A method and system for analyzing electrical equipment accuracy

Abstract

The present invention discloses a method and system for analyzing the accuracy of electrical equipment, which relate to the field of data analysis technology. The method comprises: obtaining a plurality of first sampling points of the electrical equipment and a plurality of second sampling points of the test voltage; based on each of the second sampling points, a test device performs an accuracy test on each of the first sampling points to obtain test accuracy data; based on rated data, obtains rated accuracy data of each of the first sampling points; averages the test accuracy data and the rated accuracy data to obtain test average data and rated average data respectively; performs polynomial fitting calculation on the test average data and the rated average data to obtain a polynomial; performs polynomial evaluation on the polynomial and the rated average data to obtain an evaluation vector; based on the evaluation vector and the rated average data, uses a drawing device to draw an image to obtain an analysis image, and obtains an analysis result based on the analysis image. The method can solve the problem of inaccurate accuracy analysis caused by test data errors and a small number of sampling points.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method and system for analyzing the accuracy of electrical equipment. Background Art

[0002] With the gradual development of distribution networks and the continuous increase in line loads, line signal acquisition and analysis, as well as rapid fault repair, have become key to the safe and stable operation of distribution networks. The signal acquisition accuracy of electrical equipment and common electrical components in distribution network lines is one of the main factors affecting the safe operation of distribution networks. Therefore, accuracy testing and data analysis are required during the design and development of electrical equipment and components such as transformers. However, the main problems with current accuracy testing are as follows:

[0003] (1) In actual accuracy testing, it is difficult to test every voltage or current position within the full voltage range. Generally, the sampling points are set by setting a test point table;

[0004] (2) The test equipment itself has certain systematic errors, which leads to certain errors in the selection of sampling points and the corresponding precision measurement data.

[0005] Therefore, due to the errors in the test data and the small number of sampling points, there are certain limitations when directly using the test data for accuracy analysis. Summary of the Invention

[0006] In order to solve the problem of inaccurate precision analysis caused by test data errors and a small number of sampling points, the present invention provides an electrical equipment precision analysis method, the method comprising: obtaining several first sampling points of the electrical equipment and several second sampling points of the test voltage; based on each of the second sampling points, a test device performs a precision test on each of the first sampling points to obtain test precision data; based on the rated data, obtains the rated precision data of each of the first sampling points; divides the test precision data and the rated precision data equally to obtain test average data and rated average data respectively; performs polynomial fitting calculation on the test average data and the rated average data to obtain a polynomial; performs polynomial evaluation on the polynomial and the rated average data to obtain an evaluation vector; based on the evaluation vector and the rated average data, uses a drawing device to draw an image to obtain an analysis image, and obtains an analysis result based on the analysis image.

[0007] Principle of the invention: Data such as voltage and current are relatively important landmark data in the power system, and their parameters are often in a floating state. They are not a fixed value, but float around a fixed value. They are floating data. The data are corrected and analyzed by the polynomial fitting method, and point data are used to capture discrete data of power equipment in a continuously changing state to obtain accuracy, effectively restore the true accuracy change of the data, and multiple averaging can further reduce the error caused by the test data, and more accurately restore the accuracy change trend; using drawing equipment, the accuracy change curve is drawn in the full process and full voltage range, reducing the error caused by the small number of sampling points, and obtaining clearer accuracy change rules and trends, which can effectively improve the accuracy and convenience of electrical equipment accuracy test data analysis, and provide design guidance for the design optimization of electrical equipment and the accuracy range.

[0008] Furthermore, the specific steps of obtaining the test average data include: in the test accuracy data, starting from the second item, calculating the average value of each item and its previous item to obtain the test average data;

[0009] The specific steps of obtaining the rated average data include: in the rated accuracy data, starting from the second item, calculating the average value of each item and its previous item to obtain the rated average data.

[0010] The average value is calculated multiple times to further reduce the error caused by the test data and more accurately restore the accuracy change trend.

[0011] Furthermore, the first calculation method for equally dividing the rated accuracy data is:

[0012]

[0013] The second calculation method for evenly dividing the test accuracy data is:

[0014]

[0015] Among them, a n Indicates rated average data, A n Indicates rated average data, A n+1 Indicates A n The latter term, b n Indicates the test average data, B n Indicates the test average data, B n+1 Indicates B n The second term, m, represents the number of elements evenly divided by the spacing points.

[0016] Furthermore, the specific step of obtaining the first sampling point includes: obtaining historical fault data of the power equipment, obtaining first data based on the historical fault data, the first data including the fault point, the fault type of the fault point, the number of faults at the fault point, the fault processing time of the fault point, the fault area of ​​the fault point, and the fault operating parameters of the fault point, the fault operating parameters including fault current data, fault voltage data, and fault traveling wave data; obtaining the first sampling point based on a preset frequency and the number of faults;

[0017] The specific steps of obtaining the second sampling point include: obtaining a fault voltage based on the fault type and the fault operating parameters; and obtaining the second sampling point based on the fault voltage.

