527results about "Testing using optic methods" patented technology

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 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 high-precision defect detection system for a track inspection robot, which relates to the technical field of track inspection and comprises a multi-mode visual acquisition module, a time sequence synchronization module, a data fusion processing module and a defect identification output module, by adopting a multi-modal visual fusion detection technology, various modal visual technologies of visible light vision, infrared vision and ultraviolet vision are combined, fusion processing is carried out on all modal image data, visual defects such as cracks and abrasion on the surface of a track can be clearly recognized through visible light vision, and the visual performance of the track is improved. The infrared vision can detect temperature abnormity of a track component to find out potential poor contact and other problems, the ultraviolet vision can be used for detecting corona discharge and other phenomena, track information is comprehensively obtained, the accuracy and reliability of defect recognition are effectively improved, defects which are difficult to find in a single mode can be detected, and the detection efficiency is improved. And powerful support is provided for safe operation and maintenance of the track.

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 is suitable for the technical field of performance detection, and provides a method for detecting the ablation resistance of a burning-resistant nozzle and an arc contact, and the method comprises the steps: collecting a temperature field matrix, an arc image sequence, an electrical parameter time sequence and a surface displacement curve of an ablation region, and generating a multi-modal data set; based on the data set, extracting spatial features of the ablation core region through a cavity convolution layer and a channel attention mechanism; inputting a preset bidirectional LSTM network, fusing thermal stress data of the COMSOL simulation model to drive gating weight update, and extracting time sequence evolution characteristics; generating a space-time fusion feature vector containing the temperature field gradient, the arc form change rate and the surface roughness evolution feature; inputting the space-time fusion feature vector, the thermal stress distribution data and the material phase change threshold parameter into a constructed ablation prediction model for training; the ablation prediction model after training is completed is optimized; and a performance prediction result is generated in real time by using the optimized ablation prediction model, so that the detection accuracy is effectively 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 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 switch cabinet discharge detection method and system, and relates to the technical field of power equipment state monitoring and fault diagnose.The light intensity attenuation rate and the vibration frequency are collected through a photonic crystal fiber, the concentration of decomposed gas is detected in combination with a Raman spectrum, and a space-time joint feature matrix is generated; performing space weight distribution on the space-time joint feature matrix based on the switch cabinet structure, and inputting the space weight to an RBF-SVM (Radial Basis Function-Support Vector Machine) to generate discharge type probability distribution; based on a high-frequency energy parameter and a gas decomposition concentration parameter in the spatio-temporal joint characteristic matrix, inputting the parameters into a multiple linear regression model, calculating an insulation aging index, and judging an insulation degradation grade according to a threshold value; the photonic crystal fiber is used for collecting the light intensity attenuation rate and the vibration frequency, the Raman spectrum is combined for detecting the decomposition gas concentration to generate the space-time joint characteristic matrix, synchronous monitoring of various physical and chemical parameters in the switch cabinet is achieved, and the accuracy and comprehensiveness of recognition of different types of discharge phenomena are improved.

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 relates to the technical field of anomaly detection, in particular to a pantograph arcing anomaly detection and early warning method based on fusion of ultraviolet light and infrared imaging, and the method comprises the steps: collecting an ultraviolet spectrum signal of a pantograph region in real time, and extracting arcing feature data; acquiring infrared temperature distribution images of the pantograph and the overhead line system, and extracting temperature abnormal region data; timestamp synchronization and space coordinate mapping are carried out on the arcing characteristic data and the temperature anomaly region data, and a fusion data set with time-space alignment is generated; on the basis of the fused data set, calculating the space-time similarity between the arcing feature data and the temperature anomaly region data, and outputting an arcing-temperature correlation index; constructing a Gaussian mixture model, and dynamically judging an abnormal level; and when the abnormal grade exceeds a preset threshold value, a grading early warning signal is triggered, and a fault positioning report including an arcing position and a temperature abnormal range is generated. The method has high engineering practicability, and has important significance for guaranteeing safe and stable operation of a rail transit power supply system.

