Error measurement system and method of metering transformer

Abstract

The invention discloses an error measurement system and method for a metering mutual inductor, and the system comprises an acquisition module which is used for collecting the multi-dimensional operation monitoring data of the metering mutual inductor; the processing module is used for performing conversion processing on the multi-dimensional operation monitoring data to obtain processed data; the first determining module is used for determining an abnormal metering transformer according to the processing data; and the alarm module is used for calculating an error value of the abnormal metering transformer, and giving an alarm prompt when the error value is greater than a preset error threshold value. The method comprises the following steps: firstly, carrying out preliminary anomaly detection, determining an abnormal metering mutual inductor, analyzing the error of the abnormal metering mutual inductor, reducing the number of measurement mutual inductors, further reducing measurement data, improving data processing efficiency, and improving the accuracy of metering error calculation.

Description

Technical Field

[0001] The present invention relates to the technical field of error detection, and in particular to an error measurement system and method for a metering transformer. Background Art

[0002] At present, with the further deepening of power system reform and the gradual establishment of the power spot trading market, the reliability of power grid power supply and the accuracy and stability of electric energy metering devices have received great attention from the power grid and other parties. As a widely used key electrical metering instrument, the transformer is directly related to the fairness and justice of electricity transaction settlement and the safe and stable operation of the power grid. However, the current transformer and voltage transformer are affected by the environment, the operating frequency of the primary side of the transformer, the magnetic field, etc., and the secondary side output results may have deviations. In order to ensure the safe operation of the power grid, it is necessary to quickly measure the errors of the current transformer and voltage transformer so that the current transformer and voltage transformer can be calibrated according to the measured errors.

[0003] In the field current transformer detection, in order to obtain the operating error of the current transformer, the current method is to compare the measurement error of the transformer under test and the advanced standard in the power-off state. However, in actual verification, it is found that some line transformers cannot be detected without power off, otherwise it will affect the production and life in the area; some lines can be powered off, but the workload of measuring the error of the transformer offline is large, the detection cycle is long, and some lines are even difficult to process the transformer offline. At the same time, when measuring the error of the metering transformer, more measurement data is obtained, and the calculation efficiency is relatively slow. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the above-mentioned technology to a certain extent. To this end, the purpose of the present invention is to provide a metering transformer error measurement system and method, firstly perform preliminary abnormality detection, determine abnormal metering transformers, analyze the errors of abnormal metering transformers, reduce the number of measuring transformers, and then reduce measurement data, improve data processing efficiency, and improve the accuracy of metering error calculation.

[0005] To achieve the above object, an embodiment of the present invention provides an error measurement system for a metering transformer, comprising: The acquisition module is used to collect multi-dimensional operation monitoring data of the metering transformer; A processing module, used for converting and processing the multi-dimensional operation monitoring data to obtain processed data; A first determination module, used for determining an abnormal metering transformer according to the processed data; The alarm module is used to calculate the error value of the abnormal metering transformer and issue an alarm prompt when the error value is greater than a preset error threshold.

[0006] According to some embodiments of the present invention, the acquisition module includes: A sampling assembly, used for connecting the metering winding of the metering transformer with the sampling channel; The transmission module is used to detect the multi-dimensional operation monitoring data of each metering transformer and transmit the multi-dimensional operation monitoring data to the processing module; wherein the multi-dimensional operation monitoring data includes a current signal.

[0007] According to some embodiments of the present invention, the processing module includes: An amplification and filtering module, used for amplifying and filtering the current signal; The AD analog-to-digital conversion module is used to convert the current signal into a digital signal and transmit it to the first determination module.

[0008] According to some embodiments of the present invention, the first determining module includes: An analysis module, used for performing data analysis on the processed data based on the FFT algorithm to obtain analysis results; the analysis results include characteristic parameters of each data point in the IMF component; the characteristic parameters include amplitude and phase between the metering transformers; A calculation module is used to calculate a first current vector sum of a metering transformer of an input branch; calculate a second current vector sum of a metering transformer of an output branch; calculate a difference between the first current vector sum and the second current vector sum; when it is determined that the difference is greater than a preset difference, determine an abnormal branch including an abnormal metering transformer; and locate the abnormal metering transformer in the abnormal branch based on a KCL algorithm.

