692results about "Testing using optic methods" patented technology

The invention provides an online monitoring method and device for the insulation performance of a power cable. The method comprises the steps that a multi-mode sensor network is constructed to synchronously collect multi-dimensional cable data, an electromagnetic-thermal-chemical multi-physics field coupling model is established, and the insulation state of the cable is analyzed in real time. And monitoring the insulation defect and predicting the residual service life by using the mixed deep learning model. And dynamically generating an early warning threshold value by adopting a digital twinning method based on spatio-temporal characteristics, and adaptively adjusting a monitoring strategy. According to the invention, multi-dimensional data are synchronously acquired through the multi-modal sensor network, and the limitation of single parameter monitoring is broken through. The insulation degradation characteristics of the cable are accurately captured through multi-dimensional data fusion, and quantitative description of energy transfer and material evolution is achieved through a constructed coupling model. And through a residual network and a cavity convolution space encoder, combining a bidirectional LSTM-GRU time encoder, and extracting spatial and temporal characteristics of the cable. The cross-modal attention mechanism fuses multi-source features, and improves the accuracy of defect identification and life prediction.

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 provides a detection method and system for an insulation detection function. Wherein in the operation state of the to-be-detected electrical equipment, the temperature change of the surface of the to-be-detected electrical equipment is monitored in real time through a non-contact sensor, and a dynamic thermogram is formed; according to a temperature fluctuation mode of a preset area in the dynamic thermogram, in combination with an environment temperature compensation algorithm, identifying a potential insulation defect area, and generating a preliminary insulation defect report; for the insulation defect area marked in the preliminary insulation defect report, simulating electromagnetic field distribution conditions of the insulation defect area under different working conditions by adopting an electromagnetic field analysis technology, evaluating the aging degree and damage risk of the insulation material, and obtaining an evaluation result; and integrating the preliminary insulation defect report and the evaluation result, calculating the probability that the insulation defect area develops into an actual fault through a fault prediction model, and outputting a final insulation performance detection report. According to the technical scheme provided by the invention, the operation efficiency and safety of the equipment are improved.

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 discloses a GIS partial discharge fault detection device and method based on D-S evidence multi-source information fusion, and relates to the technical field of power system control, and the device comprises a signal collection module which is used for collecting an electromagnetic wave signal, a mechanical vibration signal, an ultrasonic signal and a partial discharge optical radiation signal generated by the partial discharge in GIS equipment; the GIS partial discharge fault detection module is used for processing, storing and sending the signals acquired by the signal acquisition module; the coordinator is used for realizing data transmission between the GIS partial discharge fault detection module and the background terminal; and the background terminal is used for receiving the processing information of the GIS partial discharge fault detection module and judging the fault type of the GIS equipment. According to the GIS partial discharge fault diagnosis system, various signals generated by partial discharge in the GIS equipment are detected through the information acquisition module, and the relationship between the characteristic data and the GIS partial discharge fault is established, so that diagnosis of the GIS partial discharge fault is realized, and the fault judgment efficiency of the background terminal is 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 the technical field of power equipment monitoring, in particular to a live equipment real-time detection and fault early warning system which comprises a corona detection module, an environment monitoring module, a data integration and transmission module, a data analysis module, a fault prediction module and an early warning module. Wherein the corona detection module is used for capturing a corona discharge phenomenon on the surface of equipment; the data integration and transmission module is used for synchronizing and integrating output data of the two modules; the data analysis module is used for quantifying the discharge intensity and range and performing correction based on the environmental data; the fault prediction module is used for generating a discharge trend prediction result; the early warning module is used for triggering an early warning signal and automatically generating an early warning information report; according to the invention, through real-time monitoring, environment correction, trend prediction and multi-channel early warning, the problems of real-time performance, accuracy and foresight of corona discharge detection are solved, and the operation safety and fault prevention capability of live equipment are significantly improved.

