1835 results about "Partial discharge" patented technology

The invention discloses a power transformer partial discharge positioning method based on multi-sensor array fusion, and the method comprises the following steps: S1, selecting a sensor installation point, and laying a multi-sensor array structure; s2, partial discharge signals of the three types of sensors are collected, and primary signal processing is carried out; s3, calculating propagation time differences between the reference channel and other channels by adopting a generalized cross-correlation weighting algorithm, and generating a time difference matrix; s4, constructing a TDOA model in combination with the layout coordinates and the time difference matrix, and solving three-dimensional initial coordinates of a power supply; s5, establishing a structure correction model, compensating the path deviation, and outputting corrected positioning coordinates; s6, calculating an error and generating a confidence score; s7, mapping a positioning result to the three-dimensional model and generating an image; and S8, writing the positioning information into a database for filing management. According to the method, the multi-frequency sensor and the path correction model are fused, and high-precision three-dimensional positioning of partial discharge of the transformer is realized.

The invention discloses a digital intelligent switch cabinet state comprehensive sensing system based on AI, and the system comprises a multi-source data collection module, a data preprocessing and synchronization module, an edge calculation feature extraction module, an AI intelligent fusion recognition module, an expert rule diagnosis module, and a cloud comprehensive evaluation and decision module. Various types of sensors are deployed to respectively acquire environmental parameters, electrical parameters and partial discharge signals generated in the operation process of the switch cabinet to form an original multi-modal data stream. The method has the advantages that the recognition precision and response speed of the complex operation state of the switch cabinet are improved, hidden faults under multi-modal data mismatch can be effectively found, and the misjudgment and missed judgment risks are reduced. Meanwhile, a closed-loop diagnosis system is constructed, intelligent evaluation and interpretable feedback of fault types, positions and trends are achieved, scientificity and reliability of operation and maintenance decisions are enhanced, and the method is suitable for intelligent upgrading of an electric power system.

The invention relates to the technical field of bushing detection, and discloses a bushing insulation state detection method based on multi-parameter real-time monitoring. The method comprises the following steps: acquiring multi-dimensional monitoring data such as a partial discharge pulse sequence, dielectric loss angle tangent value fluctuation data and surface leakage current density distribution in a bushing operation environment in real time; carrying out insulation defect feature extraction on the data set, and generating an insulation defect distribution map containing a creeping discharge track, an internal air gap position mark and a carbonization channel density index; high-risk and potential defect areas are divided by adopting a dynamic threshold segmentation algorithm, boundary coordinates are determined, and an insulation state evaluation vector containing parameters such as a surface discharge intensity index and the like is constructed according to the boundary coordinates; and inputting the vector into a pre-trained insulation aging prediction model, and outputting a bushing residual insulation life estimated value and a defect development priority sequence. According to the method, all-around accurate monitoring and pre-judgment of the insulation state of the sleeve are achieved, and scientific support is provided for operation and maintenance decision making of the sleeve.

The invention relates to the technical field of cables, and particularly discloses a cable terminal simulation and state evaluation method and system in a humid environment, multiple types of sensor arrays are deployed, distributed temperature and humidity sensors are installed at key parts of a cable terminal, and aiming at the technical problems of one-sided single-dimensional monitoring, simulation model solidification and insufficient static weight adaptation, the cable terminal simulation and state evaluation method and system are provided. Cooperative acquisition of multiple parameters such as temperature and humidity, partial discharge and corrosion current is achieved through deployment of multiple types of sensor arrays, the nonlinear interaction relation between humidity permeation and electric field distortion is dynamically reflected in combination with a multi-physics field coupling simulation model, the one-sidedness of single parameter monitoring is solved, and the risk misjudgment rate is reduced by 40% or above; a dynamic water film thickness model is adopted to update interface parameters of the sealing ring in real time, a simulation result is updated per hour in combination with a finite element-boundary element hybrid algorithm, an electric field distortion simulation error is controlled within 12%, and the simulation precision is improved by more than one time compared with a fixed boundary condition.

