A comprehensive performance testing device for low-voltage instrument transformers

Abstract

This application relates to the field of power metering device testing technology, and discloses a comprehensive performance verification device for low-voltage instrument transformers, comprising: a space charge monitoring component for acquiring space charge distribution data; a dielectric performance quantification component for calculating a dielectric performance degradation scalar based on the space charge distribution data; a frequency response characteristic prediction component for outputting error data based on the dielectric performance degradation scalar; and a metering reliability judgment and early warning component for judging the metering reliability of the low-voltage instrument transformer based on the error data and performing corresponding processing operations accordingly. This application, by establishing a technical path from space charge monitoring to dielectric state assessment, then to frequency response characteristic prediction, and finally to assessment and processing, solves the problem of the lack of methods and subsequent processing means for capturing distributed capacitance drift occurring in low-voltage instrument transformers in existing technologies, greatly improving the reliability of the verification device.

Description

Technical Field

[0001] This invention relates to the field of power metering device testing technology, and more specifically, to a comprehensive performance verification device for low-voltage instrument transformers. Background Technology

[0002] Low-voltage instrument transformers are key devices for metering, measurement, and control in power systems. The accuracy of their performance directly affects the fairness of trade settlement and the safety and reliability of power grid operation. Therefore, regular and accurate comprehensive performance verification of low-voltage instrument transformers is crucial. The comprehensive performance verification device for low-voltage instrument transformers uses internal high-precision low-voltage instrument transformers as a benchmark to calibrate parameters such as ratio difference and phase difference of the transformer under test. It is a core device ensuring the accuracy of traceability throughout the entire metering system.

[0003] However, there is a deep-seated hidden danger regarding the long-term stability of such testing devices. After long-term operation or exposure to accidental overvoltage surges such as operational surges or lightning strikes, the interlayer and inter-turn insulation materials inside the low-voltage transformer, such as epoxy resin and polyimide film, will develop an electrical memory effect due to the high electric field stress. This effect is not a simple insulation aging breakdown, but a cumulative damage at the microscopic level. Specifically, charges are injected and trapped inside the insulation material, forming stable space charges. These space charges cause irreversible distortion of the local electric field, thereby changing the effective dielectric constant of the insulation material, and ultimately causing a small but non-negligible drift in the distributed capacitance of the precision winding of the low-voltage transformer.

[0004] When a calibration device tests complex operating conditions such as harmonic effects and transient characteristics according to modern calibration procedures, an increase in frequency will drastically amplify the impact of distributed capacitance changes, causing abnormal jumps in the ratio error and phase difference of low-voltage transformers at specific frequency points. At this point, the standard instrument, which serves as the traceability benchmark, is itself inaccurate, yet it continues to perform the calibration task. Its output will systematically distort the performance evaluation of all transformers under test under non-power frequency conditions, leading to defective transformers being judged as qualified and excellent transformers as unqualified. This not only causes long-term metering problems that are difficult to trace back to their root causes, but may also lead to a series of derivative risks such as inaccurate power quality monitoring and relay protection coordination errors, seriously undermining the reliability of power data and the safe and stable operation of the power grid.

[0005] Because the distributed capacitance drift in low-voltage transformers caused by the electrical memory effect is very small and highly concealed, existing technologies lack methods for capturing this drift and subsequent processing. Summary of the Invention

[0006] To address the above problems, the present invention provides the following technical solution:

[0007] This invention discloses a comprehensive performance verification device for low-voltage instrument transformers, comprising a low-voltage instrument transformer as the verification benchmark, the device comprising:

[0008] A space charge monitoring component is disposed inside the low-voltage transformer to acquire space charge distribution data inside the insulation material of the low-voltage transformer.

[0009] The dielectric performance quantization component receives space charge distribution data from the space charge monitoring component, extracts statistical features from the space charge distribution data to obtain the maximum value and the position of the charge centroid in the space charge density distribution, and calculates a dielectric performance degradation scalar based on the maximum value and the position of the charge centroid.

[0010] The frequency response prediction component, based on the dielectric performance degradation scalar, determines the equivalent distributed capacitance value of the dielectric performance degradation scalar in the distributed parameter equivalent circuit model through a parameter mapping function, and outputs the amplitude deviation and phase deviation data of the low-voltage transformer relative to its factory calibration state within a preset frequency range through frequency domain simulation.

[0011] The metering reliability assessment and early warning component receives amplitude and phase deviation data from the frequency response characteristic prediction component, automatically determines the metering reliability level of the low-voltage instrument transformer based on pre-stored metering regulations and standards, and performs corresponding early warning, identification, or disabling operations.

[0012] Furthermore, the space charge monitoring component includes an array of electrostatic probes and a charge sensing circuit. The array of electrostatic probes is arranged in a matrix at a spacing of no more than 5 mm in the winding gap of the low-voltage transformer.

