190 results about "High frequency transducer" patented technology

The invention proposes a GIS equipment partial discharge mode recognition method based on deep learning, and relates to the technical field of GIS partial discharge recognition, and the method comprises the steps: collecting a partial discharge signal of GIS equipment through an ultrahigh frequency sensor, and carrying out the signal conditioning; performing digital sampling and processing on the conditioned partial discharge signal to generate a phase-resolved pulse sequence (PRPS) map, and performing intelligent noise reduction processing on the PRPS map, the PRPS map including three-dimensional information of phase, amplitude and frequency; inputting the PRPS atlas into a preset neural network model, and extracting atlas features; and identifying and classifying the partial discharge type based on the extracted map features, and outputting an identification result. According to the method, automatic acquisition, intelligent processing and accurate identification of the partial discharge signals are realized, and compared with a traditional manual identification method, the identification efficiency and accuracy are greatly improved, and reliable technical support is provided for state monitoring and fault diagnosis of GIS equipment.

A GIS partial discharge defect category intelligent identification method fusing optical and electrical characteristics comprises the following steps: acquiring an optical signal of partial discharge through an optical detection system, and extracting light intensity and spectral distribution characteristics; meanwhile, an ultrahigh frequency (UHF) sensor is used for collecting electromagnetic wave signals, and time domain and frequency domain characteristics of the electromagnetic wave signals are extracted; de-noising the small optical signal by adopting spatial filtering, and processing an electromagnetic wave signal by adopting a wavelet de-noising method; fusing the denoised signals by using a multi-modal deep neural network; splicing the two types of features and carrying out dimensionality reduction through a principal component analysis method to obtain a fusion feature vector; and finally, constructing a dual-channel CNN model for training and evaluation, optimizing the model by using a cross entropy loss function and an Adam optimizer, adjusting hyper-parameters and a network structure through a verification set, improving the generalization ability of the model, and finally obtaining a GIS partial discharge defect category classification model. According to the invention, precise and intelligent identification of GIS partial discharge defects is realized.

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 relates to the field of partial discharge source positioning, in particular to a large-scale substation multi-partial discharge source positioning method and system, and the method comprises the steps: forming a sensor array through a movable ultrasonic sensor and an ultrahigh frequency sensor, and collecting ultrasonic signals and ultrahigh frequency signals generated by partial discharge sources respectively; denoising the collected discharge signals, and calculating the distance between the partial discharge source and the sensor array; denoised ultrasonic array signals are divided into a plurality of narrowband signals by discrete Fourier transform, weighted covariance matrixes of all narrowband signals after focusing transformation are subjected to mean value calculation to obtain weighted covariance matrixes of focusing frequency points, and the weighted covariance matrixes of the focusing frequency points are subjected to unitary transformation to obtain a weighted covariance matrix of a plurality of narrowband signals. Estimating the number of partial discharge sources according to a signal source number estimation equation; and carrying out spectrum peak search in a specified space parameter range according to a preset step length to obtain a space parameter corresponding to each partial discharge source, and obtaining a partial discharge source coordinate in a three-dimensional space.

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 partial discharge positioning method based on multichannel signal processing, and relates to the field of fault diagnosis of high-voltage electrical equipment, and the method comprises the steps: S1, collecting multichannel electromagnetic wave signals generated by partial discharge through a plurality of ultrahigh frequency sensors distributed in GIS equipment; s2, preprocessing the collected electromagnetic wave signal, including noise suppression and time difference calculation; s3, based on the time difference and amplitude information of the multi-channel electromagnetic wave signals, calculating the position of a partial discharge source by adopting a fusion algorithm and combining a double-end positioning formula; and S4, displaying the position and the discharge intensity of the partial discharge source through the monitoring host, and generating a related diagnosis report. According to the invention, precise positioning of the partial discharge source in the GIS equipment can be realized, so that the positioning precision and the anti-interference capability are improved, and safe operation and maintenance of the GIS equipment are ensured.

The invention provides a high-voltage circuit breaker partial discharge monitoring and early warning method and system, and relates to the technical field of partial discharge monitoring, and the method comprises the steps: obtaining the feature information of a target circuit; a circuit analysis model is called for analysis, and a target analysis result is obtained and comprises a plurality of target point locations; dynamically monitoring the first target point location through an ultrahigh frequency sensor to obtain first partial discharge information; calling a signal analysis model for processing and analysis to obtain a first signal feature set; activating a fault diagnosis model to analyze the first signal feature set to obtain a first diagnosis tag; and when the partial discharge fault of the target high-voltage circuit breaker at the first target point location is displayed, a first early warning signal is sent out. The technical problems that in the prior art, partial discharge monitoring mostly depends on artificial experience or fixed rules, intelligent analysis means are lacked, misjudgment or missed judgment is prone to occurring, accurate positioning is difficult, and the safety of the high-voltage circuit breaker is reduced are solved.

