296 results about "Ultrahigh frequency" patented technology

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 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 discloses a GIS online monitoring method based on multi-state quantity integration, and relates to the field of GIS online monitoring, and the method comprises the steps: deploying an array composed of an ultrahigh frequency sensor, an ultrasonic sensor, a gas density sensor, an optical fiber temperature sensor and a vibration sensor based on a GIS equipment cavity structure zone; the method comprises the following steps: synchronously acquiring an ultrahigh-frequency electromagnetic wave signal, an ultrasonic signal, a gas density signal, a local temperature signal and a shell vibration signal in an operation process of GIS equipment so as to obtain a multi-dimensional original state quantity sequence; according to the method, multiple types of sensors are accurately distributed and controlled for the GIS cavity structure partition, and through time alignment, adaptive weight fusion and time-space convolution feature extraction, hidden dangers such as a partial discharge source can be accurately positioned, and a state quantity can be predicted by means of a correlation evolution equation. Meanwhile, mechanisms such as sensor fault automatic elimination and complementation, prediction accuracy verification and the like guarantee data reliability, equipment states can be mastered in real time, and faults can be warned in advance.

The invention discloses a partial discharge online monitoring method based on an ultrahigh frequency original signal, and relates to the field of partial discharge monitoring, and the method comprises the steps: autonomously switching an ultrahigh frequency signal capturing frequency band according to the real-time electromagnetic spectrum distribution of a monitoring environment, and obtaining an original signal sequence covering a potential partial discharge feature; the method comprises the following steps: preprocessing an original signal sequence through multi-scale pulse contour extraction, and suppressing environmental noise interference to retain pulse characteristics of partial discharge signals; through real-time electromagnetic spectrum analysis, ultrahigh frequency capture frequency bands are automatically switched, environmental noise changes are dynamically adapted, original signal capture accuracy is improved, through multi-scale pulse contour extraction preprocessing, noise interference is effectively suppressed, key pulse features are reserved, and the accuracy of signal capture is improved. A multi-dimensional feature data set constructed based on the pulse peak gradient, the duration spectrum and the waveform similarity provides a comprehensive basis for partial discharge recognition.

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 discloses a live detection method for insulation defects of high-voltage power equipment. The method comprises the following steps: firstly, synchronously arranging ultrahigh frequency sensors and acoustic emission sensors on a plurality of monitoring points on the surface of a gas insulated switchgear shell to form an array, and synchronously acquiring signals for filtering pretreatment; then, pulse events are extracted from the two types of signals respectively, and associated pulses in a time window are matched into matched pulse pairs representing the same discharge source; then, carrying out time-frequency transformation on the ultrahigh frequency and acoustic emission pulse waveform in each matched pulse pair, carrying out joint noise reduction by calculating a coherence coefficient between time-frequency distribution matrixes, and extracting a joint feature vector containing an energy ratio, a time parameter ratio and a frequency difference from the denoised time-frequency matrix; according to the invention, multi-source signals are fused, and high-sensitivity detection, high-precision positioning and high-accuracy identification of insulation defects are realized.

The invention belongs to the technical field of metal crack detection, and discloses a metal microcrack ultrasonic detection method, which comprises the following modules: an ultrahigh frequency composite array probe with a center frequency dynamic adjustable range of 5-50 MHz; the dual-mode excitation and receiving module is used for independently generating a pulse reflection mode excitation signal and a diffraction mode chirp frequency modulation signal; the phase-controlled synthetic aperture dynamic focusing module is used for realizing + / -60-degree deflection and sub-wavelength level focusing of acoustic beams; the nonlinear harmonic enhancement module is used for exciting a crack nonlinear effect by superposing 1-2MHz fundamental waves; and a time-space-frequency three-domain data fusion module. According to the method, signal-to-noise ratio collapse caused by the diffraction effect in a traditional method is overcome, more importantly, the system has low-frequency penetrability and high-frequency resolution at the same time due to 80%-120% of broadband coverage of chirp frequency modulation signals, the diffraction signal gain is increased by 20 dB or above, a near-surface blind area is compressed to be within 0.2 mm, and the system can be applied to the field of radar imaging. And the core pain point of missing detection of the near-surface microcracks of the thin-wall component is solved.