[0009] According to some embodiments of the present invention, the analysis module comprises: A first decomposition module is used to generate a data curve based on the processed data, and perform STL decomposition on the data curve to obtain periodic terms; The second decomposition module is used to perform EMD decomposition on the data curve to obtain multiple IMF components; The second determination module is used to determine, for each IMF component, the period length of the period in which each data point in the IMF component is located; and determine the analysis result according to the period length of the periodic item, the period length of the IMF component, and the period length of the period in which each data point in the IMF component is located.

[0010] According to some embodiments of the present invention, the alarm module includes: A third determination module is used to use a nonlinear kernel function to perform data feature space mapping on the characteristic parameters of the abnormal metering transformer, use a support vector machine algorithm to perform regression calculation on the characteristic parameters in the feature space, and determine linear and nonlinear regression models; The fourth determination module is used to determine the error value of the abnormal metering transformer through linear and nonlinear regression models, and issue an alarm when the error value is greater than a preset error threshold.

[0011] According to some embodiments of the present invention, the system further comprises: An acquisition module is used to acquire disturbance data of abnormal metering transformers; wherein the disturbance data includes temperature parameters, environmental pollution parameters, humidity parameters and secondary load parameters; The correction module is used to correct the error value of the abnormal metering transformer according to the disturbance data to obtain a corrected error value.

[0012] According to some embodiments of the present invention, the system further comprises: A sorting module, used for calculating the abnormal coefficient of each abnormal metering transformer before the alarm module calculates the error value of the abnormal metering transformer, sorting them according to the abnormal coefficient, and calculating the error value of the abnormal metering transformer according to the sorting result; The sorting module calculates the abnormal coefficient of each abnormal metering transformer, including: Calculates the actual wear life of abnormal metering sensors within a preset time period : in, The standard loss life of the abnormal metering sensor within a preset time period; is the actual operating temperature during the preset time period; is the ambient temperature; is the rated operating temperature of the abnormal metering sensor; Measure the actual life loss of the sensor within a preset time period based on anomalies , calculate the abnormal coefficient of the abnormal measurement sensor : in, is a natural constant.

[0013] According to some embodiments of the present invention, the system further comprises: an abnormality type determination module, configured to: Acquire a preset number of signals of the abnormal metering sensor based on a preset time interval, arrange the preset number of signals based on a time sequence, and obtain an abnormal signal vector; Analyze and calculate the abnormal signal vector to determine the attribute information of the signal in the abnormal measurement sensor; Comparing the attribute information with a plurality of preset attribute information in a preset database, determining the abnormal type of the abnormal metering sensor according to the comparison result and displaying it; Calculate the difference between the attribute information and several preset attribute information in the preset database respectively: in, The attribute information is the same as the first The difference value of preset attribute information; is the number of feature vectors included in the attribute information and is also the number of feature vectors included in the preset attribute information, and the number of feature vectors included in the attribute information is equal to the number of feature vectors included in the preset attribute information; Attribute information No. feature vectors; Preset attribute information No. feature vectors; The minimum difference value is determined, and the type of preset attribute information corresponding to the minimum difference value is used as the abnormal type of the abnormal measurement sensor and displayed.

[0014] In one embodiment, an error measurement method of an error measurement system of a metering transformer includes: Collect multi-dimensional operation monitoring data of metering transformers; Convert and process the multi-dimensional operation monitoring data to obtain processed data; Determine the abnormal metering transformer based on the processed data; Calculate the error value of the abnormal metering transformer and issue an alarm when the error value is greater than the preset error threshold.

