A misalignment analysis method of an intelligent measurement switch

Abstract

This invention discloses a misalignment analysis method for intelligent measurement switches, relating to the field of power equipment condition monitoring technology. The specific steps of this method are as follows: First, multi-physics field synchronous sampling is performed to acquire electrical, magnetic, temperature, and vibration field data; then, a dual-domain benchmark model is calibrated to determine algorithm parameters and safety thresholds; next, misalignment is determined and its root cause is traced, calculating the deviation and locating the failure unit; then, multi-field coupled attribution quantization is carried out to calculate the contribution weight of each field; finally, a misalignment boundary condition library is established to calculate the risk degree and delineate the safety boundary. This invention, through multi-physics field synchronous acquisition and a dual-domain benchmark model, accurately determines the misalignment state, traces the root cause, filters interference, and improves the accuracy of determination; it overcomes the limitations of one-sidedness by utilizing the coupled attribution quantization algorithm; and it constructs a misalignment boundary condition library to achieve proactive risk prediction, identify risks in advance, provide a basis for preventative maintenance, ensure metering accuracy, and improve equipment stability and safety.

Description

Technical Field

[0001] This invention relates to the field of power equipment condition monitoring technology, specifically to a method for analyzing the inaccuracy of an intelligent measuring switch. Background Technology

[0002] Intelligent metering switches are core hardware devices in the metering and control of power systems, widely used in various power supply and consumption scenarios in power networks. Their metering accuracy directly affects the fairness of power transaction settlement, the scientific nature of power grid dispatching and operation, and the overall stability of the power system. As the power industry rapidly develops towards intelligence and digitalization, the operating conditions of the power grid are becoming increasingly complex. Intelligent metering switches are always operating in a multi-physical field coupling environment, including electric, magnetic, temperature, and vibration fields. The parameter changes and interactions of these physical fields can easily lead to metering inaccuracies. The large-scale expansion of power networks and the diversified changes in electricity loads further enhance the requirements for the real-time and accuracy of intelligent metering switch metering status monitoring. The industry urgently needs a set of inaccuracy analysis technology that can adapt to complex operating conditions and achieve full-dimensional feature perception to ensure the continuous and stable metering performance of measurement equipment and meet the development needs of high-quality operation of the power system.

[0003] Traditional intelligent measurement switch misalignment analysis methods mostly focus on detection and analysis within a single physical field or a single feature dimension. They fail to achieve simultaneous acquisition and comprehensive consideration of multi-physical field feature data, and cannot accurately capture the misalignment patterns of equipment under the coupled effects of factors in various fields. This results in a lack of comprehensiveness in the judgment of misalignment status. At the same time, the misalignment judgment logic of traditional methods is relatively simple and easily affected by external factors such as instantaneous interference from the power grid, which can easily lead to misjudgment or omission. Furthermore, there is a lack of effective feature decoupling methods for tracing the root cause of misalignment, making it difficult to accurately distinguish between different misalignment causes such as equipment failure and power grid signal distortion. It is also impossible to accurately locate the failed hardware unit. In addition, traditional technologies can only perform passive diagnosis after obvious misalignment occurs in the equipment. They cannot quantify the degree of influence of various factors on misalignment, nor can they predict the risk of measurement misalignment in advance, making it difficult to adapt to the development requirements of preventive operation and maintenance in power systems. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method for misalignment analysis of intelligent measuring switches. This method relies on multi-physics field synchronous sampling to acquire full-dimensional feature data, determines deviation algorithm parameters and safety thresholds through dual-domain benchmark model calibration, achieves accurate misalignment determination through multi-cycle verification, and completes root cause tracing and failure unit location by using feature deviation decoupling. Simultaneously, through a multi-physics field coupling attribution quantification algorithm, the contribution weight of each field and coupling effect to misalignment is clarified. A misalignment boundary condition library is constructed by combining equipment rated parameters and component tolerance thresholds, calculating real-time misalignment risk and delineating multi-level safety boundaries. This method overcomes the limitations of traditional analysis, achieving accurate diagnosis, quantitative attribution, and early prediction of misalignment, providing a scientific basis for the operation and maintenance management of intelligent measuring switches, and effectively ensuring equipment metering accuracy and stable operation of the power system.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for analyzing the inaccuracy of an intelligent measuring switch, the specific steps of which are as follows: S1, Multi-physics field synchronous sampling: Synchronously collect full feature data of the electric field, full feature data of the magnetic field, full node data of the temperature field, and full working condition data of the vibration field; S2, Dual-domain reference model calibration: Based on the electric domain reference feature data and magnetic domain reference feature data collected under standard rated operating conditions, calibrate the algorithm parameters for the dual-domain synchronization feature deviation of the electric and magnetic domains, and determine the safety threshold for the dual-domain feature deviation under normal metering conditions; the dual-domain synchronization feature deviation is used to characterize the degree of synchronization difference between the electric domain features and the magnetic domain features. S3, Inaccuracy Judgment and Root Cause Tracing: Based on the electric and magnetic domain data collected in real time in step S1, the real-time dual-domain feature deviation is calculated using the deviation algorithm calibrated in step S2. The inaccuracy state is determined by comparing it with the safety threshold. Then, the root cause of inaccuracy is distinguished and the failure unit is located by decoupling the dual-domain feature deviation. S4, Multi-field coupling attribution quantization: Based on the time series data of the misalignment obtained in step S3 and the temperature field data and vibration field data obtained in step S1, the contribution weight of each physical field to the misalignment is calculated by the multi-physical field coupling misalignment contribution weight quantization algorithm. S5, Inaccuracy Boundary and Risk Prediction: Based on the contribution weight obtained in step S4, the deviation time series data obtained in step S3, and the rated parameter limits of the intelligent measurement switch and the tolerance threshold of the components, an inaccuracy boundary condition library is established, and the real-time measurement inaccuracy risk degree is calculated based on the inaccuracy boundary condition library, thereby defining the measurement inaccuracy safety boundary.

[0006] Furthermore, in S1, during multi-physics field synchronous sampling, synchronous acquisition is performed using a unified clock trigger signal. The full characteristic data of the electric domain includes instantaneous current data, instantaneous voltage data, amplitude-frequency characteristic data, phase-frequency characteristic data, voltage RMS data, current RMS data, and metering error timing data. The full characteristic data of the magnetic domain includes magnetic field strength data, magnetic flux data, hysteresis loop characteristic data, permeability data, and magnetic flux distortion rate data. The full node data of the temperature field includes terminal temperature data, magnetic core temperature data, PCB board temperature data, and cavity ambient temperature data. The full operating condition data of the vibration field includes opening and closing impact vibration data and line mechanical vibration data.

[0007] Furthermore, in S2, the standard rated operating conditions in the dual-domain reference model calibration are: ambient temperature of 20℃±2℃, no external vibration input, input signal of rated sinusoidal voltage, input signal of rated sinusoidal current, total harmonic distortion rate of no more than 1%, and no DC bias component. The dual-domain synchronization feature deviation algorithm parameters include time synchronization fidelity coefficient, electro-magnetic reference constitutive mapping operator, and electric domain reference feature vector. The dual-domain feature deviation safety threshold is determined according to the normal distribution 3σ criterion.