The invention discloses a series ultrahigh frequency sensing GIS three-dimensional positioning and monitoring system and method, and belongs to the technical field of high-voltage equipment state monitoring. The system comprises a sensing layer, a processing layer, a transmission layer and an application layer, wherein the sensing layer synchronously collects ultrahigh-frequency electromagnetic signals, sound wave vibration signals and SF6 gas state parameters through a plurality of serial multi-physical-quantity sensing nodes; the processing layer eliminates interference based on signal homology verification, calls an intelligent response model to dynamically compensate propagation path errors in combination with a GIS three-dimensional structure model and real-time environment data, outputs accurate three-dimensional coordinates of the partial discharge source by adopting a progressive time difference of arrival positioning algorithm, and identifies fault types and risk levels by fusing multi-dimensional features; the transmission layer encrypts and uploads the diagnosis report; and the application layer maps defect positions in the three-dimensional visual model and automatically generates an operation and maintenance work order according to risk levels. According to the invention, high-precision positioning, multi-source fusion diagnosis and closed-loop operation and maintenance are realized, and the accuracy and intelligent level of GIS equipment monitoring are remarkably improved.

The embodiment of the invention relates to the technical field of ultrahigh frequency passive RFID, in particular to an intelligent storage RFID robot reading perception and trajectory optimization method and system. According to the method, the position coordinates of the goods shelf are obtained by building the model and utilizing the label characteristic values, the interaction state of the UHF RFID robot and the goods shelf can be sensed, the distribution state of the goods is further sensed, and the number and the position of the missed goods are estimated. Furthermore, the advancing track of the UHF RFID robot system in the warehouse management scene is designed and optimized according to the sensing result, so that the reading accuracy and the checking efficiency of the mobile RFID system are 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.

The invention relates to a partial discharge type identification method and device, computer equipment, a readable storage medium and a program product. The method comprises the steps that discharge signals including a current signal, an ultrasonic signal and an ultrahigh frequency signal are obtained and subjected to feature extraction, feature data corresponding to the discharge signals are obtained, the feature data are input into a graph convolutional network model, feature fusion can be conducted on the feature data, and a fused multi-modal feature vector is obtained; and inputting the fused multi-modal feature vector into a preset depth transfer learning model to obtain a predicted partial discharge identification type corresponding to the discharge signal. The method can improve the identification accuracy and reliability of the partial discharge type, and is of great significance to guarantee the stable operation of a power system.

The invention provides a GIS insulation defect evaluation method and device and electronic equipment, and relates to the field of data processing. According to the method, a cross-modal synchronous data stream formed by an ultrahigh frequency sensor and a mechanical vibration sensor on a GIS (Geographic Information System) is obtained, event slicing is firstly carried out, multi-dimensional features are extracted, and candidate events are screened in combination with a sparse labeling library; and generating a propagation time delay window and a path priority table by using a three-dimensional geometric model, completing cross-modal pairing and time registration, outputting a registration result, performing array manifold sparse reconstruction separation, and eliminating multipath interference to obtain purification components. And constructing a cross-modal event chain graph based on the purification components, executing spatial positioning and physical quantity inversion under the constraint of the chain graph, and finally generating a defect evaluation report in combination with a historical labeling result, thereby realizing high-precision detection and evaluation of GIS insulation defects. By implementing the technical scheme provided by the invention, the accuracy of insulation defect evaluation can be improved.

The invention discloses an active clamping resonant drive circuit and device. The active clamping resonant drive circuit comprises a power supply module, an energy storage clamping module, a switch group module, an additional capacitor module and an output drive module. The device corresponds to the circuit. According to the application, through the energy storage clamping module and the combination of the switch group module, wide-range duty ratio adjustment of quasi-square wave driving output can be realized under the frequency of tens of megahertz, duty ratio requirements of different working conditions can be adapted, frequency band work with a certain width can be supported through the same set of circuit parameters, and the design flexibility of the ultrahigh frequency power converter is greatly improved; the circuit redesign cost in different frequency scenes is reduced; through the energy storage clamping module, the coupling degree of a driving waveform and line parasitic parameters is effectively reduced, so that the driving circuit still has good stability during high-frequency work, the anti-interference capability is enhanced, the adverse effect of the parasitic parameters or external interference on the driving performance is avoided, and the reliability of high-frequency work of the power electronic switching device is guaranteed.

The invention discloses a partial discharge signal multi-source cooperative detection method and system based on time-frequency fusion analysis, and relates to the technical field of multi-source cooperative detection, and the method comprises the following steps: obtaining partial discharge original signals of an electric signal channel and an ultrahigh frequency channel, respectively carrying out the time-frequency transformation, and generating a multi-source time-frequency characteristic spectrum; extracting high-frequency noise modes of different channels under a non-partial discharge condition based on the multi-source time-frequency characteristic spectrum, and constructing a cross-source interference fingerprint database; based on the cross-source interference fingerprint database, carrying out frequency-band-by-band difference mapping calculation on actually acquired multi-source time-frequency characteristics to generate a difference characteristic matrix; and introducing reverse consistency constraint into the differential feature matrix, carrying out weighted fusion to obtain multi-source coupling time-frequency feature mapping, and outputting a detection result of the partial discharge signal. According to the method, the multi-source time-frequency characteristic spectrum is constructed to carry out band-by-band differential mapping on the actual signal, so that cross-source noise interference is inhibited, and the accuracy and robustness of partial discharge detection in a strong noise environment are improved.