The invention discloses a fault identification early warning method and system for a power transmission line, and the method comprises the steps: receiving a fault flashover signal, determining a generation region of the fault flashover signal, and obtaining a target image; determining whether the power transmission line in the occurrence area has abnormal discharge or not; if yes, performing image recognition to obtain a to-be-detected fault source; obtaining a tower gradient, an electromagnetic field and a radar change of a tower area in the occurrence area; determining a motion path of a current fault source, determining the type of the fault source through image snapshot, and determining a target fault source; obtaining meteorological data through a micro-meteorological sensor and network meteorological weather, and obtaining future and real-time galloping swing and galloping frequency of the power transmission line; and starting image snapshot to determine a target probability and an image of a secondary fault caused by a target fault source on the power transmission line by combining a galloping amplitude, a galloping frequency, a tree barrier, an ultrahigh engineering vehicle, a floating object, a lightning stroke and a value greater than an early warning value, determining a high-risk interval section, and sending an early warning picture and a video to a background in real time.

The invention discloses an intelligent operation and maintenance robot real-time state monitoring system and method based on artificial intelligence, and belongs to the technical field of artificial intelligence. An intelligent operation and maintenance robot arrives at each equipment monitoring point of a transformer substation, a communication machine room or a data center to acquire multi-dimensional data; when arriving at a monitoring point, acquiring real-time electrical parameter data, and fusing the real-time electrical parameter data with the multi-dimensional data to obtain a data set; constructing a main analysis model by combining a convolutional neural network with a long short-term memory network, inputting the trained main analysis model, and outputting a current state evaluation result of the equipment; cooling medium parameters are measured, enthalpy difference changes in the cooling process are calculated, and the heat exchange efficiency of the cooling system is quantified; constructing a transformer cooling system performance evaluation model by using a support vector regression algorithm, and outputting a current heat dissipation capability coefficient of the cooling system; and judging whether the cooling system meets the heat dissipation requirement or not by combining the current heat dissipation capability coefficient of the cooling system with the current state evaluation result of the equipment.

The invention relates to the technical field of partial discharge detection of power equipment, and discloses a KNN algorithm-based photoacoustic feature combined particle identification method and system, optical and acoustic signals caused by particles in different shapes under alternating voltage are obtained through experiments, and mathematical modeling is performed on particle motion discharge based on particle motion and discharge theories; and then, time sequence distribution characteristics of optical and ultrasonic signals triggered by the particles are mastered, a particle type identification algorithm based on a KNN model is constructed, and particle shape identification and mixed particle component identification are realized. In conclusion, the method combines an optical detection method and an ultrasonic method to form photoacoustic combined detection, excavates combined feature information and establishes a particle discharge combined detection method, and has important significance on application of a particle discharge optical detection method and multi-parameter combined detection; and the method has outstanding value for maintaining safe and stable operation of power equipment and even a power system.

Provided are a fault diagnosis apparatus and method based on fluorescence multivariate correction analysis of transformer oil, relating to the field of transformer fault diagnosis technology. Thus, the problem of large volume and weight of the apparatus, high costs, and inconvenience to use caused when in the related art, a fluorescence spectrometer is directly used to acquire the fluorescence spectrum of transformer oil is solved. The monochromatic excitation light of an optimal excitation wavelength generated by a fluorescence excitation source is used to excite the transformer oil in a fluorescence excitation detection apparatus to generate fluorescence. The fluorescence excitation detection apparatus generates the fluorescence according to the input monochromatic excitation light and inputs the fluorescence to a fluorescence signal acquisition and analysis apparatus. Since the fluorescence signal acquisition and analysis apparatus acquires and analyzes the fluorescence signal emitted by the transformer oil by using a multivariate correction filter group, the emission monochromator component of the fluorescence spectrometer is replaced. In this manner, the equipment costs and the equipment volume are reduced, and data processing is rapid. Thus, the cost performance of the fault detection of the transformer oil is improved, thereby implementing the engineering application of the fluorescence monitoring technology in the online diagnosis of the transformer failure.

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 a battery arc testing device and a battery arc testing method. The device comprises a closed container, a positive electrode plate, a negative electrode plate, an environment module, a testing circuit and a processor, the positive electrode plate, the negative electrode plate and the environment module are arranged in the closed container, the first side of the positive electrode plate and the first side of the negative electrode plate are arranged in the environment module, the second side of the positive electrode plate and the second side of the negative electrode plate penetrate through the closed container through wires and are connected with the test circuit, and the test circuit is further connected with the processor. The processor is used for controlling the test circuit to generate different test signals under the condition that the closed container is filled with the environmental gas, and acquiring arc signals generated by the positive electrode plate and the negative electrode plate under the different test signals. The electrolyte or the battery active material powder is filled in the environment module to simulate the electrolyte environment in the battery, and the environment gas is filled in the closed container to simulate the external environment after the electric arc is generated, so that the test accuracy can be improved.