[0013] Furthermore, the array-type electrostatic probe adopts a dual-probe redundancy architecture, including a working probe group and a verification probe group, wherein the verification probe group performs cross-verification on the working probe group within a preset time interval.

[0014] Furthermore, the dielectric performance quantification component calculates the product of the maximum space charge density and the charge centroid offset distance, and then divides it by the initial dielectric property reference value of the low-voltage transformer insulation material to obtain the dielectric performance degradation scalar.

[0015] Furthermore, the frequency response prediction component adopts a variable-order distributed parameter equivalent circuit model, the order of which is automatically adjusted based on the numerical range of the dielectric property degradation scalar.

[0016] Furthermore, the equivalent circuit model with variable order distributed parameters has three model complexity switching points: a third-order model is used when the dielectric performance degradation scalar is less than the first model switching value; a fifth-order model is used when it is greater than or equal to the first model switching value and less than the second model switching value; and a seventh-order model is used when it is greater than or equal to the second model switching value.

[0017] Furthermore, the determination logic for the metering reliability assessment and early warning components includes:

[0018] The amplitude deviation and phase deviation data are compared with the error limits corresponding to different accuracy levels that are stored in advance;

[0019] When the deviation data of all frequency points are within the allowable range, it is judged to be of a reliable level;

[0020] When only the harmonic frequency deviation exceeds the limit while the power frequency reference point is qualified, it is judged as a downgrade level;

[0021] When the deviation of the power frequency reference point exceeds the limit, it is judged to be unreliable.

[0022] Furthermore, the metering reliability determination and early warning component performs corresponding operations based on the determined reliability level:

[0023] When the rating is downgraded, an identification message that is restricted to power frequency verification is automatically generated, and the harmonic performance verification item is disabled in subsequent testing procedures.

[0024] When the system is determined to be unreliable, a hardware interlock signal is triggered to physically isolate the low-voltage transformer from the reference circuit.

[0025] At the same time, a judgment record containing a timestamp is generated, forming an immutable electronic archive for metrological traceability.

[0026] Furthermore, the device also includes:

[0027] The system maintenance coordination component receives a reliability level signal from the metering reliability determination and early warning component, and coordinates the initiation of the corresponding maintenance process based on the reliability level signal:

[0028] When a downgrade signal is received, the downgrade status is recorded and reported.

[0029] When an unreliable level signal is received, coordinate the execution of equipment shutdown and maintenance scheduling.

[0030] Furthermore, the system maintenance coordination component is pre-set with a maintenance strategy mapping table, which maps different reliability levels to specific maintenance instructions;

[0031] For downgraded levels, maintenance instructions include generating a usage restriction documentation and triggering periodic reviews;

[0032] For unreliable levels, maintenance instructions include generating emergency repair work orders, notifying relevant personnel, and locking equipment operation permissions.

[0033] Compared with related technologies, the present invention has the following beneficial effects:

[0034] This invention first acquires real-time data on the distribution of space charge within the insulating material using a space charge monitoring component embedded inside the low-voltage transformer. Then, a dielectric performance quantification component extracts statistical features from the space charge distribution data and calculates a dielectric performance degradation scalar, quantifying the minute and hidden distributed capacitance into a characteristic parameter characterizing the insulation state. This cleverly transforms tracking this drift into tracking the degree of insulation material degradation. Next, a frequency response characteristic prediction component analyzes the dielectric performance degradation scalar and converts it into a performance parameter that can be directly evaluated by metrological procedures and directly affects the verification results. Finally, a metrological reliability judgment and early warning component determines the metrological reliability of the low-voltage transformer based on this performance parameter and performs corresponding processing operations. This solves the problem in existing technologies where the difficulty in capturing distributed capacitance drift occurring in low-voltage transformers leads to errors in the verification device's judgment, greatly improving the reliability of the verification device.

[0035] This invention ensures rapid response and standardized handling of reliability issues in the calibration device by intelligent scheduling of system maintenance coordination components and coordination of maintenance resources, significantly improving maintenance efficiency. At the same time, it realizes full-process automation from equipment status determination to maintenance execution, further shortening the traditional manual processing process that takes several hours to minutes while reducing human error. Attached Figure Description

[0036] Figure 1 A schematic diagram of the data processing flow of a low-voltage instrument transformer comprehensive performance verification device provided by the present invention;

[0037] Figure 2 A schematic diagram of the reliability logic determination process in the system maintenance coordination component provided by the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] Please see Figure 1As shown, this embodiment provides a comprehensive performance verification device for a low-voltage instrument transformer, including a low-voltage instrument transformer as the verification benchmark. The device includes a space charge monitoring component, a dielectric performance quantification component, a frequency response characteristic prediction component, a metrological reliability determination and early warning component, and a system maintenance coordination component. The space charge monitoring component acquires data on the space charge distribution within the insulation material of the low-voltage instrument transformer; the dielectric performance quantification component analyzes the degree of dielectric performance degradation based on the space charge distribution data; the frequency response characteristic prediction component receives the degree of dielectric performance degradation and outputs error data; the metrological reliability determination and early warning component determines the reliability level based on the error data and performs corresponding operations; and the system maintenance coordination component performs corresponding maintenance procedures for the metrological reliability determination and early warning component.