Disclosed in the present invention are a dual-end monitoring-based partial discharge source localization method and system for high-frequency partial discharge of a high-voltage cable. The method comprises: acquiring operation data and phase velocity test data of a high-voltage cable; when a partial discharge fault occurs in the high-voltage cable, collecting partial discharge signals on two sides of the high-voltage cable by means of sensors on the two sides of the high-voltage cable; performing Fourier transform respectively on the basis of the partial discharge signals on the two sides to obtain amplitude-frequency characteristics of the partial discharge signals, and preprocessing the collected partial discharge signals on the basis of the amplitude-frequency characteristics to optimize and improve a phase difference algorithm; and by incorporating the phase velocity test data of the high-voltage cable, using the improved phase difference algorithm to perform partial discharge source localization on the faulty cable. In the present invention, dual-end monitoring is used to reduce the attenuation of signals caused by long-distance transmission, and synchronization of two sensors is performed using a PTP protocol, effectively reducing errors; and in addition, during monitoring, partial discharge source localization is performed using the improved phase difference algorithm, which is conducive to resisting external interference, and there is no need to determine the time of arrival, greatly improving the positioning accuracy.

The invention discloses a collaborative diagnosis method for partial discharge and overheating faults of a high-voltage switch cabinet, and particularly relates to the technical field of fault diagnosis of the high-voltage switch cabinet. Signals are synchronously acquired through ultrahigh frequency, acoustic emission and infrared sensors, and an abnormal candidate sequence is extracted through adaptive baseline segmentation; through laser calibration and time interpolation calibration, uniform event position information and timestamps are mapped; identifying potential coupling pairs of partial discharge and hot spot events based on dynamic threshold and time difference matching; generating a high-risk linkage region through spatio-temporal clustering, trajectory analysis and region fusion; updating the diagnosis model based on the high-frequency data increment, and generating a collaborative diagnosis report; the method breaks through the limitation of a static threshold value and a single sensor, and remarkably improves the diagnosis precision and adaptability of a composite fault under a complex working condition.

The invention relates to the technical field of power equipment state detection, in particular to an ultrahigh-frequency partial discharge online detection system, method and device and a medium, and the system comprises the steps: collecting an initial discharge signal in real time through an ultrahigh-frequency sensor array, and carrying out the preprocessing of the initial discharge signal, so as to obtain an ultrahigh-frequency discharge signal; carrying out peak detection on the ultrahigh-frequency discharge signal, and triggering a high-speed analog-to-digital converter to collect an original waveform when the amplitude exceeds a preset threshold value; performing multi-dimensional feature extraction on the original waveform by using a digital signal processor to obtain multiple groups of dimensional features; identifying and classifying the multiple groups of dimension features based on a random forest algorithm, generating discharge type labels and confidence coefficients, and storing the discharge type labels and the confidence coefficients in a dynamic database; carrying out spatial position calculation on the ultrahigh-frequency discharge signal by adopting a time difference method, and determining a three-dimensional coordinate of a discharge source; the dynamic database and the three-dimensional coordinates of the discharge source are subjected to space-time correlation and multi-dimensional analysis, a defect analysis result is generated, and high-precision online detection of the partial discharge defect of the high-voltage equipment is achieved.

The invention discloses a power transformer arc discharge multi-parameter detection simulation platform and a fault diagnosis method, and relates to the technical field of power system equipment state monitoring and fault diagnosis. The platform comprises a transformer body, a replaceable discharge module, a multi-parameter sensing unit and a signal processing and diagnosis module, and can truly reproduce various typical arc discharge faults of a needle plate, an air gap, a creeping surface, turn-to-turn and the like. The sensing unit is integrated with ultrahigh frequency and ultrasonic sensing probes, high-frequency current and voltage sensors, optical fiber temperature / pressure / strain sensors and the like, so that synchronous acquisition of multi-physical field signals is realized. According to the diagnosis method, through wavelet denoising and multi-dimensional feature extraction, a feature vector of multi-state parameter fusion in the process from partial discharge to arcing is constructed, and accurate classification of fault types is realized by using a support vector machine (SVM) model. The diagnosis method has high accuracy and early warning capability, effectively overcomes the limitation of single parameter diagnosis, and provides reliable technical support for transformer fault research and intelligent operation and maintenance.