The invention discloses an ultrahigh-frequency radio frequency tableware batch identification and transaction control system based on artificial intelligence, and the system comprises a tableware radio frequency tag module which is used for embedding an ultrahigh-frequency RFID tag in a tableware body; the radio frequency reading and signal acquisition module is used for outputting structured data records; the artificial intelligence identification and error correction module is used for generating a credible tableware identifier set and a state feature set; the array antenna control module is used for dynamically adjusting a beam direction angle and a transmitting power parameter value of the array antenna; the edge computing transaction processing module is used for inputting the trusted tableware identifier set into an edge computing transaction processing flow and executing repeated charging risk verification; the payment control module is used for returning a payment result; and the background management and tracking module is used for generating operation statistics and abnormal record entries. The system has high-precision tableware identification, low-delay settlement and abnormity prevention and control capabilities, and is suitable for automatic identification and intelligent settlement management of batch tableware in a catering scene.

The invention discloses a transformer substation safety risk prediction and alarm system based on edge calculation, and belongs to the technical field of power system safety monitoring. The system comprises a sensing layer equipment group, an edge computing device, a cloud edge collaborative management platform and an intelligent alarm linkage system. The sensing layer equipment group acquires multi-source data in real time through a broadband current transformer, an ultrahigh frequency partial discharge detection array and a micro-meteorological monitoring module; the edge computing device performs local risk prediction by adopting a lightweight model generated by knowledge distillation, and realizes millisecond response in combination with FPGA hardware acceleration; the cloud edge collaboration platform realizes model dynamic optimization through federated learning and incremental updating; and the intelligent alarm system triggers a light stripe coding alarm and priority instruction sequence based on the multi-source fusion confidence coefficient. The problems that a traditional system is lagged in response, slow in model updating, difficult in multi-source data fusion, weak in privacy protection and the like are solved, and real-time prediction and accurate disposal of the safety risk of the transformer substation are achieved.

The invention provides a GIS partial discharge positioning method based on deep learning and acoustoelectric combination, and relates to the field of data processing. The method comprises the following steps: firstly, acquiring an original ultrahigh frequency signal and an original ultrasonic signal of a target detection entity, constructing an acoustoelectric signal matrix, and performing short-time Fourier transform to obtain a complex spectrum tensor and a power spectrum tensor; and then, extracting a high-dimensional feature embedding tensor by using a double-branch deep learning network, and generating a time-frequency masking tensor and a source confidence tensor through a multi-source sub-ion network to complete source-by-source spectrum reconstruction. And a time difference of arrival matrix is calculated by using a phase transformation generalized cross-correlation method, a sensor space coordinate is combined to be input into the depth regression positioning sub-network, and a discharge source is output to predict three-dimensional coordinates and regression uncertainty. And finally, fusing the regression uncertainty and the source confidence tensor to construct a confidence interval, and generating a visual positioning result with an ellipsoid confidence boundary. By implementing the technical scheme provided by the invention, the accuracy of partial discharge positioning is improved.

The invention relates to the technical field of power equipment state operation early warning, and discloses a power equipment state operation time sequence early warning system and method, and the system comprises a data collection module which collects the multi-source operation data of power equipment through infrared, ultrasonic TEV, high-frequency, ultrahigh-frequency and vibration sensors; the data preprocessing module is used for carrying out normalization processing on the collected multi-source operation data by adopting an extreme value method or an averaging method and mapping the data to a [-1, 1] interval; and the time sequence analysis modeling module is used for constructing a Prophet time sequence model for the normalized data, and decomposing a trend item, a season item and a holiday item. A dynamic factor is added in weight updating through a dynamic parameter adjustment method, network training is effectively prevented from falling into a local minimum point, fluctuation in the learning period is reduced, the learning rate is flexibly adjusted according to error changes in combination with an adaptive learning rate algorithm, and slow convergence caused by the too low learning rate is avoided.

