Calculation method, system and storage medium of error electric quantity under electric energy metering failure

Abstract

The present invention specifically relates to a method, system and storage medium for calculating the amount of electricity error under an electric energy metering fault, and belongs to the technical field of electric energy metering. The method includes the following steps: first, historical operation data is collected, and a judgment data set L is constructed in the collected historical operation data to judge the degree of deviation between the estimated electricity consumption calculated by the current integration method and the actual electricity consumption. When the degree of deviation is large, correction and compensation are performed. The present invention increases the judgment of the deviation between the estimated electricity consumption calculated by the current integration method and the actual electricity consumption, and at the same time, correction and compensation are performed on the estimated electricity consumption calculated by the current integration method, which greatly improves the accuracy of the current integration method in calculating the amount of electricity error under an electric energy metering fault, so that the electricity refund and compensation work can be carried out fairly and justly. At the same time, the present invention builds a system platform and storage medium that carries the method for calculating the amount of electricity error under an electric energy metering fault, so that the calculation method can be more smoothly applied to different scenarios.

Description

Technical Field

[0001] The present invention specifically relates to a method, system and storage medium for calculating error electric quantity under electric energy metering failure, and belongs to the technical field of electric power metering. Background Art

[0002] High-voltage customers account for more than half of the total electricity consumption in the society, and they have the characteristics of small user volume and large single-household electricity consumption. Therefore, the accuracy of electricity metering for high-voltage customers is directly related to the economic interests of both the supplier and the seller. For individual customers, monthly electricity consumption is closely related to their electricity prices and transaction assessments; for power supply companies, the accuracy of customer electricity consumption is closely related to their transaction forecasts and internal operations. Power supply companies monitor customer electricity consumption through electricity metering devices to achieve accurate measurement of customer electricity consumption, but the measurement accuracy of electricity metering devices is closely related to whether their wiring is correct and whether the voltage and current circuits are normal. According to statistics, most of the metering failures and electricity errors in electricity metering devices are caused by abnormal voltage secondary circuits.

[0003] When metering failure and power errors occur, the existing technology uses historical power consumption for the same period or assumes three-phase load balance, and uses the correction coefficient method to calculate the error power during the metering failure period. Since the customer's current and voltage are not completely balanced in actual operation, but fluctuate in real time, the error power calculated by the correction coefficient method assuming three-phase load balance will inevitably be different from the actual error current, which is easy to cause disputes with customers. At the same time, the inaccurate calculation of the error power will inevitably make it difficult to conduct the power refund work fairly and impartially. Summary of the invention

[0004] The first technical problem to be solved by the present invention is: how to accurately calculate the error electric quantity during the fault period when a fault occurs in the electric energy metering device.

[0005] The second technical problem to be solved by the present invention is: how to propose a calculation system and storage medium for the error electric quantity under electric energy metering failure, which can carry the calculation method of the error electric quantity under electric energy metering failure.

[0006] In order to solve the above-mentioned first technical problem, the first technical solution proposed by the present invention is:

[0007] A method for calculating the error power under electric energy metering failure includes the following steps:

[0008] Step 1: According to the collection frequency Continuously collect data from the three-phase four-wire electric energy metering device N times, where N is a natural number; obtain historical operation data of the three-phase four-wire electric energy metering device in N consecutive fixed time periods t in the normal operation time period T, pre-process the collected historical operation data in the N consecutive fixed time periods t and collect all the pre-processed historical operation data to form a historical operation data sequence;

[0009] Step 2: Perform a first sampling without replacement in the historical running data sequence, and establish a judgment data set L according to the result of the first sampling. The specific steps are as follows:

[0010] Step 2.1: Set the time window g and rolling step s of the first sampling, wherein g and s satisfy the following formula (1):

[0011]

[0012] In formula (1), T0 is the total time length of the historical operation data sequence;

[0013] In the total time length T0 of the historical operation data sequence, an initial time T1 is selected b As the first sampling start time point of the first sampling, extract the historical operation data with a time length of g from the historical operation data sequence, substitute all the historical operation data extracted in the first sampling into the following formula (2) and use the current integration method to calculate the first sampling time period [T1 b ,T1 b +g]Estimated electricity consumption

[0014]