[0018] Taking sampling at points that are more prone to failure can more accurately restore the accuracy changes of data before and after the failure; taking testing at voltage values ​​that are more likely to cause failure can more accurately restore the accuracy changes of data before and after the failure.

[0019] Considering the wide variety of electrical equipment and the complex operating environments, its operation is easily affected by external factors. Many large-scale power outages in distribution networks are caused by weather. Severe weather conditions can have a significant impact on distribution network infrastructure, making them more susceptible to failures. Introducing weather factors and restoring the actual environment for accuracy testing can obtain more realistic accuracy data, thereby providing a more accurate analysis of the accuracy of electrical equipment.

[0020] Furthermore, the method also includes: obtaining historical weather data based on the historical fault data, preprocessing the historical weather data to obtain second data, dividing the second data into seasons based on a preset temperature range to obtain different seasonal data; extracting weather characteristics of the seasonal data, constructing initial feature sets of different seasons based on the weather characteristics, dividing the initial feature sets into levels based on preset fault levels to obtain several level feature sets; performing feature selection on the level feature sets to obtain different seasonal feature sets; training a model based on the seasonal feature sets to obtain a weather model; the weather model predicts the first sampling point based on the preset fault type to obtain sampling weather; based on the sampling weather and each of the second sampling points, the testing equipment performs an accuracy test on each of the first sampling points to obtain weather test accuracy data, and updates the test accuracy data to weather test accuracy data.

[0021] The impact of temperature is different in different seasons. Dividing weather data according to temperature to obtain the characteristics of different seasons can improve the accuracy of weather feature classification. Dividing features according to fault level can obtain weather characteristics of different fault levels, making classification more accurate and restoring the weather environment of different faults. Selecting more representative weather features for training makes the weather environment more representative, and the electrical equipment accuracy data under this environment more representative, so the accuracy changes under the influence of weather can be obtained more accurately.

[0022] Furthermore, the specific steps of obtaining the level feature set include: obtaining the cumulative fault handling time based on the fault handling time; obtaining the fault duration ratio based on the fault handling time and the cumulative fault handling time; obtaining the fault area ratio based on the fault area and the preset area; and performing level division based on the fault duration ratio and the fault area ratio to obtain the level feature set.

[0023] The impact of a fault is determined based on the fault handling time and fault area, and then its level is divided.

[0024] Furthermore, the specific steps of obtaining the seasonal feature set include:

[0025] S1. Randomly obtain a feature sample of the hierarchical feature set;

[0026] S2. Extracting K adjacent features of the feature samples from each level feature set;

[0027] S3, calculating the similarity between the feature sample and the adjacent features;

[0028] S4, repeating S1 to S3 until the similarities of all feature samples are calculated;

[0029] S5. Select the similarity based on the preset similarity to obtain the sample similarity;

[0030] S6. Obtain the seasonal feature set based on the sample similarity.

[0031] The features with larger similarity differences are selected, which are representative and their weather environment is more representative.

[0032] Considering both accuracy and stability is crucial for ensuring reliable system or device performance and accurate data. High accuracy means the output is very close to the true value, while high stability ensures that this accuracy remains constant over time and under varying conditions. Therefore, this paper considers stability to provide a more comprehensive analysis of electrical equipment.

[0033] Furthermore, the method also includes: obtaining a number of precision lines based on the analysis image, and obtaining a number of extreme values ​​based on the precision lines; judging whether the number of the extreme values ​​is greater than a preset number, and if so, obtaining a trend line based on the extreme values, calculating a first angle of the trend line, and obtaining a stable value; if not, obtaining a tangent of the precision line, calculating a second angle of the tangent, and obtaining the stable value; obtaining a new analysis result based on the stable value and the analysis result, and updating the analysis result to the new analysis result.

[0034] Furthermore, the specific steps of obtaining the trend line include: based on the extreme value, obtaining the precision line within the preset range, obtaining the first line, obtaining the lowest point and the highest point of the first line, connecting any low point or high point between the lowest point and the highest point, and obtaining the trend line.

[0035] A trend line is a straight line drawn by connecting the highest or lowest accuracy points in the past accuracy trend of an electrical device. It is intended to show the accuracy change over the full voltage range.

[0036] Trend lines can analyze and predict accuracy trends, detect reversals or accelerated rises and falls in accuracy, and can also provide guidance for the design of power equipment.

