273results about "Testing using acoustic measurements" patented technology

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 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 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 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 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 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.

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 relates to the technical field of intelligent diagnosis of power equipment, and discloses a switch cabinet partial discharge diagnosis method based on multi-information fusion and dynamic intelligent regulation, which comprises the following steps: firstly, collecting multi-source partial discharge signals and constructing a defect basic database, then carrying out multi-source feature generation, principal component dimension reduction and BPNN key feature screening on the signals, and carrying out multi-source feature extraction on the signals; then, a dynamic BPA value is constructed based on the key features; and then, the defect type is identified by fusing multi-source information through an improved D-S evidence theory, and the defect is positioned in combination with a TDOA and an optimization algorithm. According to the method, the accuracy and reliability of defect type judgment after multi-source information fusion are improved.

The invention discloses an identification and positioning method for power cable defects and short-circuit faults. The defect identification and positioning method comprises the following steps: establishing a power cable transmission line simulation model and a partial discharge simulation model by using electromagnetic transient simulation software; obtaining a partial discharge signal according to the power cable transmission line simulation model and the partial discharge simulation model, and identifying the partial discharge signal based on an SSA-BP algorithm; performing preliminary positioning on the power cable by adopting an ultrasonic, high-frequency and ultrahigh-frequency detection method; accurate positioning of power cable defects is realized through an infrared heat and sound wave positioning principle. According to the method, a simulation analysis method is adopted to obtain a sample, and then accurate identification of a cable fault is realized through a BP neural network (SSA-BP) optimized based on a sparrow search algorithm. And meanwhile, accurate cable fault positioning is realized aiming at different fault positions.

The invention discloses a multi-mode signal fusion switch cabinet partial discharge detection and positioning method and device, and belongs to the technical field of power equipment monitoring, and the method comprises the steps: arranging an ultrahigh frequency sensor, an ultrasonic sensor and a traveling wave signal sensor on a switch cabinet, and synchronously collecting electromagnetic wave, sound wave and traveling wave signals; filtering the electromagnetic wave signal, the sound wave signal and the traveling wave signal, and enhancing the filtered signal to obtain a filtered enhanced signal; carrying out the positioning and classification of the partial discharge signals through the multi-modal feature fusion of the filtered enhanced signals; and triggering graded alarm according to a positioning result, and periodically starting a self-checking program to verify the reliability of the sensor and the algorithm. Physical characteristics of different discharge types are covered, the detection comprehensiveness is improved, the noise interference problem is solved, and the data fusion efficiency and the positioning precision are improved; the system health state is monitored in real time, and long-term operation stability is ensured.

The invention belongs to the technical field of power equipment signal processing, and particularly relates to a multi-channel signal reverberation suppression method based on phase weighted cross-correlation optimization, which comprises the following steps: S1, acquiring an original multi-channel time domain signal matrix; s2, estimating propagation time delay of each channel relative to the reference channel; s3, enabling all channels to realize time domain alignment in a direct sound main energy section; s4, processing to obtain a multi-channel time-frequency domain signal; s5, performing reverberation suppression by adopting a WPE algorithm to obtain a multi-channel time-frequency domain signal after reverberation removal; before the WPE algorithm is executed, through a Bayesian optimization method based on a Gaussian process, a prediction order and prediction delay parameter combination enabling the SI-SNR of the multi-channel dereverberation signal to be maximum is searched, and the prediction order and prediction delay parameter combination is applied to the WPE algorithm; s6, reconstructing the multi-channel time-frequency domain signal after reverberation removal into a time domain signal; and S7, calculating a normalization coefficient, and performing amplitude scaling. According to the method, the dereverberation ultrasonic signal with high fidelity, high consistency and high robustness can be obtained in a complex reverberation environment.

The invention provides a cable fault diagnosis method and system based on multi-modal composite fusion. The method comprises the following steps: synchronously acquiring and performing time-space alignment on distributed acoustic, temperature and high-frequency pulse signals along a cable path; the acoustic signal is decoupled into an internal event channel and an external interference channel by time domain polarization analysis. And calculating the acoustic nonlinear index of the candidate event in the internal channel, dynamically correcting the judgment threshold value of the candidate event by using the real-time temperature signal, and performing cross-modal correlation verification with the high-frequency pulse signal so as to confirm that the candidate event is an electrical discharge event. And if the judgment result is true, an acoustic wave field data array of the event is extracted, and a passive synthetic aperture focusing algorithm is applied to reconstruct a two-dimensional morphological acoustic image of the fault source. And finally, integrating all information to generate a comprehensive diagnosis report. The technical problem that a traditional distributed sensing method can only provide one-dimensional fault positioning and cannot perform high-confidence qualitative and multi-dimensional morphological representation on the fault is solved.