The invention provides an insulator pollution grade evaluation method based on multispectral analysis, and relates to the technical field of data analysis, and the insulator pollution grade evaluation process comprises the steps of precise alignment of a camera carried by an unmanned aerial vehicle body, multispectral camera image collection, image processing and image evaluation. The unmanned aerial vehicle body master control comprises a flight path control system, a flight attitude control system and an automatic navigation positioning calculation system, the unmanned aerial vehicle is used for carrying a multispectral camera, the multispectral camera is installed in the center of the unmanned aerial vehicle, and meanwhile a temperature detection probe and a time degree detection probe are installed on the two sides of the unmanned aerial vehicle respectively. The unmanned aerial vehicle can acquire the height image of the insulator in advance, then the unmanned aerial vehicle calculates the vertical horizontal difference from the unmanned aerial vehicle to the insulator through the automatic navigation positioning calculation system, and the unmanned aerial vehicle flies to the periphery of the insulator after accurately acquiring the position information of the insulator, so that manual information acquisition is replaced.

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 GIS partial discharge acoustic-optical-electric combined detection composite sensor comprises a composite sensing device installed on a GIS, and the composite sensing device comprises a sealing plate, a dielectric layer plate, an upper polar plate, an optical fiber connector, a fluorescent optical fiber, a feed rod and a limiting device; the limiting device comprises a fixed ring seat, a rotating mechanism, a synchronizing mechanism and an adjustable limiting mechanism; the fixed ring seat is fixedly mounted on the lower surface of the sealing plate; the rotating mechanism is rotationally arranged at the bottom of the fixed ring seat; the synchronizing mechanism is rotationally arranged in the fixed ring seat; the adjustable limiting mechanism is arranged on the edge of the fixing ring base in a sliding and penetrating mode and meshed with the synchronizing mechanism. Through the adjustable limiting mechanism, the position of the sliding limiting seat is reasonably adjusted according to the thickness of the head of the bolt, so that it is guaranteed that the adjusted sliding limiting seat can make contact with and press the head of the bolt, using is more flexible, the signal conditioning amplifier and the photoelectric converter are matched, and the problem of false alarm of partial discharge is solved.

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 provides a temperature rise test system, and relates to the field of heating characteristic testing of power equipment. The system comprises a power supply control system, an electric field regulation and control system, an environment simulation system, a discharge detection system, a three-dimensional temperature field monitoring system and a processing system. The power supply control system simulates equipment operation voltage; the electric field regulation and control system simulates a non-uniform electric field in the equipment; the environment simulation system simulates external environment conditions; the discharge detection system detects the partial discharge condition of the equipment under the simulation condition, obtains discharge parameters and transmits the discharge parameters to the processing system; the three-dimensional temperature field monitoring system monitors temperature distribution on the surface and in the equipment and transmits the temperature distribution to the processing system; and the processing system determines an association relationship between the partial discharge condition and the temperature rise based on the temperature distribution and the discharge parameters. Through cooperation of multiple systems, the influence of the external environment and internal electrical parameters on heating of the equipment is comprehensively simulated, and the heating behavior of the equipment under the actual working condition is reflected more truly, so that the accuracy of temperature rise simulation is improved.

The invention belongs to the field of vehicle pantograph overhauling, and discloses a pantograph head anomaly detection method. The method comprises the following steps: acquiring infrared data and arcing ultraviolet light characteristic quantity of an area between a carbon slide plate of a pantograph head and a contact line; determining whether arcing is generated based on the infrared data and the arcing ultraviolet light characteristic quantity; under the condition that arcing is generated, the duration and temperature of each arcing are determined; determining whether the arcing is an over-limit arcing based on at least one of the duration and the temperature; determining the number of times of over-limit arcing and the cumulative time in a predetermined time period; and when at least one of the number of times and the accumulated time reaches a set standard, triggering early warning. According to the pantograph head anomaly detection method, detection data of different dimensions such as temperature detection and ultraviolet light detection are fused, the detection accuracy is improved, meanwhile, a fine anomaly alarm scheme is formulated for the abnormal condition of the surface of the carbon contact strip caused by arcing, faults can be conveniently found and processed in time, and the operation safety of a vehicle is 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.