159 results about "High frequency transducer" patented technology

The invention relates to the technical field of cable partial discharge detection and diagnosis, provides a high-voltage cable multi-parameter intelligent diagnosis method, system and equipment and a storage medium, and aims to solve the problems of low accuracy and poor reliability of high-voltage cable multi-parameter diagnosis caused by multiple factors. The method comprises the following steps: decomposing a partial discharge signal after interference filtering, extracting a time domain amplitude change rate and an energy distribution parameter, and generating a multi-dimensional time-frequency domain feature vector; calibrating a multi-dimensional time-frequency domain feature vector, carrying out nonlinear manifold mapping, generating a low-dimensional feature vector matched with dielectric spectrum characteristics, matching a partial discharge type with the highest similarity with an identification result, judging an insulation degradation level in combination with a final loss threshold interval, and generating a comprehensive diagnosis report. According to the invention, the ultrahigh-frequency sensor array captures the partial discharge signal matched with the dielectric spectrum, precise identification of the discharge type and dynamic evaluation of the insulation degradation level are realized through multi-scale analysis and deep learning fusion, and the reliability of high-voltage cable diagnosis is improved.

The invention discloses a GIS partial discharge on-line monitoring method, and relates to the technical field of power equipment monitoring and fault diagnosis, and the method comprises the following steps: a detection system carries out the real-time collection of an electrical signal caused by a partial discharge phenomenon in the operation process of GIS equipment through a high-frequency sensor disposed on the GIS equipment, therefore, a data basis for subsequent distortion analysis and positioning is obtained. Partial discharge signals are collected in real time through the high-frequency sensor, signal distortion is evaluated in combination with a machine learning model, the time domain and the frequency domain of the signals are accurately corrected by adopting a dynamic time warping algorithm and a frequency compensation means, and the real characteristics of the signals are recovered. According to the method, the signal positioning precision is improved, the GIS equipment fault detection capability is enhanced, potential risks are warned in advance, equipment faults and safety accidents are reduced, and the safety and stability of a power system are remarkably improved.

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 transformer bushing high-frequency partial discharge signal on-line monitoring method and system, and relates to the technical field of electrical equipment insulation state monitoring, and the method comprises the steps: synchronously collecting signals based on a high-frequency current sensor and an ultrahigh-frequency sensor; performing mixed noise reduction and feature extraction according to the collected signals; and carrying out discharge type identification on the processed data by constructing a lightweight convolutional neural network, and carrying out early warning and positioning in combination with a dynamic alarm threshold. The method can significantly improve the signal-to-noise ratio and enhance the weak discharge signal detection capability by combining the dual-mode sensor with a hybrid noise reduction strategy of wavelet packet decomposition and adaptive filtering, adopts the lightweight convolutional neural network, supports edge equipment to implement reasoning, realizes intelligent identification of the discharge type, and improves the detection accuracy of the weak discharge signal. The insulation state of the transformer bushing is monitored online in real time, off-line dependence is reduced, the service life of equipment is prolonged, and the intelligent level of operation and maintenance of a power system is improved.

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 multi-parameter ultrahigh frequency sensor performance comprehensive evaluation method, which belongs to the technical field of sensor performance evaluation, and effectively breaks through the limitation of the traditional static test on a transient interference response blind area through a dynamic interference field simulation test and an original fidelity-false triggering joint evaluation model. The adaptive adjustment mechanism of the multi-source interference field can vividly simulate the space-time non-uniform characteristic of electromagnetic noise in an industrial field, and in combination with a time-frequency domain composite evaluation algorithm, not only is the signal distortion degree of the sensor in typical interference modes such as frequency sweeping and pulse train quantified, but also the error action acceleration rule caused by high-frequency-band noise coupling is disclosed. The dynamic immunity evaluation capability enables a designer to accurately position the electromagnetic shielding weakness of the sensor, optimizes a front-end filter circuit or a signal processing algorithm in a targeted manner, and remarkably improves the reliability of a product in strong interference scenes such as radar detection and wireless communication.