[0015] The present invention proposes a metering transformer error measurement system and method, which firstly performs preliminary abnormality detection, determines the abnormal metering transformer, analyzes the error of the abnormal metering transformer, reduces the number of measuring transformers, and further reduces the measurement data, improves data processing efficiency, and improves the accuracy of metering error calculation.

[0016] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0017] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 is a block diagram of an error measurement system for a metering transformer according to an embodiment of the present invention; Figure 2 The present invention is a flowchart of a method for measuring an error of a metering transformer according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0020] like Figure 1 As shown, an embodiment of the present invention provides an error measurement system for a metering transformer, comprising: The acquisition module is used to collect multi-dimensional operation monitoring data of the metering transformer; A processing module, used for converting and processing the multi-dimensional operation monitoring data to obtain processed data; A first determination module, used for determining an abnormal metering transformer according to the processed data; The alarm module is used to calculate the error value of the abnormal metering transformer and issue an alarm prompt when the error value is greater than a preset error threshold.

[0021] The working principle of the above technical solution is as follows: the acquisition module collects and obtains the multi-dimensional operation monitoring data of the metering transformer, and comprehensively reflects the various states of the transformer during the working process. The processing module performs amplification and filtering, and converts the signal into a digital signal. The first determination module determines the abnormal metering transformer according to the processed data; the alarm module calculates the error value of the abnormal metering transformer, and when the error value is greater than the preset error threshold, an alarm prompt is issued to facilitate accurate determination of the error characteristics of the abnormal metering transformer and timely processing. Calibrate or replace the abnormal metering transformer. Adjust the installation position or working environment of the metering transformer to reduce the source of error. Strengthen the monitoring and maintenance of the metering transformer to ensure its long-term stable operation.

[0022] The beneficial effects of the above technical solution are as follows: through the steps of collection, processing, analysis and alarm, accurate measurement and effective monitoring of the metering transformer error are achieved. This can not only improve the stability and safety of the power system, but also reduce the economic losses caused by the transformer error. First, a preliminary abnormality detection is performed to determine the abnormal metering transformer, analyze the error of the abnormal metering transformer, reduce the number of measuring transformers, and then reduce the measurement data, improve the data processing efficiency, and improve the accuracy of the metering error calculation.

[0023] According to some embodiments of the present invention, the acquisition module includes: A sampling assembly, used for connecting the metering winding of the metering transformer with the sampling channel; The transmission module is used to detect the multi-dimensional operation monitoring data of each metering transformer and transmit the multi-dimensional operation monitoring data to the processing module; wherein the multi-dimensional operation monitoring data includes a current signal.

[0024] The working principle of the above technical solution: The sampling component is responsible for connecting the metering winding of the metering transformer to the sampling channel. In this way, when the metering transformer is working, the key data such as the current signal it generates can be captured by the acquisition module through the sampling channel. As an important part of multi-dimensional operation monitoring data, the current signal can directly reflect the performance of the metering transformer in the current conversion process. By monitoring the changes in the current signal, possible errors or faults in the metering transformer can be discovered in time. The transmission module usually adopts a high-speed, low-latency communication protocol to ensure the real-time and integrity of the data. It also has strong anti-interference capabilities to cope with complex electromagnetic environments.

[0025] The beneficial effect of the above technical solution is that the acquisition module can accurately capture and transmit the multi-dimensional operation monitoring data of the metering transformer through the coordinated work of the sampling component and the transmission module.

[0026] According to some embodiments of the present invention, the processing module includes: An amplification and filtering module, used for amplifying and filtering the current signal; The AD analog-to-digital conversion module is used to convert the current signal into a digital signal and transmit it to the first determination module.

[0027] The working principle of the above technical solution: Since the current signal collected from the metering transformer may be relatively weak and may contain some noise or interference components, it is necessary to amplify it to a suitable level through an amplifier circuit and remove noise and interference through a filter to improve the signal-to-noise ratio. The AD analog-to-digital conversion module converts continuous analog signals into discrete digital signals through the process of sampling and quantization. Sampling refers to taking values ​​of analog signals at certain time intervals, while quantization converts the sampled analog values ​​into the closest digital values.

