Metal oxide arrester leakage current on-line monitoring system and method

Abstract

The invention specifically relates to a metal oxide arrester leakage current on-line monitoring system and method, and relates to the technical field of power equipment monitoring, and the system comprises a multi-physical field sensing array module; a synchronization and data aggregation gateway module; the central analysis server is connected with the fusion module; and a multi-dimensional state evaluation and decision module. According to the invention, through the collaborative design of the electrical, acoustic and thermal three-dimensional sensing units, the problems of early defect misjudgment and interference signal misjudgment caused by the fact that traditional MOA monitoring only depends on power frequency parameters are solved; nanosecond-level partial discharge pulses are captured in the electrical dimension; the acoustic dimension realizes three-dimensional positioning of defects; the thermal dimension is combined with an environment compensation algorithm, so that tiny temperature rise can be identified; and in cooperation with triple cross validation of a space-time correlation algorithm, internal defects and external interference are effectively distinguished.

Description

An online monitoring system and method for leakage current of metal oxide surge arresters Technical Field

[0001] This invention relates to the field of power equipment monitoring technology, and in particular to an online monitoring system and method for leakage current of metal oxide surge arresters. Background Technology

[0002] Metal oxide surge arresters (MOAs) are critical devices in power systems for suppressing lightning overvoltages and switching overvoltages, and their operating status directly affects the safety and stability of the power system. During long-term operation, MOAs are susceptible to defects such as aging of the resistor elements, moisture absorption, partial discharge, and structural loosening due to environmental factors such as electric field, temperature, and humidity. If these defects are not detected and addressed in a timely manner, they may lead to MOA failure, causing damage to power equipment or even large-scale power outages.

[0003] Existing MOA monitoring technologies mostly focus on monitoring conventional parameters in a single dimension, such as judging the insulation status of the MOA solely by monitoring power frequency resistive current. These technologies have significant limitations: traditional power frequency resistive current monitoring cannot capture high-frequency transient pulse signals generated by partial discharges within the MOA, making it difficult to detect partial discharge defects at an early stage; single-dimensional monitoring data lacks correlation, cannot effectively distinguish between internal defects and external interference, is prone to misjudgment, and is difficult to achieve precise spatial location of defects.

[0004] Therefore, an online monitoring system and method for leakage current of metal oxide surge arresters is proposed to address the aforementioned problems. Summary of the Invention

[0005] The purpose of this invention is to provide an online monitoring system and method for leakage current of metal oxide surge arresters in order to solve the above-mentioned problems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an online monitoring system for leakage current of a metal oxide surge arrester (MOA), comprising: a multi-physics sensing array module configured to capture characteristic signals of internal defects of the MOA from three dimensions—electrical, acoustic, and thermal—through a dedicated monitoring unit; a synchronization and data aggregation gateway module configured to provide a unified high-precision time reference and complete the synchronous sampling, preprocessing, and alignment of multi-source signals; a central analysis server and fusion module configured to perform MOA defect type discrimination and spatial positioning based on a spatiotemporal correlation algorithm; and a multi-dimensional state assessment and decision-making module configured to output anomaly assessment coefficients to match defect levels based on spatiotemporal correlation analysis results, combined with fault type discrimination rules and quantitative assessment models, and perform corresponding processing based on the defect levels.

[0007] Preferably, the multi-physics sensing array module specifically includes: an electrical dimension: based on a broadband current sensor, acquiring corresponding data through conventional monitoring channels and pulse capture channels to output the effective value of power frequency resistive current, full-cycle current waveform, partial discharge pulse waveform, pulse count rate, pulse occurrence timestamp, and voltage phase; an acoustic dimension: arranging three acoustic emission sensors on the outer surface of the MOA bushing at the top, middle, and bottom; if the MOA height exceeds 3m, adding two more sensors at the upper middle and lower middle sections to form a 5-point array; and obtaining the original acoustic emission waveform, characteristic parameters, and sound source arrival time at each sensor based on the acquired data. The time difference and sensor operating status are considered. The thermal dimension involves acquiring infrared energy radiated from the MOA surface through infrared detection components, a gimbal mechanism, and environmental compensation sensors, converting it into electrical signals, and generating a thermal image. The environmental compensation algorithm, based on the Stefan-Boltzmann law, combines ambient temperature, humidity, and wind speed parameters to correct for atmospheric attenuation and surface emissivity affecting the temperature measurement results. A temperature difference map is generated by comparing the temperature with historical data from the same period, highlighting areas of abnormal temperature rise. The final output includes an infrared thermal image of the MOA surface, maximum / minimum temperature values ​​and coordinates, a temperature gradient distribution curve, the area and temperature rise value of areas of abnormal temperature rise, a comparison map of historical temperatures under the same operating conditions, and environmental parameters.

[0008] Preferably, the synchronization and data aggregation gateway module specifically includes: a GPS / BeiDou module receiving satellite signals, generating a clock signal, and distributing the clock signal to the current monitoring unit, the acoustic emission sensor array, and the infrared thermal imaging unit via optical fiber or Ethernet; each unit calibrating its local sampling clock based on the clock signal; and the signals from each sensing unit being preprocessed and then connected to the synchronization acquisition card, which starts sampling under a unified clock beat, converts analog signals into digital signals, and adds a unified timestamp to each frame of data.

