A measurement error analysis system for smart electric energy meters

Abstract

The present invention provides a smart energy meter measurement error analysis system, belonging to the field of energy meter technology, and comprising a collection device, a rejection device, an error data calculation device, a mean calculation device, and a coefficient calculation device. The collection device collects the effective power of a sub-meter and the total power of the total meter. The rejection device rejects data from sub-meters whose effective power is lower than a predetermined value. The error data calculation device calculates the meter box error value before and after the rejection. The mean calculation device is used to calculate the effective power average of the sub-meters before and after the rejection. The coefficient calculation device is used to calculate the pre-rejection Pearson correlation coefficient between the effective power of the sub-meter before rejection and the pre-rejection meter box error value. The smart energy meter measurement error analysis system provided by the present invention accurately obtains the operating status of each sub-meter, can promptly determine the metering error status of the sub-meter, is easy to implement, and saves labor costs.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric energy meters, and more specifically, relates to a measurement error analysis system for intelligent electric energy meters. Background Art

[0002] As a terminal product of the smart grid, the energy meter is the foundation of smart grid construction and a key focus of smart grid policies worldwide. New and practical functions are constantly being added, with a clear trend towards modularization, networking, and systematization. Smart meters are essential equipment for smart grid data collection, responsible for collecting, measuring, and transmitting raw energy data. They also serve as the foundation for information integration, analysis, optimization, and presentation. The metering error of energy meters directly impacts the economic benefits of both the power supplier and the user, placing high demands on the accuracy of energy metering. The metering accuracy of smart meters is therefore crucial. Currently, metering error analysis plays a crucial role, impacting the meter's operational performance and electricity safety. Summary of the Invention

[0003] The purpose of the present invention is to provide a smart electric energy meter measurement error analysis system to solve the technical problems existing in the prior art of low accuracy of electric energy measurement and high use of human resources.

[0004] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: to provide a smart electricity meter measurement error analysis system, comprising an acquisition device, a rejection device, an error data calculation device, a mean calculation device and a coefficient calculation device arranged in sequence; the acquisition device is used to collect the effective power of each sub-meter in the meter box and the total power of the total meter input into the meter box; the rejection device is used to reject the data of the sub-meter whose effective power is lower than a fixed value; the error data calculation device is used to calculate the meter box error value before rejection and the meter box error value after rejection; the mean calculation device is used to calculate the effective power average of the sub-meter before rejection and the effective power average of the sub-meter after rejection; the coefficient calculation device is used to calculate the Pearson correlation coefficient before rejection between the effective power of the sub-meter before rejection and the meter box error value before rejection.

[0005] In a possible implementation, the collection device is used to record the number of the sub-tables and the number of collection points.

[0006] In a possible implementation, the elimination device eliminates the data of the sub-tables whose effective power is lower than a set value, and records the number of the remaining sub-tables as the number of corresponding collection points.

[0007] In a possible implementation, the meter box error value before elimination and the meter box error value after elimination are calculated by eliminating the effective power and the total power of the sub-meter.

[0008] In a possible implementation, the total power of the meter box after the elimination is the total power minus the sum of the rated powers of the eliminated sub-meters.

[0009] In a possible implementation, the mean value calculation device is used to calculate the mean effective power of the meter box before elimination of the error value of the meter box before elimination and calculate the mean effective power of the meter box after elimination of the error value of the meter box after elimination.

[0010] In a possible implementation, the coefficient calculation device is used to calculate the post-elimination Pearson correlation coefficient between the effective power of the sub-meter after elimination and the post-elimination meter box error value.

[0011] In a possible implementation, the collection device includes a monitoring module for removing abnormal sub-tables.

[0012] In a possible implementation, the coefficient calculation device calculates the error coefficient and uploads it to the server for use in monitoring the error of the sub-meter in the meter box.

[0013] In a possible implementation, the absolute values of the Pearson correlation coefficients before elimination are sorted from small to large, and the average of the remaining Pearson correlation coefficients before elimination and the Pearson correlation coefficients after elimination is taken.