[0008] Furthermore, in S2, the mathematical expression for the dual-domain synchronization feature deviation algorithm in the dual-domain baseline model calibration is: In the formula, Let be the deviation of the dual-domain features at time t, and let be a dimensionless relative value characterizing the degree of synchronization difference between the electric and magnetic domain features; This is the time synchronization fidelity coefficient, a dimensionless coefficient ranging from 0.99 to 1. It is calculated based on the timestamp synchronization accuracy of the sampling channel; higher synchronization accuracy results in higher fidelity. The closer the value is to 1; Let be the electric domain feature column vector after Z-score dimensionless preprocessing at time t. is the magnetic domain feature column vector after Z-score dimensionless preprocessing at time t; It is an electro-magnetic reference constitutive mapping operator, a dimensionless matrix that matches the dimension of the feature vector. It is obtained by fitting and calculating the dimensionless electric domain reference feature data and magnetic domain reference feature data collected under standard rated operating conditions through a one-to-one correspondence, and it varies with the metering characteristics of the intelligent measurement switch. For Hadamah accumulation; The dimensionless electric domain reference characteristic column vector under standard rated operating conditions L2 norm; electric domain reference feature column vector It is calculated by taking the average value of the dimensionless electrical domain reference characteristic data of the rated load range under standard rated operating conditions, and varies with the rated parameters of the intelligent measuring switch. It is calculated from real-time dimensionless electric and magnetic domain data and calibration parameters.

[0009] Furthermore, in step S3, during the inaccuracy determination and root cause tracing, when comparing the inaccuracy status with the safety threshold, the real-time dual-domain feature deviation of a single power frequency cycle calculated by the deviation algorithm calibrated in step S2 is first compared with the safety threshold for dual-domain feature deviation determined in step S2. When the real-time dual-domain feature deviation of a single power frequency cycle is less than or equal to the safety threshold for dual-domain feature deviation, the current power frequency cycle metering status is determined to be normal. When the real-time dual-domain feature deviation of a single power frequency cycle is greater than the safety threshold for dual-domain feature deviation, the current power frequency cycle is marked as normal. The period is an abnormal cycle. Electric and magnetic domain data are continuously collected for the next three power frequency cycles. The deviation algorithm calibrated in step S2 is used to calculate the real-time dual-domain feature deviation for each power frequency cycle. When the real-time dual-domain feature deviation for three consecutive power frequency cycles is greater than the dual-domain feature deviation safety threshold, the intelligent measurement switch is determined to be in a measurement inaccuracy state. When the real-time dual-domain feature deviation for two or fewer power frequency cycles within three consecutive power frequency cycles is greater than the dual-domain feature deviation safety threshold, it is determined to be an instantaneous interference anomaly, and the measurement inaccuracy state determination is not triggered.

[0010] Furthermore, in S3, the specific steps for distinguishing the root cause of inaccuracy and locating the failed unit through dual-domain feature deviation decoupling in the process of determining and tracing the inaccuracy in the inaccuracy determination and root cause tracing are as follows: First, feature extraction is performed on the real-time acquired electrical domain data and magnetic domain data to obtain real-time electrical domain feature sequences and real-time magnetic domain feature sequences. The power grid input signal feature components in the real-time electrical domain feature sequences are separated and the corresponding feature offsets are calculated. The feature offsets are removed from the real-time dual-domain feature deviations to obtain the dual-domain feature deviations corresponding to the switch body. The magnetic domain feature deviations between the real-time magnetic domain feature sequences and the magnetic domain reference feature sequences under standard rated operating conditions, and the electrical domain feature deviations between the real-time electrical domain feature sequences and the electrical domain reference feature sequences under standard rated operating conditions are calculated respectively. The magnetic domain feature deviations and the electrical domain feature deviations are compared. The characteristic deviation values ​​are synchronized with the changes in the dual-domain characteristic deviation degree corresponding to the switch body. When the magnetic domain characteristic deviation value exceeds the fluctuation range of the magnetic domain characteristic reference under standard rated operating conditions and is synchronized with the change in the dual-domain characteristic deviation degree corresponding to the switch body, the magnetic characteristic degradation of the current transformer is marked as the root cause of inaccuracy. When the electrical domain characteristic deviation value exceeds the fluctuation range of the electrical domain characteristic reference under standard rated operating conditions and is synchronized with the change in the dual-domain characteristic deviation degree corresponding to the switch body, the electrical parameter drift of the sampling circuit is marked as the root cause of inaccuracy. When the characteristic offset corresponding to the characteristic component of the grid-side input signal is not zero and cannot be compensated by the switch body, the grid-side signal distortion is marked as the root cause of inaccuracy. Based on the marked root cause of inaccuracy, the corresponding hardware unit is matched to complete the location of the hardware unit corresponding to the root cause of inaccuracy.

[0011] Furthermore, in S4, the mathematical expression for the multi-physics coupling misalignment contribution weight quantization algorithm in multi-field coupling attribution quantization is: In the formula, The contribution weight corresponding to the i-th physical field parameter. For the i-th physical field parameter, Let j be the physical field parameter. The deviation of the two-domain features; Let be the sensitivity coefficient of the dual-domain characteristic deviation to the i-th physical field parameter. The sensitivity coefficient of the dual-domain feature deviation to the j-th physical field parameter is calculated by the numerical difference method based on the synchronously acquired time-series data of the dual-domain feature deviation and the corresponding physical field parameter. It is used to characterize the rate of change of the dual-domain feature deviation with the corresponding physical field parameter, and its value is dynamically updated with the real-time physical field parameter. The misalignment elastic coefficient is calculated by multiplying the sensitivity coefficient, the real-time value of the corresponding physical field parameter, and the real-time value of the dual-domain characteristic deviation. The value is calculated by taking the absolute value of the misalignment elasticity coefficient and then dividing it by the sum of the absolute values ​​of the misalignment elasticity coefficients of all physical field parameters. The value ranges from 0 to 1. The sum is 1.

[0012] Furthermore, in S4, the multi-field coupling attribution quantization includes temperature drift physical field, vibration acceleration physical field, and electromagnetic interference physical field. The calculation objects contributing weights include single physical field independent action terms, temperature and vibration coupling terms, temperature and electromagnetic interference coupling terms, vibration and electromagnetic interference coupling terms, and temperature, vibration, and electromagnetic interference three-field coupling terms.

[0013] Furthermore, in S5, the mathematical expression of the multi-field coupled inaccuracy risk degree and boundary determination algorithm in the inaccuracy boundary and risk prediction is as follows: In the formula, Let t be the measure of inaccuracy risk. The deviation of the two-domain feature at time t. The contribution weight corresponding to the i-th physical field parameter. Let be the real-time acquired value of the i-th physical field parameter at time t. Let be the rated limit tolerance value of the i-th physical field parameter; The parameters are determined based on the rated operating parameter limits of the intelligent measurement switch and the tolerance threshold of the components, and vary with the hardware parameters of the intelligent measurement switch. The real-time load rate of the i-th physical field parameter is calculated by comparing the real-time acquired value of the physical field parameter with the rated ultimate tolerance value, and the value ranges from 0 to 1. The value is calculated by multiplying the contribution weight of each physical field parameter by the corresponding real-time load rate and then summing the results. The value ranges from 0 to 1. The value is calculated by multiplying the deviation of the two-domain feature at time t by the summation result above, and the value ranges from 0 to positive infinity.