The invention relates to the technical field of cable monitoring, and discloses a cable defect monitoring and positioning method based on big data sample statistical analysis, and the method comprises the following modules: a multi-source sensor array module which is used for setting a regular triangle array according to a specific interval along a cable path, an ultrahigh-frequency partial discharge sensor, a fiber grating temperature sensor and an acceleration vibration sensor are integrated, and partial discharge, temperature and vibration multi-dimensional physical signals are synchronously collected under the sampling frequency meeting the Shannon sampling theorem; and the space-time synchronization module is used for the GPS clock module and the I EEE1588 protocol to perform nanosecond synchronization of the sensor array, and eliminating inherent delay and global clock offset of the sensor through timestamp calibration. According to the cable defect monitoring system, comprehensive and accurate data acquisition, high-quality data preprocessing and multi-source information effective fusion are realized through multi-module collaborative operation, and the accuracy and reliability of the whole cable defect monitoring work are improved. The problem that cable defect monitoring is not comprehensive and accurate enough is solved.

The utility model relates to an ultrahigh frequency resonance excitation low power consumption electromagnetic flowmeter and a special chip thereof, a sensor core is installed in a sensor core waterproof box, the sensor core waterproof box is installed at the middle position of a sensor core support, and the sensor core waterproof box divides a measuring pipeline in the sensor core support into two parts. Signal processing PCBs are installed on the front face and the back face of the sensor core support respectively, special chips are arranged in the two signal processing PCBs respectively, and the sensor core support is installed in a metal pipeline. According to the utility model, the purpose of reducing power consumption is realized by shortening the excitation time and forming resonance by using inductance and capacitance of the excitation coil; and the excitation coil and the measurement electrode are manufactured by adopting the multi-layer PCB, so that the manufacturing cost is greatly reduced.

According to the fault early warning system for the wind power booster station provided by the invention, the limitation of traditional single-point monitoring is broken through by fusing the infrared ultrasonic technology and the ultrahigh frequency technology through the non-contact sensor, so that the early fault detection rate is greatly improved; the composite heat dissipation system suppresses the temperature rise of a transformer hot spot based on a dynamic heat management mechanism of a phase change material and a heat pipe, and the energy-saving efficiency is improved; the edge calculation early warning center converts temperature, partial discharge, strain and other multi-source heterogeneous data into a unified health degree index through a weighted fusion algorithm, and in combination with an equipment topology correlation analysis model, the positioning accuracy of a fault source is greatly improved, and the early warning response time delay is greatly reduced.

The invention provides a multi-mode fusion switch cabinet partial discharge on-line monitoring system and method, and the system comprises a space setting module which employs a preset space analysis algorithm, obtains an installation point position set according to the space structure of a switch cabinet, and carries out the installation of the installation point position set; the data acquisition module is used for acquiring data acquired by the ultrahigh frequency sensor, the ultrasonic sensor and the temperature sensor, preprocessing the data and then sending the data to the signal processing module; carrying out power frequency noise suppression on the ultrahigh frequency signal and the ultrasonic signal through a self-adaptive filter circuit, and extracting partial discharge characteristics from the filtered signal by adopting a wavelet transform algorithm; and the AI model fusing the convolutional neural network and the long-short-term memory network performs fusion analysis on the partial discharge characteristics and the temperature sensor data, realizes partial discharge mode identification and insulation degradation trend prediction, and outputs early warning information. According to the technical scheme provided by the invention, high-precision and anti-interference discharge detection can be realized.

The invention relates to the technical field of electrical equipment fault diagnosis, in particular to an acoustic-electric joint detection method, system and device for internal defects of GIS equipment and a medium, and the method comprises the steps: synchronously collecting an ultrasonic signal detected by a grating fiber ultrasonic sensing module and an ultrahigh frequency electromagnetic wave signal detected by an ultrahigh frequency sensing module; performing feature extraction on the ultrasonic signal and the ultrahigh-frequency electromagnetic wave signal to obtain a first feature parameter and a second feature parameter respectively; based on the arrival time difference of the ultrahigh-frequency electromagnetic wave signal and the ultrasonic wave signal, calculating the initial space position of the defect by adopting a time difference positioning method; and performing conjoint analysis on the first characteristic parameter and the second characteristic parameter in combination with the initial space position of the defect so as to identify the defect type. Through the arrangement, sound and electricity detection information can be deeply fused, a novel joint detection method with mutual verification and mutual promotion of positioning and identification is realized, and the integrity, accuracy and reliability of GIS internal defect diagnosis are effectively improved.