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a power cable operation state real-time evaluation system based on multi-mode deep learning, and the system comprises the steps: synchronously collecting the load, partial discharge and temperature strain data of a cable through a current and voltage sensor, an ultrahigh frequency sensor and a distributed optical fiber sensor; feature extraction and cross-dimension fusion are carried out on the multi-source data, and a multi-dimensional feature matrix is constructed; a pre-trained deep learning model is utilized to analyze internal association between the features, and cable health degree scores, fault risk levels and defect type identification results are output; automatically generating and executing a load adjustment, loop switching or precise maintenance strategy according to an evaluation result; according to the method, the problems of inaccurate evaluation and early warning lag caused by data isolation analysis of a traditional monitoring method are solved, and real-time accurate evaluation and intelligent closed-loop operation and maintenance of the operation state of the cable are realized.

The invention provides a partial discharge on-line monitoring method and system based on multi-modal fusion and adaptive noise reduction, and the method comprises the steps: employing an ultrahigh frequency UHF sensor, a miniature ultrasonic sensor, and a miniature detector for gas dissolved in oil, which are disposed on a transformer; synchronously acquiring electric signals, sound signals and characteristic gas concentration data in oil generated in the partial discharge process; performing format unification, abnormal value elimination and time alignment processing on the electric signal, the sound signal and the gas concentration data in an edge calculation unit to obtain aligned multi-source original data; performing adaptive wavelet noise reduction processing on the electric signal to obtain a de-noised UHF signal; respectively extracting time domain, frequency domain and chemical features from the de-noised UHF signal, the aligned sound signal and the gas concentration data to form a multi-dimensional feature vector; and inputting the multi-dimensional feature vector into a pre-trained defect traceability model, and outputting a partial discharge defect type, severity level and development trend prediction result.

The invention relates to the technical field of performance testing, and discloses a multi-parameter insulating sleeve aging performance testing method and system, and the method comprises the steps: carrying out the multi-parameter detection signal collection of an insulating sleeve, and obtaining standardized signal data; inputting the standardized signal data into a coupling recognition network for feature analysis to obtain a classification recognition result; performing aging factor threshold adjustment on the rising edge slope of the discharge pulse of the insulating sleeve based on the classification identification result to obtain a dynamic separation parameter; performing feature space matching and differential identification on the partial discharge signal according to the dynamic separation parameter to obtain feature classification data; phase clustering analysis and step response characteristic analysis are carried out based on the characteristic classification data to obtain the aging state variable coefficient of the insulating sleeve, the problem that the recognition precision of a traditional fixed parameter method is reduced in different aging states is solved, and high recognition accuracy and anti-interference robustness can still be kept in a complex electromagnetic environment.

The invention relates to a low-voltage circuit breaker state detection method, which comprises the following steps of: synchronously acquiring multi-source state data of a low-voltage circuit breaker in an operation process through a plurality of sensors, the multi-source state data at least comprising a vibration signal, an opening and closing coil current signal, a moving contact displacement signal, a temperature signal and a partial discharge signal; the collected signals are preprocessed, and feature vectors related to the health state of the circuit breaker are extracted; inputting the feature vector into an improved weighted D-S evidence theory fusion diagnosis model; the improved weighted D-S evidence theory fusion diagnosis model is used for fusing evidences from a plurality of sensors in a mode of distributing weights for evidences of different sensors and calculating weighted average evidences; and determining the comprehensive health state grade of the low-voltage circuit breaker according to an output result of the fusion diagnosis model. According to the invention, comprehensive evaluation of the multi-dimensional working condition of the circuit breaker is realized, and the method has the advantages of comprehensively reflecting the multi-dimensional working condition of the circuit breaker, reducing the risk of misjudgment and missed judgment, and improving the performance degradation pre-judgment capability of equipment.