[0028] The beneficial effect of the above technical solution is that the processing module can accurately convert the collected current signal into a digital signal that can be used for subsequent analysis and judgment through the coordinated work of the amplification and filtering module and the AD analog-to-digital conversion module. This provides a reliable data basis for the entire error measurement system, thereby ensuring the accuracy and reliability of the measurement.

[0029] According to some embodiments of the present invention, the first determining module includes: An analysis module, used for performing data analysis on the processed data based on the FFT algorithm to obtain analysis results; the analysis results include characteristic parameters of each data point in the IMF component; the characteristic parameters include amplitude and phase between the metering transformers; A calculation module is used to calculate a first current vector sum of a metering transformer of an input branch; calculate a second current vector sum of a metering transformer of an output branch; calculate a difference between the first current vector sum and the second current vector sum; when it is determined that the difference is greater than a preset difference, determine an abnormal branch including an abnormal metering transformer; and locate the abnormal metering transformer in the abnormal branch based on a KCL algorithm.

[0030] The working principle of the above technical solution: The analysis module uses the FFT (Fast Fourier Transform) algorithm to conduct in-depth analysis of the data transmitted from the processing module. The FFT algorithm is an efficient spectrum analysis method that can convert time domain signals into frequency domain signals, thereby revealing the distribution characteristics of the signal in frequency. Through FFT analysis, the characteristic parameters of each data point in the IMF (Intrinsic Mode Function) component can be obtained. These characteristic parameters are crucial for determining the amplitude and phase relationship between metering transformers. The characteristic parameters include key information such as the amplitude, phase, and frequency of the current signal of each metering transformer. Calculate the current vector sum composed of the current signals of each metering transformer in the input branch. (The amplitude and phase of each current signal are vector-added) Similarly, calculate the current vector sum composed of the current signals of each metering transformer in the output branch. Calculate the difference between the first current vector sum and the second current vector sum. This difference reflects the degree of current imbalance between the input branch and the output branch. If the difference is greater than the preset difference threshold, it is determined that there is an abnormal branch. This threshold is usually set according to the actual situation of the system and the error requirements. Finally, in the determined abnormal branch, the KCL (Kirchhoff's Current Law) algorithm is used to locate the specific abnormal metering transformer. In the KCL algorithm, at a node, the sum of the current flowing in is equal to the sum of the current flowing out. By comparing the amplitude and phase relationship of the current signal of each metering transformer, the metering transformer that does not conform to the KCL law, that is, the abnormal metering transformer, can be found.

[0031] The beneficial effects of the above technical solution are as follows: the first determination module can accurately determine whether there is an abnormal metering transformer and locate the specific abnormal position through the collaborative work of the analysis module and the calculation module, thereby ensuring the accuracy and reliability of the entire error measurement system.

[0032] According to some embodiments of the present invention, the analysis module comprises: A first decomposition module is used to generate a data curve based on the processed data, and perform STL decomposition on the data curve to obtain periodic terms; The second decomposition module is used to perform EMD decomposition on the data curve to obtain multiple IMF components; The second determination module is used to determine, for each IMF component, the period length of the period in which each data point in the IMF component is located; and determine the analysis result according to the period length of the periodic item, the period length of the IMF component, and the period length of the period in which each data point in the IMF component is located.