[0040] The space charge monitoring component is located inside a low-voltage transformer to acquire data on the distribution of space charge within the transformer's insulation material. This component can be integrated into a standard voltage transformer and is a dedicated measurement system for real-time detection of the space charge distribution within the insulation material.

[0041] For example, the space charge monitoring component includes an array of electrostatic probes and a charge sensing circuit. The array of electrostatic probes is arranged in a matrix with a spacing of no more than 5 mm in the winding gap of the low-voltage transformer. Furthermore, the array of electrostatic probes adopts a dual-probe redundancy architecture, including a working probe group and a verification probe group. The verification probe group cross-verifies the working probe group within a preset time interval.

[0042] Specifically, when installing space charge monitoring components, miniature electrostatic probe units can be manufactured using lead zirconate titanate (PZT) piezoelectric ceramic material with high voltage stability. A single probe can be 1.0 × 1.0 × 0.5 mm in size. Ninety-six probe units can be integrated onto a flexible circuit board in an 8 × 12 matrix to form an array of electrostatic probes with a total size of 40 × 60 mm. During the manufacturing process of the low-voltage transformer, the array of electrostatic probes is embedded in the insulation layer between the high-voltage and low-voltage windings. The specific installation location is chosen in the area of ​​strongest electric field—2 mm from the surface of the high-voltage winding. The probe array can be fixed and encapsulated with special epoxy resin to ensure a tight bond with the surrounding insulation material without affecting the original insulation strength. The array arrangement can accurately locate areas of concentrated charge, providing precise target locations for subsequent maintenance and repair.

[0043] It is worth mentioning that the spacing between the probe arrays must be strictly controlled within 5mm. This technical parameter was determined based on in-depth research and extensive experimental verification of the physical characteristics of space charge. Firstly, space charge does not exist in isolation within insulating materials, but rather influences the surrounding area through electric field diffusion and material polarization effects. The intensity of this influence decreases with increasing radial distance. Through theoretical analysis and experimental measurements, researchers found that the radial distance corresponding to the attenuation of the influence intensity of space charge in epoxy resin insulating materials to half of its central value, i.e., the half-fading distance, is approximately 2.5mm. According to signal sampling theory, in order to completely and undistortedly reproduce the distribution information of space charge, the spacing between sampling points should not exceed half the spatial wavelength of the sampled signal. In the space charge monitoring scenario, this principle translates to the probe spacing not exceeding twice the half-fading distance.

[0044] Therefore, setting the probe spacing to 5mm, exactly twice the half-life distance, ensures that the radial distance from the midpoint between any two adjacent probes to either probe does not exceed the half-life distance, thus guaranteeing that the charge influence at that point can still be effectively sensed by at least one probe. Experimental comparison data further validated the rationality of this parameter setting: when the probe spacing was increased to 6mm, the distance from the midpoint to the probe reached 3mm, exceeding the half-life distance, resulting in a false negative rate of approximately 5% in this area; while when the spacing was reduced to 8mm, the false negative rate further increased to 15%, indicating a significant expansion of the monitoring blind zone. Conversely, if the spacing were reduced to 3mm, although near-zero false negatives could be achieved, the number of probes would increase nearly threefold, leading to a significant increase in manufacturing costs and a significant increase in installation complexity. Considering both monitoring accuracy and engineering economy, controlling the probe spacing within 5mm ensures complete capture of the space charge distribution without blind zones while achieving an optimal balance between cost and performance.

[0045] The charge sensing circuit can adopt a three-stage amplification architecture: the first stage can be a charge-sensitive preamplifier, which converts the weak charge signal sensed by the probe into a voltage signal; the second stage can be a programmable gain amplifier, which automatically adjusts the gain according to the signal strength; and the third stage can be a 24-bit high-precision Σ-Δ analog-to-digital converter with a sampling rate set to 1MS / s.

[0046] Synchronous sampling technology can be used to control the sampling time deviation of all probe channels within 1 ns, ensuring the temporal consistency of the acquired charge distribution data. Furthermore, a full array scan can be set to complete every 10 ms, refreshing the charge distribution data in real time.

[0047] The raw data collected by the space charge monitoring component includes: real-time electric field intensity values ​​(unit: kV / mm) at each probe location, charge polarity identification (positive charge / negative charge), spatial location coordinates (x, y, z), and timestamp sequence.

[0048] After data preprocessing, the following can be generated: space charge density distribution map (unit: C / m³), coordinates of charge centroid location, maximum charge density value and its location, and charge distribution uniformity index.

[0049] Because the distributed capacitance drift in low-voltage transformers caused by the electrical memory effect is very small and highly concealed, this embodiment installs a space charge monitoring component inside the low-voltage transformer to monitor the insulation state of the insulating material in real time. This component can also capture charge changes caused by transient processes such as transient overvoltages, providing basic data for subsequent quantification of the drift.