[0015] In formula (2), is the three-phase average voltage during the first sampling period; and are respectively the a-phase, b-phase and c-phase voltages of the three-phase four-wire electric energy metering device at the tth moment; is the three-phase average current during the first sampling period, and are respectively the a-phase, b-phase and c-phase currents of the three-phase four-wire electric energy metering device at the moment t; is the average power factor during the first sampling period; P t is the active power of the three-phase four-wire electric energy metering device at the tth moment; Q t is the reactive power of the three-phase four-wire electric energy metering device at the tth moment;

[0016] The first sampling time period [T1 b ,T1 b +g] Actual power consumption

[0017]

[0018] In formula (3), is in the]T1 b ,T1 b +g[the final amount of the three-phase four-wire electric energy metering device at the end; In the [T1 b ,T1 b +g] the starting power of the three-phase four-wire electric energy metering device at the beginning;

[0019] Step 2.2: As the starting time point of the second sampling of the first sampling, extract the historical operation data of time length g from the historical operation data sequence, substitute all the historical operation data extracted by the second sampling into formulas (2) and (3) to calculate the second sampling time period [T1 b +s,T1 b +g+s]Estimated electricity consumption and actual power consumption Repeat the above steps until the nth sampling is completed, and obtain the nth sampling time period [T1 b +(n-1)*s,T1 b +g+(n-1)*s] estimated power consumption and actual power consumption The n pairs of estimated power consumption and actual power consumption obtained from the first sampling to the nth sampling in the first sampling are collected to form a judgment data set L, as shown in the following formula (4):

[0020]

[0021] Step 3: Construct the test statistic t0 of the paired t test based on all the data in the judgment data set L. If the test statistic t0 satisfies the following formula (5):

[0022] -t α / 2,n-1 <t0<t α / 2,n-1 (5)

[0023] In formula (5), t α / 2,n-1 It is the upper quantile of the t distribution with n-1 degrees of freedom, indicating the critical value of the right tail at a significance level of α / 2, -t α / 2,n-1It represents the critical value of the left tail at the significance level of α / 2;

[0024] It is considered that the difference between the estimated power consumption calculated by the current integration method according to formula (2) and the actual power consumption calculated by formula (3) is not large, and the estimated power consumption during the fault period is calculated according to formula (2) as the error power under the power metering fault;

[0025] If the test statistic t0 does not satisfy formula (5), it is considered that the difference between the estimated power consumption calculated by the current integration method according to formula (2) and the actual power consumption calculated by formula (3) is large, and correction compensation is required. The correction coefficient δ is calculated and the correction coefficient δ is multiplied by the estimated power consumption during the fault period calculated by formula (2) to obtain the error power under the electric energy metering fault after correction and compensation.

[0026] Further, the historical operation data collected in step 1 includes the starting power of the three-phase four-wire power metering device at the beginning of the fixed time period t. At the end of the fixed time period t, the final power of the three-phase four-wire power metering device and the three-phase current data under the fixed time period t Three-phase voltage data Total power factor Three-phase power factor Total active power P t , three-phase AC active power Total reactive power Q t , three-phase AC reactive power The preprocessing includes data type conversion and standardization.

[0027] Furthermore, the calculation of the test statistic t0 in step 3 is shown in the following formula (6):

[0028]

[0029] In formula (6), μ is the mean of all estimated power consumptions in the judgment data set L; μ′ is the mean of all actual power consumptions in the judgment data set L; Δ0 is the difference between μ and μ′.

[0030] Further, the specific correction and compensation process in step 3 is as follows:

[0031] Step 3.1: Perform a second sampling of external rolling with replacement and internal rolling without replacement in the historical running data sequence. The specific process is as follows:

[0032] Step 3.1.1: Set the internal rolling time window g without replacement for the second samplingin and rolling step length s in , the g in and in Satisfies the following formula (7):

[0033]

[0034] Set the rolling step size s of the second sampling to roll with replacement out , the out Satisfies the following formula (8):

[0035] 1≤s out ≤g in (8);