[0033] The working principle of the above technical solution is as follows: the first decomposition module is responsible for converting the processed data (such as current signals) into a data curve and performing STL (Seasonal-Trend decomposition using LOESS, seasonal-trend decomposition based on local weighted regression) decomposition on it. STL decomposition is a method for decomposing time series data, which can decompose data into trend terms, periodic terms and residual terms. Through STL decomposition, the first decomposition module can extract the periodic terms in the data curve. The periodic terms reflect the periodic changes in the data, which is of great significance for understanding the working state and error sources of the metering transformer. The second decomposition module performs EMD (Empirical Mode Decomposition) decomposition on the data curve. EMD is an adaptive data decomposition method that can decompose nonlinear and non-stationary data into a series of intrinsic mode function (IMF) components and a residual term. Through EMD decomposition, the second decomposition module can extract multiple IMF components. Each IMF component represents an inherent mode or vibration in the data, which is very useful for analyzing the behavioral characteristics of the metering transformer under complex working conditions. The period length of the periodic term is calculated to reflect the main periodic changes in the data. For each IMF component, calculate its period length and the period length of each data point in it. This helps to understand the natural modes and vibrations in the data. Compare the period length of the periodic term with the period length of the IMF component to analyze the correlation and difference between them. Analyze the changes in the period length of each data point in the IMF component to understand the dynamic characteristics of the data. If the period length of the periodic term is similar or equal to the period length of an IMF component, this may indicate that the IMF component is related to the main periodic changes in the data. If there are multiple periods of different lengths in the IMF component, this may reflect multiple vibration modes or periodic changes in the data. By analyzing the changes in the period length in the IMF component, the working status and error sources of the metering transformer in different time periods can be inferred. If the period length of an IMF component changes abnormally (such as suddenly becoming longer or shorter), this may indicate that the metering transformer has a fault or an increase in error during that time period. Set a threshold range for the period length to detect whether there is an abnormal period length in the IMF component. If the cycle length of a certain IMF component exceeds the set threshold range, it will be marked as an abnormal component and further checked to see if there is a problem with the related metering transformer.

[0034] Beneficial effects of the above technical solution: The analysis results include characteristic parameters (such as amplitude, phase, cycle length, etc.) of each data point in the IMF component, as well as the relationship between these characteristic parameters and the amplitude and phase relationship between the metering transformer. By comprehensively analyzing the cycle length of the periodic term, the cycle length of the IMF component, and the cycle length of the cycle in which each data point in the IMF component is located, the working state and error source of the metering transformer can be accurately determined. The analysis module can more deeply understand the periodicity and inherent patterns in the processed data through the combined use of STL decomposition and EMD decomposition, thereby extracting characteristic parameters useful for determining the error of the metering transformer.

[0035] According to some embodiments of the present invention, the alarm module includes: A third determination module is used to use a nonlinear kernel function to perform data feature space mapping on the characteristic parameters of the abnormal metering transformer, use a support vector machine algorithm to perform regression calculation on the characteristic parameters in the feature space, and determine linear and nonlinear regression models; The fourth determination module is used to determine the error value of the abnormal metering transformer through linear and nonlinear regression models, and issue an alarm when the error value is greater than a preset error threshold.

[0036] The working principle of the above technical solution is as follows: the third determination module uses a nonlinear kernel function to map the characteristic parameters of the abnormal metering transformer to the data feature space. The purpose of this step is to convert the original characteristic parameters into a higher-dimensional or more complex feature space in order to better capture the nonlinear relationship between the data. Commonly used nonlinear kernel functions include Gaussian kernel (RBF kernel), polynomial kernel, etc. In the feature space, the third determination module uses the support vector machine (SVM) algorithm to perform regression calculation on the characteristic parameters. SVM is a powerful machine learning algorithm that is particularly suitable for processing high-dimensional data and nonlinear relationships. Through SVM regression, a linear and / or nonlinear regression model can be determined to predict or estimate the error value of the abnormal metering transformer. The characteristic parameters of the abnormal metering transformer are input into the regression model, and the corresponding output value (i.e., the error value) is calculated. In order to determine whether the error of the abnormal metering transformer exceeds the acceptable range, the fourth determination module will set a preset error threshold. This threshold is usually set according to the actual needs and error requirements of the system. If the calculated error value is greater than the preset error threshold, the fourth determination module will issue an alarm prompt. This prompt includes sound and light alarms, screen display of alarm information, and sending alarm signals to remote monitoring systems.