[0009] Preferably, the central analysis server and fusion module specifically include: performing noise reduction and enhancement processing on the synchronized raw data, and extracting key feature parameters that can characterize the defect state of MOA: current signal, including power frequency parameter extraction and partial discharge feature extraction; acoustic emission signal, including time domain feature extraction, frequency domain feature extraction and time difference extraction; infrared temperature signal, including temperature field feature extraction.

[0010] Preferably, the process further includes spatiotemporal correlation analysis: calculating the correlation coefficient between the current pulse and the acoustic emission signal within the same time window; if the correlation coefficient is ≥ a set threshold, and the phase characteristics of the current pulse and the energy characteristics of the acoustic emission signal meet preset conditions, then it is determined to be a valid partial discharge event; the current pulse characteristics and acoustic emission characteristics of the valid partial discharge event are combined to form a... Fingerprints are matched with a pre-defined defect fingerprint database to initially determine the discharge type. Linear regression or exponential regression models are used to fit the resistive current change curve and the temperature rise change curve in the abnormal temperature rise region, respectively. The Pearson correlation coefficient between the two curves is calculated. If the correlation coefficient is greater than or equal to the value corresponding to a pre-defined strong positive correlation, and the absence of synchronous acoustic emission signal or the correlation between acoustic emission signal and current signal is less than a pre-defined threshold, it is determined that the power loss is increased due to aging or moisture in the resistor element. If the resistive current increase precedes the temperature rise change, and the lag time is less than or equal to a pre-defined duration, it is further verified that the internal insulation performance has degraded.

[0011] Preferably, the process further includes spatial positioning and correlation analysis: based on the principle of spherical wave propagation, a set of positioning equations is established, and the weighted least squares method is used to solve the set of equations, with the weight coefficients set based on the signal amplitude of each sensor; the three-dimensional coordinates obtained from positioning are converted into relative coordinates of the MOA body; the spatial coordinates of the defects located by acoustic emission are converted into pixel coordinates of the infrared thermogram, and a mapping relationship between the three-dimensional spatial coordinates and the pixel coordinates of the thermogram is established; if the pixel coordinates of the thermogram corresponding to the acoustic emission positioning point fall within the abnormal temperature rise area, then the acoustic-thermal correlation is completed; if a valid partial discharge event is detected at the location corresponding to the acoustic-thermal correlation, then the electrical-acoustic-thermal triple verification is completed, confirming that there is a serious internal defect at that location.

[0012] Preferably, the multi-dimensional state assessment and decision-making module specifically includes: obtaining the average partial discharge amount within a preset time window, setting a threshold for the average partial discharge amount, subtracting the threshold for the average partial discharge amount from the average partial discharge amount, and retaining the value greater than 0 if the result is greater than 0, and marking it as an abnormal partial discharge value; obtaining the abnormal partial discharge values ​​for each time window, and arranging the obtained abnormal partial discharge values ​​in descending order of numerical value to obtain a set of abnormal partial discharge values; extracting the three largest abnormal partial discharge values ​​from the set, and using the two largest abnormal partial discharge values ​​as one side and the height perpendicular to the side of a triangle to construct a triangle; then using the smallest abnormal partial discharge value in the set of abnormal partial discharge values ​​as the height of the triangle to construct a triangular pyramid, calculating the volume of the triangular pyramid, and obtaining the abnormal discharge quantification value.

[0013] Preferably, the method further includes: calculating the center coordinates of the multiple positioning coordinates based on the three-dimensional spatial coordinates of the defect obtained by multiple positioning by the acoustic emission sensor array; obtaining the spatial distance from each positioning coordinate to the center coordinate; calculating the standard deviation of each distance, taking the reciprocal of the standard deviation, and recording it as the anomaly concentration; dividing the abnormal temperature rise region using the region growing method: taking the pixel with temperature > overall average temperature + 0.5℃ as the seed point, expanding to 8 neighborhoods until the temperature of the neighboring pixels is lower than the threshold; obtaining the average temperature of all pixels in the abnormal region and the average temperature of all pixels on the entire MOA surface, calculating the difference between the two average temperatures, and recording it as the abnormal temperature rise value.

[0014] Preferably, the method further includes: normalizing the abnormal discharge quantification value, abnormal concentration, and abnormal temperature rise value, respectively, pre-setting weight factors for the abnormal discharge quantification value, abnormal concentration, and abnormal temperature rise value, and multiplying the abnormal discharge quantification value, abnormal concentration, and abnormal temperature rise value with their corresponding weight factors to obtain the abnormality evaluation coefficient.

[0015] A method for online monitoring of leakage current in a metal oxide surge arrester (MOA) includes: capturing characteristic signals of internal defects of the MOA from three dimensions (electrical, acoustic, and thermal) using a dedicated monitoring unit; providing a unified high-precision time reference to complete synchronous sampling, preprocessing, alignment, transmission, and storage of multi-source signals; performing noise reduction processing on the synchronized data and extracting key characteristic parameters from the electrical, acoustic, and thermal dimensions to provide data support for defect analysis; identifying defect types through PD fingerprint recognition and thermo-electric correlation analysis, and locking the spatial location of defects by combining acoustic emission three-dimensional localization and triple cross-validation; and obtaining an anomaly evaluation coefficient by calculating and weighting the abnormal discharge quantification value, abnormal concentration, and abnormal temperature rise value.