[0036] Step 3.1.2: Select an initial time in the total time length T0 of the historical operation data sequence As the first sampling start time point of the first internal no-replacement rolling of the second sampling, a time length of g is extracted from the historical running data sequence. in , substitute all the historical running data extracted from the first sampling into formulas (2) and (3) to calculate the first sampling time period [T2 b ,T2 b +g in ]Estimated power consumption and actual power consumption

[0037] by As the second sampling start time point of the first internal no-replacement rolling of the second sampling, a time length of g is extracted from the historical running data sequence. in , substitute all the historical running data of the second sampling into formulas (2) and (3) to calculate the second sampling time period [T2 b +s in ,T2 b +g in +s in ]Estimated power consumption and actual power consumption Repeat the above steps until the N0th sampling is completed, and the N0th sampling time period [T2 b +(N0-1)*s in ,T2 b +g in +(N0-1)*s in ]Estimated power consumption and actual power consumption At this time, the first internal rolling sampling without replacement is completed, and a total of N0 paired estimated power consumption and actual power consumption data extracted by the first internal rolling sampling without replacement are collected;

[0038] Step 3.1.3: After the first internal rolling sampling without replacement is completed, the second external sampling with replacement rolling step s out Increase the rolling time window g within the second sampling without replacement in The second sampling is repeated without replacement within the rolling new time window g. new , as shown in the following formula (9),

[0039] g new =g in +s out (9)

[0040] Roll the new time window g according to the internal no-replacement new , rolling step length s in The second internal rolling sampling without replacement of the second sampling is performed according to the principle of step 3.1.2 to obtain N1 paired estimated power consumption and actual power consumption data; repeat the above steps for a total of k-1 times to obtain N2, N3, N4, and so on. k The sample size is N = N0 + N1 + ... + N. k A large sample data set is used as a correction and compensation data set;

[0041] Step 3.1.4: Substitute all data pairs in the correction compensation data set into the following formula (10) to calculate the correction coefficient δ:

[0042]

[0043] In formula (10), is the jth actual power consumption data in the correction and compensation data set; is the jth estimated power consumption data in the correction compensation data set;

[0044] The estimated power consumption during the fault period is calculated according to formula (2), and the calculated result is multiplied by the result of formula (10) to obtain the error power under the corrected and compensated power metering fault.

[0045] In order to solve the above-mentioned second technical problem, the second technical solution proposed by the present invention is:

[0046] A system for calculating error electric quantity under electric energy metering failure, comprising:

[0047] Data acquisition and processing module, according to the acquisition frequency Continuously collect data N times from the three-phase four-wire electric energy metering device to obtain historical operation data of the three-phase four-wire electric energy metering device in N consecutive fixed time periods t in the normal operation time period T, and pre-process the collected historical operation data;

[0048] A sampling module capable of performing a first sampling without replacement rolling in all the historical operation data collected by the data collection and processing module, and establishing a judgment data set L according to the result of the first sampling;

[0049] A judgment module, capable of constructing a test statistic t0 of a paired t test in the judgment data set L, and judging whether there is a large deviation between the estimated power consumption calculated by the current integration method and the actual power consumption through the test statistic t0;

[0050] The compensation model can correct and compensate the estimated power consumption calculated by the current integration method when the judgment module determines that there is a large deviation between the estimated power consumption calculated by the current integration method and the actual power consumption.

[0051] To solve the above-mentioned second technical problem, the third technical solution adopted by the present invention is: a computer-readable storage medium, characterized in that: the computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the method for calculating the error power under the above-mentioned electric energy metering failure are implemented.

[0052] Beneficial effects: 1. The present invention adds the judgment of the deviation between the estimated power consumption calculated by the current integration method and the actual power consumption, and at the same time corrects and compensates for the situation where the deviation between the estimated power consumption calculated by the current integration method and the actual power consumption is large. The correction and compensation process constructs a correction and compensation data set with a large number of samples through external rolling with replacement and internal non-rolling sampling. The correction and compensation coefficient is calculated in the correction and compensation data set, and the correction and compensation coefficient is used to correct and compensate for the estimated power consumption with large deviation calculated by the current integration method, which greatly improves the accuracy of the current integration method in calculating the error power under the electric energy metering fault, so that the power refund work can be carried out fairly and justly. 2. The present invention builds a system platform and storage medium that can carry the calculation method of the error power under the electric energy metering fault, so that the calculation method of the error power under the electric energy metering fault can be more smoothly applied to different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of the method for calculating the error electricity quantity under the electric energy metering failure.