The invention relates to a power transformer with an active anti-deflagration function and a monitoring method. The power transformer comprises a transformer oil tank, an ascending flanged base, a high-voltage bushing, a short-circuit copper sheet, a neutral point bushing, a high-frequency sensor and an ultrasonic sensor. The ascending flanged base and the neutral bushing are connected to the top of the transformer oil tank; one end of the high-voltage bushing is connected with the ascending flanged base through a bolt; two ends of the short-circuit copper sheet are respectively connected with two ends of any bolt; the neutral point bushing is grounded through a grounding wire; and the high-frequency sensor is respectively connected in series with the short-circuit copper sheet and the grounding wire. The ultrasonic sensors are arranged in the ascending flanged base and the transformer oil tank respectively, and the ultrasonic sensors in the transformer oil tank are distributed on the upper portion, the middle portion and the lower portion of the transformer oil tank. According to the power transformer, two kinds of partial discharge signals at different positions are collected, whether discharge occurs at different positions in the transformer or not is accurately judged in combination with a logic judgment method, the severity degree is judged, and therefore early warning or tripping instructions are accurately output at the initial stage of a fault to remove the fault, and operation safety is ensured.

The invention discloses a direct-current GIS (gas insulated switchgear) partial discharge detection method, which comprises the following steps: acquiring multi-modal sensing data in a direct-current GIS, the multi-modal sensing data comprising any two or more of current sensor data, optical sensor data, ultrasonic sensor data, infrared sensor data and ultrahigh-frequency sensor data; the collected multi-mode sensing data is preprocessed; performing feature extraction on the preprocessed multi-modal sensing data by using a deep learning model; and synthesizing the extracted features by using data fusion to obtain partial discharge information consistency explanation of the multi-modal sensing data. According to the method, multiple partial discharge detection methods can be comprehensively configured according to the requirements of the operation condition of the direct current GIS, so that the partial discharge condition of the direct current GIS can be more effectively evaluated.

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 provides an active suppression method and system for partial discharge ultrahigh frequency signal background noise. The method comprises the following steps: determining the arrangement positions of an ultrahigh frequency sensor and a noise sensor on the outer wall of a GIS (Gas Insulated Switchgear); collecting a partial discharge signal of the mixed noise through an ultrahigh frequency sensor; after the minimum collection period of the noise signals is determined, the noise signals are collected through a noise sensor; inputting the collected noise signal into a spectrum analyzer for spectrum analysis; extracting an obvious narrow-band noise frequency band in a frequency domain, and determining the frequency band and phase jitter of the narrow-band noise frequency band; and active suppression of the narrow-band noise is carried out on the frequency band with the narrow-band noise through the suppression formula of the narrow-band noise interference. According to the invention, the technical problem that the accuracy and the reliability of interference suppression are restricted due to poor narrow-band interference applicability of partial discharge ultrahigh-frequency signals of GIS equipment in a field environment is solved.

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 provides an ultrahigh frequency sensor performance attenuation upper limit verification method considering environmental influence. The method comprises the following steps: injecting a discharge signal at a sensor adjacent to a sensor to be tested by using a standard sensor; calculating the performance attenuation upper limit of the sensor under the humidity according to the local actual relative humidity; receiving a partial discharge signal by using a to-be-tested sensor; and acquiring partial discharge signal data received by the sensor to be tested, determining that the sensor to be tested has a performance fault when the signal attenuation amplitude is higher than the performance attenuation upper limit, and needing to perform verification. The sensor verification method suitable for different on-site environments is considered, and the accuracy of sensor verification 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 a method and a device for identifying surface flashover of an internal insulating part of a high-voltage tank type circuit breaker, and relates to the technical field of high-voltage test and detection. The method comprises the following steps: S1, additionally arranging a built-in ultrahigh frequency sensor on a cover plate of the high-voltage tank type circuit breaker; s2, acquiring an electromagnetic pulse signal in the high-voltage tank-type circuit breaker by using a built-in ultrahigh-frequency sensor, and extracting signal characteristics of the electromagnetic pulse signal; and S3, comparing the signal feature of the extracted electromagnetic pulse signal with the first standard feature of the electromagnetic pulse signal of partial discharge and the second standard feature of the electromagnetic pulse signal of surface flashover of the insulating part, and judging whether the extracted electromagnetic pulse signal is generated by surface flashover or not. According to the method and the device, whether the surface flashover occurs on the internal insulating part or not can be effectively judged in the predelivery test and on-site handover test processes of the high-voltage tank type circuit breaker.

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 utility model relates to a GIS equipment partial discharge intelligent positioning detection device based on an ultrahigh frequency detection method. The device is mainly composed of an ultrahigh frequency sensor, a coaxial cable, an electric power equipment partial discharge diagnosis positioning device and an electric power equipment partial discharge diagnosis positioning software operation platform. The device detects partial discharge defects and other information in GIS equipment through a built-in or external high-precision sensor, the information is transmitted to the electrical equipment partial discharge diagnosis and positioning device through a coaxial cable, data analysis is carried out, then the data is transmitted to electrical equipment partial discharge diagnosis and positioning software for analysis and processing, and waveform features are extracted. The partial discharge detection device has the advantages of real-time detection, accurate positioning, live detection, portability, detachability, simple operation, strong anti-interference capability and the like, improves the working efficiency of the partial discharge detection device and the service life of GIS equipment, and provides guarantee for maintaining safe and stable operation of an electric power system.

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.