[0014] Furthermore, in S5, in the inaccuracy boundary and risk prediction, the measurement inaccuracy safety boundary is divided into three numerical intervals: the measurement inaccuracy risk degree greater than or equal to 1 corresponds to the critical inaccuracy interval, the measurement inaccuracy risk degree greater than or equal to 0.8 and less than 1 corresponds to the high inaccuracy risk interval, and the measurement inaccuracy risk degree less than 0.8 corresponds to the safe operation interval. The inaccuracy boundary condition library stores the safety boundary values ​​under different combinations of temperature, vibration and electromagnetic interference.

[0015] Compared with existing technologies, the misalignment analysis method of this intelligent measuring switch has the following advantages: I. This invention acquires multi-dimensional feature data synchronously through multiple physical fields and combines it with a dual-domain benchmark model calibration to form a standardized deviation judgment system. This enables accurate judgment and root cause tracing of the measurement inaccuracy status of intelligent measurement switches. By setting multi-cycle verification inaccuracy judgment rules, it effectively filters abnormal signals caused by instantaneous interference, avoids misjudgment problems caused by single-cycle detection, and significantly improves the accuracy and objectivity of inaccuracy status judgment. It breaks through the limitations of traditional single-dimensional detection. At the same time, by using the dual-domain feature deviation decoupling method, it can effectively distinguish different types of inaccuracy root causes, accurately match and locate the corresponding failed hardware units, making the investigation of inaccuracy problems more targeted, reducing indiscriminate investigation steps, significantly improving the efficiency of fault handling, and reducing the manpower and time costs of equipment operation and maintenance.

[0016] Second, this invention utilizes a multi-physics field coupling attribution quantification algorithm to quantitatively analyze the contribution of the independent action of each physical field and the inter-field coupling effect to measurement inaccuracies. This allows inaccuracy attribution to align with the complex operating conditions of actual equipment, overcoming the limitations of traditional methods that only consider the influence of a single physical field. By combining the rated parameter limits of intelligent measurement switches and the tolerance thresholds of components, an inaccuracy boundary condition library covering all operating conditions is constructed. Through a risk degree algorithm, the real-time measurement inaccuracy risk degree is calculated and multi-level safety boundary intervals are defined, realizing a shift from passive diagnosis after inaccuracy to proactive risk prediction before inaccuracy. This enables the early identification of measurement inaccuracy risks under different operating conditions, providing a scientific basis for preventive maintenance of equipment, continuously ensuring the measurement accuracy of measurement switches, improving the overall stability and safety of equipment operation, and extending the effective service life of equipment.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 A flowchart of a misalignment analysis method for an intelligent measuring switch; Figure 2 This is a schematic diagram illustrating the data transmission between the steps of a misalignment analysis method for an intelligent measuring switch. Figure 3 A flowchart for calibrating a two-domain baseline model. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] The misalignment analysis method for intelligent measuring switches of this invention is based on multi-physics field synchronous sensing and electro-magnetic dual-domain reference model calibration technology. Through step-by-step execution of misalignment state determination, misalignment root cause tracing, multi-field coupling attribution quantification, and misalignment boundary and risk prediction, it achieves accurate identification of the measurement misalignment state of the intelligent measuring switch, rapid location of the misalignment root cause, quantitative analysis of misalignment influencing factors, and early prediction of misalignment risks. This solves the technical problems of traditional measuring switch misalignment detection relying solely on a single electrical domain parameter, inability to locate the misalignment root cause, difficulty in quantifying the effects of multi-physics field coupling, and lack of an effective risk prediction mechanism. Figure 1 As shown. The overall implementation process of this method consists of five continuous and interconnected steps: multi-physics field synchronous sampling, dual-domain benchmark model calibration, misalignment determination and root cause tracing, multi-field coupling attribution quantification, and misalignment boundary and risk prediction. Each step is linked to form a complete misalignment analysis closed-loop system, which can be adapted to low-voltage and medium-voltage intelligent measuring switches with different rated parameters and different operating conditions. The specific implementation methods of each step are described in detail below.

[0022] S1, Multi-physics synchronous sampling: This step is the data source acquisition stage of the entire misalignment analysis method. The core implementation logic is to use a unified clock trigger signal to synchronously collect the full-dimensional characteristic data of the electric field, magnetic field, temperature field and vibration field of the intelligent measurement switch through a dedicated data acquisition unit, so as to ensure the time consistency of the data of each physical field and provide complete and accurate raw data for subsequent steps such as model calibration and misalignment judgment.

[0023] Sampling triggering and acquisition device configuration: Synchronous sampling is performed using a 1kHz unified clock trigger signal, with the clock signal synchronization accuracy controlled within 1ms to ensure that the timestamps of each physical field data acquisition are perfectly matched. Four dedicated acquisition units are configured: an electric field data acquisition unit, a magnetic field data acquisition unit, a temperature field data acquisition unit, and a vibration field data acquisition unit. Each acquisition unit is physically connected to the corresponding monitoring point of the intelligent measurement switch, and all acquisition units are equipped with a signal conversion module, which can convert the acquired analog signals into digital signals in real time and add high-precision timestamps to the converted digital signals.

[0024] Specific data acquisition methods for each physical field: Full-feature data acquisition in the electrical domain: A high-precision electrical parameter acquisition instrument is used as the core of the acquisition. The acquisition points are set at the inlet and outlet of the intelligent measuring switch and the internal sampling circuit. The acquisition accuracy is 0.1mA for instantaneous current and 0.1V for instantaneous voltage, with a voltage RMS resolution of 0.01V and a current RMS resolution of 0.01A. The instantaneous current and voltage data are continuously acquired at a sampling frequency of 1kHz. The amplitude-frequency characteristic data and phase-frequency characteristic data acquisition frequency band covers the 50Hz power frequency to 2kHz harmonic frequency band. The metering error time series data is acquired according to the power frequency cycle, with no less than 200 data points acquired in each power frequency cycle, to fully record the real-time change law of the electrical domain characteristics.

[0025] Full-feature data acquisition in the magnetic domain: A Hall magnetic field sensor array is used as the core of the acquisition. The sensor array is evenly arranged around the magnetic core of the intelligent measuring switch, the current transformer body and other key monitoring positions of magnetic characteristics. The magnetic field strength acquisition range is 0-1000Gs and the acquisition accuracy is 1Gs. Magnetic field strength data and magnetic flux distortion rate data are acquired in real time. Magnetic flux data is calculated by spatial integration of magnetic field strength. Hysteresis loop feature data is acquired at the frequency of drawing a complete hysteresis loop every 1 power frequency cycle. Key features of hysteresis loop such as saturation magnetic induction intensity, remanence, and coercivity are extracted simultaneously. Magnetic permeability data is obtained by calculating the ratio of magnetic flux to magnetic field strength.

[0026] Temperature field full-node data acquisition: A combination of distributed fiber optic temperature sensors and patch thermocouple sensors is used for acquisition. Patch thermocouple sensors are attached to the surfaces of key heat-generating components such as terminals, magnetic cores, and PCB boards, while distributed fiber optic temperature sensors are arranged inside the switch cavity. The acquisition accuracy is 0.1℃ and the sampling frequency is 10Hz. The acquired data includes the real-time temperature value of each monitoring point and the rate of temperature change per unit time, which fully reflects the temperature distribution and temperature change trend of each node inside the switch.