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 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 a multi-mode signal fusion switch cabinet partial discharge detection and positioning method and device, and belongs to the technical field of power equipment monitoring, and the method comprises the steps: arranging an ultrahigh frequency sensor, an ultrasonic sensor and a traveling wave signal sensor on a switch cabinet, and synchronously collecting electromagnetic wave, sound wave and traveling wave signals; filtering the electromagnetic wave signal, the sound wave signal and the traveling wave signal, and enhancing the filtered signal to obtain a filtered enhanced signal; carrying out the positioning and classification of the partial discharge signals through the multi-modal feature fusion of the filtered enhanced signals; and triggering graded alarm according to a positioning result, and periodically starting a self-checking program to verify the reliability of the sensor and the algorithm. Physical characteristics of different discharge types are covered, the detection comprehensiveness is improved, the noise interference problem is solved, and the data fusion efficiency and the positioning precision are improved; the system health state is monitored in real time, and long-term operation stability is ensured.

The invention discloses a GIS ultrahigh frequency sensor ultrasonic cooperative detection system based on piezoelectric energization, and relates to the field of electric power detection, and the system comprises an excitation module which is used for transmitting a piezoelectric excitation signal with a specific frequency to a preset detection region in GIS equipment, so as to trigger the potential partial discharge acoustoelectric signal radiation of the region; the sensing module is used for receiving ultrahigh-frequency electromagnetic signals and ultrasonic vibration signals radiated by partial discharge in the GIS equipment; the excitation frequency, the filtering bandwidth and the sampling rate are dynamically optimized according to the detection result, the detection accuracy and efficiency are continuously improved, and reliable and effective guarantee is provided for safe operation and defect early warning of GIS equipment.

The invention relates to a ring main unit intelligent detection and fault early warning method and system. The method comprises the following steps: deploying an ultrahigh frequency sensor, an infrared thermal imager, an ultraviolet sensor and a high-frequency current transformer, and synchronously acquiring multi-modal data such as partial discharge, temperature distribution, corona discharge and grounding current of the ring main unit; carrying out feature extraction on the collected data, wherein the feature extraction comprises a time-frequency statistical feature and a temperature-load correlation feature of the partial discharge signal; the Dempster-Shafer evidence theory is utilized to carry out fusion calculation on the multi-source feature evidence, and confidence coefficients for insulation degradation, poor contact, arc faults and other fault types are obtained; arc fault prediction is carried out in combination with a long-short-term memory network model; and finally executing a grading early warning strategy according to the fault confidence. The problems of low detection reliability and fault early warning lag of a single sensor are solved, early accurate diagnosis and predictive maintenance of the insulation fault of the ring main unit are realized, and the operation safety and reliability of power equipment are remarkably improved.

The invention provides a Ti65 pipe external corrosion quantitative risk comprehensive evaluation method and system, and relates to the technical field of pipe detection, and the method comprises the following steps: carrying out data collection and preprocessing, carrying out comprehensive geometric model construction, carrying out comprehensive risk index calculation and grade division, and carrying out risk early warning and decision output. And finally carrying out dynamic optimization and model verification. The temperature, chloride ion concentration, microorganism density and stress data are synchronously collected through a heterogeneous sensor combination, multi-field coupling analysis of internal and external corrosion factors is achieved, the risk source of the whole life cycle of the pipeline is covered, in-situ real-time processing is achieved through a high-frequency sensor and edge calculation, physical invasion of the pipeline is not needed, delay is greatly reduced, and the detection accuracy is improved. And the dynamic monitoring requirement is met.