The invention discloses a partial discharge intelligent detection method and a partial discharge intelligent detection system. The method comprises the following steps: firstly, synchronously acquiring a multi-channel ultrasonic signal and a surface infrared image sequence of to-be-detected power equipment; performing pulse detection, cross-channel clustering based on a sliding time window and time difference positioning on the ultrasonic signal to obtain a preliminary discharge point; the multi-moment infrared images are intelligently selected and fused based on the ultrasonic pulse active time period, and a target surface infrared image highlighting the abnormal temperature rise area is generated; performing spatial aggregation on the ultrasonic positioning point and the infrared abnormal region to form a suspected sound-heat associated discharge source; respectively extracting waveform features and temperature features from the aggregated ultrasonic pulse set and the infrared region, fusing the waveform features and the temperature features, and inputting the fused features into a dual-channel neural network model; and finally, the model outputs a discharge type identification result and a discharge intensity evaluation value of the suspected discharge source. According to the invention, through deep space-time fusion and feature level fusion, the positioning precision and identification accuracy of partial discharge detection are significantly improved.

The invention provides a transformer intelligent monitoring method and system based on artificial intelligence. The method comprises the steps that multi-dimensional data in the operation process of a transformer are collected in real time; establishing a cross-dimensional correlation model by using multi-dimensional data based on an attention mechanism in combination with a graph neural network algorithm; performing fault trend analysis based on fusion data output by the cross-dimension correlation model, predicting a fault change trend in a period of time in the future, and generating a fault prediction result; an equipment digital mirror image is constructed by adopting a data twinning technology, a physical process of fault development is simulated through coupling simulation of a magnetic field, an electric field and a temperature field driven by real-time monitoring data, and virtual-real linkage early warning is carried out in combination with a fault prediction result; through deep fusion of an artificial intelligence technology and transformer partial discharge monitoring, accurate identification and development trend prediction of partial discharge faults are realized, accurate fault identification can still be carried out even under a small sample condition, and the safety and reliability of power equipment operation are significantly improved.

A partial discharge inspection and positioning apparatus and method for a ring main unit. The partial discharge inspection and positioning apparatus is mounted on the ring main unit. A three-phase partial discharge signal acquired from a charged indicator inside the ring main unit is received by means of a three-phase signal processing system in an apparatus housing of the partial discharge inspection and positioning apparatus, the three-phase partial discharge signal is analyzed to generate analysis data, and then the analysis data is transmitted to a handheld terminal for detection and analysis, so as to implement detection of a PRPD pattern and inter-phase positioning of a partial discharge source.

The invention discloses a partial discharge fusion detection method based on multiple sensors, and relates to the field of power system detection, and the method comprises the steps: S1, multi-modal sensor cooperative deployment: firstly, after the multi-modal sensor cooperative deployment in S1 is completed, S2, time-space synchronization data collection is carried out, and then S3, signal preprocessing and feature extraction are carried out after the time-space synchronization data collection in S2 is completed; and S3, after signal preprocessing and feature extraction are completed, performing S4, feature level fusion innovation, when the S4, feature level fusion innovation is completed, performing S5, a decision level fusion mechanism, and finally performing S6, three-dimensional visualization and early warning after the S5, the decision level fusion mechanism is completed. According to the partial discharge fusion detection method based on the multiple sensors, in the strong interference environment of the frequency converter, the false alarm rate is compressed from 68% to 11% through multi-sensor cooperative noise reduction and dynamic weight distribution, and the anti-interference capability of the system is remarkably improved; and centimeter-level positioning precision is realized, and powerful support is provided for accurate fault positioning.

The invention discloses a composite voltage generation device and a cable insulation performance evaluation system and method, and relates to the technical field of high-voltage insulation testing. The composite voltage generating device comprises a synchronous controller, a direct current source, an impact source, an IGBT active protection unit and a magnetic isolation coupler, and by means of the excellent characteristics of the IGBT and the magnetic isolation coupler, the superposition time sequence accuracy of direct current and impact voltage is improved, and waveform distortion is reduced. The IGBT active protection unit comprises a first IGBT and a second IGBT, and direct-current voltage polarity switching can be realized through selective conduction of the IGBTs; the RC circuit is connected in parallel between the collector electrode of the first IGBT and the emitter electrode of the second IGBT, reverse current flowing back to the direct current source direction can be discharged through the RC circuit, the risk of energy flowing back is reduced, meanwhile, the magnetic isolation coupler can completely block direct current, and an impact source is protected against the influence of direct current voltage; according to the invention, the authenticity and reliability of cable insulation performance evaluation can be effectively improved.