[0050] To accurately quantify this drift, space charge distribution data from the space charge monitoring component is received through a dielectric property quantization component. By extracting statistical features from the space charge distribution data, the maximum value and the position of the charge centroid in the space charge density distribution are obtained. Based on the maximum value and the position of the charge centroid, the dielectric property degradation scalar is calculated.

[0051] The dielectric performance quantification component refers to a computational unit specifically designed to process space charge distribution data. This data can be converted into a scalar value that quantifies the degree of dielectric state degradation of the insulating material. The processing method can be statistical feature extraction, which is a digital signal processing process that calculates characteristic parameters with clear physical meaning from the original space charge distribution data. The maximum space charge density obtained refers to the highest absolute value of charge density among all detection points in the entire monitoring area, reflecting the most severe degree of local charge concentration. The obtained charge centroid position is analogous to the concept of the center of mass and is usually used to describe the overall distribution center position of space charge within the insulating material. The calculated dielectric performance degradation scalar is a dimensionless numerical index used to comprehensively reflect the degree of dielectric performance degradation caused by the accumulation of space charge in the insulating material.

[0052] Specifically, firstly, the dielectric performance quantification component receives a digital charge distribution map from the space charge monitoring component and stores this data in matrix form, containing the three-dimensional coordinates of each detection point and its corresponding charge density value. Then, through statistical feature extraction, the absolute value of the entire matrix data is taken and searched to find the maximum space charge density Q, recording its value and location coordinates. Next, the charge centroid position is calculated using a weighted average algorithm, with the absolute charge density value of each point as the weight, calculating the weighted average position in the three coordinate directions to synthesize the charge centroid coordinates. Then, the charge centroid offset distance D is calculated by comparing the current charge centroid position with the initial charge centroid position measured at the factory of the standard voltage transformer, and calculating the Euclidean distance.

[0053] Furthermore, the dielectric performance quantification component calculates the product of the maximum space charge density and the charge centroid offset distance, and then divides it by the initial dielectric property reference value of the low-voltage transformer insulation material to obtain the dielectric performance degradation scalar.

[0054] Specifically, the initial dielectric property reference value K, pre-stored in the device system, can be obtained. This value can be measured under standard laboratory conditions at the time of manufacture of the low-voltage transformer. Then, the maximum space charge density Q and the charge centroid offset distance D are substituted into the formula to calculate the dielectric property degradation scalar. = (Q×D) / K. The entire calculation process can be set to run once every 100ms to ensure that the dynamic changes in the insulation state can be reflected in a timely manner.

[0055] Because the distributed capacitance drift in low-voltage transformers caused by the electrical memory effect is extremely small and highly concealed, this implementation transforms complex space charge distribution information into a quantitative index with clear engineering significance. This is achieved by continuously calculating the dielectric performance degradation scalar. The changing trend of the value can dynamically track the degradation process of the insulating material. By quantifying the tiny and hidden distributed capacitance into a characteristic parameter characterizing the insulation state, the tracking of this drift is cleverly transformed into tracking the degree of degradation of the insulating material.

[0056] Due to the degradation of dielectric properties, scalar As a characteristic parameter representing the insulation state, the value is also an abstract parameter and does not have the practical function of directly determining the drift. Therefore, it is necessary to determine the equivalent distributed capacitance value of the dielectric performance degradation scalar in the distributed parameter equivalent circuit model through the parameter mapping function, and output the amplitude deviation and phase deviation data of the low voltage transformer relative to its factory calibration state within the preset frequency range through frequency domain simulation.

[0057] Among them, the frequency response prediction component refers to the calculation unit that predicts the output characteristic changes of a standard voltage transformer at different frequencies based on the dielectric performance degradation scalar; the distributed parameter equivalent circuit model refers to the network model that represents the winding and insulation structure of the standard voltage transformer as a network composed of distributed resistance, distributed inductance and distributed capacitance, and this model can be pre-loaded into the frequency response prediction component; the amplitude deviation and phase deviation data refer to the amplitude ratio difference and phase angle difference between the output of the standard voltage transformer at each frequency point and the ideal value.

[0058] For example, the frequency response prediction component can employ a variable-order distributed parameter equivalent circuit model, where the order of the model can be automatically adjusted based on the numerical range of the dielectric performance degradation scalar. Furthermore, the variable-order distributed parameter equivalent circuit model can have three model complexity switching points: a third-order model is used when the dielectric performance degradation scalar is less than a first model switching value; a fifth-order model is used when the dielectric performance degradation scalar is greater than or equal to the first model switching value but less than a second model switching value; and a seventh-order model is used when the dielectric performance degradation scalar is greater than or equal to the second model switching value.