[0037] The present invention also provides an electrical equipment accuracy analysis system, the system comprising:

[0038] Sampling point unit: used to obtain several first sampling points of the electrical equipment and several second sampling points of the test voltage;

[0039] Testing unit: configured to perform an accuracy test on each of the first sampling points based on each of the second sampling points to obtain test accuracy data; and obtain rated accuracy data of each of the first sampling points based on rated data;

[0040] Data processing unit: used for equally dividing the test accuracy data and the rated accuracy data to obtain test average data and rated average data respectively; performing polynomial fitting calculation on the test average data and the rated average data to obtain a polynomial; performing polynomial evaluation on the polynomial and the rated average data to obtain an evaluation vector;

[0041] Analysis unit: used for performing image drawing using a drawing device based on the evaluation vector and the rated average data to obtain analysis results.

[0042] The principle and effect of this system are similar to those of this method, so the system will not be described in detail.

[0043] One or more technical solutions provided by the present invention have at least the following technical effects or advantages:

[0044] 1. Data such as voltage and current are relatively important landmark data in the power system. Their parameters are often in a floating state. They are not fixed values, but float around fixed values. They are floating data. The data are corrected and analyzed through the polynomial fitting method, and point data is used to capture discrete data of power equipment in a continuously changing state to obtain accuracy, effectively restore the true accuracy change of the data, and multiple averaging can further reduce the error caused by the test data and more accurately restore the accuracy change trend; using drawing equipment, the accuracy change curve is drawn in the full process and full voltage range to reduce the error caused by the small number of sampling points, and obtain clearer accuracy change rules and trends, which can effectively improve the accuracy and convenience of electrical equipment accuracy test data analysis, and provide design guidance for the design optimization of electrical equipment and the accuracy range.

[0045] 2. Introducing weather factors and restoring the real environment for accuracy testing can obtain more realistic accuracy data, thereby conducting a more accurate analysis of the accuracy of electrical equipment.

[0046] 3. Introducing stability and obtaining trend lines and tangents can analyze and predict accuracy trends, discover reversals or accelerated increases and decreases in accuracy, and more comprehensively analyze the accuracy of electrical equipment, which can also provide guidance for the design of power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of the present invention, and do not constitute a limitation of the embodiments of the present invention;

[0048] Figure 1 It is a flow chart of an electrical equipment accuracy analysis method in the present invention;

[0049] Figure 2 It is a schematic diagram of an upward trend line;

[0050] Figure 3 It is a schematic diagram of the downward trend line;

[0051] Figure 4 This is a schematic diagram of the test curve of the three-phase voltage ratio difference accuracy of the first type transformer under the full voltage range;

[0052] Figure 5 This is a schematic diagram of the test curve of the three-phase voltage ratio difference accuracy of the second type transformer under the full voltage range;

[0053] Figure 6 It is a schematic diagram of the voltage ratio difference accuracy curve of the pole-mounted switch pole under full-process, full-voltage, high and low temperature test conditions;

[0054] Figure 7This is a schematic diagram of the current ratio difference accuracy curve of the pole-mounted switch zero-sequence current transformer under full current high and low temperature test conditions;

[0055] Figure 8 It is a schematic diagram of the current phase difference accuracy curve of the pole-mounted switch zero-sequence current transformer under full current high and low temperature test conditions;

[0056] Among them, A-lowest point, B-highest point, C-low point, D-high point. DETAILED DESCRIPTION

[0057] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features therein can be combined with each other without conflict.

[0058] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0059] Example 1

[0060] refer to Figure 1 , this embodiment provides an electrical equipment accuracy analysis method, the method comprising:

[0061] A plurality of first sampling points of the electrical equipment and a plurality of second sampling points of the test voltage are obtained; in this embodiment, the electrical equipment may include a generator, a transformer, a power line, a circuit breaker, and the like.

[0062] The specific step of obtaining the first sampling point includes: obtaining historical fault data of the power equipment, and obtaining first data based on the historical fault data, wherein the first data includes the fault point, the fault type of the fault point, the number of faults of the fault point, the fault processing time of the fault point, the fault area of ​​the fault point, and the fault operation parameters of the fault point, wherein the fault operation parameters include fault current data, fault voltage data, and fault traveling wave data;

[0063] Based on the preset frequency and the number of faults, the first sampling point is obtained; if the fault frequency of each fault point is calculated by the number of faults, and if the number of faults / total number of faults>the preset frequency, the point is selected as the sampling point;

[0064] The specific steps of obtaining the second sampling point include: obtaining a fault voltage based on the fault type and the fault operating parameters; if fault voltages under different fault types are selected, obtaining the second sampling point based on the fault voltages.

[0065] Based on each of the second sampling points, the testing device performs an accuracy test on each of the first sampling points to obtain test accuracy data, which is recorded as A=[A1, A2, ..., A N ]; Based on the rated data, the rated accuracy data of each of the first sampling points is obtained, and the rated accuracy data represents the accuracy data calculated under the rated voltage data, which is recorded as B = [B1, B2, ..., B N ], N represents the number of data; in this embodiment, the test equipment can be a primary and secondary fusion precision test platform.