[0027] Vibration field full-condition data acquisition: A three-axis accelerometer is used as the core of the acquisition. The sensor is installed at locations prone to vibration, such as the switch body, the opening and closing mechanism, and the line connection end. When acquiring the impact vibration data of opening and closing, the sampling frequency is increased to 5kHz to ensure that the instantaneous characteristics of the impact vibration are captured. When acquiring the mechanical vibration data of the line, the sampling frequency is set to 200Hz. The acquired data includes three dimensions of characteristics: vibration acceleration, vibration frequency, and vibration amplitude, which fully covers the vibration state of the switch under all operating conditions.

[0028] Initial storage of collected data: After each acquisition unit completes data acquisition and signal conversion, it transmits the timestamped digital signal to the data buffer module in real time. The data buffer module groups and stores the data according to the physical field type and sets up a data backup mechanism to avoid the loss of original data. All stored data retains timestamp information, providing a foundation for timing alignment processing in subsequent steps, such as... Figure 2 As shown.

[0029] S2, Dual-domain baseline model calibration: This step establishes a precise benchmark reference system for misjudgment. The implementation basis is the standard rated operating condition of the intelligent measurement switch. Under this operating condition, the calibration of the algorithm parameters for the synchronous characteristic deviation of the electric and magnetic domains is completed. The safety threshold for the dual-domain characteristic deviation under normal measurement conditions is determined through statistical analysis. The specific implementation is divided into four sub-steps: benchmark data acquisition, benchmark data preprocessing, algorithm parameter calibration, and safety threshold determination. Each sub-step is executed sequentially and progressively.

[0030] Standard rated operating condition definition: The specific parameters of the standard rated operating condition in this invention are as follows: ambient temperature is 20℃±2℃, no external vibration input (vibration acceleration amplitude <0.01m / s²), input signal is the sinusoidal voltage and sinusoidal current of the rated specification of the intelligent measuring switch, total harmonic distortion rate is not greater than 1%, and there is no DC bias component (DC bias intensity <1Gs). Under this operating condition, the metering state of the intelligent measuring switch is ideal and normal, and it can be used as the reference operating condition for the calibration of the dual-domain reference model.

[0031] Baseline data acquisition: Under the aforementioned standard rated operating conditions, electrical and magnetic reference characteristic data were collected covering five load ranges: no load, 25% rated load, 50% rated load, 75% rated load, and 100% rated load. For each load range, the intelligent measuring switch was kept stable and interference-free. At least 50 sets of stable data for each continuous power frequency cycle were collected for each load range, with each set containing all characteristic data for both the electrical and magnetic domains under the corresponding load range. After collection, all data were grouped and labeled, indicating the corresponding load range, power frequency cycle number, and collection time, forming the original reference dataset.

[0032] Baseline data preprocessing: The original benchmark dataset is subjected to two processes in sequence: filtering and denoising, and normalization, to eliminate interference information and dimensional differences in the data, providing high-quality benchmark data for algorithm parameter calibration. Filtering and noise reduction: A Butterworth low-pass filter is used to filter all reference data. The filter cutoff frequency is set to 500Hz to accurately filter out high-frequency interference signals in the data and retain effective characteristic signals. Normalization: The Z-score normalization method is adopted to uniformly map the filtered electric and magnetic domain feature data to a dimensionless numerical range that follows a standard normal distribution, completely eliminating the calculation deviation caused by different physical quantities due to different dimensions and numerical ranges, and ensuring the mathematical consistency of subsequent electric-magnetic feature mapping operations and deviation calculations; the preprocessed dimensionless reference data is stored in a dedicated reference database as the sole data basis for algorithm parameter calibration.

[0033] Dual-domain synchronization feature deviation algorithm parameter calibration: Based on the preprocessed benchmark data, the time synchronization fidelity coefficient κ and the electromagnetic reference constitutive mapping operator in the dual-domain synchronization feature deviation algorithm are calibrated sequentially. Electric domain reference eigenvector The three key parameters, along with their calibration methods and value rules, are as follows: Time synchronization fidelity coefficient κ: Calculated based on the timestamp synchronization accuracy of each sampling channel, with a value ranging from 0.99 to 1. The higher the timestamp synchronization accuracy of the sampling channel, the closer the value of κ is to 1; for example, when the timestamp synchronization accuracy of the sampling channel is 0.5ms, κ is 0.995, and when the synchronization accuracy is 0.1ms, κ is 0.999. Electromagnetic reference constitutive mapping operator Based on the one-to-one correspondence between the preprocessed electric domain reference feature data and the magnetic domain reference feature data, the least squares method is used for fitting and generation. It is a matrix with the same dimension as the feature vector, and its values ​​are dynamically updated as the measurement characteristics of the smart measurement switch change. Electric Domain Reference Eigenvector The mean value was calculated by taking the average of the electrical domain reference characteristic data within the 100% rated load range under standard rated operating conditions. The Laida criterion was used during the calculation to remove outlier data points, ensuring the accuracy of the mean. The value is adjusted accordingly as the rated parameters of the intelligent measuring switch change.

[0034] The mathematical expression for the dual-domain synchronization feature deviation algorithm is: The mathematical expression for the dual-domain synchronization feature deviation algorithm is: In the formula, Let be the deviation of the dual-domain features at time t, and let be a dimensionless relative value characterizing the degree of synchronization difference between the electric and magnetic domain features; This is the time synchronization fidelity coefficient, a dimensionless coefficient ranging from 0.99 to 1. It is calculated based on the timestamp synchronization accuracy of the sampling channel; higher synchronization accuracy results in higher fidelity. The closer the value is to 1; Let be the electric domain feature column vector after Z-score dimensionless preprocessing at time t. is the magnetic domain feature column vector after Z-score dimensionless preprocessing at time t; It is an electro-magnetic reference constitutive mapping operator, a dimensionless matrix that matches the dimension of the feature vector. It is obtained by fitting and calculating the dimensionless electric domain reference feature data and magnetic domain reference feature data collected under standard rated operating conditions through a one-to-one correspondence, and it varies with the metering characteristics of the intelligent measurement switch. For Hadamah accumulation; The dimensionless electric domain reference characteristic column vector under standard rated operating conditions L2 norm; electric domain reference feature column vector It is calculated by taking the average value of the dimensionless electrical domain reference characteristic data of the rated load range under standard rated operating conditions, and varies with the rated parameters of the intelligent measuring switch. It is calculated from real-time dimensionless electric and magnetic domain data and calibration parameters.

[0035] Determination of safety threshold for dual-domain feature deviation: Based on the calibrated dual-domain synchronous feature deviation algorithm, the dual-domain feature deviation values ​​corresponding to all preprocessed benchmark data in the benchmark database are calculated. Statistical analysis is performed on all calculated values, and the mean plus three times the standard deviation is taken as the safety threshold for dual-domain feature deviation under normal measurement conditions, using the normal distribution 3σ criterion. This threshold is a key basis for subsequent inaccuracy determination. After calibration, it is stored in the algorithm parameter library along with the three algorithm parameters mentioned above. Figure 3 As shown.

[0036] S3, Inaccuracy Detection and Root Cause Analysis: This step is the core judgment and source tracing link of the misalignment analysis. The data source is the electric and magnetic domain data collected in real time by the multi-physics field synchronous sampling step. The algorithm is based on the deviation algorithm and the determined safety threshold determined by the dual-domain benchmark model calibration step. The specific implementation is divided into three sub-steps: misalignment state judgment, misalignment root cause differentiation, and failure unit location. These steps respectively achieve accurate identification of misalignment state, clear differentiation of misalignment root cause, and rapid location of failure hardware unit.