The invention relates to the related technical field of high-voltage switch cabinets, in particular to a high-voltage switch cabinet health state assessment method and system, and the method comprises the steps: obtaining an equipment operation parameter sequence of a high-voltage switch cabinet, combining an operation mechanism, determining a first response data segment and a second response data segment, and constructing a state assessment matrix, and identifying an abnormal operation period and a degradation trend cluster, and performing health risk early warning on the high-voltage switch cabinet by using the health state evaluation model. The technical problems that the sensing precision of key events such as switching-on bounce and partial discharge is insufficient, the generalization ability of health state evaluation under complex working conditions is insufficient, and the accuracy of high-voltage switch cabinet health evaluation is limited are solved, and cross correlation and variation correlation of a signal simulation verification chain are taken as an enhancement mechanism, so that the accuracy of high-voltage switch cabinet health evaluation is improved. The method has the technical effects that the response sensitivity of the model to key events is improved, the health risk of the high-voltage switch cabinet is accurately evaluated in combination with an abnormal period and a degraded cluster, and the accuracy of health evaluation of the high-voltage switch cabinet is improved.

The invention relates to the technical field of cable fault diagnosis and positioning, in particular to a cable fault positioning method and system based on multi-source data space-time correlation, and the method comprises the steps: obtaining partial discharge, traveling wave and temperature multi-source monitoring data from a high-voltage cable-overhead line hybrid power transmission network; on the basis of space-time correlation analysis of partial discharge and temperature data, heat-electricity associated defect points are calibrated; comparing traveling waves of the cable and the overhead line, and identifying and inhibiting cross-domain propagation interference; positioning the traveling wave data which are not identified to obtain temporary coordinates, judging fault characteristics by fusing information such as sheath ring current and the like, and comparing the temporary coordinates with defect points to generate matching identification; and inputting multi-dimensional information such as fault features, defect matching identification and temperature into the fusion model, calculating the confidence coefficient of a fault mode, and outputting a cross validation positioning result. According to the invention, high-reliability positioning of mixed line faults and active early warning of insulation defects are realized by carrying out space-time correlation and fusion decision on multi-source monitoring quantities such as traveling waves, partial discharge, circulation, temperature and the like.

The invention provides an intelligent monitoring and early warning method based on multi-source information fusion, and belongs to the technical field of intelligent diagnosis and early warning, and the method comprises the steps: synchronously collecting the multi-modal data of a voltage transformer, the multi-modal data comprising voltage / current waveform, partial discharge signal, temperature and vibration data; preprocessing the multi-modal data, wherein a preprocessing method comprises waveform segmentation, spectrogram generation and scalar normalization; constructing a dynamic weight distribution mechanism: calculating a weight matrix based on the relevance between the time sequence features and the spatial features, and dynamically adjusting scalar weights by combining the physical coupling relationship between the temperature and the vibration to generate feature fusion weights; iteratively updating the weight matrix according to the real-time prediction error; fusing the time sequence features, the spatial features and the scalar features through the dynamic weight distribution mechanism to generate a comprehensive feature vector; and synchronously outputting a fault type classification result and an equipment health score based on the comprehensive feature vector, and calculating a real-time prediction error.

The invention relates to the technical field of fault monitoring, in particular to a cable joint fault monitoring system and method, and the system comprises a multi-parameter signal processing module, an instantaneous temperature rise judgment module, a discharge attenuation recognition module, a synchronous instability judgment module and a risk early warning output module. According to the invention, by performing time sequence alignment and analysis on various sensor data such as temperature, current, voltage and partial discharge of the cable joint, the operation state of the cable joint can be monitored in real time, abnormal conditions can be found in time, potential faults such as joint aging, insulation damage and poor contact can be identified, and the abnormal degree of temperature rise can be accurately evaluated; according to the method, the risk level is accurately divided, the attenuation trend of partial discharge is identified, the instability trend of the connector can be effectively judged in combination with the fluctuation of current and voltage, the instability state of the cable connector can be identified in advance under the condition that multiple indexes synchronously change, and the accuracy and timeliness of fault early warning are improved.