[0016] In summary, due to the adoption of the above technical solutions, the beneficial effects of this invention are as follows: 1. This invention solves the problem of early defect omission and interference signal misjudgment caused by traditional MOA monitoring relying solely on power frequency parameters through the collaborative design of electrical, acoustic, and thermal sensing units; it captures nanosecond-level partial discharge pulses in the electrical dimension; it achieves three-dimensional defect localization in the acoustic dimension; it can identify minute temperature rises by combining environmental compensation algorithms in the thermal dimension; and it effectively distinguishes between internal defects and external interference by using triple cross-validation with spatiotemporal correlation algorithms.

[0017] 2. This invention constructs a quantitative evaluation system by using geometric modeling and statistical analysis. It converts discrete discharge data into abnormal discharge quantitative values ​​by calculating the volume of a triangular pyramid. Combined with indicators such as location concentration and temperature rise, it generates anomaly evaluation coefficients, which solves the subjective problem of traditional operation and maintenance relying on experience thresholds. The quantitative results can directly match the defect level, enabling operation and maintenance personnel to accurately grasp the severity and spatial location of defects, avoiding blind inspections or preventive replacements. Attached Figure Description

[0018] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: FIG1 is a system structure diagram of the present invention; FIG2 is a method flowchart of the present invention. Detailed Implementation

[0019] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0020] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0021] Example 1

[0022] The specific implementation method is described in detail with reference to Figures 1 and 2.

[0023] Figure 1 is a structural block diagram of an online monitoring system for leakage current of a metal oxide surge arrester provided in an embodiment of the present invention. It shows the connection relationship between the multi-physics sensing array module and the multi-dimensional state assessment and decision module, and marks the main functional interaction flow of each module.

[0024] Figure 2 is a flowchart of an online monitoring method for leakage current of a metal oxide surge arrester (MOA) according to an embodiment of the present invention, showing the complete steps from capturing the characteristic signals of internal defects of the MOA to obtaining the anomaly evaluation coefficient.

[0025] In this embodiment, MOA is an abbreviation for Metal-Oxide Arrester; the following content will refer to Metal-Oxide Arrester as MOA.

[0026] The multi-physics sensing array module is configured to accurately capture characteristic signals of internal defects in the MOA from three dimensions: electrical, acoustic, and thermal, through a dedicated monitoring unit, providing high-quality data for subsequent analysis. Specifically, it includes: a high-bandwidth leakage current monitoring unit: its core function is to break through the limitations of traditional power frequency resistive current monitoring, focusing on the ability to capture high-frequency transient currents, directly sensing nanosecond-level current pulse signals generated by partial discharge inside the MOA, while also taking into account conventional power frequency parameter monitoring, realizing the dual functions of conventional condition monitoring and fault feature capture.

[0027] Wideband current sensor: Type: Rogowski coil or flexible current sensor, offering non-contact measurement advantages to avoid affecting the normal operation of MOA; Bandwidth: DC-5MHz, ensuring coverage of the entire frequency band from power frequency (50Hz) to partial discharge pulses (several MHz); Accuracy parameters: Power frequency current measurement accuracy ≤ ±0.5%FS, pulse current measurement error ≤ ±3%, meeting quantitative analysis requirements; Anti-interference design: Built-in double-layer electromagnetic shielding cover, using twisted shielded wire for signal transmission to suppress electromagnetic field coupling interference on site.

[0028] High-speed data acquisition channels: Dual-channel independent design: Channel 1 (conventional monitoring channel) sampling rate 1kS / s-10kS / s, 16-bit sampling accuracy, used to acquire conventional parameters such as power frequency resistive current and capacitive current; Channel 2 (pulse capture channel) sampling rate ≥10MS / s, 12-bit sampling accuracy, supports continuous sampling or triggered sampling mode, specifically for capturing partial discharge pulses; Triggering mechanism: Employs threshold triggering and phase triggering dual modes; Threshold triggering allows setting pulse amplitude thresholds (e.g., 50pC corresponding to a current threshold), while phase triggering can lock a specific phase range of power frequency voltage (e.g., 0°-90°, 180°-270°) to avoid false triggering; Data cache: Built-in 16GB high-speed cache, capable of temporarily storing 1 hour of continuous pulse waveform data to prevent data loss.

[0029] The conventional monitoring channel converts the current signal sensed by the sensor into a voltage signal through an integrating circuit. After low-pass filtering, the signal is sampled to calculate parameters such as the effective value of the power frequency resistive current and harmonic content. The pulse capture channel monitors the current signal in real time. When a transient pulse exceeding the threshold is detected, high-speed sampling is immediately initiated to record the complete waveform of the pulse (rising edge, peak value, and falling edge), and the power frequency voltage phase at the moment of pulse occurrence is simultaneously marked. Electrical dimension: Based on a wideband current sensor, corresponding data is acquired through the conventional monitoring channel and the pulse capture channel to output the effective value of the power frequency resistive current, the full-cycle current waveform, the partial discharge pulse waveform (including amplitude, rise time ≤50ns, duration ≤1μs), the pulse count rate, the timestamp of the pulse occurrence, and the voltage phase. Multi-channel acoustic emission sensor array: Core function: Sensing ultrasonic signals (frequency range 100kHz-1MHz) generated inside the MOA due to faults such as partial discharge (electron avalanche, streamer discharge), resistor cracking, and loose internal structure, and realizing three-dimensional spatial positioning of the fault point based on the time difference analysis of the array layout.