[0066] Divide the test accuracy data and the rated accuracy data equally to obtain the test average data and the rated average data, respectively, which are recorded as a=[a1, a2, ..., a N-1 ] and b=[b1,b2,...,b N-1 ];

[0067] The specific steps of obtaining the test average data include: in the test accuracy data, starting from the second item, calculating the average value of each item with its previous item to obtain the test average data; the calculation method that can be used is:

[0068]

[0069] Among them, a n Indicates rated average data, A n Indicates rated average data, A n+1 Indicates A n The second term, m, represents the number of elements evenly divided by the spacing points.

[0070] The specific steps of obtaining the rated average data include: in the rated accuracy data, starting from the second item, calculating the average value of each item with its previous item to obtain the rated average data. The calculation method that can be used is:

[0071]

[0072] Among them, b n Indicates the test average data, B n Indicates the test average data, B n+1 Indicates B nThe latter term, m represents the number of spacing point average elements. A polynomial fitting curve is calculated for the test average data and the rated average data, and the calculation data set is recorded as [a, b, x], where x represents the number of polynomial calculations, which is used to adjust the fitting calculation accuracy of the polynomial calculation, thereby obtaining a polynomial and a polynomial coefficient, which are recorded together as P; a polynomial evaluation is performed on the polynomial P and the rated average data, such as using a direct method and the Qin Jiushao (Horner) algorithm to evaluate the polynomial, to obtain an evaluation vector F; based on the evaluation vector F and the rated average data, an image is drawn using a drawing device to obtain an analysis image, and an analysis result is obtained based on the analysis image. In this embodiment, the drawing device can be a MATLAB tool, a GNU Octave tool, a Scilab tool, a Spyder tool, a FreeMat tool, and the like.

[0073] Polynomial curve fitting is a common data fitting method that describes the relationship between data by constructing a polynomial function. In principle, polynomial curve fitting works by finding a polynomial function with minimal error that best fits the given data points. The coefficients of the polynomial are then adjusted to create a fitting curve that best matches the data. The degree of fit of the fitted curve can be evaluated by calculating the error between the fitted curve and the original data. Common error assessment methods include the least squares method, which minimizes the sum of the squares of the vertical distances between all data points and the curve.

[0074] Example 2

[0075] On the basis of Example 1, in this embodiment, the method further includes:

[0076] Historical weather data is obtained based on the historical fault data, and the historical weather data is preprocessed to obtain second data, such as deduplication and other processing operations. The second data is divided into seasons based on a preset temperature range to obtain different seasonal data; for example, the time when the average maximum temperature in a set time period is higher than a first preset temperature is divided into summer; the time when the average minimum temperature in a set time period is lower than a second preset temperature is divided into winter; and the time other than summer and winter is divided into spring and autumn.

[0077] Extracting weather features of the seasonal data using machine learning or other methods, where the weather features may include temperature, humidity, time, and wind speed, constructing initial feature sets for different seasons based on the weather features, and classifying the initial feature sets based on preset fault levels to obtain a plurality of graded feature sets;

[0078] Among them, the specific steps of obtaining the level feature set include: obtaining the cumulative fault handling time based on the fault handling time; obtaining the fault duration ratio based on the fault handling time and the cumulative fault handling time, such as fault duration ratio = fault handling time / cumulative fault handling time; obtaining the fault area ratio based on the fault area and the preset area, such as fault area ratio = fault area / preset area; performing level division based on the fault duration ratio and the fault area ratio, such as z = p*fault area ratio + q*fault area ratio, q and p both represent weight coefficients, z represents a numerical value, and range division is performed according to the size of z to obtain the level feature set.

[0079] Performing feature selection on the level feature set to obtain different seasonal feature sets; the specific steps include:

[0080] S1. Randomly obtain a feature sample of the hierarchical feature set;

[0081] S2. Extracting K adjacent features of the feature samples from each level feature set;

[0082] S3, calculating the similarity between the feature sample and the adjacent features;

[0083] S4, repeating S1 to S3 until the similarities of all feature samples are calculated;

[0084] S5. Select the similarity based on the preset similarity to obtain the sample similarity;

[0085] S6. Obtain the seasonal feature set based on the sample similarity.

[0086] In this embodiment, adjacent features can be obtained by slicing, KNN algorithm or k-nearest neighbor algorithm, and similarity can be obtained by using algorithms such as Euclidean distance, Manhattan distance, Chebyshev distance, cosine similarity and Jaccard similarity.

[0087] The model is trained based on the seasonal feature set to obtain a weather model; in this embodiment, the model can be a deep learning model, an SVM model, a neural network model, etc.

