157results about "Testing using optic methods" patented technology

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 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 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 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.

The invention provides an electrical equipment overheating hidden danger early warning method and system based on unmanned aerial vehicle multi-spectral imaging, and belongs to the technical field of power equipment state monitoring, and the method comprises the steps: firstly obtaining ultraviolet photons and spatial point cloud data collected by an unmanned aerial vehicle; a three-dimensional model is constructed based on the point cloud, and electric field distribution information is obtained through finite element analysis. And the photon data and the electric field information are combined, and the discharge charge quantity and distribution are obtained through space inversion correction. And calculating the discharge current density by using an energy dissipation model, and determining the discharge thermal power loss density in combination with the local electric field. And finally, a steady-state heat conduction equation is solved based on the heat conduction and dissipation parameters, temperature field distribution under heat balance is calculated, and early warning information is generated when the steady-state temperature exceeds a heat-resistant threshold value. According to the invention, early-stage accurate prediction and positioning of the hidden overheating fault of the electrical equipment caused by insulation degradation are realized.

The invention discloses a defect positioning method, system and device based on vehicle-mounted fusion detection and a medium, and belongs to the technical field of distribution line fault detection. The method comprises the steps that a coordinate system is established according to the environment of a detected vehicle; the method comprises the following steps: collecting detection data through a vehicle-mounted probe, and preprocessing the detection data; when the vehicle is detected to be in a denial environment, optimizing the preprocessed detection data by constructing an acoustic reflection path model and a dynamic mask, and extracting infrared partial discharge features and acoustic imaging features; judging the discharge condition of the distribution line by using an acoustic infrared registration fusion mechanism; the infrared partial discharge features and the acoustic imaging features are fused, a fusion detection result is obtained, and a defect part is judged in combination with a line equipment drawing or a three-dimensional model; and combining the defect part with the coordinate system to obtain a defect coordinate, and rechecking the defect part by using the detection equipment according to the defect coordinate. According to the invention, the problem that accurate fault detection and fault positioning of the fault part of the distribution line cannot be realized in the prior art is solved.

The invention provides a transformer internal partial discharge detection method, device and equipment and a storage medium, and belongs to the field of transformer monitoring, and the method comprises the steps: obtaining an ultrahigh frequency electric signal, an optical signal and an acoustic signal generated by transformer internal partial discharge through an ultrahigh frequency sensor, an optical sensor and an optical fiber Fabry-Perot ultrasonic sensor; a multi-state quantity integrated sensing SoC chip on the circuit board is used for conditioning and digitizing the signals to generate a digitized signal flow; inputting the signal flow into a state sensing special edge computing NPU chip, processing the signal flow by using a graph attention network algorithm, calculating an attention coefficient between sensor nodes, and fusing the multi-modal signals to generate a fusion signal; and determining the partial discharge type according to the fusion signal. According to the invention, three sensors are internally integrated, signal conditioning digitalization is carried out by using the SoC chip, multi-mode signals are fused through the NPU chip, high-precision and high-reliability identification is realized, and the difficulty of an external sensor is overcome.

The invention relates to the technical field of power equipment detection, and discloses a GIS foreign matter recognition and positioning method and system based on a multi-sensing double-order fusion network, and the method comprises the steps: synchronously collecting ultrahigh frequency, ultrasonic wave and pulse current signals of GIS partial discharge through a multi-mode sensor, and constructing a discharge pulse correlation octree after clock synchronization and adaptive noise reduction; foreign matter recognition is completed by means of feature-level and decision-level dual-order fusion, and accurate positioning of a discharge source is achieved by combining initial positioning of a time difference method and bisecting face iteration. And furthermore, the foreign matter motion trail is restored, the discharge risk is evaluated, and a graded early warning instruction is output. According to the method, multi-modal features can be effectively associated, information redundancy is eliminated, GIS foreign matter identification accuracy and positioning precision are improved, and dynamic assessment and graded early warning of discharge risks are realized.