[0030] Acoustic emission sensor: Type: Piezoelectric ceramic acoustic emission sensor, center frequency 150kHz-300kHz (matching the main frequency of the acoustic signal of the internal discharge of MOA); Performance indicators: sensitivity ≥80dB (reference sensor: R15α), resolution ≤10μV, operating temperature range -40℃-85℃, meeting the operating environment requirements of outdoor power equipment; Customized design: the sensor end face is made of ceramic material, and the acoustic impedance is matched and optimized with the MOA bushing to reduce acoustic signal reflection loss; built-in electromagnetic shielding layer (copper mesh, grounding terminal) to suppress electromagnetic interference generated by on-site GIS, transformers and other equipment.

[0031] Acoustic dimension: Three acoustic emission sensors are arranged on the outer surface of the MOA sleeve at the top (10cm from the top), middle (midpoint of the sleeve), and bottom (10cm from the bottom). If the MOA height exceeds 3m, two more sensors are added at the upper middle and lower middle, forming a 5-point array. High-temperature resistant silicone coupling agent is used for bonding and fixing, and a stainless steel protective shell (with reserved heat dissipation holes) is added to the outside to prevent wind, rain, and dust corrosion, while ensuring that the sensors are in close contact with the sleeve surface. Spacing design: The spacing between adjacent sensors is determined according to the MOA diameter (generally 30cm-50cm) to ensure that the time difference between the sound source signal arriving at each sensor is in the microsecond range (1μs-10μs), meeting the positioning accuracy requirements.

[0032] When a fault occurs inside the MOA, the generated ultrasonic signal propagates in the form of a spherical wave and reaches each sensor in the array in sequence. Each sensor converts the acoustic signal into an electrical signal, which is then amplified and transmitted to the data aggregation gateway, synchronously recording the arrival time of the signal from each channel (with an accuracy of 0.1 μs). Based on the time difference between each channel and the sound velocity in the MOA sleeve (approximately 5000 m / s in the ceramic sleeve and approximately 2500 m / s in the composite sleeve), the three-dimensional coordinates of the sound source are calculated using a spatial geometric algorithm.

[0033] Based on the collected data, the original waveforms, characteristic parameters, time difference of sound source to each sensor, and sensor working status of each channel acoustic emission are obtained; Miniature infrared thermal imaging unit: Core function: Non-contact acquisition of two-dimensional temperature field distribution on MOA surface, accurate identification of abnormal temperature rise areas caused by internal defects (such as increased power loss due to aging of resistor sheet, Joule heat generated by partial discharge), and elimination of interference from external factors such as ambient temperature, wind speed, and sunlight.

[0034] Infrared detection components: Detector type: uncooled focal plane microbolometer, resolution ≥320×240 pixels, pixel pitch ≤17μm; Temperature performance: temperature range -20℃-150℃, temperature accuracy ±2% or ±2℃ (maximum value), thermal sensitivity (NETD) ≤50mK, ensuring the detection of minute temperature rises (≥0.5℃); Optical system: focal length 12mm, field of view 25°×19°, capable of covering an entire MOA with a height ≤5m; The lens is made of germanium glass with an anti-reflection coating to reduce the influence of ambient light reflection.

[0035] Auxiliary Modules: Gimbal Mechanism: Employs a miniature gimbal driven by a stepper motor, with a horizontal rotation range of 0°-360°, a vertical rotation range of -10°-90°, and a positioning accuracy of ±0.1°. It supports automatic cruise scanning (scanning by top-middle-bottom partitions) or fixed-point monitoring; Environmental Compensation Sensors: Integrates a digital temperature sensor (accuracy ±0.1℃), a humidity sensor, and a wind speed sensor (range 0-30m / s, accuracy ±0.2m / s) to collect environmental parameters in real time; Protective Design: The entire structure uses an IP65-rated protective shell and has a built-in heating defrosting device to prevent lens fogging in low-temperature and high-humidity environments.

[0036] Thermal dimension: Infrared energy radiated from the MOA surface is acquired through infrared detection components, a gimbal mechanism, and environmental compensation sensors, converted into electrical signals, and generated into a thermal image. The environmental compensation algorithm is based on the Stefan-Boltzmann law and combines ambient temperature, humidity, and wind speed parameters to correct the influence of atmospheric attenuation and surface emissivity (MOA ceramic sleeve emissivity ≈ 0.85, composite sleeve ≈ 0.90) on the temperature measurement results. By comparing with historical temperature data of the same period (same load, same environmental conditions), a temperature difference map is generated to highlight abnormal temperature rise areas. The final output includes an infrared thermal image of the MOA surface (pseudo-color / grayscale), the highest / lowest temperature values ​​and coordinates, a temperature gradient distribution curve (unit: ℃ / cm), the area and temperature rise value of the abnormal temperature rise area (ΔT≥0.5℃), a comparison map of historical temperatures under the same operating conditions, and environmental parameters (temperature, humidity, wind speed).