The invention relates to the technical field of power capacitors, and discloses a power capacitor explosion-proof test method, system, medium and equipment, and the method comprises the steps: constructing a composite stress field according to the actual working condition of a power capacitor, and injecting a multi-band harmonic current sequence; three-dimensional strain propagation characteristics of the power capacitor outer shell are captured in real time, a strain-discharge time-space correlation model is established, a structural integrity attenuation curve of the power capacitor outer shell is obtained accordingly, and when the slope of the attenuation curve breaks through a preset critical topological form, a multi-stage linkage protection device is triggered to implement energy discharge; and extracting a characteristic spectral line of a plasma spectrum formed by the self-healing characteristic of the metallized film in the energy discharge process, constructing a multi-stage evaluation index through the offset of the characteristic spectral line, and generating an explosion-proof performance grade evaluation result. According to the invention, accurate simulation of the composite stress field and dynamic monitoring and early warning of the power capacitor can be realized, and the accuracy of early warning and explosion resistance evaluation is improved.

The invention relates to the technical field of power system equipment state monitoring and fault diagnosis, and discloses an arc light online monitoring method and system based on a spectrum sensing technology, and the method comprises the steps: arranging a first spectrum sensor and a second spectrum sensor corresponding to a detected target, and respectively detecting optical signals in different wavelength ranges; collecting optical data according to a set period, and identifying abnormal features according to a preset template; when the double abnormal features exist, the duration of the double abnormal features is calculated, and if the duration exceeds the corresponding reference duration, a duration reference value is generated; and performing weighted calculation on the time length analysis value, if the time length analysis value is within a preset range, giving a partial discharge abnormal prompt, and resetting the reference value. Multi-wavelength spectrum monitoring is utilized, non-contact and high-sensitivity detection of partial discharge is achieved, electrical equipment is prevented from being damaged, fault hidden dangers are found in time, and safe and stable operation of the equipment is guaranteed.

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 an acceleration method, device and equipment for partial discharge detection of a switch cabinet and a storage medium, and relates to the technical field of power equipment fault detection, partial discharge signals are collected in real time through a multi-parameter sensor, multi-source signal alignment is realized by combining a hardware trigger mechanism and timestamp synchronization, noise components are separated through multi-scale wavelet transform, and the partial discharge detection of the switch cabinet is realized. The method comprises the following steps: extracting time-frequency joint features through discrete wavelet transform, reducing dimensionality by adopting a principal component analysis method, taking low-dimensional feature vectors as input limited by a Gaussian mixture model (GMM), taking feature vectors limited by the GMM as input of an SVM classifier, outputting a classification result, and performing online judgment according to the classification result. When the output is partial discharge, an alarm prompt is triggered through the interface module; and when the output is normal, real-time monitoring is continued. According to the invention, signal processing and classification are accelerated by using an FPGA parallel processing architecture, and real-time early warning of power equipment faults is realized.

The invention provides a novel environment-friendly insulating gas surface flashover characteristic evaluation method based on surface charge on-line measurement, which comprises an electric field measurement system based on an electric field induced second harmonic effect (EFISH), a coupling high-voltage surface flashover experiment system, and an evolutionary process of an electric field in a process of applying high voltage to surface flashover, the evolutionary process of charge distribution is obtained based on the electrostatic field source relation, the starting moment of surface flashover is determined by measuring sound, light and electromagnetic signals in the surface flashover process, and the starting area of the surface flashover is observed through a high-speed camera; and associating the relationship between the charge and the surface flashover, and quantitatively explaining the induction effect of the charge on the flashover.

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 relates to the technical field of substation detection equipment, in particular to a fault discharge detection device for a main transformer of a substation, which comprises a base, a first bearing plate is arranged on the side of a vertical supporting beam, a right-angle support is connected to a first servo rotating seat on the side of the first bearing plate, and a second bearing plate is fixedly mounted on the right-angle support. A horizontal supporting arm is installed on a second servo rotating seat on the second bearing plate, a bearing rotary table is installed at one end of the outer side of the horizontal supporting arm, a stabilizing screw rod is installed on a loading plate at one end of the outer side of the bearing rotary table, an adjusting screw rod on the periphery of the stabilizing screw rod is sleeved with a detection probe rod, and a third bearing plate is fixedly installed on the other side of the loading plate; an infrared camera probe is arranged on the outer side of the third bearing plate. According to the invention, through a matching structure of a mechanical-electrical integration design and a space kinematic chain, full-angle coverage of the main transformer of the transformer substation can be realized, a safe operation space is given to electric power engineering personnel, and the core problems of high signal supply omission ratio and high personnel safety risk are solved.

