334results about "Testing using optic methods" patented technology

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 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 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 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 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 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 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 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.

The application 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, which comprises the following steps: S1: acquiring 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, acquiring a multi-dimensional feature vector, and learning the internal correlation among features by using a multi-modal deep network to obtain joint multi-modal features; S4: constructing a fault type recognition model based on Bayesian inference and Monte Carlo sampling, and acquiring a fault type classification result; S5: combining deep learning and a physical model to acquire interval and positioning information of fault occurrence and a fault grade; and S6: acquiring the fault grade based on the fault type classification result and the interval and positioning information of fault occurrence, and generating a diagnosis report for early warning pushing. The application realizes high-precision identification, positioning and risk assessment of high-voltage cable insulation faults.

The invention relates to the technical field of capacitors, in particular to an explosion-proof test method for a power capacitor. Injecting a multi-band harmonic current sequence into the composite stress field, capturing three-dimensional strain propagation characteristics of the capacitor shell in real time, and establishing a strain-discharge space-time correlation model according to the three-dimensional strain propagation characteristics; a capacitor shell structure integrity attenuation curve is obtained according to the strain-discharge time-space correlation model, 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 plasma spectrum fingerprint formed by the self-healing characteristic of the metallized film in the energy discharge process, and reversely reconstructing an explosion-proof performance grade evaluation system of the capacitor through the offset of a characteristic spectral line. The full-process closed-loop test of the capacitor from stress loading, failure early warning to performance evaluation is realized, and the environment simulation authenticity, the failure early warning accuracy and the performance evaluation scientificity of the explosion-proof test are improved.

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 relates to a gas discharge detection method, system and equipment based on an ultraviolet spectrum and a medium. The method comprises the following steps: transmitting a frequency-modulated ultraviolet light beam to a gas chamber to be detected, synchronously collecting and processing a transmission light signal in a branching manner, and simultaneously obtaining a modulated spectrum signal for gas absorption analysis and a discrete photon pulse signal for detecting a discharge event; respectively processing the two paths of signals, and extracting sulfur dioxide concentration information and photon radiation characteristics; fusing the two types of features to construct a dynamic feature vector, and obtaining a discharge state grade by using a dynamic Bayesian network diagnosis model; and finally, combining the concentration and state information, and outputting the membership degree of the discharge duration through fuzzy logic. By adopting the method, the fast process and the slow process of partial discharge can be cooperatively monitored, and the early detection sensitivity and the diagnosis reliability are improved.

The invention discloses a bubble defect detecting and positioning system and method based on X-ray enhanced partial discharge, and belongs to the technical field of nondestructive testing of insulating materials, the system comprises a shielding box slidably mounted on a three-dimensional linear guide rail, an X-ray machine and a red laser are embedded in the shielding box, and double-oblique-angle cameras are mounted on two sides of the three-dimensional linear guide rail; the laser positioning synchronization device is installed on the movable collimation X-ray scanning module, and the movable collimation X-ray scanning module emits an X-ray beam towards the basin-type insulator to be detected; the laser positioning synchronization device emits a laser beam towards the basin-type insulator to be detected; the laser beam is coaxial with the X-ray beam; the double-oblique-angle camera shoots the basin-type insulator to be detected; the pressurization circuit is respectively connected with the pressurization electrode and the partial discharge detection system, and the pressurization electrode is electrically contacted with the basin-type insulator to be detected; the intelligent control platform is connected with the voltage increasing circuit, the movable collimation X-ray scanning module, the laser positioning synchronization device, the double-oblique-angle camera and the partial discharge detection system.

The invention relates to the technical field of transformer arc fault detection, in particular to an optical fiber sensing detection method, system and device for an arc fault in a transformer bushing and a storage medium. Acquiring internal pressure and flow velocity data of the casing by constructing an optical fiber sensing detection model, identifying fault feature points and determining a pressure-flow velocity relationship; establishing a pressure change prediction model in combination with the environmental parameters to perform first prediction, and analyzing pressure and flow velocity dynamic characteristics to obtain a dual-drive fault threshold to perform second prediction; and an arc fault diagnosis model is constructed based on the double-parameter coupling matrix, and a time-varying early warning model is established in combination with fault evolution to realize fault prediction. According to the invention, the technical problem that the traditional detection method cannot monitor the arc fault in the bushing in real time is solved, the accurate identification of fault features, the early warning of precursor signals and the accurate prediction of residual life are realized, the detection precision and real-time performance are obviously improved, and reliable guarantee is provided for the safe operation of the transformer.

The invention discloses a transformer insulating sleeve fault diagnosis method and device and electronic equipment. The method comprises the following steps: acquiring an infrared image and a visible light image of a to-be-detected insulating pipe sleeve in a transformer; respectively carrying out feature extraction on the infrared image and the visible light image by adopting a scale invariant feature transformation method to obtain an infrared feature point set and a visible light feature point set; performing feature fusion based on the infrared feature point set and the visible light feature point set to obtain a target fusion image; multiple features in the target fusion image are extracted, and the multiple features at least comprise a thermal image temperature feature, a texture feature and a shape feature; and obtaining a thermal fault diagnosis result of the to-be-detected insulating sleeve by adopting a pre-trained insulating sleeve fault diagnosis model based on the multiple characteristics. According to the invention, the technical problem of low potential thermal fault diagnosis accuracy of the transformer insulating sleeve in the prior art is 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 high-voltage cable intermediate joint partial discharge detection method and system, a storage medium and an electronic terminal.Partial discharge signals are collected through multi-sensor fusion, and adaptive filtering anti-interference processing, characteristic parameter extraction and a time difference positioning algorithm are combined; high-sensitivity detection, accurate positioning and severity evaluation of partial discharge of the intermediate joint of the high-voltage cable are realized, so that the problems of low sensitivity, weak anti-interference capability and insufficient positioning accuracy of an existing detection method are solved, the detection sensitivity is remarkably improved, weak partial discharge signals can be effectively captured, and the detection accuracy is improved. And reliable technical support is provided for safe operation and maintenance of the high-voltage cable intermediate joint.