[0037] The synchronization and data aggregation gateway module is configured to provide a unified high-precision time reference and complete the synchronous sampling, preprocessing, and alignment of multi-source signals. It is the hardware core for deep fusion of multiple information sources. Specifically, it includes: Clock synchronization process: The GPS / BeiDou module receives satellite signals, tames a high-stability temperature-controlled crystal oscillator (OCXO) to generate a high-precision 10MHz clock and PPS signal, and distributes the clock signal to the current monitoring unit, acoustic emission sensor array, and infrared thermal imaging unit through fiber optic or Ethernet. Each unit calibrates its local sampling clock based on this clock signal to ensure that the clock phase of all acquisition channels is consistent; Data acquisition process: The signals from each sensing unit are preprocessed... After processing, the data is connected to a synchronous acquisition card. The acquisition card starts sampling under a unified clock beat, converts analog signals into digital signals, and adds a unified microsecond-level timestamp to each frame of data. Data preprocessing process: The acquired digital signals are initially processed: the current signal is filtered using a moving average to remove power frequency interference, the acoustic emission signal is filtered using a bandpass filter (100kHz-1MHz) to separate the effective signal, and the infrared temperature data is subjected to bad pixel removal (based on neighborhood mean replacement). Data transmission and storage: The preprocessed data is transmitted to the central analysis server in real time via gigabit Ethernet, and is also backed up and stored on a local solid-state drive to ensure that the data is not lost.

[0038] The central analysis server and fusion module are configured to use a spatiotemporal correlation algorithm as the core for MOA defect type identification and spatial localization; specifically, this includes: denoising and enhancing the synchronized raw data, and extracting key feature parameters that characterize the MOA defect state: Current signal: a wavelet threshold denoising algorithm is used, selecting the db4 wavelet basis, decomposing into 5 layers, and using an improved threshold function for high-frequency coefficients (…). ,in The standard deviation of noise. Data length processing effectively suppresses noise such as power frequency harmonics and electromagnetic interference.

[0039] Power frequency parameter extraction: Fourier transform is performed on the noise-reduced power frequency current waveform to extract the fundamental (50Hz) resistive current RMS value, 3rd / 5th / 7th harmonic content, and total harmonic distortion (THD); the rate of change of resistive current is calculated using a sliding window (window size 1 minute); Partial discharge feature extraction: Time domain analysis is performed on the pulse waveform to extract pulse amplitude, rise time, fall time, pulse width, and peak factor (peak / RMS); Statistical analysis is performed on the pulse sequence to extract pulse count rate (times / minute), average discharge quantity, and discharge quantity distribution histogram; Spectral analysis (FFT) is performed on the pulse waveform to extract frequency domain features such as peak frequency, spectral centroid, and spectral width; Acoustic emission signal: An adaptive noise cancellation algorithm is used, with acoustic signals without detected faults as reference noise. Background noise is canceled in real time using an adaptive filter; envelope detection (Hilbert transform) is performed on the denoised signal to extract the envelope curve of the acoustic emission signal; time-domain feature extraction: parameters such as peak value, amplitude, energy (area under the envelope curve), count (number of pulses exceeding the threshold), duration, rise time, and fall time are extracted from the envelope curve; frequency-domain feature extraction: short-time Fourier transform (STFT) is performed on the original acoustic emission signal to obtain the time-frequency matrix, and parameters such as peak frequency, main frequency band energy ratio (energy in the 150kHz-300kHz band / total energy), and spectral entropy are extracted; time difference extraction: based on the peak time of the acoustic emission signal of each channel, the time difference of the sound source arriving at different sensors is calculated; infrared temperature signal: a temperature correction model is established based on ambient temperature, humidity, and wind speed parameters. ;in, , , The fitting coefficients (calibrated experimentally) are the fitting coefficients. For ambient temperature, Relative humidity, For wind speed; temperature field feature extraction: extract the highest temperature, lowest temperature, average temperature, and temperature range (the difference between the highest and lowest temperatures) from the compensated infrared thermogram; use the region growing method to segment the abnormal temperature rise area, and extract the area, coordinates of the region center, and average temperature rise value within the region; calculate the temperature gradient (temperature range / distance from the center of the temperature rise area to the normal area).

[0040] It also includes spatiotemporal correlation analysis: PD fingerprint recognition algorithm: Core logic: Partial discharge events simultaneously generate electrical pulses and acoustic emission signals, and the occurrence times of the two are fixedly correlated (the acoustic signal lags the electrical signal by ≤1μs, determined by the propagation time from the discharge region inside the MOA to the sensor); calculate the correlation coefficient between the current pulse and the acoustic emission signal within the same time window (±1μs). If the correlation coefficient ≥ a set threshold, and the phase characteristics of the current pulse (e.g., mainly concentrated near the voltage peak) and the energy characteristics of the acoustic emission signal meet preset conditions, it is determined to be a valid partial discharge event; the process of calculating the correlation coefficient between the current pulse and the acoustic emission signal includes: extracting the current pulse signal sequence and the acoustic emission signal sequence corresponding to the same partial discharge event within the same time window; calculating the mean of the current pulse sequence and the mean of the acoustic emission signal sequence respectively; calculating the correlation coefficient using the Pearson correlation coefficient formula; the above correlation coefficient calculation process is a direct reference to existing technology, therefore the details of each step are not explained; combining the current pulse characteristics (amplitude, phase distribution) and acoustic emission characteristics (peak frequency, energy) of the valid partial discharge event to form Fingerprints are matched with a pre-defined defect fingerprint database (such as moisture discharge, insulation aging discharge, and metal particle discharge) to preliminarily determine the discharge type. A thermo-electric correlation analysis algorithm is used: linear regression or exponential regression models are used to fit the resistive current change curve and the temperature rise curve of the abnormal temperature rise region, respectively. The Pearson correlation coefficient between the two curves is calculated. If the correlation coefficient is greater than or equal to the pre-defined value corresponding to a strong positive correlation, and there is no synchronous acoustic emission signal or the correlation between the acoustic emission signal and the current signal is less than a pre-defined threshold, then it is determined that the power loss is increased due to aging or moisture in the resistor element. If the resistive current increase precedes the temperature rise change, and the lag time is less than or equal to a pre-defined duration (72 hours), it is further verified as internal insulation performance degradation (aging / moisture), because increased resistor element loss gradually leads to heat accumulation and temperature rise.