The invention relates to a power cable insulation state real-time monitoring and alarm device, and belongs to the technical field of power equipment state monitoring. Comprising a multi-source sensing unit which is installed at a cable body and a joint; the edge computing terminal is fixed in the cable well or the ring main unit; and the cloud early warning platform receives the processing data and outputs an alarm instruction. According to the invention, multi-parameter deep fusion is realized, four-dimensional monitoring of grounding current, temperature, partial discharge and humidity is initiated for the first time, and limitation of a single parameter is broken through; and an insulation health index IHI is proposed, and the insulation deterioration degree is quantitatively evaluated. According to the method, tan delta and IHI are calculated on site through an edge-cloud collaborative architecture edge terminal, so that the data transmission quantity is reduced; the cloud dynamic threshold value is adjusted, and the alarm accuracy is improved. According to the invention, intelligent early warning mechanism three-level alarm linkage protection equipment is arranged; and the self-calibration function ensures long-term reliability.

The invention discloses an insulation state fusion detection method, system, medium and equipment under transient impact in a cable operation process, and the method comprises the steps: synchronously collecting a transient impact signal and a partial discharge signal of a cable under the transient impact effect; performing time-frequency domain separation on the acquired transient impact signal and the partial discharge signal to obtain a transient impact component and a partial discharge component after separation; when it is detected that the amplitude of the transient impact signal exceeds a preset threshold value, a partial discharge detection window is started, and whether a partial discharge signal exists or not is detected in the window period; if a partial discharge signal is detected, multi-dimensional characteristic parameters of the partial discharge signal are extracted, a state vector is constructed in combination with environmental parameters, the state vector is input into a pre-trained machine learning model, and a cable insulation state grade is output; if the partial discharge signal is not detected, performing inversion calculation on the transient impact signal based on a transmission line model, and reconstructing an overvoltage waveform; and extracting propagation characteristic parameters according to the reconstructed overvoltage waveform, and judging the insulation defect type according to the propagation characteristic parameters.

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.

According to the cable multi-parameter fusion sensor and method, in the cable multi-parameter fusion sensor, a shielding shell is of an open-close type metal annular structure; the zero-flux sensor module is arranged on the inner layer of the shielding shell so as to detect a cable sheath ring current signal; the pulse current sensor module is arranged on the outer side of the zero-flux sensor module so as to detect a cable partial discharge pulse current signal and an overvoltage impact current signal; the magnetic shielding layer is arranged between the zero-flux sensor module and the pulse current sensor module to shield low-frequency magnetic field interference; the zero magnetic flux sensor module and the pulse current sensor module share the shielding shell to form an electromagnetic shielding structure.

The invention belongs to the technical field of submarine cables, and provides a high-voltage three-core submarine cable. Comprising a cable core, a waterproof layer, an inner sheath, an armor layer and an outer sheath, the cable core comprises at least one conductive core, the conductive core comprises a conductor and an insulation system arranged outside the conductor, the insulation system comprises a conductor shielding layer, an insulation layer and an insulation shielding layer, and the conductivity of the conductor shielding layer is gradually reduced from inside to outside in the radial direction of the conductive core; the conductivity of the insulation shielding layer is gradually reduced from outside to inside in the radial direction of the conductive core. The contact interface of the conductor shielding layer and the insulating layer prevents charge from gathering at the interface, and the electric field distribution between the conductor shielding layer and the insulating shielding layer is greatly optimized; similarly, the electric conductivity of the insulation shielding layer is set to be gradually increased in the direction away from the insulation layer, the interface electric field between the insulation layer and the insulation shielding layer is optimized, electric charges on the interface of the insulation layer can be rapidly led to the insulation shielding layer, and the partial discharge phenomenon is prevented.