[0037] The beneficial effect of the above technical solution is that the alarm module can accurately determine the error value of the abnormal metering transformer through the coordinated work of the third determination module and the fourth determination module, and issue an alarm prompt when necessary. This provides strong support for timely discovery and handling of metering transformer faults, thereby ensuring the stability and reliability of the entire system.

[0038] According to some embodiments of the present invention, the system further comprises: An acquisition module is used to acquire disturbance data of abnormal metering transformers; wherein the disturbance data includes temperature parameters, environmental pollution parameters, humidity parameters and secondary load parameters; The correction module is used to correct the error value of the abnormal metering transformer according to the disturbance data to obtain a corrected error value.

[0039] The working principle of the above technical solution: Temperature parameter: Temperature changes may cause the material inside the metering transformer to expand or contract, thereby affecting its electrical performance. Environmental pollution parameter: Pollutants (such as dust, salt spray, etc.) may adhere to the surface or inside of the metering transformer, resulting in a decrease in electrical insulation performance, thereby affecting the measurement accuracy. Humidity parameter: Excessive humidity may cause corrosion of metal parts inside the metering transformer or affect the electrical insulation performance. Secondary load parameter: The metering transformer is usually connected to the secondary circuit, and changes in the secondary load (such as changes in the secondary impedance of the current transformer) may affect the accuracy of its output current and voltage. A correction data table is constructed based on historical data, and the correction data table is queried based on the disturbance data to determine the correction coefficient. The error value of the abnormal metering transformer is corrected based on the correction coefficient to obtain the corrected error value.

[0040] The beneficial effect of the above technical solution is: the influence of various disturbance factors on the error of the metering transformer is more comprehensively considered, thereby improving the accuracy and reliability of error measurement.

[0041] According to some embodiments of the present invention, the system further comprises: A sorting module, used for calculating the abnormal coefficient of each abnormal metering transformer before the alarm module calculates the error value of the abnormal metering transformer, sorting them according to the abnormal coefficient, and calculating the error value of the abnormal metering transformer according to the sorting result; The sorting module calculates the abnormal coefficient of each abnormal metering transformer, including: Calculates the actual wear life of abnormal metering sensors within a preset time period : in, The standard loss life of the abnormal metering sensor within a preset time period; is the actual operating temperature during the preset time period; is the ambient temperature; is the rated operating temperature of the abnormal metering sensor; Measure the actual life loss of the sensor within a preset time period based on anomalies , calculate the abnormal coefficient of the abnormal measurement sensor : in, is a natural constant.

[0042] The working principle and beneficial effects of the above technical solution: The abnormal coefficient L is a comprehensive indicator that reflects the change in the loss life of the sensor due to actual working conditions (such as temperature) and the potential impact of such changes on sensor performance and errors. According to the calculated abnormal coefficient L, the abnormal metering transformers are sorted. The sorting results can be used to determine the priority of processing abnormal metering transformers. When processing each abnormal metering transformer, the alarm module can use the sorting results provided by the sorting module to calculate its error value, and determine whether an alarm prompt needs to be issued based on the preset error threshold. The introduction of the sorting module and the calculation of the abnormal coefficient can more accurately evaluate the potential problems and error size of each abnormal metering transformer, thereby optimizing the processing order and improving the efficiency and accuracy of the overall system. This helps to promptly discover and solve problems with metering transformers and ensure the stable and safe operation of the power system.

[0043] According to some embodiments of the present invention, the system further comprises: an abnormality type determination module, configured to: Acquire a preset number of signals of the abnormal metering sensor based on a preset time interval, arrange the preset number of signals based on a time sequence, and obtain an abnormal signal vector; Analyze and calculate the abnormal signal vector to determine the attribute information of the signal in the abnormal measurement sensor; Comparing the attribute information with a plurality of preset attribute information in a preset database, determining the abnormal type of the abnormal metering sensor according to the comparison result and displaying it; Calculate the difference between the attribute information and several preset attribute information in the preset database respectively: in, The attribute information is the same as the first The difference value of preset attribute information; is the number of feature vectors included in the attribute information and is also the number of feature vectors included in the preset attribute information, and the number of feature vectors included in the attribute information is equal to the number of feature vectors included in the preset attribute information; Attribute information No. feature vectors; Preset attribute information No. feature vectors; The minimum difference value is determined, and the type of preset attribute information corresponding to the minimum difference value is used as the abnormal type of the abnormal measurement sensor and displayed.