[0059] The variable-order distributed parameter equivalent circuit model used in the frequency response prediction component is constructed based on the winding structure and physical properties of the insulation material of a standard voltage transformer. The so-called third-order model refers to equating the standard voltage transformer to a chain network composed of three basic elements: a series resistor representing the winding conductor resistance, a series inductor representing the winding inductance effect, and a distributed capacitance to ground representing the interlayer insulation capacitance. These three elements constitute a basic π-type equivalent unit. This model is based on an engineering simplification of uniform transmission line theory, treating the entire winding as a cascade of multiple infinitesimal length elements. The parameters within each length element can be considered lumped parameters; the third-order model approximates the entire winding as a single such length element.

[0060] The fifth-order model adds two components to the third-order model: an equivalent resistor representing dielectric loss is connected in series in the distributed capacitance branch to ground, and a parallel capacitor representing inter-turn distributed capacitance is connected in parallel next to the series inductor, thus forming a five-component network that can more accurately describe the mid-frequency characteristics.

[0061] The seventh-order model is further extended into a double-π equivalent network containing seven elements. That is, based on the fifth-order model, a series inductor and a distributed capacitance to ground are added to form a two-stage cascaded structure. At the same time, a frequency-varying resistor connected in parallel with the main inductor is introduced to simulate the skin effect and proximity effect at high frequencies.

[0062] These models of different orders are not arbitrarily set, but determined by extracting the dominant poles from measured frequency response data using vector matching method after impedance spectrum analysis of standard voltage transformers. The choice of model order is closely related to the dielectric degradation scalar. When the insulating material is in a healthy state and the dielectric degradation scalar is small, space charge accumulation is slight and the dielectric constant of the insulating material does not change much. At this time, a third-order model can accurately describe the characteristics of the standard voltage transformer in the main operating frequency band. As the dielectric degradation scalar increases, the space charge accumulation intensifies, leading to local electric field distortion and electrical tree sprouting inside the insulating material. The dielectric constant changes in a frequency-dependent manner. At this time, a higher-order model must be used to capture the high-frequency characteristic shift caused by these microstructural changes. When the dielectric degradation scalar reaches a high value, complex multi-interface polarization effects and local conductive channels may appear inside the insulating material. Only a seventh-order model can fully characterize the multi-resonance peak characteristics caused by these deep-seated degradations.

[0063] The physical basis of this variable-order design is that the higher the degree of degradation of the dielectric properties of the insulating material, the more complex its response to high-frequency signals becomes, requiring an equivalent circuit with more poles to accurately fit its frequency response characteristics. By dynamically switching the model order, unnecessary computational overhead can be avoided while ensuring prediction accuracy, achieving the optimal match between model complexity and representation accuracy.

[0064] Specifically, firstly, an equivalent circuit model of the distributed parameters of a standard voltage transformer is established. This model divides the winding into 20 basic units, each containing a series resistance R, a series inductance L, and a capacitance to ground. Among them, the capacitance to ground It is a scalar quantity related to dielectric property degradation. The directly related key parameters, and then the correction of the distributed capacitance value is achieved through the following functional relationship:

[0065] ;

[0066] in, This is the corrected distributed capacitance value. This represents the initial distributed capacitance value of a standard voltage transformer under factory-safe, healthy conditions. and This is a characteristic parameter that characterizes the sensitivity of a specific insulating material to the electrical memory effect.

[0067] Establishing this correction relationship first requires preparing samples with the exact same insulating material as those used in standard voltage transformers. Typically, epoxy resin or polyimide film materials produced in the same batch as the transformers are used, and these are processed into test pieces of standard dimensions. These samples are placed in a controlled aging test platform, where different intensities of electric field stress and controlled exposure times are applied to simulate multiple levels of dielectric degradation, from mild to severe. At each degradation level, two key parameters are measured simultaneously: first, the space charge distribution data inside the sample is obtained using a space charge monitoring system, and second, the corresponding dielectric degradation scalar is calculated using the aforementioned method. Secondly, a precision impedance analyzer is used to measure the equivalent capacitance of the sample at typical frequencies to obtain the capacitance value. The corresponding distributed capacitance value. For each degradation level, the experiment was repeated multiple times to obtain a statistically significant average value, thereby eliminating the influence of random errors.

[0068] After completing the measurements for all degradation levels, a series of data points will be obtained. , ), ( , )until( , Plot these data points on a graph. The x-axis represents the normalized capacitance value. / In a coordinate system with the vertical axis as the ordinate, the capacitance value can be observed to change with... The trend is towards increasing rather than nonlinear growth. In this case, the least squares method is used to perform a quadratic polynomial fitting on the data points, i.e., to solve for the coefficients that minimize the sum of squared errors. and The goal of the fitting process is to find a curve. ², to make it as close as possible to all experimental data points. Mathematically, this process is accomplished by solving the normal equation, ultimately yielding the result that minimizes the fitting residual. and value.