[0088] The weather model predicts the first sampling point based on the preset fault type to obtain sampling weather; based on the sampling weather and each of the second sampling points, the testing equipment performs an accuracy test on each of the first sampling points to obtain weather test accuracy data, and updates the test accuracy data to weather test accuracy data.

[0089] Example 3

[0090] refer to Figure 2-Figure 3Based on the above embodiment, in this embodiment, the method further includes:

[0091] obtaining a plurality of precision lines based on the analysis image, and obtaining a plurality of extreme values ​​based on the precision lines;

[0092] Determine whether the number of extreme values ​​is greater than a preset number; if so, obtain a trend line based on the extreme values, calculate a first angle of the trend line, and obtain a stable value; if not, obtain a tangent line of the precision line, calculate a second angle of the tangent line, and obtain the stable value;

[0093] A new analysis result is obtained based on the stable value and the analysis result, and the analysis result is updated to the new analysis result.

[0094] Among them, the specific steps of obtaining the trend line include: based on the extreme value, obtaining the precision line within the preset range, obtaining the first line, obtaining the lowest point and the highest point of the first line, connecting any low point or high point between the lowest point and the highest point, and obtaining the trend line.

[0095] Example 4

[0096] On the basis of the above embodiment, this embodiment provides an electrical equipment accuracy analysis system, the system comprising:

[0097] Sampling point unit: used to obtain several first sampling points of the electrical equipment and several second sampling points of the test voltage;

[0098] Testing unit: configured to perform an accuracy test on each of the first sampling points based on each of the second sampling points to obtain test accuracy data; and obtain rated accuracy data of each of the first sampling points based on rated data;

[0099] Data processing unit: used for equally dividing the test accuracy data and the rated accuracy data to obtain test average data and rated average data respectively; performing polynomial fitting calculation on the test average data and the rated average data to obtain a polynomial; performing polynomial evaluation on the polynomial and the rated average data to obtain an evaluation vector;

[0100] Analysis unit: used for performing image drawing using a drawing device based on the evaluation vector and the rated average data to obtain analysis results.

[0101] Example 5

[0102] Based on the above embodiment, in this embodiment, the method also includes obtaining the maximum value, minimum value, average value of the accuracy curve and the difference between the accuracy curves for calculation and analysis. The above parameters are the inherent numerical characteristics of the curve. Such characteristics can effectively help analyze the accuracy status of electrical equipment and the reasons for changes under certain conditions.

[0103] It also includes analysis and calculation of span drift and zero votes of test accuracy data.

[0104] Zero drift refers to the phenomenon that the reference value (called zero point) of the test equipment output value drifts when there is no external force.

[0105] Span drift refers to the change in the response of the test equipment to a constant stimulus (i.e., the measured value) within a specified time under specified conditions.

[0106] Example 6

[0107] refer to Figure 4-Figure 5 On the basis of the above embodiments, in this embodiment, a primary-secondary fusion test platform is used as the test equipment to perform voltage ratio difference accuracy tests on two different types of incoming line side voltage transformer components, and data processing and comparative analysis are performed on the ratio difference accuracy of the two types of transformers.

[0108] The sampling point table of the primary and secondary fusion test platform is set to 1% U N , 5% U N , 10% U N , 20% U N , 30% U N , 40% U N , 60% U N , 80% U N , 100% U N and 120% U N There are ten sampling points in total, U N Represents the rated voltage. The full voltage range voltage accuracy ratio test is performed on two types of voltage transformer components through the test platform. The array after the sampling point table is calculated through the rated voltage data is recorded as the rated data array, and the test data corresponding to the sampling point is recorded as the test data array.

[0109] The rated data array of the first type of mutual inductor is V1, and the test data array is EV1; the rated data array of the second type of mutual inductor is V2, and the test data array is EV2.

[0110] An averaging calculation is performed on the rated data arrays V1 and V2, as well as the test data arrays EV1 and EV2, of the two types of transformers. The starting and ending values ​​of the averaging calculation are adjacent data in the arrays. The averaging calculation is performed on the data in the arrays in order, with the number of averaging elements set to 10. The resulting arrays are recorded as the rated averaging arrays v1 and v2, and the test averaging arrays ev1 and ev2, respectively.

[0111] Polynomial fitting calculations are performed on the rated equal-division array v1 and the test equal-division data array ev1 of the first type of mutual inductor, as well as the rated equal-division array v2 and the test equal-division data array ev2 of the second type of mutual inductor. The degree of the polynomial returned by the required fitting calculation is set to 10. The arrays of numbers after the fitting calculation are recorded as PV1 and PV2, which represent the polynomial coefficients of the fitting calculation of the ratio difference accuracy data of the two types of mutual inductors, respectively.

