentially confirmed to be real discharge and has confidence reaching the standard; the problem of distinguishing a real discharge event from multi-source interference is solved, and the real discharge event is reliably identified.

The invention discloses a power distribution cable fault processing method and device, electronic equipment and a storage medium, and relates to the technical field of power distribution cable fault processing, and the method comprises the steps: firstly collecting multi-mode fault signals, such as a high-frequency traveling wave signal, an ultrasonic partial discharge signal and an environment parameter signal, of a power distribution cable; and distribution network topological structure information such as line length, branch number, node position, wave impedance parameters and the like is synchronously obtained. Then high-frequency traveling wave features are extracted, a positioning strategy is determined according to the number of line branches, and fault position coordinates are obtained in cooperation with the traveling wave features and topological information. Then partial discharge features and environment features are extracted respectively, the three types of features are fused to form a fusion feature set, the feature set is analyzed through a fault classification model optimized by the improved whale algorithm, and fault type information is obtained. And finally, associating fault positions and types, generating a fault processing scheme containing a positioning result, fault causes and operation and maintenance priorities, and realizing accurate positioning, classification and operation and maintenance guidance of the distribution cable faults.