The invention belongs to the technical field of circuit breaker discharge monitoring, and particularly relates to a circuit breaker partial discharge monitoring system which comprises a sensing acquisition module, a data acquisition and preprocessing module, a signal identification and diagnosis module, an alarm and decision module, a communication and visualization module and a system management and storage module. According to the circuit breaker partial discharge monitoring system provided by the invention, the high-frequency current sensor, the ultrasonic sensor, the ultrahigh-frequency electromagnetic wave sensor and the environmental parameter sensor are integrated, so that multi-dimensional detection of electric, magnetic and acoustic signals generated in a partial discharge process and an interference environment is realized, and the signal credibility and the recognition precision are effectively improved; and the problems of false alarm and missing alarm of a single sensor are avoided.

The invention relates to a 10kV switch cabinet intelligent automatic operation monitoring and early warning method and system and a medium, and the method specifically comprises the following steps: 1, collecting the partial discharge data information of each key part, including a sound wave signal, an instantaneous pulse current signal and a high-frequency electromagnetic wave signal; 2, carrying out data processing, and obtaining a partial discharge abnormal point data value of each key part; 3, realizing partial discharge early warning of the switch cabinet by adopting a fault early warning method based on current and sound wave abnormal point data of the switch cabinet; 4, collecting the temperature of each key part and the environment temperature; 5, carrying out data processing, and obtaining a change relation function of the temperature rise of each key part along with the current; step 6, establishing a switch cabinet dynamic early warning model to realize dynamic temperature rise early warning; the method has the advantages that the operation and maintenance cost is reduced, the equipment operation efficiency is improved, potential safety hazards are found in time, and remote monitoring and early warning are achieved.

The invention discloses a partial discharge detection method and device based on discharge spectrum chromaticity, and belongs to the technical field of power systems, and the method comprises the steps: collecting a plurality of discharge optical signals of switch cabinet equipment; for each discharge optical signal, carrying out chromaticity analysis on the RGB monochromatic light signal, and respectively calculating color chaos degree, spectrum color gamut deviation degree, spectrum chromaticity dynamic coupling parameters and hue purity ratio; calculating a chromaticity difference weight based on the full-band optical signal and the RGB monochromatic light signal; performing weighted integration on the color chaos degree, the spectrum color gamut deviation degree, the spectrum chromaticity dynamic coupling parameter and the hue purity ratio based on the chromaticity difference weight to obtain a chromaticity comprehensive magnitude; calculating the mean value of the comprehensive chromaticity values of the plurality of discharge optical signals; and comparing the chromaticity comprehensive magnitude mean value with the plurality of judgment chromaticity ranges, and determining a discharge type detection result of the switch cabinet equipment. According to the invention, the problem of low accuracy of partial discharge detection in the prior art can be solved.

The invention discloses a power distribution equipment leakage point detection system and method, and belongs to the technical field of electrical equipment detection, and the method comprises the steps: obtaining infrared thermal imaging data, ultrasonic detection data and environment parameter data of power distribution equipment, and fusing the data into a multi-mode sensor data set; extracting multi-dimensional physical features of data in the multi-modal sensor data set, and generating a multi-modal fusion feature vector; calculating based on the multi-modal fusion feature vector to obtain a comprehensive electric leakage probability index; acquiring an equipment operation state of the power distribution equipment, and generating a self-adaptive alarm threshold value in combination with pre-acquired historical electric leakage data; and comparing the comprehensive electric leakage probability index with a self-adaptive alarm threshold, and when the comprehensive electric leakage probability index exceeds the self-adaptive alarm threshold, generating an electric leakage alarm signal. According to the invention, a dynamic threshold decision-making method with multi-modal sensing data fusion and equipment operation state self-adaption is adopted, so that accurate detection, positioning and early warning of the leakage point of the power distribution equipment can be realized.