[0112] The polynomial calculation is performed using the fitting calculation polynomial coefficient array PV1 and the rated equal-division array v1 of the first type of transformer. The returned array is recorded as FV1, which is used to represent the ratio difference accuracy data of the first type of transformer after the fitting calculation. Similarly, the above polynomial calculation is performed for the second type of transformer. The returned array is recorded as FV2, which represents the ratio difference accuracy data of the second type of transformer after the fitting calculation.

[0113] FV1 and FV2 after polynomial calculation are used as evaluation calculation arrays, and image rendering and data processing are performed respectively with the rated average calculation arrays v1 and v2.

[0114] The A, B, and C three-phase mutual inductors of the two types of mutual inductors are tested on the primary and secondary fusion test platform under the full voltage range. After the above data processing, the three voltage ratio difference accuracy curves corresponding to the two types of mutual inductors are drawn using MATLAB as shown below. Figure 4 and Figure 5 shown.

[0115] As can be seen from the figure, compared with the second type of transformer, the ratio difference accuracy of the first type of transformer has better consistency, and the ratio difference accuracy curve of the three-phase voltage transformer is flatter and the span of the ratio difference accuracy curve is smaller. Therefore, the first type of transformer has more advantages in the ratio difference accuracy test of the voltage transformer.

[0116] Example 7

[0117] refer to Figure 6 Based on the above embodiment, in this embodiment, a primary and secondary fusion test platform is used as the test equipment, and a high and low temperature box is used as the temperature setting equipment for the pole-mounted switch pole, and the installed pole-mounted switch pole is subjected to a voltage ratio difference accuracy test under full-process, full-voltage, high and low temperature test conditions.

[0118] The process is as follows:

[0119] 1. Test the voltage difference accuracy of the pole under normal temperature conditions;

[0120] 2. Place the pole in a high and low temperature box to conduct voltage ratio difference accuracy test at room temperature;

[0121] 3. Set the high and low temperature box to 70°C and maintain it for 16 hours. Wait until the pole reaches the temperature to be determined and then conduct the voltage ratio difference accuracy test under high temperature conditions.

[0122] 4. Set the high and low temperature box to -40°C and maintain it for 16 hours. After that, wait for the pole to reach the temperature to be determined and then conduct the voltage ratio difference accuracy test under low temperature conditions.

[0123] 5. After completing the above tests, wait for the pole to return to normal temperature, and then test the voltage ratio difference accuracy under normal temperature conditions after experiencing high and low temperatures.

[0124] In the above test, the voltage ratio difference accuracy test data of each stage is recorded.

[0125] Similar to the sixth embodiment, the rated data and the test data obtained from the test are recorded as a rated data array V and a test data array EV respectively.

[0126] The sampling point table sets ten sampling points, namely 1% U N , 5% U N , 10% U N , 20% U N , 30% U N , 40% U N , 60% U N , 80% U N , 100% U N and 120% U N .

[0127] The rated data array V and the test data array EV are equally divided, and the number of elements is set to 10; the arrays after equal division are recorded as the rated equal-divided array v1 and the test equal-divided data array ev1 respectively.

[0128] A polynomial fitting calculation is performed using the rated average array v1 and the test average data array ev1. The degree of the polynomial returned by the required fitting calculation is set to 10, and the arrays of numbers after the fitting calculation are recorded as PV.

[0129] The polynomial calculation is performed using the fitted polynomial coefficient array PV and the rated equal-division array v1 of the first type mutual inductor. The returned array is recorded as FV, ​​which serves as the ratio error accuracy data after the fitting calculation.

[0130] The test data of each stage described in the test process are processed separately using the above data processing process and the images are drawn using MATLAB to obtain the voltage ratio difference accuracy curve of the pole under the full process and full voltage. Figure 6 shown.

[0131] As can be seen from the figure, the high and low temperature box will have a certain impact on the voltage ratio accuracy of the pole, and when the temperature changes, the voltage ratio accuracy curve of the pole will move in the negative direction. After returning to normal temperature after high and low temperature conditions, the voltage ratio accuracy curve will be close to the ratio accuracy curve at normal temperature, but it has not reached the position of the voltage ratio accuracy curve under normal temperature conditions.

[0132] In addition, data processing and analysis can be performed on each ratio error accuracy curve, and the following can be obtained through calculation:

[0133] The voltage ratio accuracy span of the pole at normal temperature outside the high and low temperature box is 0.0892%, and the zero drift is 0.2296%. From this, it can be seen that the voltage accuracy of the pole itself is positive; the zero drift of the voltage ratio accuracy of the pole at high temperature inside the high and low temperature box is -0.1419%, and the zero drift at low temperature is -0.0368%. From this, it can be seen that the voltage ratio accuracy of the pole under high and low temperature conditions is negative; the voltage ratio accuracy difference between the pole inside and outside the high and low temperature box is -0.1152, which means that the high and low temperature box has a negative effect on the voltage ratio accuracy of the pole; after high and low temperature test conditions, the voltage ratio accuracy difference between the pole at high temperature and normal temperature is 0.2703, and the voltage ratio accuracy difference between high temperature and normal temperature is 0.1642. From this, it can be seen that the voltage ratio accuracy of the pole is more affected by high temperature conditions.