The invention discloses an electromagnetic vibration table for an uncooled infrared machine core assembly adopting multiple sensors, and relates to the technical field of vibration tables. By arranging four groups of vibration testing mechanisms of low frequency, intermediate frequency, high frequency and ultrahigh frequency, vibration waves of different frequencies can be synchronously or alternately generated, complex operation conditions of the uncooled infrared movement in real environments such as transportation jolting, mechanical shock and high-frequency vibration can be simulated, and vibration testing of different vibration frequencies can be performed on a plurality of uncooled infrared movement assemblies at the same time. Wherein the low-frequency vibration testing mechanism and the intermediate-frequency vibration testing mechanism can simulate low-frequency vibration in a transportation process, the high-frequency vibration testing mechanism can simulate high-frequency noise interference in operation of the uncooled infrared movement, and the ultrahigh-frequency vibration testing mechanism can simulate an instantaneous impact working condition of the uncooled infrared movement in an extreme environment. Therefore, each uncooled infrared movement to be tested can undergo vibration tests in various modes, full-band coverage tests are realized, and the accuracy and credibility of test results are greatly improved.

The invention provides a collaborative optimization method of a mixed water electrolysis hydrogen production system considering seasonal wind and light fluctuation. The method comprises the following steps of: analyzing a wind-solar power frequency spectrum by adopting a variational mode decomposition algorithm, and extracting three intrinsic mode function components of ultrahigh frequency, transition frequency band and ultralow frequency; establishing a multi-time-scale collaborative optimization framework comprising a seasonal scheduling layer, an intra-day optimization layer and a real-time control layer; the ultra-low frequency component is distributed to an alkaline electrolytic bath, and the ultra-high frequency component is distributed to a proton exchange membrane electrolytic bath; an electric-thermal-life multi-field coupling efficiency optimization model is constructed, and multi-objective optimization of efficiency maximization and life loss and operation cost minimization is achieved; and an equipment switching critical point calculation method based on an efficiency curve second derivative and a gradient protection strategy are adopted. The method can effectively improve the adaptability of the system to seasonal fluctuation, the operation economy and the service life of equipment.

The invention relates to the technical field of partial discharge detection and sensor verification, and particularly discloses an ultrahigh frequency sensor relative amplitude online verification method for transformer bushing defects. The method comprises the following steps: firstly, spraying conductive paint into a lower porcelain bushing of the transformer bushing to form a creeping discharge channel, and constructing a transformer bushing operation simulation device; then, a standard calibration pulse generator is utilized to inject a discharge signal equivalent to a 10pC electric charge quantity at the defect position of the sleeve; then, the ultrahigh frequency sensor to be tested receives a partial discharge signal, and a relative amplitude model is established based on sensor frequency response, dielectric constant, signal propagation attenuation and installation conditions; and finally, comparing the actually measured signal amplitude with the calculated relative amplitude threshold, and if the amplitude is lower than the threshold, judging that the performance of the sensor is degraded. According to the method, the installation condition correction model is introduced, online evaluation of the sensitivity of the ultrahigh-frequency sensor under the condition that equipment is not disassembled is achieved, the real performance of the sensor in a complex field environment can be accurately reflected, and the reliability and maintenance efficiency of a transformer bushing partial discharge monitoring system are remarkably improved.

The invention discloses an environment-friendly electrical equipment early-stage defect rapid identification and evaluation method and system, and the method comprises the steps: a multi-modal data obtaining step: obtaining multi-modal data of GIS equipment, the multi-modal data comprising voiceprint signals, ultrasonic signals, ultrahigh frequency signals and physical parameters of insulating gas; an anomaly analysis step: if the multi-modal data is abnormal, performing a fault diagnosis step; and a fault diagnosis step of performing fusion diagnosis analysis based on the voiceprint signal, the ultrasonic signal, the ultrahigh frequency signal, the temperature, the pressure, the density and the mixing ratio to obtain the fault type and the fault severity. According to the invention, by combining online monitored multi-modal data, physical parameters are utilized to make up for the accidental defect of electric signal capture, the defect that misjudgment may be caused only by means of instantaneous acoustoelectric signals is avoided, and analysis of equipment fault types and severity is realized.

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 discloses a GIS partial discharge fault diagnosis method based on a double neural network model, and the method comprises the steps: obtaining a partial discharge signal ultrahigh frequency signal feature vector and a gas decomposition product concentration change feature vector through analyzing a GIS partial discharge signal and a gas decomposition product absorption spectrogram, respectively inputting the feature vectors into an LSTM network and a Transformer model, and carrying out the fault diagnosis of the GIS partial discharge fault. And outputting respective diagnosis results, building a prediction fusion model, carrying out prediction analysis by using the model, and finally outputting a fault diagnosis result. According to the GIS partial discharge fault diagnosis method based on the double neural network model disclosed by the invention, the problem of inaccurate partial discharge type identification caused by single detection method and incapability of avoiding external interference in the prior art is solved.