[0014] The beneficial effect of the smart electric energy meter measurement error analysis system provided by the present invention is that: compared with the existing technology, the smart electric energy meter measurement error analysis system of the present invention uses a collection device to collect the effective power of each sub-meter in the meter box and the total power of the total meter, the elimination device eliminates the data of the sub-meter whose effective power is lower than a fixed value, the error data calculation device calculates the meter box error value, the mean calculation device calculates the effective power average and the effective power mean of the sub-meter, and the coefficient calculation device calculates the effective power and the Pearson correlation coefficient of the sub-meter before elimination. Therefore, the operating status of each sub-meter can be accurately obtained, and the measurement error of the sub-meter can be judged in time, which is easy to implement and saves labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 A schematic diagram of the structure of a smart electricity meter measurement error analysis system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] It should be noted that when an element is referred to as being “fixed on” or “disposed on” another element, it may be directly on the other element or indirectly on the other element. When an element is referred to as being “connected to” another element, it may be directly connected to the other element or indirectly connected to the other element.

[0019] It should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0020] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0021] See also Figure 1 The intelligent electric energy meter measurement error analysis system provided by the present invention is now described. The intelligent electric energy meter measurement error analysis system includes a collection device, a rejection device, an error data calculation device, a mean value calculation device, and a coefficient calculation device, which are sequentially arranged. The collection device is used to collect the effective power of each sub-meter in the meter box and the total power of the main meter input to the meter box. The rejection device is used to reject the data of sub-meters whose effective power is lower than a fixed value. The error data calculation device is used to calculate the meter box error value before rejection and the meter box error value after rejection. The mean value calculation device is used to calculate the effective power average of the sub-meters before rejection and the effective power average of the sub-meters after rejection. The coefficient calculation device is used to calculate the Pearson correlation coefficient before rejection between the effective power of the sub-meters before rejection and the error value of the meter box before rejection.

[0022] Compared with the prior art, the intelligent electric energy meter measurement error analysis system provided by the present invention uses a collection device to collect the effective power of each sub-meter in the meter box and the total power of the total meter, a rejection device rejects the data of the sub-meter whose effective power is lower than a fixed value, an error data calculation device calculates the meter box error value, a mean calculation device calculates the effective power average and the effective power mean of the sub-meter, and a coefficient calculation device calculates the effective power and the Pearson correlation coefficient of the sub-meter before rejection. Therefore, the operating status of each sub-meter can be accurately obtained, the measurement error of the sub-meter can be judged in time, the implementation is convenient, and labor costs are saved.

[0023] Specifically, the data collection device is used to collect the effective power of each sub-meter within the meter box, as well as the total power of the main meter input into the meter box. The number of sub-meters is recorded as m1, and the number of data collection points is recorded as n1. If multiple sub-meters are set in a meter box, the number of sub-meters in this case is recorded as m1.

[0024] A rejection device is used to remove data from sub-meters whose effective power is lower than a set value. The number of remaining sub-meters is recorded as m2, and the number of collection points corresponding to m2 is n2. Since there are many sub-meters in the meter box, it cannot be guaranteed that all sub-meters are operating normally at the time of detection. Therefore, abnormal sub-meters must be discarded first. The rejection method is as follows: if the effective power of a sub-meter is less than a minimum error value, the sub-meter is discarded. The minimum error value mentioned here is data that can be known at the time of production. Of course, it can also be manually defined during detection. Simply compare the power of the sub-meter with the normal operation of the sub-meter. If the power of the sub-meter is not working properly at this time, the sub-meter needs to be rejected. According to this method, all abnormal sub-meters are eliminated, and the number of remaining valid sub-meters is recorded as m2. In other words, m2-m1 sub-meters are eliminated.