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 mechanical arm vibration suppression, in particular to a mechanical arm tail end active vibration suppression method and system based on predictive compensation, and the method comprises the following steps: S1, collecting the three-axis acceleration, angular velocity and vibration spectrum data of a mechanical arm in real time through a high-frequency IMU sensor; monitoring a rolling angle, a pitch angle and a yaw angle of a base attitude through an auxiliary IMU sensor, and fusing double IMU data through a Kalman filtering algorithm; s2, generating a pre-compensation torque based on the fused data, optimizing a model gain coefficient in combination with a real-time deviation between a frequency spectrum actually measured by a high-frequency IMU sensor and a model prediction frequency spectrum, and correcting and outputting the pre-compensation torque in real time; and S3, the corrected compensation torque is injected into a feed-forward channel of a joint motor control loop, vibration suppression torque is output to a joint execution mechanism through a high-gain controller, and residual vibration at the tail end of the mechanical arm is counteracted in real time. According to the invention, an active vibration suppression system can be constructed to solve the problem of real-time vibration suppression under multi-source disturbance coupling.

The invention provides an energy storage power station fault positioning method, system and device and a medium, and relates to the technical field of energy storage power stations, multiple energy storage bin nodes in an energy storage power station are detected based on multiple ultrasonic sensors, and detected sound signals are transmitted to a monitoring terminal; if the monitoring terminal obtains that the sound signal detected by the first ultrasonic sensor is abnormal, re-checking detection is carried out on the abnormal energy storage bin node; if the abnormal state is detected, performing window scanning on the abnormal energy storage bin node; and if the waveform similarity between the ultrahigh-frequency signal generated by the central ultrahigh-frequency sensor and the ultrahigh-frequency signal of the first ultrahigh-frequency sensor exceeds a preset waveform threshold value, determining that the abnormal energy storage bin node is a fault energy storage bin node. Through cooperative monitoring of multiple sensors, the fault position in the energy storage power station can be rapidly and accurately positioned, the fault finding time is shortened, and the operation reliability and stability of energy storage power station equipment are effectively improved.

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 embodiment of the invention provides a zinc layer thickness control method and system, and relates to the technical field of zinc layer thickness control, the method comprises the steps that real-time feature data of a molten zinc liquid film attached to the surface of a steel strip is obtained, and the real-time feature data is obtained through a non-contact high-frequency sensor array arranged in the width direction of the steel strip; according to the instant characteristic data and a preset mode library, identifying a behavior mode of the molten zinc liquid film; adjusting purging parameters of the air knife according to the behavior mode to obtain target purging parameters; and the molten zinc liquid film is purged according to the target purging parameters, and the target purging parameters are fed back and adjusted according to the purged molten zinc liquid film. The stability and uniformity of the thickness of the zinc layer can be improved.

The invention provides a high-frequency FBG sensor with symmetrical elliptical flexible hinges and a monitoring method of the high-frequency FBG sensor, and relates to the technical field of optical fiber sensing, the sensor comprises a fiber Bragg grating, a base, a first elliptical flexible hinge, a second elliptical flexible hinge, a first mass block and a second mass block; the left side of the base is connected with a first elliptical flexible hinge; the first elliptical flexible hinge is connected with a first mass block; the right side of the base is connected with a second elliptical flexible hinge; the second elliptical flexible hinge is connected with a second mass block; the fiber bragg grating is arranged between the first mass block and the second mass block. As a flexible hinge design, the symmetrical elliptical flexible hinge solves the problem that the motion range is limited, achieves a larger motion range, increases the sensitivity, and keeps high precision at the same time.

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 chip-based cross-modal space-time coupling unmanned vehicle grading emergency response method. The method comprises the following steps: a sensor aligns a local clock based on a global clock signal; determining the length of a sliding window according to the sampling frequency of the sensor, and inserting an interpolation frame between two adjacent frames of the low-frequency sensor so as to align the timestamp of the low-frequency sensor with the timestamp of the high-frequency sensor; the compensation weight is dynamically adjusted through the time when the chip receives the data of the sensor; processing the multi-modal data stream by using dual-channel deep network fusion, and calculating the shielding rate of a visual sensor image and the deviation degree between the current frame density and the historical average density of the laser radar point cloud; shielding and failure are distinguished through cross-modal verification; the out-of-control probability of the unmanned vehicle is obtained based on a dynamic Bayesian network; and formulating a corresponding hierarchical response strategy according to the risk level of the risk decision engine, and mapping a signal of the hierarchical response strategy to a vehicle end executor by bypassing a software intermediate layer. According to the invention, low-delay and high-precision vehicle out-of-control pre-judgment and graded active safety control are realized.