The invention discloses a multifunctional power equipment partial discharge comprehensive detection device, and relates to the technical field of power equipment state monitoring and fault diagnosis. The system comprises a multi-mode sensor interface (TEV / HFCT / UHF / air coupling ultrasound), a phase reference and cross-channel alignment unit, a physical scene adaptive window estimation module, an event sparse state fusion unit and a joint criterion and control unit. The device adapts the time difference of arrival and a phase window and generates a frequency band weight according to equipment geometry / medium information, event level judgment is completed under the common constraint of delta t / delta phi consistency, spectral kurtosis and fusion score in combination with an event purification mask and an evidence score, PRPD / PRPS is output, and graded alarming is performed; a coaxial composite ultrasonic wave concentrator improves the acoustic signal-to-noise ratio, and a calibration and traceability unit (a detachable UHF calibration cavity, a fast edge source and a probe identification memory) realizes link sensitivity and group delay field compensation. Power-off-free and cross-scene high-precision detection is realized, false alarm and missing detection are reduced, and consistency and traceability are enhanced.

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 an electric wire and cable partial discharge online detection and insulation defect evaluation system. The system comprises a distributed sensor array, a signal preprocessing unit, a feature extraction module, a defect evaluation unit, a positioning module and an early warning unit. Multiple sensors cooperatively detect, a distributed sensor array realizes full-line coverage, weak discharge signals are effectively captured, and the detection sensitivity reaches a picobank (pC) level. According to the dual positioning algorithm, an improved TDOA algorithm and a PSO algorithm are combined, and the positioning error is smaller than or equal to 0.5 m (laboratory environment) and smaller than or equal to 1.5 m (actual engineering environment) and is improved by 30%-50% compared with a traditional method. Time-frequency feature depth extraction: wavelet packet transformation and S transformation are combined to realize 0.1 microsecond-level time resolution and 5kHz-level frequency resolution of discharge signals, and different types of discharge are accurately identified.

The invention relates to the technical field of electrical equipment fault diagnosis, in particular to an acoustic-electric joint detection method, system and device for internal defects of GIS equipment and a medium, and the method comprises the steps: synchronously collecting an ultrasonic signal detected by a grating fiber ultrasonic sensing module and an ultrahigh frequency electromagnetic wave signal detected by an ultrahigh frequency sensing module; performing feature extraction on the ultrasonic signal and the ultrahigh-frequency electromagnetic wave signal to obtain a first feature parameter and a second feature parameter respectively; based on the arrival time difference of the ultrahigh-frequency electromagnetic wave signal and the ultrasonic wave signal, calculating the initial space position of the defect by adopting a time difference positioning method; and performing conjoint analysis on the first characteristic parameter and the second characteristic parameter in combination with the initial space position of the defect so as to identify the defect type. Through the arrangement, sound and electricity detection information can be deeply fused, a novel joint detection method with mutual verification and mutual promotion of positioning and identification is realized, and the integrity, accuracy and reliability of GIS internal defect diagnosis are effectively improved.

The invention provides a switch cabinet partial discharge on-line monitoring method based on cloud-side cooperation, and belongs to the technical field of switch cabinet partial discharge detection. Comprising the steps that an edge computing device performs partial discharge preliminary monitoring by using discrete signals collected by an ultrasonic sensor and a ground wave sensor on a switch cabinet; if the partial discharge event is preliminarily monitored, discrete signals collected by all ultrasonic sensors and ground wave sensors on the corresponding switch cabinet before and after partial discharge are sent to cloud computing equipment; and the cloud computing device carries out partial discharge type identification and partial discharge position positioning according to the received discrete signals. According to the method, a collaborative architecture of edge preliminary monitoring and cloud deep analysis is adopted, and lightweight real-time monitoring is executed on the edge side; according to the invention, an intelligent trigger uploading mechanism based on an event is adopted, and the original sensing data before and after the event occurrence moment are uploaded only after the partial discharge event is preliminarily confirmed at the edge side, so that the communication bandwidth demand and the cloud processing load are greatly reduced.

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.

The invention belongs to the technical field of GIS fault detection, and discloses a GIS latent insulation fault diagnosis method and system under strong noise, and the method achieves the omnibearing signal capture through synchronously obtaining optical, mechanical, electrical and acoustic signals, and solves a problem of weak signal leak detection. For strong noise interference, noise and fault characteristic frequency bands are effectively separated by using multi-modal signal difference and constructing a time domain and wavelet domain cascaded double-layer dictionary. A discriminative sparse model is combined with a K-SVD algorithm to carry out end-to-end iterative optimization, sparse features with higher discriminative force are automatically mined, and the problem of deep feature mining is solved. For the problem of weak generalization ability of small samples, a classifier error term is introduced into an objective function, a joint optimization objective function fusing reconstruction and classification errors is constructed, dictionary learning and classifier training are combined into one, the generalization ability of the model is significantly enhanced, and the method is suitable for large-scale popularization and application. Therefore, timely and accurate identification of the latent insulation fault under the conditions of strong noise and small samples is realized.