The invention relates to the technical field of pre-diagnosis and health management, in particular to a low-voltage switch cabinet insulation safety diagnosis method based on discharge characteristic analysis, and the method comprises the steps: synchronously collecting original optical signals through a multispectral sensor array; performing energy compensation by using a multipath reflection equivalent attenuation model to generate reconstructed spectral line intensity data; determining a virtual time zero point by using an autocorrelation function and generating virtual phase analysis atlas data; and constructing an energy spectrum gravity center space coordinate in the energy spectrum feature space to extract a gravity center red shift feature quantity. According to the method, through multi-band relative attenuation rate compensation, power frequency period statistical peak value extraction and energy spectrum space gravity center mapping, the diagnosis problem that the insulation state is difficult to quantitatively evaluate due to signal distortion, reference phase loss and fuzzy characteristic evolution in a non-line-of-sight environment is solved.

The invention provides a power frequency arc intelligent diagnosis method, device and equipment and a medium, and relates to the technical field of power frequency arc diagnos.The method comprises the steps that optical signal data and analysis data of gas dissolved in oil are obtained; the optical signal data and the dissolved gas analysis data are preprocessed, and a training data set is constructed based on the preprocessed data and the corresponding manual distribution labels; the training data set and a preset target loss function are adopted to train a double-flow neural network model, a target fault diagnosis model is obtained, and the double-flow neural network model comprises an optical signal network branch, an analysis data network branch and a fusion module; and inputting the real-time monitoring data into the target fault diagnosis model to obtain a target fault diagnosis result. Through the intelligent diagnosis method for the power frequency arc, early warning and accurate fault type diagnosis of the power frequency arc can be realized.

The invention discloses a GIS metal particle defect detection test platform and a detection method, and relates to the technical field of high-voltage electrical equipment detection and fault diagnosis, and the platform comprises a GIS equipment module which is used for simulating an actual GIS equipment operation environment and an internal metal particle defect scene; the alternating current voltage module is used for applying processing frequency voltage to the GIS equipment module; the impulse voltage module is used for superposing impulse voltage on the basis of power frequency voltage to realize electric stress coupling; the physical vibration module is used for applying controllable mechanical vibration to the GIS equipment module, simulating impact disturbance generated by switch operation, and enabling internal metal particles to be in a suspended or moving state; the protection device is used for ensuring the effective superposition of the power frequency voltage and the impulse voltage and blocking the reverse voltage; and the detection equipment is used for monitoring the motion trail, the charge characteristic and the partial discharge signal of the metal particles in the GIS equipment module in real time so as to carry out defect mechanism analysis. According to the invention, complex working conditions in GIS operation can be truly simulated.

The invention provides a robot and a partial discharge monitoring system and method based on the robot, and the robot comprises a robot main body, an adsorption module, a dynamic visual sensing module and a communication module, when the robot is located in oil-immersed electrical equipment, the robot moves along an inspection path through the adsorption module or is adsorbed to the oil-immersed electrical equipment, and the robot is located in the oil-immersed electrical equipment. Event stream data of the oil-immersed electrical equipment is obtained through the dynamic vision sensing module, and the communication module is used for sending the event stream data to external control equipment, so that the external control equipment obtains partial discharge information of the oil-immersed electrical equipment based on processing of the event stream data. According to the invention, the problems of limited motion control and limited fault monitoring capability of the existing electrical equipment internal inspection robotic fish under an electrified condition can be solved.

The invention relates to the field of electrical equipment detection, in particular to a transformer partial discharge detection method and system based on optical signal characteristics. The method comprises the following steps: acquiring an optical signal generated by partial discharge in the transformer and converting the optical signal into a voltage pulse signal; synchronizing the signal with the power frequency voltage of the transformer, and recording the peak amplitude of each pulse signal and the corresponding power frequency voltage phase angle to form a discharge data point set; carrying out statistical distribution analysis on the phase angles of the data points in a power frequency period of 0-360 degrees, and extracting a characteristic phase interval representing a discharge phase aggregation rule; and matching the characteristic phase interval with a predefined discharge type discrimination rule, and determining the partial discharge type. The method solves a problem that a conventional optical detection method cannot accurately recognize the partial discharge type of the transformer, is high in electromagnetic interference resistance, and improves the detection precision and engineering practicality.