[0134] Example 8

[0135] refer to Figure 7-Figure 8 Based on the above embodiment, in this embodiment, the primary and secondary fusion test platform is used as the test equipment, and the high and low temperature box is used as the temperature setting equipment of the pole switch to test the sampling accuracy of the zero-sequence current transformer under full current high and low temperature test conditions of the pole switch.

[0136] The process is as follows:

[0137] 1. Place the pole switch in a high and low temperature box to conduct zero-sequence current ratio difference and phase difference accuracy test at room temperature;

[0138] 2. Set the high and low temperature box to 70°C for 16 hours, and wait until the pole switch reaches the to-be-determined temperature value to conduct the zero-sequence current ratio difference phase difference accuracy test under high temperature conditions;

[0139] 3. Set the high and low temperature box to -40°C and maintain it for 16 hours. After that, wait for the pole switch to reach the to-be-determined temperature value to conduct the zero-sequence current ratio difference phase difference accuracy test under low temperature conditions.

[0140] 4. After completing the above tests, wait for the pole switch to return to normal temperature, and then test the zero-sequence current ratio difference phase difference accuracy under normal temperature conditions after experiencing high and low temperatures.

[0141] Similar to Example 6, the sampling point data and test data obtained from the test are recorded as the rated data array I0, the current ratio difference test data array AI01, and the current phase difference test data array AI02 respectively.

[0142] The sampling point table sets eight sampling points, namely 1%I N , 5%I N , 10%I N , 20%I N , 60% I N , 80% I N , 100% I N and 120% I N , where I N In the primary and secondary fusion test platform, the rated current is set to 600A, I N Indicates rated current.

[0143] The rated data array I0 and the ratio difference test data arrays AI01 and AI02 are averaged, and the number of elements is set to 12; the arrays after average division are recorded as the rated average calculation array i0 and the test data average calculation arrays Ai01 and Ai02 respectively.

[0144] The rated equal distribution calculation array i0 and the test data equal distribution calculation arrays Ai01 and Ai02 are used for polynomial fitting calculation. The degree of the polynomial returned by the required fitting calculation is set to 10. The polynomial coefficient arrays after the fitting calculation are recorded as PI01 and PI02 respectively.

[0145] The polynomial calculation is performed using the fitting calculation polynomial coefficient arrays PI01 and PI02 and the equally divided calculation array i0. The returned arrays are recorded as FI01 and FI02, which serve as the ratio difference and phase difference accuracy data after the fitting calculation of the pole-mounted switch zero-sequence current transformer.

[0146] The test data of each stage described in the test process are processed separately using the above data processing process and the images are drawn using MATLAB.

[0147] in Figure 7 This is the ratio error accuracy curve of the pole-mounted switch zero-sequence current transformer under full current high and low temperature test conditions. Figure 8 This is the phase difference accuracy curve of the pole-mounted switch zero-sequence current transformer under full current and high and low temperature test conditions.

[0148] from Figure 7It can be seen that the trend of the ratio difference accuracy curve of the zero-sequence current of the pole switch under high and low temperature conditions is relatively close. After returning to normal temperature under high and low temperature conditions, the zero-sequence current ratio difference accuracy curve is slightly positive compared with the normal temperature condition. Figure 8 It can be seen that high and low temperature conditions have a greater impact on the phase difference accuracy curve trend of the pole-mounted switch zero-sequence current. After returning to normal temperature after high and low temperature conditions, the zero-sequence current phase difference accuracy curve is more positive than that under normal temperature conditions.

[0149] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0150] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for analyzing the accuracy of electrical equipment, characterized in that: The method comprises: Acquiring a plurality of first sampling points of the electrical equipment and a plurality of second sampling points of the test voltage; Based on each of the second sampling points, the testing device performs an accuracy test on each of the first sampling points to obtain test accuracy data; based on the rated data, obtains rated accuracy data of each of the first sampling points; Evenly dividing the test accuracy data and the rated accuracy data to obtain test averaged data and rated averaged data respectively; Performing a polynomial fitting calculation on the test average data and the rated average data to obtain a polynomial; Performing polynomial evaluation on the polynomial and the rated equally divided data to obtain an evaluation vector; Based on the evaluation vector and the rated average data, an image is drawn using a drawing device to obtain an analysis image, and an analysis result is obtained based on the analysis image.