The invention relates to the technical field of GIS basin-type insulator performance evaluation, and discloses a GIS basin-type insulator performance evaluation method and system, and the method comprises the steps: synchronously collecting multi-physical field monitoring data through an ultrahigh frequency UHF sensor, an ultrasonic sensor array, an SF6 gas decomposition product detection unit and a thermal infrared imager; performing preprocessing and feature extraction on the monitoring data to obtain a multi-dimensional feature parameter set; performing weighted fusion calculation on the feature parameter set based on a fusion model of combined weighting of an analytic hierarchy process (AHP) and an entropy weight method, and outputting a health state evaluation index; comparing the evaluation index with a preset threshold interval to determine a performance grade and sending out an early warning; and automatically generating an evaluation report containing historical trends and maintenance suggestions. According to the GIS basin-type insulator performance evaluation method and system, multi-source information fusion and intelligent evaluation are achieved, the accuracy and reliability of state evaluation are remarkably improved, and technical guarantee is provided for safe operation of a power grid.

The invention discloses a self-adaptive ultrahigh frequency partial discharge signal extraction device and method based on dual-mode noise reference, and relates to the technical field of communication, and the device comprises a dual-sensor cooperative collection system which comprises a directional ultrahigh frequency sensor group and a noise feature learning module; the self-adaptive noise elimination device is connected with the dual-sensor collaborative acquisition system; and the intelligent signal identification system is connected with the self-adaptive noise elimination device. An auxiliary sensor in the directional ultrahigh frequency sensor group is specially used for collecting environmental background noise, and the noise feature learning module is used for extracting a time-frequency domain feature spectrum of the background noise by using Hilbert-Huang transform, so that the device can know the feature change of the noise in real time. Meanwhile, a dynamic threshold value adjusting unit in the self-adaptive noise elimination device can automatically adjust a signal triggering threshold value according to the noise energy level, automatic tracking of noise changes is achieved, and the adaptability of the device to the complex noise environment is greatly improved.

The present invention relates to a high-frequency sensor suitable for short-range detection, comprising a first shell, a piezoelectric ceramic piece and a first matching layer. The piezoelectric ceramic piece is arranged in the first shell, and the piezoelectric ceramic piece is connected to one end face of the first matching layer. The first matching layer is embedded in the first shell, and the other end of the first matching layer protrudes out of the first end of the first shell. In the above-mentioned high-frequency sensor suitable for short-range detection, after the first matching layer is embedded in the first shell, on the one hand, the first matching layer is embedded and fixed in the first shell, and the entire high-frequency sensor suitable for short-range detection has a relatively stable structure; on the other hand, since the end of the first matching layer protrudes out of the first end of the first shell, the binding force of the first shell on the surrounding areas of the first matching layer can be reduced, which can greatly reduce the phenomenon of insufficient echo signals. That is, it can be suitable for short-range detection and has a good echo signal.

The invention relates to a high-precision sensing device capable of being used for GIS accidental partial discharge positioning and type diagnosis. The high-precision sensing device comprises an ultrahigh frequency sensor and a data acquisition system. The ultrahigh frequency sensor is responsible for capturing weak accidental partial discharge signals generated in the GIS equipment; the captured original signal is then sent to a filtering and amplifying module and a synchronization module for hardware preprocessing, and is digitalized through a high-speed sampling assembly; the digitized mass data are transmitted to the high-performance GPU parallel computing unit, and the data are efficiently analyzed by utilizing the strong parallel processing capability of the high-performance GPU parallel computing unit. According to the invention, the equipment sampling rate is improved to a high sampling rate of 5GHz, so that the partial discharge pulse sampling interval of the GIS partial discharge monitoring equipment reaches 0.2 ns, and the sampling points and the data volume are greatly increased; gPU parallel computing is combined and utilized to greatly improve the pulse processing capacity, so that the system can effectively find and capture intermittent and accidental partial discharge signals, and the detection rate of the intermittent and accidental signals is remarkably improved.