The invention discloses a switch cabinet electric arc light detection method and system based on multispectral fusion, and relates to the technical field of electric power system intelligent detection, and the method comprises the following steps: S1, building a cross-spectrum unified time reference, and carrying out the synchronous comparison of an infrared light signal, a visible light signal and an ultraviolet light signal, extracting a temperature transition trigger feature and a strong light disturbance spectrum feature, and generating a time synchronization deviation vector; and S2, constructing an anti-fact spectrum playback model based on the time synchronization deviation vector, generating a shadow spectrum sequence, positioning an infrared response threshold lifting reason through energy difference analysis, calculating a spectrum overlapping risk interval and duration, and outputting a threshold disturbance distribution diagram. According to the method, infrared anomalies are identified through cross-spectrum time alignment, anti-fact playback and differential analysis, channel recovery is guided in combination with springback prediction and time sequence correction, and a spectrum broken chain window is constructed through dislocation sampling and phase regulation and control, so that the accuracy and the anti-interference capability of arc identification are effectively improved.

The invention relates to the technical field of partial discharge detection, in particular to an ultraviolet partial discharge detection method and device, a storage medium and an ultraviolet sensor, the ultraviolet partial discharge detection method is applied to a gallium nitride ultraviolet sensor, and the ultraviolet partial discharge detection method comprises the following steps: acquiring an initial ultraviolet detection signal and an environmental parameter detection signal, preprocessing the initial ultraviolet detection signal and the environmental parameter detection signal to obtain an actual ultraviolet detection signal and a drift compensation value; when it is determined that the gallium nitride ultraviolet sensor is in a real partial discharge state according to the actual ultraviolet detection signal, compensating the actual ultraviolet detection signal according to the drift compensation value; and acquiring the real ultraviolet partial discharge intensity corresponding to the initial ultraviolet detection signal according to the compensated actual ultraviolet detection signal. Therefore, the detection precision and stability of the ultraviolet signal can be improved, excessive deviation of a detection result caused by interference of environmental factors is avoided, and the occurrence frequency of false alarms is remarkably reduced.

The invention relates to a transformer abnormal sound source positioning method and system, and belongs to the technical field of power equipment state evaluation, and the method comprises the steps: constructing a Bayesian neural network embedded with a voiceprint physical mechanism, and enabling a sound wave propagation equation to serve as a physical constraint to be embedded into the Bayesian neural network; reconstructing the voiceprint signal by adopting a compressed sensing technology to obtain a reconstructed voiceprint field; designing a multi-task objective function including data fitting, physical constraint and positioning loss, and optimizing data fitting, physical constraint and positioning precision to obtain a trained Bayesian neural network; based on a gradient sound source inversion positioning algorithm and the trained Bayesian neural network, sparse regularization is combined to obtain a prediction result of accurate positioning; and a prediction result is visualized to a three-dimensional model of the transformer, and the position of an abnormal sound source is visually displayed. According to the method, the limitation of a traditional method in a complex environment is overcome, and high-precision and high-robustness transformer abnormal sound source positioning is realized.

The invention discloses a high-precision voltage regulating system of a power frequency no-partial-discharge test transformer, and the system comprises a heterogeneous multi-stage power module which is formed by connecting an H-bridge unit group and a matrix converter in parallel, the H-bridge unit group outputs a fundamental voltage component, and the matrix converter compensates a high-frequency harmonic component; the multi-physics field digital twinning unit integrates an electromagnetic field solver, a thermodynamic model and a mechanical deformation simulation module and predicts the multi-physics field coupling state of the insulation system in real time; the dynamic impedance matching network comprises an adjustable inductor array and a digital capacitor bank, and automatically adjusts filtering parameters according to a load impedance measurement value; and the control module is used for deploying a deep learning model and is used for optimizing a modulation strategy and a protection threshold on line. According to the method, the voltage regulation precision and the waveform purity can be improved, the stability under the wide-range working condition is guaranteed, the breakdown hidden danger is recognized in advance through multi-physics field coupling simulation, and partial discharge development is restrained from the source.

The invention relates to the technical field of life evaluation, in particular to a circuit breaker life evaluation method and system for switching an alternating current filter in an extra-high voltage converter station, and the method comprises the steps: collecting multi-dimensional operation data in the operation process of a circuit breaker in real time; extracting a high-frequency-band energy ratio as a contact system abrasion loss characteristic parameter; establishing an arcing energy accumulation model according to the arc light intensity time sequence signal, and calculating an effective arcing energy value of a single operation; constructing a life weight distribution model, inputting the wear loss characteristic parameters, the arcing energy accumulation value and the environment temperature and humidity data into the model, and outputting a mechanical wear index, an electric corrosion index and an environment degradation index; establishing a three-axis linkage correction function; generating a comprehensive life evaluation index based on the corrected mechanical wear index, electric corrosion index and environmental degradation index, and triggering an early warning signal when the index exceeds a preset threshold value; according to the invention, by establishing the life evaluation model based on multi-dimensional data fusion, the precision and reliability of life prediction of the circuit breaker of the extra-high voltage converter station are significantly improved.