[0037] Determination of inaccuracy status: The misjudgment method adopts a rule of initial judgment in a single power frequency cycle plus verification in three consecutive power frequency cycles to avoid misjudgment caused by instantaneous interference. The specific implementation method is as follows: First, the real-time acquired electric and magnetic domain data are preprocessed with Z-score dimensionless conversion consistent with the reference data. Then, the deviation degree of the real-time dual-domain feature is calculated using the deviation degree algorithm calibrated in the algorithm parameter library, and the value is compared with the dual-domain feature deviation safety threshold. If the deviation of the real-time dual-domain feature in a single power frequency cycle is less than or equal to the safety threshold, the metering status of the intelligent measurement switch in the current power frequency cycle is determined to be normal. If the real-time dual-domain feature deviation of a single power frequency cycle is greater than the safety threshold, the current power frequency cycle is immediately marked as an abnormal cycle, and the electric domain data and magnetic domain data of the next three power frequency cycles are continuously collected. The real-time dual-domain feature deviation of each power frequency cycle is calculated using the same algorithm. If the real-time dual-domain feature deviation is greater than the safety threshold for three consecutive power frequency cycles, the intelligent measurement switch is determined to be in a state of measurement inaccuracy, and the subsequent process of inaccuracy root cause identification and failure unit location is triggered. If, within three consecutive power frequency cycles, the real-time dual-domain characteristic deviation exceeds the safety threshold for two or fewer power frequency cycles, it is determined to be an instantaneous interference anomaly. The measurement inaccuracy status determination is not triggered, but the anomaly information is only recorded in the data analysis database for subsequent switch operation status trend analysis.

[0038] Distinguishing the root causes of inaccuracy: The identification of the root causes of inaccuracies is based on the decoupling of dual-domain feature deviations. By eliminating grid-side interference components, it focuses on the inaccuracy causes of the switch itself. The specific implementation consists of four sub-steps: feature extraction, grid-side component elimination, feature deviation calculation, and root cause labeling. Feature extraction: The sliding window method is used to extract features from the real-time acquired electric and magnetic domain data. The window size is set to 1 power frequency cycle and the step size is set to 0.5 power frequency cycles. Real-time electric domain feature sequences and real-time magnetic domain feature sequences are extracted from the real-time data. Power grid side component removal: The characteristic components of the power grid side input signal are separated from the real-time electrical domain characteristic sequence by Fourier decomposition, and the corresponding characteristic offset is calculated. The characteristic offset is removed from the real-time dual-domain characteristic deviation to obtain the dual-domain characteristic deviation caused only by the intelligent measurement switch itself. Characteristic deviation calculation: Calculate the magnetic characteristic deviation between the real-time magnetic characteristic sequence and the reference magnetic characteristic sequence under standard rated operating conditions, and the electric characteristic deviation between the real-time electric characteristic sequence and the reference electric characteristic sequence under standard rated operating conditions. The deviation value is the absolute value of the difference between the real-time characteristic value and the reference characteristic value. Simultaneously, determine the characteristic reference fluctuation range under standard rated operating conditions, where the magnetic characteristic reference fluctuation range is ±5% of the reference characteristic value, and the electric characteristic reference fluctuation range is ±3% of the reference characteristic value. Root cause identification: The Pearson correlation coefficient is used to calculate the synchronicity between the changes in the magnetic domain characteristic deviation value and the electrical domain characteristic deviation value and the corresponding dual-domain characteristic deviation degree of the switch body. A correlation coefficient greater than 0.8 is considered highly synchronized. If the magnetic domain characteristic deviation value exceeds the magnetic domain characteristic reference fluctuation range and is highly synchronized with the corresponding dual-domain characteristic deviation degree of the switch body, the magnetic characteristic degradation of the current transformer is identified as the root cause of inaccuracy. If the electrical domain characteristic deviation value exceeds the electrical domain characteristic reference fluctuation range and is highly synchronized with the corresponding dual-domain characteristic deviation degree of the switch body, the electrical parameter drift of the sampling circuit is identified as the root cause of inaccuracy. If the characteristic offset corresponding to the characteristic component of the grid-side input signal is not zero and cannot be compensated by the switch body, the grid-side signal distortion is identified as the root cause of inaccuracy.

[0039] Failure Element Location: Based on the aforementioned sources of misalignment, the corresponding hardware units within the intelligent measurement switch are matched to achieve precise location of the hardware units responsible for the misalignment: If the root cause of the misalignment is the degradation of the magnetic characteristics of the current transformer, the corresponding hardware unit related to the magnetic characteristics, such as the current transformer body, magnetic core, and magnetic shielding components, should be located. If the inaccuracy is caused by the drift of electrical parameters in the sampling circuit, the relevant hardware unit in the sampling circuit, such as the sampling resistor, operational amplifier, AD conversion chip, and filter capacitor, should be located. If the inaccuracy is caused by signal distortion on the power grid side, the corresponding hardware unit for power grid side signal processing, such as the power grid incoming line, line filtering unit, and surge suppressor, should be located.

[0040] Once the location is determined, a failure unit location report is generated, which marks the name, installation location, abnormal characteristics, and characteristic deviation values ​​of the failure unit, providing a clear direction for the troubleshooting of intelligent measurement switches.

[0041] S4, Multi-field Coupled Attribution Quantization: This step is the quantitative analysis of the factors affecting inaccuracy. Based on the time-series data of inaccuracy deviation obtained from the inaccuracy judgment and root cause tracing steps, and the temperature field data and vibration field data obtained from the multi-physics field synchronous sampling steps, electromagnetic interference data is introduced as supplementary physical field data. Through the multi-physics field coupling inaccuracy contribution weight quantification algorithm, the contribution weight of each physical field and each coupling term to the inaccuracy of the intelligent measurement switch is calculated to identify the main causes of inaccuracy. The specific implementation is divided into five sub-steps: independent variable set construction, data preprocessing, sensitivity coefficient calculation, inaccuracy elasticity coefficient calculation, and contribution weight calculation.

[0042] Setting of independent and dependent variables: The time-series data of the misalignment deviation is used as the dependent variable to reflect the change in the degree of misalignment of the intelligent measuring switch. Temperature field data, vibration field data, and electromagnetic interference data are used as basic independent variables. Among them, the electromagnetic interference data is collected by the electromagnetic interference monitoring module inside the switch to characterize the intensity of electromagnetic interference during the switch operation. Temperature, vibration, and electromagnetic interference indirectly affect the dual-domain characteristic deviation by changing the magnetic characteristics of the current transformer and the electrical parameters of the sampling circuit. The two are implicitly causally related and have no explicit analytical function relationship. Therefore, a data-driven numerical method is used to calculate the degree of correlation between them.