[0025] The error data calculation device is used to calculate the error value of the meter box before elimination based on the effective power and total power of the m1 sub-meters before elimination, and to calculate the error value of the meter box after elimination based on the effective power of the m2 sub-meters after elimination and the total power of the meter box after elimination, wherein the total power of the meter box after elimination refers to the total power minus the sum of the rated powers of the eliminated sub-meters; since there may be abnormal sub-meters before elimination, but the abnormal sub-meter may or may not affect the final error, in order to avoid blind elimination and the accuracy of subsequent errors, the data before and after elimination are calculated uniformly at this time and then processed subsequently.

[0026] Since the number of sub-meters is reduced after elimination, but the power supplied by the entire meter box is not reduced, it is necessary to subtract the rated power corresponding to the sub-meter so that the power supplied by the meter box matches the remaining normal sub-meters.

[0027] The calculation formula after removing the error value of the front meter box is the total power minus the sum of the effective powers of m1 sub-meters.

[0028] The calculation formula is:

[0029] Among them, S 1j represents the power deviation value of the meter box at the jth data collection point, m1 is the number of sub-meters before elimination, n1 is the number of collection points before elimination, P ij represents the power reading of the ith submeter at the jth data collection point, T j Represents the total power reading at the jth data collection point.

[0030] The calculation formula of the error value of the meter box after elimination is the total power minus the sum of the effective power of the m2 sub-meters.

[0031] The calculation formula is:

[0032] Among them, S 2j represents the power deviation value of the meter box at the jth data collection point, m2 is the number of sub-meters after elimination, n2 is the number of collection points after elimination, P ij represents the power reading of the ith submeter at the jth data collection point, T j Represents the total power reading at the jth data collection point.

[0033] (4) a mean value calculation device for calculating the mean effective power of the m1 sub-meters before elimination, and for calculating the mean effective power of the m2 sub-meters after elimination, and for calculating the mean effective power of the meter box before elimination of the meter box error value, and for calculating the mean effective power of the meter box after elimination of the meter box error value;

[0034] Among them, the calculation formula for the mean effective power of the sub-table before elimination is:

[0035]

[0036] in, Represents the mean value of n1 power data in the i-th sub-table.

[0037] The calculation formula for the mean effective power of the sub-meter after elimination is:

[0038]

[0039] in, Represents the mean value of n2 power data in the i-th sub-table.

[0040] The calculation formula for the mean effective power of the front meter box after excluding the error value of the front meter box is:

[0041]

[0042] The calculation formula for the mean effective power of the meter box after eliminating the error value of the meter box is:

[0043]

[0044] The coefficient calculation device is used to calculate the Pearson correlation coefficient before elimination between the effective power of the m1 sub-meters before elimination and the meter box error value before elimination, and to calculate the Pearson correlation coefficient after elimination between the effective power of the m2 sub-meters after elimination and the meter box error value after elimination; the Pearson correlation coefficients before and after elimination are calculated to obtain a group of m1 coefficient data and a group of m2 coefficient data.

[0045] The calculation formula of Pearson correlation coefficient before elimination is:

[0046]

[0047] The calculation formula of Pearson correlation coefficient after elimination is:

[0048]

[0049] Sort the absolute values of the Pearson correlation coefficients before elimination from smallest to largest, delete the first m1-m2 Pearson correlation coefficients before elimination, and average the remaining Pearson correlation coefficients before elimination and the Pearson correlation coefficients after elimination. In other words, sort the data of the first group of m1 coefficients from smallest to largest in absolute value. If the number of eliminated sub-tables is 1, delete the smallest data so that the number of remaining data is the same as the number of data in the second group, that is, after elimination, that is, m1 = m2. At this point, two groups of coefficient data with the same number are obtained. The coefficient data of these two groups are averaged to obtain a single average value, which is the final error coefficient.