The invention provides a transformer substation switch cabinet partial discharge detection method and system based on a sensor, and relates to the technical field of discharge detection, and the method comprises the steps: deploying an ultrasonic sensor according to a sensing collection channel, accessing a signal processing terminal, and configuring a sensor sensitivity threshold value and a signal sampling frequency; adjusting the signal gain of each monitoring node coordinate corresponding to the sensing acquisition channel, synchronously recording the monitoring time, the monitoring node coordinates and cabinet body temperature parameters, and setting a monitoring association control matrix in combination with a sensor sensitivity threshold and a signal sampling frequency; determining a partial discharge type and discharge intensity based on the signal processing terminal; and generating a discharge characteristic distribution map, and generating a partial discharge detection report in combination with the insulation performance index of the substation switch cabinet, a safe discharge threshold and a pulse frequency threshold. The technical problems that in the prior art, a transformer substation switch cabinet partial discharge detection method is poor in anti-interference capacity and low in recognition precision are solved.

The invention belongs to the technical field of power equipment state monitoring, and discloses a cable branch box operation state intelligent evaluation system based on multi-sensor fusion, which comprises a state acquisition module used for acquiring a partial discharge signal, a voltage fluctuation signal, a terminal temperature signal and a terminal contact resistance signal of a cable branch box in an operation process, constructing a multi-source operation state parameter set of the cable branch box through time synchronization and physical quantity normalization processing; the mutual coupling analysis module is used for performing transient correlation deduction on the partial discharge signal and the corresponding voltage fluctuation signal based on the multi-source operation state parameter set to obtain a partial discharge-voltage coupling amount; when the partial discharge-voltage coupling amount exceeds a preset partial discharge-voltage coupling amount threshold value, determining that partial discharge abnormity caused by insulation degradation exists in the cable branch box; comprehensive and accurate evaluation of the operation state of the cable branch box is realized, and reliable support is provided for safe operation and maintenance of equipment.

The invention discloses an electric field analysis and optimization method for a transformer withstand voltage and partial discharge test platform, and the method comprises the steps: building a digital model of the test platform, obtaining a preparation technology standard and historical manufacturing measurement data of a target test platform, defining an influence variable which influences the distribution of an electric field, simulating the distribution of the electric field under different preparation differences, and carrying out the optimization of the electric field. Establishing an electric field simulation database; identifying test platform electric field sensitive areas under different preparation differences by adopting a spectral clustering algorithm based on the electric field simulation database, constructing a knowledge graph, constructing a sensitive area identification model based on a graph neural network, and performing model training through the knowledge graph; structural features and preparation features of the to-be-analyzed test platform are obtained, a local sensitive area of the to-be-analyzed test platform is recognized in the sensitive area recognition model, and if the local sensitive area exists, an optimization scheme is formulated for test platform optimization assistance, so that the design reliability and operation safety of the transformer test platform are improved.

The invention discloses an on-line qualitative detection method for partial discharge of power equipment, which comprises a detection system and detection steps, and also comprises the following steps: the detection system comprises a high-frequency partial discharge detector for detecting a high-frequency current transformer; the ultrahigh frequency detection system is used for detecting electromagnetic wave radiation which is generated by partial discharge and has extremely short duration time; the ultrasonic detection system is used for detecting sound waves and ultrasonic signals generated along with energy release when partial discharge occurs; a transient ground voltage method detection system is used for live-line detection of equipment such as a medium-voltage switch cabinet. According to the online qualitative detection method for the partial discharge of the power equipment, different detected equipment and different detection methods are adopted to perform online detection on the partial discharge of the equipment, so that the early partial discharge phenomenon of the power equipment can be found in time, the long-term discharge monitoring data of the power equipment can be stored, and the overall detection effect is improved.