[0041] Acoustic emission three-dimensional positioning algorithm: Based on the principle of spherical wave propagation, a set of positioning equations is established: ; , For the number of sensors; Let be the coordinates of the sound source, and be the th . Installation coordinates of each sensor (pre-calibrated) For the speed of sound (ceramic sleeve) =5000m / s, composite sleeve =2500m / s), For the sound source to reach the first The time of each sensor; the weighted least squares method is used to solve the equation system, and the weight coefficients are set based on the signal amplitude of each sensor (the larger the amplitude, the higher the weight), reducing the positioning error caused by signal attenuation; the calculation process is a direct reference to existing technology and will not be elaborated here; the three-dimensional coordinates obtained from the positioning are... Convert the coordinates of the MOA body to relative coordinates (e.g., height h from the bottom, circumferential angle θ) to facilitate maintenance personnel in identifying defect locations; convert the spatial coordinates of the defect located by acoustic emission into pixel coordinates of the infrared thermal image, establishing a mapping relationship between three-dimensional spatial coordinates and thermal image pixel coordinates (based on MOA geometry and infrared camera installation angle calibration); cross-validation logic: Level 1 validation: if the thermal image pixel coordinates corresponding to the acoustic emission location point fall within the abnormal temperature rise area, then the acoustic-thermal correlation is completed; Level 2 validation: if a valid partial discharge event is detected at the same location ( If fingerprint matching is successful, the electrical-acoustic-thermal triple verification is completed, confirming that there is a serious internal defect at that location; anomaly elimination: if there is only a temperature rise or only an acoustic emission signal without a synchronous electrical signal, it is determined to be an external factor (such as surface dirt or external collision) to avoid misjudgment.

[0042] The multi-dimensional state assessment and decision-making module is configured to output anomaly assessment coefficients based on spatiotemporal correlation analysis results, combined with fault type discrimination rules and quantitative assessment models, to match defect levels and perform corresponding processing based on defect levels. Specifically, when a serious internal defect is confirmed to exist in a certain part of the MOA after secondary verification, the following analysis is performed: the average partial discharge quantity within a preset time window is obtained, a preset threshold for the average partial discharge quantity is set, the average partial discharge quantity is subtracted from the threshold, and if the resulting value is greater than 0, it is retained and marked as an abnormal partial discharge value; the abnormal partial discharge values ​​of each time window are obtained, and the obtained abnormal partial discharge values ​​are sorted in descending order of numerical value to obtain a set of abnormal partial discharge values; the three largest abnormal partial discharge values ​​are extracted from the set, and the two largest abnormal partial discharge values ​​are used as a side and the height perpendicular to the side of a triangle to construct a triangle; then the smallest abnormal partial discharge value in the set is used as the height of the triangle to construct a triangular pyramid, and the volume of the triangular pyramid is calculated to obtain the abnormal discharge quantification value.

[0043] The fuzzy state of MOA partial discharge is transformed into a precise quantitative indicator. Valid abnormal discharge data is extracted through threshold screening. Combined with geometric modeling (triangles and triangular pyramids), the discrete partial discharge quantity is transformed into a concrete abnormal discharge quantitative value. This not only avoids the randomness of single-point-of-time data, but also realizes an intuitive characterization of the defect discharge intensity, providing a calculable and comparable core basis for subsequent evaluation.

[0044] By sorting and extracting features from outliers across multiple time windows, the quantified values ​​can reflect the dynamic development trend of the discharge phenomenon. Furthermore, the calculation logic of the triangular pyramid volume cleverly integrates the extreme value characteristics of the discharge intensity, making the quantified results both highlight the core discharge information and take into account the data integrity. This provides solid support for accurately matching defect levels and formulating targeted processing strategies.

[0045] Based on the three-dimensional spatial coordinates of the defect obtained from multiple localizations using an acoustic emission sensor array, the center coordinates of the multiple localization coordinates are calculated: , To retain the required number of positioning coordinates, at least 5 iterations are needed; calculate the spatial distance from each positioning coordinate to the center coordinate: The standard deviations of each distance (i.e., the quantified values ​​of defect location concentration) were calculated as follows: ; For all The average value is calculated; the reciprocal of the standard deviation is taken as the anomaly concentration; the abnormal temperature rise area is divided using the region growing method: the pixel with temperature > overall average temperature + 0.5℃ is used as the seed point, and it is expanded to the 8-neighborhood until the temperature of the neighboring pixels is lower than the threshold; the average temperature of all pixels in the abnormal area and the average temperature of all pixels on the entire MOA surface are obtained, and the difference between the two average temperature values ​​is calculated and recorded as the abnormal temperature rise value.