The application discloses an arc fault detection device for a low-voltage circuit breaker and relates to the technical field of low-voltage circuit breaker fault detection.The device comprises a workbench, wherein an elevated platform is installed on a reference plate of the workbench;an infrared temperature measurement probe is installed on a first supporting rod of a first support;an industrial frequency magnetic field measuring device is installed on a second supporting rod of a second support;first and second inclined surfaces are symmetrically arranged on the left and right sides of a third support;third and fourth supporting rods are respectively installed on the first and second inclined surfaces;first and second noise detection probes are respectively installed on the inner ends of the third and fourth supporting rods;and a fifth supporting rod is arranged between the third and fourth supporting rods, and a third noise detection probe is installed on the bottom end of the fifth supporting rod.The device eliminates the blind area of traditional single-point detection, can capture complete high-frequency noise spectrum radiated by various types of fault arcs in the circuit breaker without dead angles, ensures complete extraction of characteristic signals, and avoids misjudgment caused by signal attenuation or directionality omission.

Disclosed is an electric power equipment internal discharge position and spectral information multiplexing photoelectric sensing device, in a bipolar plate structure, a split light guide array is inserted into the interior of the electric power equipment to collect the discharge light beams from the interior of the electric power equipment, a microlens homogenizing plate is arranged at the bottom of the split light guide array, the upper surface of the microlens homogenizing plate is etched with a microlens array for focusing light signals to form parallel light beams; a fiber panel is arranged at the bottom of the microlens homogenizing plate, the upper panel is a central-symmetrical planar multi-lobe structure to group the parallel light beams, the lower panel is connected to the upper panel through a fiber bundle, and the lower panel is a rectangular structure to group the parallel light beams from the upper panel to form photon signals; a photoelectric array includes a silicon photomultiplier array and a surface spectral coating arranged on the silicon photomultiplier array, and the multiplexing two-dimensional photoelectric array constructs a sparse spectral sensing line array based on the photon signals to generate spectral information and constructs a multi-normal intensity sensing line array to obtain position information.

The invention discloses an arc light online monitoring device for an energy storage power distribution switch cabinet, and belongs to the technical field of arc fault monitoring. Comprising a monitoring unit and a collecting unit, a quartz window short horn mouth captures 2pi spherical degree arc light at a time, and a closed plastic optical fiber sends a pulse into an integrating sphere within 50 microseconds; a black body extinction cylinder-narrow band filter-integrating sphere multi-stage attenuation is carried out, strong light is weakened firstly, then an ultraviolet characteristic spectrum is extracted, flash of a background lamp and a camera is completely eliminated, arc light position offset still keeps stable output, and high signal-to-noise ratio identification is realized; a real arc is judged according to the ratio of pure arc light to total light, and interference is avoided; after confirmation, a level in direct proportion to energy is output immediately, a background acts according to a three-level strategy of prompting, tripping and quick breaking, no optical cable is exposed in the whole process, the whole process is not influenced by dust condensate water, a closed loop from arcing to protection decision is completed within 2 ms, and real-time, reliable and maintenance-free arc light online monitoring is provided for an energy storage power distribution switch cabinet.

This invention discloses a method and apparatus for detecting partial discharge in transformers based on an acoustically coupled ring structure. The method includes: receiving a first ultrasonic signal transmitted by the transformer through an annular metal sheet and an annular metal rod of the acoustically coupled ring structure; performing frequency separation on the first ultrasonic signal to obtain multiple second ultrasonic signals corresponding to each frequency; transmitting each second ultrasonic signal to a sensing optical fiber of the acoustically coupled ring structure; receiving a sensing laser emitted by a laser using the sensing optical fiber, thereby coupling the sensing laser with each of the second ultrasonic signals and generating a backscattered light signal corresponding to each second ultrasonic signal; transmitting the backscattered light signal to an ultrasonic measurement system to detect the partial discharge fault type of the transformer. Before transmitting the ultrasonic signal to the ultrasonic measurement system, this invention uses an annular metal sheet and an annular metal rod to separate the first ultrasonic signal of the transformer into multiple second ultrasonic signals of different frequencies to improve detection efficiency.