2. The electrical equipment accuracy analysis method according to claim 1, characterized in that: The specific steps to obtain the test average data include: In the test accuracy data, starting from the second item, each item is averaged with its previous item to obtain the test average data; The specific steps to obtain the rated average data include: In the rated accuracy data, starting from the second item, each item is averaged with its previous item to obtain the rated average data.

3. The electrical equipment accuracy analysis method according to claim 2, characterized in that: The first calculation method for averaging the rated accuracy data is: The second calculation method for evenly dividing the test accuracy data is: Among them, a n Indicates rated average data, A n Indicates rated average data, A n+1 Indicates A n The latter term, b n Indicates the test average data, B n Indicates the test average data, B n+1 Indicates B n The second term, m, represents the number of elements evenly divided by the spacing points.

4. The electrical equipment accuracy analysis method according to claim 1, characterized in that: The specific steps of obtaining the first sampling point include: Acquire historical fault data of the power equipment, and obtain first data based on the historical fault data, where the first data includes a fault point, a fault type of the fault point, a number of faults at the fault point, a fault handling time of the fault point, a fault area of ​​the fault point, and fault operating parameters of the fault point, where the fault operating parameters include fault current data, fault voltage data, and fault traveling wave data; Obtaining the first sampling point based on a preset frequency and the number of faults; The specific steps of obtaining the second sampling point include: A fault voltage is obtained based on the fault type and the fault operating parameter; and the second sampling point is obtained based on the fault voltage.

5. The electrical equipment accuracy analysis method according to claim 4, characterized in that: The method further comprises: Obtaining historical weather data based on the historical fault data, preprocessing the historical weather data to obtain second data, and dividing the second data into seasons based on a preset temperature range to obtain different seasonal data; Extracting weather features from the seasonal data, constructing initial feature sets for different seasons based on the weather features, and classifying the initial feature sets based on preset fault levels to obtain a plurality of graded feature sets; Performing feature selection on the level feature set to obtain different seasonal feature sets; Training a model based on the seasonal feature set to obtain a weather model; The weather model predicts the first sampling point based on a preset fault type to obtain sampling weather; Based on the sampled weather and each of the second sampling points, the testing equipment performs an accuracy test on each of the first sampling points, obtains weather test accuracy data, and updates the test accuracy data to weather test accuracy data.

6. The electrical equipment accuracy analysis method according to claim 5, characterized in that: The specific steps to obtain the hierarchical feature set include: Obtaining a cumulative fault handling time based on the fault handling time; Obtaining a fault duration ratio based on the fault handling duration and the cumulative fault handling duration; Obtaining a fault area ratio based on the fault area and a preset area; Based on the fault duration ratio and the fault area ratio, level classification is performed to obtain the level feature set.

7. The electrical equipment accuracy analysis method according to claim 6, characterized in that: The specific steps to obtain the seasonal feature set include: S1. Randomly obtain a feature sample of the hierarchical feature set; S2. Extracting K adjacent features of the feature samples from each level feature set; S3, calculating the similarity between the feature sample and the adjacent features; S4, repeating S1 to S3 until the similarities of all feature samples are calculated; S5. Select the similarity based on the preset similarity to obtain the sample similarity; S6. Obtain the seasonal feature set based on the sample similarity.

8. The electrical equipment accuracy analysis method according to claim 7, characterized in that: The method further comprises: obtaining a plurality of precision lines based on the analysis image, and obtaining a plurality of extreme values ​​based on the precision lines; Determine whether the number of extreme values ​​is greater than a preset number; if so, obtain a trend line based on the extreme values, calculate a first angle of the trend line, and obtain a stable value; if not, obtain a tangent line of the precision line, calculate a second angle of the tangent line, and obtain the stable value; A new analysis result is obtained based on the stable value and the analysis result, and the analysis result is updated to the new analysis result.

9. The electrical equipment accuracy analysis method according to claim 8, characterized in that: The specific steps to obtain a trend line include: Based on the extreme value, the precision line within the preset range is obtained to obtain a first line, the lowest point and the highest point of the first line are obtained, and any low point or high point between the lowest point and the highest point is connected to obtain the trend line.

10. An electrical equipment accuracy analysis system, characterized in that: The system comprises: Sampling point unit: used to obtain several first sampling points of the electrical equipment and several second sampling points of the test voltage; Testing unit: configured to perform an accuracy test on each of the first sampling points based on each of the second sampling points to obtain test accuracy data; and obtain rated accuracy data of each of the first sampling points based on rated data; Data processing unit: used for equally dividing the test accuracy data and the rated accuracy data to obtain test average data and rated average data respectively; performing polynomial fitting calculation on the test average data and the rated average data to obtain a polynomial; performing polynomial evaluation on the polynomial and the rated average data to obtain an evaluation vector; Analysis unit: used for performing image drawing using a drawing device based on the evaluation vector and the rated average data to obtain analysis results.

Images