[0043] Construction of the set of independent variables: Based on the fundamental independent variables, a complete set of independent variables is constructed, including single-physics independent terms, pairwise coupled terms, and multi-field coupled terms. The characterization parameters of each type of term are as follows: Independent terms for a single physics field: including temperature drift term (characterized by the rate of temperature change in the temperature field data), vibration acceleration term (characterized by the amplitude of vibration acceleration in the vibration field data), and electromagnetic interference term (characterized by the intensity of electromagnetic interference). Pairwise coupling terms include temperature and vibration coupling terms (temperature change rate × vibration acceleration amplitude), temperature and electromagnetic interference coupling terms (temperature change rate × electromagnetic interference intensity), and vibration and electromagnetic interference coupling terms (vibration acceleration amplitude × electromagnetic interference intensity). Multi-field coupling terms: only include three-field coupling terms: temperature, vibration, and electromagnetic interference (temperature change rate × vibration acceleration amplitude × electromagnetic interference intensity).

[0044] Data preprocessing: All data within the dependent and independent variable sets are aligned to the same timestamp, with a time granularity of 1ms. Missing data are filled using linear interpolation, and outlier data are removed using the Laida criterion to ensure data integrity and consistency. At the same time, all data are standardized to eliminate the dimensional differences between different parameters, providing a unified data foundation for subsequent numerical calculations.

[0045] Calculation of sensitivity coefficient and misalignment elasticity coefficient: Sensitivity coefficient calculation: Using the sliding window numerical difference method, based on the time-series data of the time-stamp-aligned dual-domain feature deviation and the time-series data of each physical field parameter, the ratio of the deviation change to the corresponding physical field parameter change at adjacent sampling times is calculated. After smoothing by the sliding window, the real-time sensitivity coefficient is obtained. This coefficient reflects the rate of change of the deviation of the two-domain characteristics with the corresponding physical field parameters, and its value is dynamically updated with the changes of the real-time physical field parameters. Calculation of misalignment elasticity coefficient: using the formula Calculate the misalignment elasticity coefficient corresponding to each independent variable. This coefficient reflects the sensitivity of the changes in each physical field parameter and coupling term parameter to the misalignment deviation. The larger the absolute value of the coefficient, the more significant the influence of the parameter on the misalignment of the intelligent measurement switch.

[0046] Contribution weight calculation: The mathematical expression for the multiphysics coupling misalignment contribution weight quantization algorithm is: In the formula, The contribution weight corresponding to the i-th physical field parameter. For the i-th physical field parameter, Let j be the physical field parameter. The deviation of the two-domain features; Let be the sensitivity coefficient of the dual-domain characteristic deviation to the i-th physical field parameter. The sensitivity coefficient of the dual-domain feature deviation to the j-th physical field parameter is calculated by the numerical difference method based on the synchronously acquired time-series data of the dual-domain feature deviation and the corresponding physical field parameter. It is used to characterize the rate of change of the dual-domain feature deviation with the corresponding physical field parameter, and its value is dynamically updated with the real-time physical field parameter. The misalignment elastic coefficient is calculated by multiplying the sensitivity coefficient, the real-time value of the corresponding physical field parameter, and the real-time value of the dual-domain characteristic deviation. The value is calculated by taking the absolute value of the misalignment elasticity coefficient and then dividing it by the sum of the absolute values ​​of the misalignment elasticity coefficients of all physical field parameters. The value ranges from 0 to 1. The sum is 1; After the calculation is completed, the total contribution weights of single-physical field independent terms, pairwise coupled terms, and multi-field coupled terms are calculated respectively. The influence ratio of each physical field and each coupling effect on the inaccuracy of the intelligent measurement switch is clarified, and a multi-field coupling attribution quantification report is generated, marking the contribution weight value and influence level (high, medium, low) of each influencing factor.

[0047] S5, Inaccuracy Boundaries and Risk Prediction: This step is the risk warning stage of the misalignment analysis. It combines the contribution weights obtained from the multi-field coupling attribution quantification step, the deviation time series data obtained from the misalignment judgment and root cause tracing step, and the rated parameter limits of the intelligent measurement switch and the tolerance threshold of the components to establish a misalignment boundary condition library and calculate the real-time measurement misalignment risk degree. Finally, it delineates the measurement misalignment safety boundary to achieve early prediction of misalignment risk. The specific implementation is divided into three sub-steps: establishing the misalignment boundary condition library, calculating the real-time misalignment risk degree, and delineating the measurement misalignment safety boundary.

[0048] Establishment of a library of inaccurate boundary conditions: Basic data extraction: Extract the contribution weights of each single physical field independent term, pairwise coupled term, and multi-field coupled term obtained from the multi-field coupling attribution quantization step; extract the time series data of the inaccuracy deviation obtained from the inaccuracy judgment and root cause tracing steps; sort out and determine the rated operating parameter limits of the intelligent measurement switch and the tolerance threshold of each component. Rated limit tolerance values ​​are determined as follows: Based on the identified parameter limits and tolerance thresholds, the rated limit tolerance values ​​for three types of physical field parameters are determined: temperature drift, vibration acceleration amplitude, and electromagnetic interference intensity. The rated limit tolerance value for temperature drift is determined based on the tolerance temperature of the magnetic core, PCB board, and terminals; the rated limit tolerance value for vibration acceleration amplitude is determined based on the vibration tolerance parameters of the switch body and the opening and closing mechanism; and the rated limit tolerance value for electromagnetic interference intensity is determined based on the anti-interference capability of the sampling circuit and control circuit. Operating condition matrix construction: Construct an operating condition matrix covering no-load to 120% rated load, different temperature, vibration, and electromagnetic interference numerical combinations. In the operating condition matrix, each physical field parameter is taken at equal intervals, with temperature drift values ​​at 5℃ intervals, vibration acceleration amplitude values ​​at 2m / s² intervals, and electromagnetic interference intensity values ​​at 1V / m intervals, ensuring that the operating condition matrix covers all operating conditions of the intelligent measuring switch. Library entry: Enter the above basic data, rated limit tolerance values, and operating condition matrix into the inaccuracy boundary condition library. This library supports online updates and can dynamically adjust internal parameters according to changes in the operating status of the intelligent measurement switch and the aging degree of components.

[0049] Real-time inaccuracy risk calculation: The real-time measurement misalignment risk is calculated using a multi-field coupled misalignment risk and boundary determination algorithm. The mathematical expression of the multi-field coupled misalignment risk and boundary determination algorithm is as follows: In the formula, Let t be the measure of inaccuracy risk. The deviation of the two-domain feature at time t. The contribution weight corresponding to the i-th physical field parameter. Let be the real-time acquired value of the i-th physical field parameter at time t. Let be the rated limit tolerance value of the i-th physical field parameter; The parameters are determined based on the rated operating parameter limits of the intelligent measurement switch and the tolerance threshold of the components, and vary with the hardware parameters of the intelligent measurement switch. The real-time load rate of the i-th physical field parameter is calculated by comparing the real-time acquired value of the physical field parameter with the rated ultimate tolerance value, and the value ranges from 0 to 1. The value is calculated by multiplying the contribution weight of each physical field parameter by the corresponding real-time load rate and then summing the results. The value ranges from 0 to 1. The value is calculated by multiplying the deviation of the two-domain feature at time t by the summation result above, and the value ranges from 0 to positive infinity.

[0050] The system collects the values ​​of various physical field parameters in real time, continuously calculates the inaccuracy risk according to the above formula, and updates the calculation results every 100ms to realize real-time monitoring of the inaccuracy risk of the intelligent measurement switch.