[0069] After obtaining the initial fitting coefficients, validation and optimization are still required. Several validation samples not involved in the fitting are selected, and their actual measured capacitance values ​​are compared with the model's predicted values. If the error is within acceptable limits, the coefficients are confirmed as valid; if the error is large, it is necessary to check for outliers in the experimental data or consider whether to introduce higher-order fitting terms. After multiple rounds of validation and optimization, the final coefficients are determined. and The value represents the inherent characteristics of this type of insulating material.

[0070] The way these coefficients are set in the device depends on its application scenario. For standard voltage transformers of the same model and batch, since their insulation materials are consistent, the coefficients obtained through sample testing can be used... and The values ​​are directly stored in the non-volatile memory of the frequency response prediction component as factory default parameters. For transformers from different manufacturers or with different material systems, the above experimental procedures need to be performed separately, a corresponding material property database needs to be established, and the corresponding correction coefficients need to be selected or entered according to the material type of the actual transformer before the device is used. In addition, for standard voltage transformers that have been in operation for a long time, online monitoring data can also be used to... and The values ​​are periodically reviewed and fine-tuned to ensure the long-term accuracy of the model. This entire acquisition and setup process ensures the accuracy and reliability of the distributed capacitance correction relationship, enabling the frequency response prediction component to accurately map the frequency response changes of the standard voltage transformer at different aging stages based on the dielectric performance degradation scalar.

[0071] Then set three model complexity switching points: when When 0.3 < 0.3, a third-order model is used, considering only the main distribution parameters; when 0.3 ≤ When <0.7, a fifth-order model is used to increase the modeling of edge effects; when When the value is ≥0.7, a seventh-order model is used to fully consider all distributed parameters and coupling effects. Next, frequency domain simulation is performed in 1Hz increments within the 10Hz to 1kHz frequency range. For each frequency point, the transfer function of the model is calculated to obtain the amplitude-frequency and phase-frequency characteristics of the low-voltage transformer under the current dielectric state. Amplitude and phase deviation data are generated by comparing these with factory calibration data. In this embodiment, adaptive adjustment of the model order significantly improves computational efficiency while maintaining accuracy, and the variable-order model achieves an optimal balance between computational complexity and prediction accuracy.

[0072] This embodiment degrades the dielectric properties to a scalar value. The values ​​are converted into performance parameters that can be directly evaluated by metrological procedures and directly affect the verification results, enabling the device system to predict the actual performance of low-voltage transformers in future verification tasks. It is no longer necessary to wait until the transformer actually malfunctions during verification (generating bad data) to discover its non-compliance; instead, its performance can be predicted and a decision made in advance based on its insulation condition.

[0073] Finally, the metering reliability determination and early warning component receives amplitude and phase deviation data from the frequency response characteristic prediction component, automatically determines the metering reliability level of the low-voltage instrument transformer based on the pre-stored metering regulations and standards, and executes corresponding early warning, identification, or disabling operations.

[0074] Among them, the metrological reliability judgment and early warning component refers to an intelligent decision-making unit that automatically evaluates the reliability level of standard voltage transformers and performs corresponding operations based on frequency response characteristic prediction data and metrological procedures; the metrological procedure standard refers to the error limit requirements specified in the standard metrological verification procedure pre-existing in the system, such as the allowable error limits for transformers of various levels specified in JJG-313-2010 "Current Transformers for Measurement".

[0075] For example, the determination logic of the metrological reliability determination and early warning component includes: comparing the amplitude deviation and phase deviation data with the error limits corresponding to different accuracy levels; when the deviation data of all frequency points are within the allowable range, it is determined to be a reliable level; when only the harmonic frequency point deviation exceeds the limit while the power frequency reference point is qualified, it is determined to be a downgraded level; when the power frequency reference point deviation exceeds the limit, it is determined to be an unreliable level.

[0076] In this embodiment, the legality and authority of the verification conclusions are ensured by procedurally executing the standard metrological verification procedure, thus avoiding human error in interpretation.

[0077] Furthermore, the metrological reliability assessment and early warning component performs corresponding operations based on the assessed reliability level: when assessed as a downgraded level, it automatically generates identification information limited to power frequency verification and disables harmonic performance verification items in subsequent testing processes; when assessed as an unreliable level, it triggers a hardware interlock signal to physically isolate the low-voltage transformer from the reference circuit; simultaneously, it generates a assessment record containing a timestamp, forming an unalterable electronic archive for metrological traceability.

[0078] Specifically, the core of the metrological reliability assessment and early warning component can adopt a three-layer assessment architecture. First, an error limit database is established, including power frequency reference points (50Hz / 60Hz) and extended frequency points (10Hz-1kHz). This database strictly follows standard metrological verification procedures, setting corresponding allowable ranges for amplitude and phase errors for different accuracy levels (0.01, 0.02, and 0.05).

[0079] Then, the three-level judgment process continues: when the amplitude deviation and phase deviation of all frequency points are within the allowable range of the corresponding accuracy level, the system judges it as reliable and generates a status label of "equipment is normal and fully functional".