[0046] After normalizing the abnormal discharge quantification value, abnormal concentration, and abnormal temperature rise value, weight factors for the abnormal discharge quantification value, abnormal concentration, and abnormal temperature rise value are preset respectively. The abnormal discharge quantification value, abnormal concentration, and abnormal temperature rise value are multiplied with their corresponding weight factors to obtain the abnormality evaluation coefficient.

[0047] Example 2

[0048] Please refer to Figure 2. An online monitoring method for leakage current of a metal oxide surge arrester (MOA) includes the following components: Accurately capturing characteristic signals of internal defects in the MOA from three dimensions—electrical, acoustic, and thermal—through a dedicated monitoring unit, providing high-quality data for subsequent analysis; providing a unified, high-precision time reference to complete synchronous sampling, preprocessing, alignment, transmission, and storage of multi-source signals, thus solidifying the hardware foundation for deep fusion of multiple information sources; denoising the synchronized data and extracting key characteristic parameters from the electrical, acoustic, and thermal dimensions to provide data support for defect analysis; identifying defect types through PD fingerprint recognition and thermal-electric correlation analysis, and accurately locating the spatial position of defects by combining acoustic emission three-dimensional positioning and triple cross-validation; and obtaining anomaly evaluation coefficients by calculating and weighting abnormal discharge quantification values, abnormal concentration, and abnormal temperature rise values ​​to match defect levels and provide corresponding processing solutions.

[0049] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0050] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0051] It should be noted that, in this document, the use of relational terms such as "first" and "second" is merely for distinguishing one entity or operation from another, and does not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0052] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0053] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0054] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0055] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0056] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0057] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0058] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An online monitoring system for leakage current of a metal oxide surge arrester, characterized in that, include: The multi-physics sensing array module is configured to capture characteristic signals of internal defects of MOA from three dimensions: electrical, acoustic and thermal, through a dedicated monitoring unit. The synchronization and data aggregation gateway module is configured to provide a unified high-precision time base and complete the synchronous sampling, preprocessing and alignment of multi-source signals; The central analysis server and fusion module are configured to use a spatiotemporal correlation algorithm as the core to perform MOA defect type identification and spatial localization; the multi-dimensional state assessment and decision module is configured to output anomaly assessment coefficients to match defect levels based on spatiotemporal correlation analysis results, combined with fault type identification rules and quantitative assessment models, and perform corresponding processing based on defect levels.

2. The online monitoring system for leakage current of a metal oxide surge arrester according to claim 1, characterized in that, The multi-physics sensing array module specifically includes: Electrical dimension: Based on a broadband current sensor, it acquires relevant data through conventional monitoring channels and pulse capture channels to output the effective value of the power frequency resistive current, the full-cycle current waveform, the partial discharge pulse waveform, the pulse count rate, and the timestamp and voltage phase of the pulse occurrence; Acoustic dimension: Three acoustic emission sensors are arranged on the outer surface of the MOA bushing at the top, middle, and bottom. If the MOA height exceeds 3m, two more sensors are added at the upper middle and lower middle sections, forming a 5-point array; Based on the acquired data, the original acoustic emission waveform, characteristic parameters, and the time it takes for the sound source to reach each sensor are obtained for each channel. The system analyzes the temperature difference between the MOA surface and the sensor's operating status. The thermal dimension involves acquiring infrared energy radiated from the MOA surface through an infrared detection component, a gimbal mechanism, and an environmental compensation sensor. This energy is converted into electrical signals and used to generate a thermal image. The environmental compensation algorithm, based on the Stefan-Boltzmann law, incorporates ambient temperature, humidity, and wind speed parameters to correct for atmospheric attenuation and surface reflectivity affecting the temperature measurement results. A temperature difference map is generated by comparing the temperature with historical data from the same period, highlighting areas of abnormal temperature rise. The final output includes an infrared thermal image of the MOA surface, the highest / lowest temperature values ​​and coordinates, a temperature gradient distribution curve, the area and temperature rise of areas with abnormal temperature rises, a comparison map of historical temperatures under the same operating conditions, and environmental parameters.

3. The online monitoring system for leakage current of a metal oxide surge arrester according to claim 1, characterized in that, The synchronization and data aggregation gateway module specifically includes: a GPS / BeiDou module that receives satellite signals, generates a clock signal, and distributes the clock signal to the current monitoring unit, acoustic emission sensor array, and infrared thermal imaging unit via optical fiber or Ethernet. Each unit calibrates its local sampling clock based on this clock signal. The signals from each sensing unit are preprocessed and then connected to the synchronization acquisition card. The acquisition card starts sampling under a unified clock beat, converts analog signals into digital signals, and adds a unified timestamp to each frame of data.

4. The online monitoring system for leakage current of a metal oxide surge arrester according to claim 1, characterized in that, The central analysis server and fusion module specifically include: noise reduction and enhancement processing of the synchronized raw data, and extraction of key feature parameters that can characterize the defect state of MOA: current signal, including power frequency parameter extraction and partial discharge feature extraction; acoustic emission signal, including time domain feature extraction, frequency domain feature extraction and time difference extraction; infrared temperature signal, including temperature field feature extraction.