The invention discloses a method and a system for determining a corona loss variable quantity of a power transmission line based on 100Hz pure sound. The method comprises the following steps: determining a 100Hz pure sound measuring device and a corona loss measuring device of a wire; in different states, voltages of different grades are applied to the test wire in sequence; acquiring 100Hz pure sound data and corona loss data when different grades of voltages are applied to the test wire through the wire 100Hz pure sound measuring device and the corona loss measuring device; taking the 100Hz pure sound data as a horizontal coordinate and the logarithmic coordinate of the corona loss data as a vertical coordinate to obtain a curve change rule of the corona loss data of the ultra-high voltage power transmission line along with the 100Hz pure sound data, and based on the curve change rule, obtaining the corona loss variable quantity of the 100Hz pure sound power transmission line. The problem that the corona loss of a running power transmission line is difficult to measure is solved.

The present invention relates to an optical detector for a high-voltage cable accessory. The present invention further relates to a monitoring system for detecting an electrical discharge in a high-voltage cable accessory and to a method of detecting and / or monitoring electrical discharges occurring within an insulating part of such a high-voltage cable accessory. According to the present invention, said optical detector detects a radiation generated by an electrical discharge of the high-voltage cable accessory at a wavelength of the radiation generated by the electrical discharge.

The invention discloses a GIS partial discharge rapid positioning method based on a multi-normal projection principle, and relates to the technical field of partial discharge on-line monitoring, a three-dimensional photoelectric array formed by silicon optical semiconductor sensors is used for obtaining a partial discharge signal amplitude, a direction vector is constructed, and a unit direction is obtained through solving a local intersection point. And further combining with least square optimization of multi-unit rays to obtain initial position estimation of the partial discharge source. On the basis, an amplitude correction and iteration updating mechanism is introduced, observation data are corrected, and a real position is gradually approached, so that high-precision three-dimensional partial discharge source positioning in a complex electromagnetic environment is realized. Through the combination of the silicon optical sensor three-dimensional array and the improved multi-vector projection iterative algorithm, high-precision partial discharge positioning can be realized in a complex electromagnetic environment, the operation reliability and the operation and maintenance efficiency of equipment are remarkably improved, and the method has a wide engineering application prospect.

A method (400) for monitoring the health of an electric power train (12) is disclosed herein. The method of monitoring the health of the electric power train (12) includes, detecting partial discharge (404) within the electric power train (12) with a partial discharge sensor (102), imaging (406) the electric power train (12) with a camera (100) to generate an image, comparing (408) the detected partial discharge with a partial discharge threshold, comparing (410) the image of the electric power train (12) to a threshold representation, and determining (412) the health of the electric power train (12) based upon the detection of partial discharge and the image of the electric power train (12).

The invention relates to the technical field of electrical equipment testing, and particularly provides a space charge detection device and method based on optical measurement. The space charge detection device based on optical measurement comprises a sensing acquisition array which is uniformly distributed around detected equipment in the circumferential direction, full-surrounding detection of space charges is achieved, collaborative design of four optical detection technologies is adopted, multi-dimensional detection of the space charges is achieved through the complementary effect of Raman scattering, the photoacoustic effect, LIBS and the photoelectric effect, and the detection accuracy of the space charges is improved. The detection limitation of a single technology is made up; according to the space charge detection method based on optical measurement, a space-time synchronization-weight distribution-feature fusion algorithm and a signal-to-noise ratio-based adaptive weight distribution fusion algorithm are adopted, space-time synchronization calibration and Kalman filtering are combined, the problems of space-time inconsistency and random errors of multi-source data are solved, and the precision of data fusion is improved.