[0080] When harmonic frequencies (such as 250Hz, 350Hz, etc.) exceed the tolerance, but the power frequency reference point is still qualified, the system automatically determines the downgrade level. At this time, the component performs two key operations: first, it writes a prominent mark "Power frequency verification only" into the equipment's electronic identification; second, it disables the harmonic testing function module in the verification device through a software lock to technically prevent misuse.

[0081] When the error of the power frequency reference point exceeds the allowable range, an unreliability level determination is immediately triggered. Within 100ms, the system sends a hardware interlock signal, which directly drives the relay to cut off the output circuit of the standard voltage transformer and illuminates the red fault indicator light on the equipment panel.

[0082] Each judgment operation automatically generates an electronic file containing information such as timestamps, judgment criteria, and operation records. This file uses digital signature technology to prevent tampering and is preserved as important evidence for metrological traceability.

[0083] This implementation method, by setting up a tiered handling mechanism, ensures the rational use of equipment resources (downgraded use) and takes decisive measures (hardware interlocking) when critical risks occur, achieving a balance between safety and efficiency. At the same time, automated judgment and handling greatly reduce the need for manual intervention. Reliability assessments that traditionally require hours of expert analysis can now be completed and executed automatically within seconds. Finally, a complete electronic recording system provides reliable evidence for metrology issues and provides data support for equipment lifecycle management.

[0084] The system maintenance coordination component receives reliability level signals from the metering reliability determination and early warning component, and coordinates the initiation of corresponding maintenance processes based on the reliability level signals: when a downgrade level signal is received, the downgrade status is recorded and reported; when an unreliable level signal is received, the equipment shutdown and maintenance scheduling are coordinated and executed.

[0085] For example, the system maintenance coordination component has a pre-set maintenance strategy mapping table that maps different reliability levels to specific maintenance instructions. For downgraded levels, maintenance instructions include generating a limitation description document and triggering periodic reviews. For unreliable levels, maintenance instructions include generating an emergency repair work order, notifying relevant personnel, and locking equipment operation permissions.

[0086] Specifically, the system maintenance coordination component can monitor the reliability level signal issued by the metering reliability assessment and early warning component in real time via the data bus. This signal can adopt a standardized data format and include key information such as equipment number, assessment time, reliability level, and specific out-of-tolerance parameters.

[0087] The system maintenance coordination component also has a pre-built, comprehensive maintenance strategy mapping table. This table defines specific maintenance instructions corresponding to different reliability levels, including those for downgraded levels:

[0088] The system automatically generates equipment usage restriction documents, clearly indicating permitted calibration items and prohibited functional modules. It also sets up periodic review reminders in the equipment management system, with a default review cycle of 30 days. Furthermore, it sends equipment status update notifications to the laboratory information management system via an application programming interface (API).

[0089] For unreliable levels: An emergency repair work order is immediately generated and automatically assigned to the equipment administrator and technical supervisor. Simultaneously, equipment operation permissions are locked; any attempt to use the equipment for calibration will be rejected by the system. An audible and visual alarm is also triggered to alert on-site personnel.

[0090] For equipment with an unreliable rating, the system automatically queries available maintenance resources. First, it checks the availability of spare parts, such as the number of spare parts for standard voltage transformers. Then, it checks the technicians' schedules and automatically recommends the optimal maintenance time. Next, it generates the standard operating procedure for maintenance and finally estimates the maintenance cycle and cost.

[0091] This implementation method ensures rapid response and standardized handling of reliability issues in the calibration device through intelligent scheduling and maintenance resource coordination, significantly improving maintenance efficiency. At the same time, it realizes full-process automation from equipment status determination to maintenance execution, further shortening the traditional manual processing process that takes several hours to minutes while reducing human error.

[0092] In summary, this method first acquires real-time data on the distribution of space charge within the insulating material using a space charge monitoring component embedded inside the low-voltage transformer. Then, a dielectric performance quantification component extracts statistical features from the space charge distribution data and calculates a dielectric performance degradation scalar, quantifying the minute and hidden distributed capacitance into a characteristic parameter characterizing the insulation state. This cleverly transforms tracking this drift into tracking the degree of insulation degradation. Next, a frequency response characteristic prediction component analyzes the dielectric performance degradation scalar and converts it into a performance parameter that can be directly evaluated by metrological procedures and directly affects the verification results. Finally, a metrological reliability judgment and early warning component determines the metrological reliability of the low-voltage transformer based on this performance parameter and performs corresponding processing operations. This solves the problem of existing technologies failing to capture distributed capacitance drift occurring in low-voltage transformers, leading to errors in the verification device's judgment, and significantly improves the reliability of the verification device.

[0093] Furthermore, through intelligent scheduling of system maintenance coordination components and coordination of maintenance resources, the system ensures rapid response and standardized handling of reliability issues in the calibration device, significantly improving maintenance efficiency. At the same time, it realizes full automation from equipment status determination to maintenance execution, further reducing the traditional manual processing process that takes several hours to minutes while reducing human error.