The invention discloses a capacitance parameter determination method and device of an iron core sensing device for transformer partial discharge detection, electronic equipment and a storage medium, and belongs to the technical field of sensor design, and the method comprises the steps: obtaining an iron core three-dimensional model of a transformer; performing electromagnetic simulation on the iron core three-dimensional model to generate antenna characteristic parameters of the transformer iron core under each frequency in a preset frequency band; determining a target detection frequency band of the transformer core according to the antenna characteristic parameters; repeatedly executing the parameter adjustment operation of the iron core sensing device until the current test frequency band covers the target detection frequency band, and determining the capacitance parameter of the iron core sensing device; therefore, by implementing the invention, the problem that the capacitance parameters of the iron core sensing device in the prior art are not accurately determined can be solved.

A method of performing a quality inspection of a vulcanized joint of a power cable during manufacturing of the vulcanized joint, the method comprising: a) obtaining, from a laser scanner, measurements of an outer surface of an inner semiconducting layer provided over a conductor joint which joints conductors of two power cable sections, and of a transition area between the outer surface and outer surfaces of a respective inner semiconducting layer of the two power cable sections, b) obtaining, from a laser scanner, measurements of an outer surface of a tapering section of an insulation layer arranged around a respective one of the inner semiconducting layers of the two cable sections, c) processing the measurements obtained in step a) and in step b), the processing involving generating one or a respective 3-d model of the outer surfaces and evaluating an outer surface quality of the outer surfaces based on the one or more 3-d models, d) presenting a conclusion regarding surface quality based on the evaluation, e) obtaining, from a laser scanner, measurements of an outer surface of a joint insulation arranged around the inner semiconducting layer that is provided over the conductor joint, the joint insulation having been provided over the inner semiconducting layer provided around the conductor joint after step d), and f) processing measurements obtained from the laser scanner in step e), the processing involving determining an insulation thickness or an outer diameter of the joint insulation, and g1) presenting the insulation thickness or outer diameter, and / or, g2) evaluating the insulation thickness or outer diameter, and presenting a conclusion regarding the insultation thickness or outer diameter based on the comparison.

The invention relates to the field of power equipment detection and intelligent robot control, and discloses an insulator zero value detection robot control method. The method comprises the following steps: deploying a rail-mounted robot on a power transmission line, and synchronously acquiring infrared thermal imaging, ultraviolet corona, capacitance coupling voltage and laser contour four-dimensional data; performing space-time alignment and multi-modal feature extraction on the multi-source information; generating an insulator unit-level health score through cross-modal attention fusion; and judging a zero-value fault according to the scoring threshold value and triggering a rechecking or confirming process. The system comprises a double-wheel clamping driving mechanism, a multi-source sensing array and a central controller. According to the invention, full-automatic, high-precision and high-safety detection in an electrified state is realized, and the recognition accuracy and the operation efficiency are remarkably improved.

The invention discloses a method and system for detecting the insulation state of power transmission and transformation equipment based on photoelectric sensing, and relates to the technical field of insulation state detection, and the method comprises the steps: continuously monitoring a baseline optical signal and a baseline electrical signal of the power transmission and transformation equipment through a photoelectric sensor which is arranged in a wide area; starting a targeted signal acquisition mode, and obtaining a high-precision optical signal and a high-precision electrical signal of the target area; performing logic correlation analysis, evaluating the matching degree of the optical features and the electrical features, and generating a fusion diagnosis result and a confidence level thereof; and matching and determining an insulation state detection result from a preset equipment aging state mapping table. The technical problems that in the prior art, power transmission and transformation equipment insulation state detection is insufficient in accuracy, low in efficiency and difficult to comprehensively and accurately judge the equipment insulation state are solved, and the technical effects of achieving accurate and efficient detection of the power transmission and transformation equipment insulation state and improving the detection reliability are achieved.

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 discloses an ultra-long distance cable fault monitoring method and system, and belongs to the field of power system safety monitoring, and the method comprises the steps: obtaining a partial discharge high-frequency transient signal, a hyperspectral remote sensing image and atmospheric parameter data of a cable, carrying out the phase-space reconstruction of the partial discharge high-frequency transient signal, and constructing a chaotic feature; and performing atmospheric correction on the hyperspectral image based on the atmospheric parameters, generating a surface reflectance image, performing spectral unmixing on the surface reflectance image, and extracting a spectral fingerprint. The method comprises the following steps: performing space-time alignment on a partial discharge high-frequency transient signal and a hyperspectral remote sensing image to generate a space-time mapping index, and according to the space-time mapping index, pairing a chaotic feature with a spectral fingerprint to form a space-time feature pair; and predicting the fault probability of each position point of the cable in a future time range through the cross-modal fusion model, and judging a latent fault according to a threshold value. According to the invention, the problem of inaccurate monitoring caused by weak signals and index lag in the prior art can be solved.

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.