[0044] The working principle and beneficial effects of the above technical solution are as follows: based on a preset time interval, a preset number of signals of the abnormal metering sensor are obtained, and these signals are arranged in chronological order to form an abnormal signal vector. The abnormal signal vector is analyzed and calculated to extract the attribute information in the signal, which includes characteristic vectors such as the amplitude, frequency, and phase of the signal. The extracted attribute information is compared with several preset attribute information in the preset database. The preset database stores characteristic vector information corresponding to multiple abnormal types. By calculating the difference value, the preset attribute information that best matches the abnormal signal vector is found to determine the abnormal type of the abnormal metering sensor. The preset attribute information with the smallest difference value is found, and its type is used as the abnormal type of the abnormal metering sensor. The determined abnormal type is displayed so that the operator can take timely measures to deal with it. The introduction of the abnormal type determination module can more accurately identify the abnormal type of the abnormal metering sensor, thereby speeding up the fault handling speed and improving the reliability and stability of the system.

[0045] like Figure 2 As shown, an error measurement method of an error measurement system of a metering transformer includes steps S1-S4: S1. Collect multi-dimensional operation monitoring data of metering transformers; S2. Convert and process the multi-dimensional operation monitoring data to obtain processed data; S3, determining the abnormal metering transformer according to the processed data; S4. Calculate the error value of the abnormal metering transformer, and issue an alarm when the error value is greater than a preset error threshold.

[0046] The beneficial effects of the above technical solution are as follows: through the steps of collection, processing, analysis and alarm, accurate measurement and effective monitoring of the metering transformer error are achieved. This can not only improve the stability and safety of the power system, but also reduce the economic losses caused by the transformer error. First, a preliminary abnormality detection is performed to determine the abnormal metering transformer, analyze the error of the abnormal metering transformer, reduce the number of measuring transformers, and then reduce the measurement data, improve the data processing efficiency, and improve the accuracy of the metering error calculation.

[0047] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An error measurement system for a metering transformer, characterized in that: include: The acquisition module is used to collect multi-dimensional operation monitoring data of the metering transformer; A processing module, used for converting and processing the multi-dimensional operation monitoring data to obtain processed data; A first determination module, used for determining an abnormal metering transformer according to the processed data; The alarm module is used to calculate the error value of the abnormal metering transformer and issue an alarm prompt when the error value is greater than a preset error threshold.

2. The error measurement system of the metering transformer according to claim 1, characterized in that: The acquisition module comprises: A sampling assembly, used for connecting the metering winding of the metering transformer with the sampling channel; The transmission module is used to detect the multi-dimensional operation monitoring data of each metering transformer and transmit the multi-dimensional operation monitoring data to the processing module; wherein the multi-dimensional operation monitoring data includes a current signal.

3. The error measurement system of the metering transformer according to claim 2, characterized in that: The processing module comprises: An amplification and filtering module, used for amplifying and filtering the current signal; The AD analog-to-digital conversion module is used to convert the current signal into a digital signal and transmit it to the first determination module.

4. The error measurement system of the metering transformer according to claim 1, characterized in that: The first determining module includes: An analysis module, used for performing data analysis on the processed data based on the FFT algorithm to obtain analysis results; the analysis results include characteristic parameters of each data point in the IMF component; the characteristic parameters include amplitude and phase between the metering transformers; A calculation module is used to calculate a first current vector sum of a metering transformer of an input branch; calculate a second current vector sum of a metering transformer of an output branch; calculate a difference between the first current vector sum and the second current vector sum; when it is determined that the difference is greater than a preset difference, determine an abnormal branch including an abnormal metering transformer; and locate the abnormal metering transformer in the abnormal branch based on a KCL algorithm.