[0051] Delineation of safety boundaries for measurement inaccuracies: Based on the multi-field coupled inaccuracy risk degree and boundary judgment algorithm, the inaccuracy risk degree value corresponding to each parameter combination in the working condition matrix is ​​calculated; all calculated inaccuracy risk degree values ​​are divided into continuous intervals, and three measurement inaccuracy safety boundary intervals are defined: safe operation interval, high inaccuracy risk interval, and inaccuracy critical interval. The division criteria and operation and maintenance recommendations for each interval are as follows: Safe operating range: The range of values ​​corresponding to a failure risk level of less than 0.8. Within this range, the metering status of the intelligent measuring switch is normal, all characteristic parameters are within a reasonable range, and it can continue to operate stably without the need for maintenance measures. High inaccuracy risk range: This range corresponds to an inaccuracy risk level greater than or equal to 0.8 and less than 1. Within this range, the measurement accuracy of the intelligent measurement switch begins to decline, and there is a significant risk of inaccuracy. Maintenance personnel should be arranged in a timely manner to conduct a comprehensive inspection and parameter calibration of the switch. Inaccuracy Critical Range: This range corresponds to a risk level of inaccuracy greater than or equal to 1. Within this range, the inaccuracy of the intelligent measuring switch has exceeded the allowable range, and the measurement results have lost their reference value. The machine must be stopped immediately for inspection and repair, and the failed hardware unit must be replaced.

[0052] All parameters corresponding to the safety boundary intervals of all parameter combinations in the operating condition matrix are completely entered into the inaccuracy boundary condition library. When the inaccuracy risk level calculated in real time falls into the unsafe operating range, the system automatically triggers an early warning signal to remind maintenance personnel to handle it in a timely manner.

[0053] After completing the above five steps, the entire misalignment analysis process of the intelligent measurement switch is finished. The analysis reports, positioning results, and risk data generated in each step are all stored in the intelligent measurement switch operation management platform, providing comprehensive data support and decision-making basis for switch condition inspection, operation and maintenance optimization, and life assessment.

[0054] This invention provides a misalignment analysis method for intelligent measuring switches, overcoming the limitations of traditional misalignment detection relying solely on a single electrical parameter. It achieves full-dimensional acquisition of switch operating characteristics through multi-physics field synchronous sampling. By relying on a dual-domain benchmark model calibration and multi-round power frequency cycle verification judgment rule, the accuracy of misalignment state identification is improved, effectively avoiding misjudgments caused by instantaneous interference. Through dual-domain feature deviation decoupling, the root causes of misalignment are accurately distinguished and the failure units are quickly located, significantly shortening fault repair time. Based on a multi-physics field coupled misalignment contribution weight quantification algorithm, quantitative analysis of various influencing factors and their coupling effects is achieved, clarifying the main causes of misalignment. By establishing a misalignment boundary condition library and calculating real-time misalignment risk, advance prediction of measurement misalignment risk and accurate delineation of safety boundaries are achieved, upgrading the fault handling of intelligent measuring switches from post-repair maintenance to pre-prediction and condition-based maintenance. This method is highly versatile and can be adapted to intelligent measuring switches with different rated specifications and operating conditions. It has significant advantages such as accurate identification, rapid traceability, quantitative analysis, and risk prediction. It effectively improves the metering accuracy and operational reliability of intelligent measuring switches and reduces the incidence of problems such as power metering errors and power grid operation failures caused by metering inaccuracies.

[0055] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for analyzing the inaccuracy of an intelligent measuring switch, characterized in that, The specific steps of this method are as follows: S1, Multi-physics field synchronous sampling: Synchronously collect full feature data of the electric field, full feature data of the magnetic field, full node data of the temperature field, and full working condition data of the vibration field; S2, Dual-domain reference model calibration: Based on the electric domain reference feature data and magnetic domain reference feature data collected under standard rated operating conditions, calibrate the algorithm parameters for the dual-domain synchronization feature deviation of the electric and magnetic domains, and determine the safety threshold for the dual-domain feature deviation under normal metering conditions; the dual-domain synchronization feature deviation is used to characterize the degree of synchronization difference between the electric domain features and the magnetic domain features. S3, Inaccuracy Judgment and Root Cause Tracing: Based on the electric and magnetic domain data collected in real time in step S1, the real-time dual-domain feature deviation is calculated using the deviation algorithm calibrated in step S2. The inaccuracy state is determined by comparing it with the safety threshold. Then, the root cause of inaccuracy is distinguished and the failure unit is located by decoupling the dual-domain feature deviation. S4, Multi-field coupling attribution quantization: Based on the time series data of the misalignment obtained in step S3 and the temperature field data and vibration field data obtained in step S1, the contribution weight of each physical field to the misalignment is calculated by the multi-physical field coupling misalignment contribution weight quantization algorithm. S5, Inaccuracy Boundary and Risk Prediction: Based on the contribution weight obtained in step S4, the deviation time series data obtained in step S3, and the rated parameter limits of the intelligent measurement switch and the tolerance threshold of the components, an inaccuracy boundary condition library is established, and the real-time measurement inaccuracy risk degree is calculated based on the inaccuracy boundary condition library, thereby defining the measurement inaccuracy safety boundary.

2. The method for misalignment analysis of an intelligent measuring switch according to claim 1, characterized in that, In the multi-physics field synchronous sampling described in S1, synchronous acquisition is performed using a unified clock trigger signal. The full characteristic data of the electric field includes instantaneous current data, instantaneous voltage data, amplitude-frequency characteristic data, phase-frequency characteristic data, voltage RMS data, current RMS data, and measurement error timing data. The full characteristic data of the magnetic field includes magnetic field strength data, magnetic flux data, hysteresis loop characteristic data, permeability data, and magnetic flux distortion rate data. The full node data of the temperature field includes terminal temperature data, magnetic core temperature data, PCB board temperature data, and cavity ambient temperature data. The full operating condition data of the vibration field includes opening and closing impact vibration data and line mechanical vibration data.

3. The method for misalignment analysis of an intelligent measuring switch according to claim 1, characterized in that, In the S2 dual-domain reference model calibration, the standard rated operating conditions are: ambient temperature of 20℃±2℃, no external vibration input, input signal of rated sinusoidal voltage, input signal of rated sinusoidal current, total harmonic distortion rate of no more than 1%, and no DC bias component. The dual-domain synchronization feature deviation algorithm parameters include time synchronization fidelity coefficient, electro-magnetic reference constitutive mapping operator, and electric domain reference feature vector. The dual-domain feature deviation safety threshold is determined according to the normal distribution 3σ criterion.

4. The method for misalignment analysis of an intelligent measuring switch according to claim 1, characterized in that, In S2, during the dual-domain baseline model calibration, the mathematical expression for the dual-domain synchronization feature deviation algorithm is: In the formula, Let be the deviation of the dual-domain features at time t, and let be a dimensionless relative value characterizing the degree of synchronization difference between the electric and magnetic domain features; This is the time synchronization fidelity coefficient, a dimensionless coefficient ranging from 0.99 to 1. It is calculated based on the timestamp synchronization accuracy of the sampling channel; higher synchronization accuracy results in higher fidelity. The closer the value is to 1; Let be the electric domain feature column vector after Z-score dimensionless preprocessing at time t. is the magnetic domain feature column vector after Z-score dimensionless preprocessing at time t; It is an electro-magnetic reference constitutive mapping operator, a dimensionless matrix that matches the dimension of the feature vector. It is obtained by fitting and calculating the dimensionless electric domain reference feature data and magnetic domain reference feature data collected under standard rated operating conditions through a one-to-one correspondence, and it varies with the metering characteristics of the intelligent measurement switch. For Hadamah accumulation; The dimensionless electric domain reference characteristic column vector under standard rated operating conditions L2 norm; electric domain reference feature column vector It is calculated by taking the average value of the dimensionless electrical domain reference characteristic data of the rated load range under standard rated operating conditions, and varies with the rated parameters of the intelligent measuring switch. It is calculated from real-time dimensionless electric and magnetic domain data and calibration parameters.