The invention discloses an electric power inspection data visualization processing method, system and device and a medium, and belongs to the technical field of electric power inspection, and the method comprises the steps: collecting sound data and image data of an electric power fault through a monitoring device, carrying out the preprocessing of the sound data, extracting the sound wave features, and processing the image data; by identifying ultraviolet spots in the image data, when the ultraviolet spots conform to stable characteristics, recording the time as optical signal time, calculating acousto-optic time difference compensation according to the optical signal time and sound wave characteristics, constructing a compensation model according to space positioning of the monitoring equipment and the acousto-optic time difference compensation, and under constraint conditions, calculating the time difference of the monitoring equipment according to the compensation model. And outputting accurate coordinates according to the compensation model, constructing a three-dimensional model according to the accurate coordinates, and diagnosing a power fault through a three-dimensional scene to obtain an inspection report, thereby solving the problems of low efficiency and insufficient positioning precision of traditional monitoring equipment.

This application provides a method and system for early warning of overheating hazards in electrical equipment based on UAV multispectral imaging, belonging to the field of power equipment condition monitoring technology. This application first acquires ultraviolet photon and spatial point cloud data collected by a UAV. A three-dimensional model is constructed based on the point cloud, and electric field distribution information is obtained through finite element analysis. Combining photon data and electric field information, the discharge charge quantity and distribution are obtained through spatial inversion correction. Then, the discharge current density is calculated using an energy dissipation model, and the discharge heat power loss density is determined by combining the local electric field. Finally, the steady-state heat conduction equation is solved based on heat conduction parameters to calculate the temperature field distribution under thermal equilibrium. When the steady-state temperature exceeds the heat resistance threshold, an early warning message is generated. This application achieves early and accurate prediction and location of hidden overheating faults in electrical equipment caused by insulation degradation.

The invention belongs to the technical field of insulator chain detection, and discloses an insulator chain detection method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the position information of a target insulator chain, and obtaining the position information of the target insulator chain according to the position information; controlling the visual detection flying robot carrying the detection instrument through the mechanical arm to fly to a hovering position above the target insulator string, identifying the axis of the target insulator string so as to control the visual detection flying robot to rotate, and enabling the flying plane of the visual detection flying robot to be parallel to the axis, controlling the visual detection flying robot to drive a mechanical arm so as to place a detection instrument on the target insulator string, and enabling the detection instrument to complete the detection operation of the target insulator string; through the method, the detection efficiency of the insulator string is improved.

The invention relates to the technical field of integrated shooting systems, in particular to a multi-mode unmanned aerial vehicle integrated shooting system which comprises a multi-mode sensing module, an environment sensing and cooperative control unit, a data storage and transmission module, a dynamic path planning module, a central cooperative control unit and a multi-mode shooting unit. The multi-modal sensing module comprises a sensor group, an inertial measurement unit and a data fusion engine; the sensor group integrates a visible light camera, an infrared thermal imager, an ultraviolet imager, a laser radar and a millimeter-wave radar and covers different frequency bands and environmental conditions, the visible light camera clearly captures details of pole and tower hardware, the infrared thermal imager detects overheating hidden dangers of key parts of a wire joint and an insulator, the ultraviolet imager detects a corona discharge phenomenon, and the laser radar covers the laser radar. Positioning hidden dangers of insulator contamination and equipment aging; the system improves the power grid inspection efficiency, achieves the precise collection and fusion of all-scene data, and provides a high-precision, high-reliability and low-cost solution for the intelligent inspection of a power grid.

The invention relates to the technical field of fault identification, in particular to an arc fault identification method and system. The method comprises the following steps: acquiring high-frequency current characteristics and ultraviolet light accumulated energy characteristics at each sampling moment based on data preprocessed by each sensor in a target area; based on the time difference between each moment and the initial moment in the target area, a nonlinear temperature function of each moment is constructed, and the temperature change rate of each sampling moment is obtained; based on the temperature value, the humidity value and the wind speed value preprocessed at each sampling moment, obtaining a correction factor at each sampling moment; correcting the temperature change rate of the sampling moment through the correction factor of each sampling moment to obtain a temperature characteristic of the sampling moment; and constructing a fusion feature vector of each sampling moment according to the high-frequency current feature, the ultraviolet light accumulated energy feature, the temperature feature and the dominant frequency energy ratio of each sampling moment, and obtaining an arc fault identification result. According to the invention, the arc fault identification precision is effectively improved.