5. The online monitoring system for leakage current of a metal oxide surge arrester according to claim 4, characterized in that, It also includes spatiotemporal correlation analysis: calculating the correlation coefficient between the current pulse and the acoustic emission signal within the same time window; if the correlation coefficient is greater than or equal to a set threshold, and the phase characteristics of the current pulse and the energy characteristics of the acoustic emission signal meet preset conditions, it is determined to be a valid partial discharge event; the current pulse characteristics and acoustic emission characteristics of the valid partial discharge event are combined to form a... Fingerprints are matched with a pre-defined defect fingerprint database to initially determine the discharge type. Linear regression or exponential regression models are used to fit the resistive current change curve and the temperature rise change curve in the abnormal temperature rise region, respectively. The Pearson correlation coefficient between the two curves is calculated. If the correlation coefficient is greater than or equal to the value corresponding to a pre-defined strong positive correlation, and the absence of synchronous acoustic emission signal or the correlation between acoustic emission signal and current signal is less than a pre-defined threshold, it is determined that the power loss is increased due to aging or moisture in the resistor element. If the resistive current increase precedes the temperature rise change, and the lag time is less than or equal to a pre-defined duration, it is further verified that the internal insulation performance has degraded.

6. The online monitoring system for leakage current of a metal oxide surge arrester according to claim 5, characterized in that, It also includes spatial positioning and correlation analysis: based on the principle of spherical wave propagation, a set of positioning equations is established, and the weighted least squares method is used to solve the set of equations, with the weight coefficients set based on the signal amplitude of each sensor; the three-dimensional coordinates obtained from positioning are converted into relative coordinates of the MOA body; the spatial coordinates of defects located by acoustic emission are converted into pixel coordinates of infrared thermograms, and a mapping relationship between three-dimensional spatial coordinates and thermogram pixel coordinates is established; if the thermogram pixel coordinates corresponding to the acoustic emission positioning point fall within the abnormal temperature rise area, then the acoustic-thermal correlation is completed. If a valid partial discharge event is detected simultaneously at the location corresponding to the acoustic-thermal correlation, the electrical-acoustic-thermal triple verification is completed, confirming that there is a serious internal defect at that location.

7. The online monitoring system for leakage current of a metal oxide surge arrester according to claim 1, characterized in that, The multi-dimensional state assessment and decision-making module specifically includes: obtaining the average partial discharge quantity within a preset time window, setting a threshold for the average partial discharge quantity, subtracting the threshold from the average partial discharge quantity, and retaining the value greater than 0 if the result is greater than 0, marking it as an abnormal partial discharge value; obtaining the abnormal partial discharge values ​​for each time window, and sorting the obtained abnormal partial discharge values ​​in descending order of numerical value to obtain a set of abnormal partial discharge values; extracting the three largest abnormal partial discharge values ​​from the set, and using the two largest abnormal partial discharge values ​​as one side and the height perpendicular to that side to construct a triangle; then using the smallest abnormal partial discharge value in the set as the height of the triangle to construct a triangular pyramid, calculating the volume of the triangular pyramid, and obtaining the abnormal discharge quantification value.

8. The online monitoring system for leakage current of a metal oxide surge arrester according to claim 7, characterized in that, Also includes: Based on the three-dimensional spatial coordinates of the defect obtained from multiple positioning by an acoustic emission sensor array, the center coordinates of the multiple positioning coordinates are calculated; the spatial distance from each positioning coordinate to the center coordinate is obtained. The standard deviation of each distance is calculated, and the reciprocal of the standard deviation is recorded as the anomaly concentration. The abnormal temperature rise region is divided using the region growing method: the pixel with temperature > overall average temperature + 0.5℃ is used as the seed point, and it is expanded to the 8-neighborhood until the temperature of the neighboring pixels is lower than the threshold. The average temperature of all pixels in the abnormal region and the average temperature of all pixels on the entire MOA surface are obtained, and the difference between the two average temperature values ​​is recorded as the abnormal temperature rise value.

9. The online monitoring system for leakage current of a metal oxide surge arrester according to claim 8, characterized in that, Also includes: After normalizing the abnormal discharge quantification value, abnormal concentration, and abnormal temperature rise value, weight factors for the abnormal discharge quantification value, abnormal concentration, and abnormal temperature rise value are preset respectively. The abnormal discharge quantification value, abnormal concentration, and abnormal temperature rise value are multiplied with their corresponding weight factors to obtain the abnormality evaluation coefficient.

10. A method for online monitoring of leakage current in a metal oxide surge arrester, and a system for online monitoring of leakage current in a metal oxide surge arrester according to any one of claims 1-9, characterized in that, include: From electrical, acoustic, and thermal dimensions, characteristic signals of internal defects in the MOA are captured through a dedicated monitoring unit; a unified high-precision time reference is provided to complete the synchronous sampling, preprocessing, alignment, transmission, and storage of multi-source signals; noise reduction is performed on the synchronized data, and key characteristic parameters of the electrical, acoustic, and thermal dimensions are extracted to provide data support for defect analysis; defect type is identified through PD fingerprint recognition and thermo-electric correlation analysis, and the spatial location of defects is locked by combining acoustic emission three-dimensional localization and triple cross-validation; and anomaly evaluation coefficients are obtained by calculating and weighting abnormal discharge quantification values, abnormal concentration, and abnormal temperature rise values.

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