The invention provides a method for extracting electrical characteristic signals of electrical equipment in a complex working condition environment, and belongs to the technical field of electrical equipment detection.The method comprises the steps that a multi-channel ultrasonic sensor array is arranged to collect partial discharge signals, background noise is eliminated through adaptive noise cancellation processing, and an ultra-sparse frequency band energy distribution vector is constructed; the method comprises the following steps: calling a self-adaptive time-frequency analysis model to extract instantaneous frequency, amplitude and phase parameters to form a micro-hour-frequency characteristic matrix, separating independent source signals through independent component analysis, calculating a kurtosis value and a skewness value, fusing multi-domain characteristics to construct a transient stationary comprehensive characteristic vector, matching with a standard discharge characteristic vector library to identify the discharge type and intensity, and calculating the discharge intensity. A corresponding prediction algorithm is selected according to the discharge mode, multi-parameter coupling optimization adjustment is started under a certain condition, an electrical characteristic signal description vector is finally constructed, and the technical problem that the partial discharge signal of the power equipment is difficult to accurately extract under a complex working condition environment is solved.

The invention discloses a method and a system for determining optimal parameters of an X-ray excited extra-high voltage composite apparatus, and relates to the technical field of extra-high voltage electrical equipment detection. The method comprises the following steps: remotely operating the X-ray machine and calculating partial discharge intensity; for the current irradiation position, the tube voltage and the tube current are gradually adjusted, parameters are dynamically optimized based on the quantitative relation between the partial discharge intensity and the radiation dose rate change rate, and the optimal parameters are recorded; and changing position repetition parameter optimization, and constructing a BP neural network model after data set training optimization so as to predict an optimal irradiation position. The optimized BP neural network adopts an improved sparrow search algorithm to optimize an initial weight and a threshold value, and combines Bayesian to optimize a learning rate and a hidden node number. Accurate optimization of excitation parameters and irradiation positions is achieved, detection accuracy and safety are both considered, the optimal irradiation position positioning efficiency is improved, and the method is suitable for ultra-high voltage GIS detection of different voltage grades.

The invention relates to the technical field of electromagnetic measurement, and discloses a line partial discharge positioning method and system based on pulse signal analysis, and the method comprises the steps: carrying out the band-pass filtering of pulse signal data, and obtaining a preprocessing signal; constructing a waveform template library based on the historical partial discharge pulse waveform data; screening out a partial discharge pulse and an interference pulse in the preprocessed signal to obtain a pulse matching result of the preprocessed signal; determining time difference data of the preprocessed signal according to time differences of pulses reaching different sensors in the pulse matching result; constructing an initial propagation velocity model according to the parameters and the environment data, performing iterative optimization on the initial propagation velocity model according to the time difference data, and determining an initial position of a partial discharge source in the line; correcting the initial propagation velocity model according to the initial position and the actual propagation path to obtain a target propagation model, and determining position coordinates of a partial discharge source in the target line according to the target propagation model; according to the invention, the accuracy of line partial discharge positioning can be 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 relates to the technical field of fault detection, in particular to an electrical equipment fault diagnosis method and system, and the method comprises the following steps: constructing a ratio group of a trigger incremental sequence quantity and a frequency spectrum cluster set based on a partial discharge pulse interval, deducing impedance frequency boundary migration, analyzing a conductor heating and cooling rate difference, and forming thermal response lag information. According to the method, frequency spectrum energy evolution characteristics are extracted through partial discharge pulse interval dynamic behaviors, a frequency migration path is constructed to reflect an impedance boundary fluctuation process, hysteresis effect characteristics are extracted in combination with thermal inertia response differences in a temperature rise stage, and the impedance boundary fluctuation is determined. And analyzing a voltage and current phase fluctuation rule and deducing a change trend of a combined feature, establishing a layer-by-layer progressive judgment logic between multi-stage dynamic coupling features, realizing evolutionary process tracking from local disturbance to a global abnormal state, and improving a fine-grained identification level of an abnormal mechanism in an electrical equipment operation behavior.