[0054] Figure 2 is a distribution histogram of the correction coefficient δ of user 1 in implementation one.

[0055] Figure 3 is a distribution histogram of the correction coefficient δ of user 2 in implementation one.

[0056] Figure 4 is a distribution histogram of the correction coefficient δ of user 3 in implementation one. DETAILED DESCRIPTION

[0057] The following is a further description of a method, system and storage medium for calculating the error power under an electric energy metering fault in accordance with a specific embodiment of the present invention.

[0058] Embodiment 1

[0059] A method for calculating the error power under power metering failure, such as Figure 1 As shown, the following steps are included

[0060] Step 1: According to the collection frequency Continuously collect data from the three-phase four-wire electric energy metering device N times, where N is a natural number; obtain historical operation data of the three-phase four-wire electric energy metering device in N consecutive fixed time periods t in the normal operation time period T, and the historical operation data includes the starting power of the three-phase four-wire electric energy metering device at the beginning of the fixed time period t. The final power of the three-phase four-wire energy metering device at the end of the fixed time period t And the three-phase current data under a fixed time period t Three-phase voltage data Total power factor Three-phase power factor Total active power P t , three-phase AC active power Total reactive power Q t , three-phase AC reactive power Preprocessing the collected historical operation data within N consecutive fixed time periods t, including data type conversion and standardization; preprocessing and collecting the preprocessed historical operation data within N consecutive fixed time periods t to form a historical operation data sequence;

[0061] Step 2: Perform the first sampling without replacement in the historical running data sequence, and establish the judgment data set L according to the result of the first sampling. The specific steps are as follows:

[0062] Step 2.1: Set the time window g and rolling step size s of the first sampling, where g and s satisfy the following equation (1):

[0063]

[0064] In formula (1), T0 is the total time length of the historical operation data series;

[0065] In the total time length T0 of the historical running data sequence, select an initial time T1 b As the first sampling start time point of the first sampling, extract the historical operation data with a time length of g from the historical operation data sequence, substitute all the historical operation data extracted in the first sampling into the following formula (2) and use the current integration method to calculate the first sampling time period [T1 b ,T1 b +g]Estimated electricity consumption

[0066]

[0067] In formula (2), is the three-phase average voltage during the first sampling period; and They are the voltages of phase a, phase b and phase c of the three-phase four-wire electric energy metering device at time t; is the three-phase average current during the first sampling period, and They are the a-phase, b-phase and c-phase currents of the three-phase four-wire electric energy metering device at the time t; is the average power factor during the first sampling period; P t is the active power of the three-phase four-wire electric energy metering device at the tth moment; Q t is the reactive power of the three-phase four-wire energy metering device at the tth moment;

[0068] The first sampling time period [T1 b ,T1 b +g] Actual power consumption

[0069]

[0070] In formula (3), It is in [T1 b ,T1 b +g] The final amount of the three-phase four-wire electric energy metering device at the end; It is in [T1 b ,T1 b +g] The starting power of the three-phase four-wire electric energy metering device at the beginning;

[0071] Step 2.2: Take T1 b+s is taken as the starting time point of the second sampling of the first sampling, and the historical operation data with a time length of g is extracted from the historical operation data sequence. All the historical operation data extracted in the second sampling are substituted into formulas (2) and (3) to calculate the second sampling time period [T1 b +s,T1 b +g+s]Estimated electricity consumption and actual power consumption Repeat the above steps until the nth sampling is completed, and obtain the nth sampling time period [T1 b +(n-1)*s,T1 b +g+(n-1)*s] estimated power consumption and actual power consumption The n pairs of estimated power consumption and actual power consumption obtained from the first sampling to the nth sampling in the first sampling are collected to form a judgment data set L, as shown in the following formula (4):

[0072]

[0073] Step 3: Construct the test statistic t0 of the paired t test based on all the data in the judgment data set L, as shown in the following formula (6):

[0074]

[0075] In formula (6), μ is the mean of all estimated power consumption in the judgment data set L; μ′ is the mean of all actual power consumption in the judgment data set L; Δ0 is the difference between μ and μ′;