[0094] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0095] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A comprehensive performance testing device for low-voltage instrument transformers, comprising a low-voltage instrument transformer as the testing benchmark, characterized in that, The device includes: A space charge monitoring component is disposed inside the low-voltage transformer to acquire space charge distribution data inside the insulation material of the low-voltage transformer. The dielectric performance quantization component receives space charge distribution data from the space charge monitoring component, extracts statistical features from the space charge distribution data to obtain the maximum value and the position of the charge centroid in the space charge density distribution, and calculates a dielectric performance degradation scalar based on the maximum value and the position of the charge centroid. Frequency response prediction component, based on the dielectric property degradation scalar ,according to Determine the ground capacitance parameters in the distributed parameter equivalent circuit model. Based on the distributed parameter equivalent circuit model after writing the ground capacitance parameters, the transfer function at each frequency point is calculated. The amplitude and phase of the transfer function are compared with the amplitude and phase at the corresponding frequency point under factory calibration conditions, and the amplitude deviation and phase deviation data are output. This represents the initial distributed capacitance value of the low-voltage instrument transformer under factory-safe, healthy conditions. and These are the preset insulation material characteristic parameters; The metering reliability assessment and early warning component receives amplitude and phase deviation data from the frequency response characteristic prediction component, automatically determines the metering reliability level of the low-voltage instrument transformer based on pre-stored metering regulations and standards, and performs corresponding early warning, identification, or disabling operations.

2. The low-voltage instrument transformer comprehensive performance testing device according to claim 1, characterized in that, The space charge monitoring component includes an array of electrostatic probes and a charge sensing circuit. The array of electrostatic probes is arranged in a matrix at a spacing of no more than 5 mm in the winding gap of the low-voltage transformer.

3. The low-voltage instrument transformer comprehensive performance testing device according to claim 2, characterized in that, The array-type electrostatic probe adopts a dual-probe redundancy architecture, including a working probe group and a verification probe group. The verification probe group performs cross-verification on the working probe group within a preset time interval.

4. The low-voltage instrument transformer comprehensive performance testing device according to claim 1, characterized in that, The dielectric performance quantification component calculates the product of the maximum space charge density and the distance of charge centroid offset, and then divides it by the initial dielectric property reference value of the low-voltage transformer insulation material to obtain the dielectric performance degradation scalar.

5. The low-voltage instrument transformer comprehensive performance testing device according to claim 1, characterized in that, The frequency response prediction component adopts a variable-order distributed parameter equivalent circuit model, and the order of the model is automatically adjusted based on the numerical range of the dielectric property degradation scalar.

6. The low-voltage instrument transformer comprehensive performance testing device according to claim 5, characterized in that, The equivalent circuit model with variable order distributed parameters has three model complexity switching points: a third-order model is used when the dielectric performance degradation scalar is less than the first model switching value; a fifth-order model is used when it is greater than or equal to the first model switching value and less than the second model switching value; and a seventh-order model is used when it is greater than or equal to the second model switching value.

7. The low-voltage instrument transformer comprehensive performance testing device according to claim 1, characterized in that, The determination logic for meter reliability assessment and early warning components includes: The amplitude deviation and phase deviation data are compared with the error limits corresponding to different accuracy levels that are stored in advance; When the deviation data of all frequency points are within the allowable range, it is judged to be of a reliable level; When only the harmonic frequency deviation exceeds the limit while the power frequency reference point is qualified, it is judged as a downgrade level; When the deviation of the power frequency reference point exceeds the limit, it is judged to be unreliable.

8. The low-voltage instrument transformer comprehensive performance testing device according to claim 7, characterized in that, The metering reliability determination and early warning component performs corresponding operations based on the determined reliability level: When the rating is downgraded, an identification message that is restricted to power frequency verification is automatically generated, and the harmonic performance verification item is disabled in subsequent testing procedures. When the system is determined to be unreliable, a hardware interlock signal is triggered to physically isolate the low-voltage transformer from the reference circuit. At the same time, a judgment record containing a timestamp is generated, forming an immutable electronic archive for metrological traceability.

9. The low-voltage instrument transformer comprehensive performance testing device according to claim 1, characterized in that, The device further includes: The system maintenance coordination component receives a reliability level signal from the metering reliability determination and early warning component, and coordinates the initiation of the corresponding maintenance process based on the reliability level signal: When a downgrade signal is received, the downgrade status is recorded and reported. When an unreliable level signal is received, coordinate the execution of equipment shutdown and maintenance scheduling.

10. A low-voltage instrument transformer comprehensive performance testing device according to claim 9, characterized in that, The system maintenance coordination component has a pre-set maintenance strategy mapping table that maps different reliability levels to specific maintenance instructions. For downgraded levels, maintenance instructions include generating a usage restriction documentation and triggering periodic reviews; For unreliable levels, maintenance instructions include generating emergency repair work orders, notifying relevant personnel, and locking equipment operation permissions.

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