5. The error measurement system of the metering transformer according to claim 4, characterized in that: The analysis module comprises: A first decomposition module is used to generate a data curve based on the processed data, and perform STL decomposition on the data curve to obtain periodic terms; The second decomposition module is used to perform EMD decomposition on the data curve to obtain multiple IMF components; The second determination module is used to determine, for each IMF component, the period length of the period in which each data point in the IMF component is located; and determine the analysis result according to the period length of the periodic item, the period length of the IMF component, and the period length of the period in which each data point in the IMF component is located.

6. The error measurement system of the metering transformer according to claim 4, characterized in that: The alarm module comprises: A third determination module is used to use a nonlinear kernel function to perform data feature space mapping on the characteristic parameters of the abnormal metering transformer, use a support vector machine algorithm to perform regression calculation on the characteristic parameters in the feature space, and determine linear and nonlinear regression models; The fourth determination module is used to determine the error value of the abnormal metering transformer through linear and nonlinear regression models, and issue an alarm when the error value is greater than a preset error threshold.

7. The error measurement system of the metering transformer according to claim 1, characterized in that: The system also includes: An acquisition module is used to acquire disturbance data of abnormal metering transformers; wherein the disturbance data includes temperature parameters, environmental pollution parameters, humidity parameters and secondary load parameters; The correction module is used to correct the error value of the abnormal metering transformer according to the disturbance data to obtain a corrected error value.

8. The error measurement system of the metering transformer according to claim 1, characterized in that: The system also includes: A sorting module, used for calculating the abnormal coefficient of each abnormal metering transformer before the alarm module calculates the error value of the abnormal metering transformer, sorting them according to the abnormal coefficient, and calculating the error value of the abnormal metering transformer according to the sorting result; The sorting module calculates the abnormal coefficient of each abnormal metering transformer, including: Calculates the actual wear life of abnormal metering sensors within a preset time period : in, The standard loss life of the abnormal metering sensor within a preset time period; is the actual operating temperature during the preset time period; is the ambient temperature; is the rated operating temperature of the abnormal metering sensor; Measure the actual life loss of the sensor within a preset time period based on anomalies , calculate the abnormal coefficient of the abnormal measurement sensor : in, is a natural constant.

9. The error measurement system of the metering transformer according to claim 1, characterized in that: The system also includes: an abnormality type determination module, which is used to: Acquire a preset number of signals of the abnormal metering sensor based on a preset time interval, arrange the preset number of signals based on a time sequence, and obtain an abnormal signal vector; Analyze and calculate the abnormal signal vector to determine the attribute information of the signal in the abnormal measurement sensor; Comparing the attribute information with a plurality of preset attribute information in a preset database, determining the abnormal type of the abnormal metering sensor according to the comparison result and displaying it; Calculate the difference between the attribute information and several preset attribute information in the preset database respectively: in, The attribute information is the same as the first The difference value of preset attribute information; is the number of feature vectors included in the attribute information and is also the number of feature vectors included in the preset attribute information, and the number of feature vectors included in the attribute information is equal to the number of feature vectors included in the preset attribute information; Attribute information No. feature vectors; Preset attribute information No. feature vectors; The minimum difference value is determined, and the type of preset attribute information corresponding to the minimum difference value is used as the abnormal type of the abnormal measurement sensor and displayed.

10. The error measurement method of the error measurement system of the metering transformer according to any one of claims 1 to 9, characterized in that: include: Collect multi-dimensional operation monitoring data of metering transformers; Convert and process the multi-dimensional operation monitoring data to obtain processed data; Determine the abnormal metering transformer based on the processed data; Calculate the error value of the abnormal metering transformer and issue an alarm when the error value is greater than the preset error threshold.

Images