5. The method for misalignment analysis of an intelligent measuring switch according to claim 1, characterized in that, In step S3, during the inaccuracy determination and root cause tracing, when comparing the inaccuracy status with the safety threshold, the real-time dual-domain feature deviation of the single power frequency cycle calculated by the deviation algorithm calibrated in step S2 is first compared with the safety threshold for dual-domain feature deviation determined in step S2. When the real-time dual-domain feature deviation of the single power frequency cycle is less than or equal to the safety threshold for dual-domain feature deviation, the current power frequency cycle measurement status is determined to be normal. When the real-time dual-domain feature deviation of the single power frequency cycle is greater than the safety threshold for dual-domain feature deviation, the current power frequency cycle is marked as abnormal. For a constant cycle, electrical and magnetic domain data are continuously collected for the next three power frequency cycles. The deviation algorithm calibrated in step S2 is used to calculate the real-time dual-domain feature deviation for each power frequency cycle. When the real-time dual-domain feature deviation for three consecutive power frequency cycles is greater than the dual-domain feature deviation safety threshold, the intelligent measurement switch is determined to be in a measurement inaccuracy state. When the real-time dual-domain feature deviation for two or fewer power frequency cycles is greater than the dual-domain feature deviation safety threshold within three consecutive power frequency cycles, it is determined to be an instantaneous interference anomaly, and the measurement inaccuracy state determination is not triggered.

6. The method for misalignment analysis of an intelligent measuring switch according to claim 1, characterized in that, In S3, the specific steps for distinguishing the root cause of inaccuracy and locating the failed unit through dual-domain feature deviation decoupling in the process of misalignment determination and root cause tracing are as follows: First, feature extraction is performed on the real-time acquired electrical and magnetic domain data to obtain real-time electrical domain feature sequences and real-time magnetic domain feature sequences. The grid-side input signal feature components in the real-time electrical domain feature sequences are separated and the corresponding feature offsets are calculated. The feature offsets are removed from the real-time dual-domain feature deviations to obtain the dual-domain feature deviations corresponding to the switch body. The magnetic domain feature deviations between the real-time magnetic domain feature sequences and the magnetic domain reference feature sequences under standard rated operating conditions, and the electrical domain feature deviations between the real-time electrical domain feature sequences and the electrical domain reference feature sequences under standard rated operating conditions are calculated respectively. The magnetic domain feature deviations and electrical domain feature deviations are compared. The difference is synchronized with the change of the dual-domain characteristic deviation degree corresponding to the switch body. When the magnetic domain characteristic deviation value exceeds the magnetic domain characteristic reference fluctuation range under standard rated operating conditions and is synchronized with the change of the dual-domain characteristic deviation degree corresponding to the switch body, the magnetic characteristic degradation of the current transformer is marked as the root cause of inaccuracy. When the electrical domain characteristic deviation value exceeds the electrical domain characteristic reference fluctuation range under standard rated operating conditions and is synchronized with the change of the dual-domain characteristic deviation degree corresponding to the switch body, the electrical parameter drift of the sampling circuit is marked as the root cause of inaccuracy. When the characteristic offset corresponding to the characteristic component of the grid-side input signal is not zero and cannot be compensated by the switch body, the grid-side signal distortion is marked as the root cause of inaccuracy. Based on the marked inaccuracy root cause, the corresponding hardware unit is matched to complete the positioning of the hardware unit corresponding to the inaccuracy root cause.

7. The method for misalignment analysis of an intelligent measuring switch according to claim 1, characterized in that, In S4, the mathematical expression for the multi-physics coupling misalignment contribution weight quantization algorithm in multi-field coupling attribution quantization is: In the formula, The contribution weight corresponding to the i-th physical field parameter. For the i-th physical field parameter, Let j be the physical field parameter. The deviation of the two-domain features; Let be the sensitivity coefficient of the dual-domain characteristic deviation to the i-th physical field parameter. The sensitivity coefficient of the dual-domain feature deviation to the j-th physical field parameter is calculated by the numerical difference method based on the synchronously acquired time-series data of the dual-domain feature deviation and the corresponding physical field parameter. It is used to characterize the rate of change of the dual-domain feature deviation with the corresponding physical field parameter, and its value is dynamically updated with the real-time physical field parameter. The misalignment elastic coefficient is calculated by multiplying the sensitivity coefficient, the real-time value of the corresponding physical field parameter, and the real-time value of the dual-domain characteristic deviation. The value is calculated by taking the absolute value of the misalignment elasticity coefficient and then dividing it by the sum of the absolute values ​​of the misalignment elasticity coefficients of all physical field parameters. The value ranges from 0 to 1. The sum is 1.

8. The method for misalignment analysis of an intelligent measuring switch according to claim 1, characterized in that, In S4, the multi-field coupling attribution quantization includes temperature drift physical field, vibration acceleration physical field, and electromagnetic interference physical field. The calculation objects contributing weights include single physical field independent action terms, temperature and vibration coupling terms, temperature and electromagnetic interference coupling terms, vibration and electromagnetic interference coupling terms, and temperature, vibration and electromagnetic interference three-field coupling terms.

9. The method for misalignment analysis of an intelligent measuring switch according to claim 1, characterized in that, In S5, the mathematical expression of the multi-field coupled inaccuracy risk degree and boundary determination algorithm in the inaccuracy boundary and risk prediction is as follows: In the formula, Let t be the measure of inaccuracy risk. The deviation of the two-domain feature at time t. The contribution weight corresponding to the i-th physical field parameter. Let be the real-time acquired value of the i-th physical field parameter at time t. Let be the rated limit tolerance value of the i-th physical field parameter; The parameters are determined based on the rated operating parameter limits of the intelligent measurement switch and the tolerance threshold of the components, and vary with the hardware parameters of the intelligent measurement switch. The real-time load rate of the i-th physical field parameter is calculated by comparing the real-time acquired value of the physical field parameter with the rated ultimate tolerance value, and the value ranges from 0 to 1. The value is calculated by multiplying the contribution weight of each physical field parameter by the corresponding real-time load rate and then summing the results. The value ranges from 0 to 1. The value is calculated by multiplying the deviation of the two-domain feature at time t by the summation result above, and the value ranges from 0 to positive infinity.

10. The method for misalignment analysis of an intelligent measuring switch according to claim 1, characterized in that, In S5, in the inaccuracy boundary and risk prediction, the measurement inaccuracy safety boundary is divided into three numerical intervals: the measurement inaccuracy risk degree greater than or equal to 1 corresponds to the critical inaccuracy interval, the measurement inaccuracy risk degree greater than or equal to 0.8 and less than 1 corresponds to the high inaccuracy risk interval, and the measurement inaccuracy risk degree less than 0.8 corresponds to the safe operation interval. The inaccuracy boundary condition library stores the safety boundary values ​​under different combinations of temperature, vibration and electromagnetic interference.

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