[0050] The calculated error coefficient is uploaded to a server for staff to monitor the errors of the sub-meters in the meter box. To enable staff to monitor errors in real time, this embodiment also provides a real-time monitoring method: a collection device collects the total power of the meter box and the effective power of each sub-meter over a 30-minute period, and after eliminating, calculating the error value, calculating the mean, and operating the coefficient, calculates the final error coefficient for that 30-minute period. The collection device is also used to divide 24 hours into 48 30-minute periods, collect the total power of the meter box and the effective power of each sub-meter over 48 30-minute periods, and after eliminating, calculating the error value, calculating the mean, and operating the coefficient, calculate the final error coefficient for each 30-minute period to obtain a set of data. The reorganized data is plotted on a curve. If the curve resembles a straight line, the error is normal. If the curve fluctuates significantly, it indicates that a sub-meter anomaly exists at the fluctuating point. At the same time, if a sub-meter anomaly occurs, the abnormal sub-meter is automatically eliminated within the next 30 minutes, and the final error coefficient for the next 30 minutes is then calculated. The curve at the time of the anomaly and the redrawn curve after the anomaly is removed are both displayed, allowing staff to understand the situation before and after the anomaly period. The drawn curve and fluctuation points are automatically stored and displayed. In addition, an anomaly recording device is included to record information about abnormal sub-meters. This information includes the abnormal sub-meter number, effective power, total power of the meter box, and the time period when the anomaly occurred. The abnormal sub-meter refers to the point in the curve where the fluctuation occurred.

[0051] Furthermore, the error analysis method is:

[0052] S10: Collect the effective power of each sub-meter in the meter box, and the total power of the main meter input into the meter box. The number of sub-meters is recorded as m1, and the number of collection points is recorded as n1.

[0053] S20: Eliminate the data of the sub-tables whose effective power is lower than the set value, and record the number of the remaining sub-tables as m2, and the number of collection points corresponding to m2 is n2.

[0054] S30: Calculating the error value of the meter box before elimination based on the effective power and total power of the m1 sub-meters before elimination, and calculating the error value of the meter box after elimination based on the effective power of the m2 sub-meters after elimination and the total power of the meter box after elimination, wherein the total power of the meter box after elimination refers to the total power minus the sum of the rated powers of the eliminated sub-meters;

[0055] The calculation formula for the error value of the meter box before elimination is the total power minus the sum of the effective powers of m1 sub-meters.

[0056] The calculation formula is:

[0057] Among them, S 1jrepresents the power deviation value of the meter box at the jth data collection point, m1 is the number of sub-meters before elimination, n1 is the number of collection points before elimination, P ij represents the power reading of the ith submeter at the jth data collection point, T j Represents the total power reading at the jth data collection point.

[0058] The calculation formula of the error value of the meter box after elimination is the total power minus the sum of the effective power of the m2 sub-meters.

[0059] The calculation formula is:

[0060] Among them, S 2j represents the power deviation value of the meter box at the jth data collection point, m2 is the number of sub-meters after elimination, n2 is the number of collection points after elimination, P ij represents the power reading of the ith submeter at the jth data collection point, T j Represents the total power reading at the jth data collection point.

[0061] S40: Calculating the mean effective power of the m1 sub-meters before elimination, and the mean effective power of the m2 sub-meters after elimination, and the mean effective power of the meter box before elimination for calculating the meter box error value before elimination, and the mean effective power of the meter box after elimination for calculating the meter box error value after elimination;

[0062] The calculation formula for the mean effective power of the sub-meter before elimination is:

[0063]

[0064] in, Represents the mean value of n1 power data in the i-th sub-table.

[0065] The calculation formula for the mean effective power of the sub-meter after elimination is:

[0066]

[0067] in, Represents the mean value of n2 power data in the i-th sub-table.

[0068] The formula for calculating the mean effective power of the meter box before removing the error value of the meter box is:

[0069]

[0070] The calculation formula for the mean effective power of the meter box after excluding the error value of the meter box is:

[0071]

[0072] S50: Calculating the Pearson correlation coefficient before elimination between the effective power of the m1 sub-meters before elimination and the meter box error value before elimination, and calculating the Pearson correlation coefficient after elimination between the effective power of the m2 sub-meters after elimination and the meter box error value after elimination;

[0073] The calculation formula of Pearson correlation coefficient before elimination is:

[0074]

[0075] The calculation formula of Pearson correlation coefficient after elimination is:

[0076]

[0077] S60: sorting the absolute values of the Pearson correlation coefficients before elimination from small to large, deleting the first m1-m2 Pearson correlation coefficients before elimination, and taking the average of the remaining Pearson correlation coefficients before elimination and the Pearson correlation coefficients after elimination.