According to the ring main unit with the intelligent online partial discharge detection system provided by the invention, the problem of multisource data asynchronization is solved through a four-dimensional space-time synchronous acquisition mechanism, so that the time alignment error of ultrahigh frequency and ultrasonic signals is lower, and the positioning precision is higher; the edge computing unit is used for executing adaptive filtering and pulse extraction, so that the effective data compression rate is greatly increased, the bandwidth requirement is reduced, and quick response within 3 seconds is realized; cross-modal coupling factor quantification electromagnetic-acoustic emission correlation characteristics are innovatively proposed, the contribution degree of a sensor is adaptively adjusted in combination with a dynamic weight distribution strategy, and high discharge type recognition accuracy is still kept in a strong electromagnetic interference environment; the space-time heterogeneous data is cooperatively processed through the two-channel deep learning model, the composite discharge mode is effectively identified, and the false alarm rate is lower compared with a traditional method; in addition, the system automatically and incrementally trains the model per week, continuously adapts to signal attenuation caused by equipment aging, and prolongs the effective monitoring life by more than three times.

The invention discloses a cable terminal internal insulation defect development simulation test system and method, and the system comprises a system voltage control module which is used for applying a processing frequency high voltage to simulate an operation voltage environment; the interface pressure monitoring module is used for applying adjustable pressure and monitoring pressure change in real time; the interface defect detection module is used for simulating an internal insulation defect development process of the cable and collecting defect development image information; and the partial discharge detection module is used for acquiring a partial discharge signal generated in the insulation defect development process. Under the simulated actual working condition, the development process of the insulation defect of the cable terminal can be dynamically reproduced, the influence rule of the interface pressure change on defect evolution is disclosed, the partial discharge characteristic signal in the defect development process is obtained, and support is provided for the research on the insulation degradation mechanism of the vehicle-mounted high-voltage cable terminal and the safe operation of the vehicle-mounted high-voltage cable terminal.

The invention provides an unmanned aerial vehicle inspection identification method and device for a power transmission tower, and relates to the technical field of electric power facility health monitoring. Polar coordinates of stay wire endpoints are obtained through unmanned aerial vehicle laser ranging, the length of the stay wire is calculated, and the deformation abnormity degree is evaluated; on the basis of a five-channel hyperspectral imaging system, orthogonal route scanning imaging is utilized, an insulator is positioned in real time in combination with a CNN algorithm, and the state of the insulator is analyzed through a UV wave band radiation value, a discharge gradient and a temperature difference; extracting reflectivity data of a wave band of 550-700nm, and calculating a corrosion area proportion by adopting a wave band ratio method; and evaluating the health state of the iron tower by combining the stay wire deformation, the insulator abnormal state and the corrosion degree. According to the method, multi-source data and an intelligent algorithm are fused, high-precision detection of stay wire deformation, insulator defects and corrosion area is realized, the problems of low efficiency and single parameter of traditional inspection are solved, and automation and reliability of state evaluation of the power transmission tower are remarkably improved.

The invention provides a GIS equipment partial discharge type classification method based on partial discharge time domain signals, and belongs to the field of partial discharge detection and classification. The problem that the method for judging the partial discharge type through the partial discharge mode spectrogram fails due to the fact that an externally applied voltage signal cannot be obtained in the field detection process is solved. The method comprises the following steps: respectively acquiring time-domain data sets of partial discharge light of a sample in the occurrence process of three partial discharge phenomena of corona discharge, surface discharge and suspension discharge through an experimental platform, and preprocessing the time-domain data sets; adopting a dual-threshold peak searching method to perform dual-threshold peak searching on data in the time domain data sets of the three types of preprocessed partial discharge light to obtain corresponding partial discharge peak data sets, and processing the partial discharge peak data sets into corresponding partial discharge light signal amplitude difference spectrograms; partial discharge type classification is realized through the partial discharge optical signal amplitude difference spectrogram; the method is applied to partial discharge detection of the GIS equipment.