The invention provides a self-adaptive denoising method based on Meyer wavelets and an improved smooth threshold function in order to solve the problem that a transformer partial discharge high-frequency current sensor field calibration signal is seriously polluted by field electromagnetic noise interference. The method comprises the following steps: firstly, arranging a standard sensor according to an optimal cross-core distance to obtain an original noisy signal; then adaptively determining a decomposition series according to a data length, selecting Meyer wavelets to perform multi-scale decomposition, performing smooth contraction on wavelet coefficients by adopting a continuously derivable, progressive and unbiased improved threshold function, and finally reconstructing a high signal-to-noise ratio verification signal through wavelet inverse transformation; the method does not need manual intervention of the threshold value, keeps orthogonality and high-order derivability in the whole process, and is beneficial to improving the accuracy of the verification result of the high-frequency sensor to be tested.

The utility model belongs to the field of GIS partial discharge detection, and particularly relates to an optical fiber ultrasonic-ultrahigh frequency composite sensor for GIS partial discharge detection, which comprises a cover plate, an ultrasonic sensor and an ultrahigh frequency sensor, the ultrasonic sensor comprises an induction optical fiber, an input connector and an output connector, the cover plate is provided with an annular groove, the induction optical fiber is fixed in the annular groove, and the output connector is fixed in the annular groove. Two ends of the sensing optical fiber are respectively connected with the input connector and the output connector, the input connector is used for inputting an external optical signal into the sensing optical fiber, and the output connector is used for outputting the optical signal in the sensing optical fiber to external detection equipment; the ultrahigh frequency sensor comprises an inductive probe, a pressing ring, a base and an N-type connector, the base is fixed on the cover plate, the pressing ring and the inductive probe are installed on the base, the inductive probe is mushroom-shaped and comprises a head part and a connecting part, and the connecting part sequentially penetrates through the pressing ring, the base and the cover plate and is electrically connected with the N-type connector; the problems that an existing composite sensor is low in measurement precision and sensitivity and poor in anti-interference capacity are solved.

This invention provides a partial discharge detection method based on the insertion depth constraint of an ultra-high frequency (UHF) sensor. The method includes: obtaining the physical parameters of an external UHF sensor; detecting the return loss curve of the external UHF sensor in a laboratory environment using a GTEM chamber; calculating the minimum insertion depth of the external UHF sensor at the joint based on the physical parameters of the transformer equipment; calculating the maximum allowable insertion depth of the external UHF sensor based on the internal structural dimensions and insulation safety requirements of the transformer; determining the effective insertion depth range of the external UHF sensor; and measuring the partial discharge signal of the transformer. This invention utilizes a formula to calculate the reasonable insertion depth range of the external UHF sensor at the transformer tank joint, thereby solving the problems of installation depth dependence on experience and poor detection consistency in existing technologies.

A transformer partial discharge positioning detection method based on ultrahigh frequency signals is characterized by comprising the following steps: S1, estimating time delay of signals obtained by an ultrahigh frequency (UHF) sensor array based on a generalized cross-correlation method; and S2, arranging and combining signals of the UHF sensor array, and solving coordinates of a positioning point based on an improved multi-moth optimization (MFO) algorithm according to the time delay obtained in the step S1. And S3, clustering the coordinates of the plurality of positioning points obtained in the step S2 based on a density clustering algorithm (DBSCAN), and selecting the geometric center position of the category with the maximum sample number as the final local discharge source coordinate.