[0078] S70: The calculated error coefficient is uploaded to a server for staff to monitor the errors of the sub-meters in the meter box. To enable staff to monitor the errors in real time, this embodiment also provides a real-time monitoring method: the collection device collects the total power of the meter box and the effective power of each sub-meter within 30 minutes, and after elimination, error value calculation, average calculation, and coefficient operation, calculates the final error coefficient for the 30 minutes. The collection device is also used to divide 24 hours into 48 30-minute periods, collect the total power of the meter box and the effective power of each sub-meter for 48 30-minute periods, and after elimination, error value calculation, average calculation, and coefficient operation, calculate the final error coefficient for each 30-minute period to obtain a set of data. The reorganized data is plotted on a curve. If the curve is similar to a straight line, the error is normal. If the curve fluctuates greatly, it indicates that a sub-meter abnormality has occurred at the fluctuating point. At the same time, if a sub-meter abnormality occurs, the abnormal sub-meter is automatically eliminated within the next 30 minutes, and the final error coefficient for the next 30 minutes is calculated.

[0079] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A smart energy meter measurement error analysis system, characterized in that: The system comprises a collection device, a rejection device, an error data calculation device, a mean calculation device and a coefficient calculation device which are arranged in sequence; the collection device is used to collect the effective power of each sub-meter in the meter box and the total power of the total meter input into the meter box; the rejection device is used to reject the data of the sub-meter whose effective power is lower than a fixed value; the error data calculation device is used to calculate the error value of the meter box before rejection and the error value of the meter box after rejection; the mean calculation device is used to calculate the mean effective power of the sub-meter before rejection and the mean effective power of the sub-meter after rejection; the coefficient calculation device is used to calculate the Pearson correlation coefficient before rejection between the effective power of the sub-meter before rejection and the error value of the meter box before rejection; The coefficient calculation device is used to calculate the post-elimination Pearson correlation coefficient between the effective power of the sub-meter after elimination and the meter box error value after elimination; the absolute values of the Pearson correlation coefficients before elimination are sorted from small to large, and the remaining Pearson correlation coefficients before elimination and the Pearson correlation coefficients after elimination are averaged; the average of the remaining Pearson correlation coefficients before elimination and the Pearson correlation coefficients after elimination is the final error coefficient.

2. The smart energy meter measurement error analysis system according to claim 1, characterized in that: The collection device is used to record the number of the sub-tables and the number of collection points.

3. The smart energy meter measurement error analysis system according to claim 2, characterized in that: The elimination device eliminates the data of the sub-table whose effective power is lower than the set value, and records the number of the remaining sub-tables as the corresponding number of collection points.

4. The smart energy meter measurement error analysis system according to claim 1, characterized in that: The meter box error value before and after elimination is calculated by eliminating the effective power and total power of the sub-meter.

5. The smart energy meter measurement error analysis system according to claim 4, characterized in that: The total power of the meter box after removal is the total power minus the sum of the rated powers of the removed sub-meters.

6. The smart energy meter measurement error analysis system according to claim 1, characterized in that: The mean value calculation device is used to calculate the mean value of the effective power of the meter box before elimination of the error value of the meter box before elimination and calculate the mean value of the effective power of the meter box after elimination of the error value of the meter box after elimination.

7. The smart energy meter measurement error analysis system according to claim 1, characterized in that: The collection device has a monitoring module for removing abnormal sub-tables.

8. The smart energy meter measurement error analysis system according to claim 1, characterized in that: The coefficient calculation device calculates the error coefficient and uploads it to the server for use in monitoring the error of the sub-meter in the meter box.

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