[0076] If the test statistic t0 satisfies the following formula (5)

[0077] -t α / 2,n-1 <t0<t α / 2,n-1 (5)

[0078] In formula (5), t α / 2,n-1 It is the upper quantile of the t distribution with n-1 degrees of freedom, indicating the critical value of the right tail at a significance level of α / 2, -t α / 2,n-1 It represents the critical value of the left tail at the significance level of α / 2;

[0079] It is considered that the difference between the estimated power consumption calculated by the current integration method according to formula (2) and the actual power consumption calculated by formula (3) is not large. The estimated power consumption during the fault period calculated according to formula (2) is used as the error power under the power metering fault.

[0080] If the test statistic t0 does not satisfy formula (5), it is considered that the difference between the estimated power consumption calculated by the current integration method according to formula (2) and the actual power consumption calculated by formula (3) is large, and correction compensation is required. The specific correction compensation process is as follows:

[0081] Step 3.1: Perform the second sampling of external rolling with replacement and internal rolling without replacement in the historical running data sequence. The specific process is as follows:

[0082] Step 3.1.1: Set the internal rolling time window g without replacement for the second sampling in and rolling step length s in , g in and in Satisfies the following formula (7):

[0083]

[0084] Set the rolling step size s of the second sample with external rollback out ,s out Satisfies the following formula (8):

[0085] 1≤s out ≤g in (8);

[0086] Step 3.1.2: Select an initial time in the total time length T0 of the historical running data sequence As the first sampling start time point of the first internal no-replacement rolling of the second sampling, the time length of the sampling in the historical running data sequence is g in , substitute all the historical running data of the first sampling into formulas (2) and (3) to calculate the first sampling time period [T2 b ,T2 b +g in ]Estimated power consumption and actual power consumption

[0087] by As the starting time point of the second sampling of the first internal no-replacement rolling of the second sampling, the time length of the sampling in the historical running data sequence is g in , substitute all the historical running data of the second sampling into formulas (2) and (3) to calculate the second sampling time period [T2 b +s in ,T2 b +g in +s in]Estimated power consumption and actual power consumption Repeat the above steps until the N0th sampling is completed, and the N0th sampling time period [T2 b +(N0-1)*s in ,T2 b +g in +(N0-1)*s in ]Estimated power consumption and actual power consumption At this time, the first internal rolling sampling without replacement is completed, and the estimated power consumption and actual power consumption data of a total of N0 pairs extracted by the first internal rolling sampling without replacement are collected;

[0088] Step 3.1.3: After the first internal rolling sampling without replacement is completed, the second external sampling with replacement rolling step s out Increase the time window g that rolls without replacement within the second sample in As the second sampling, the new time window g is rolled without replacement. new , as shown in the following formula (9),

[0089] g new =g in +s out (9)

[0090] Roll the new time window g according to the internal no-replacement new , rolling step length s in The second sampling is performed according to the principle of step 3.1.2, and the second internal rolling sampling without replacement is performed to obtain N1 paired estimated power consumption and actual power consumption data; the above steps are repeated for a total of k-1 times to obtain N2, N3, N4, and so on. k The sample size is N = N0 + N1 + ... + N. k A large sample data set is used as a correction and compensation data set;

[0091] Step 3.1.4: Substitute all data pairs in the correction compensation data set into the following formula (10) to calculate the correction coefficient δ:

[0092]

[0093] In formula (10), is the jth actual power consumption data in the correction and compensation data set; is the jth estimated power consumption data in the correction compensation data set;

[0094] The estimated power consumption during the fault period is calculated according to formula (2), and the calculated result is multiplied by the result of formula (10) to obtain the error power under the corrected and compensated power metering fault.

[0095] The following calculation is performed on the data of three high-voltage customer meters in a city of the State Grid from 00:00 on October 1, 2023 to 23:45 on October 30, 2023. They are numbered as User 1, User 2, and User 3. The data collection frequency is 15 minutes, the number of measurements is 2880 times, and the nearest neighbor method is used to fill in missing values.

[0096] The results show that in the normal time period, the three-phase voltages of the three users all fluctuate around 60V. Starting from a certain moment, the B-phase voltage of user 1 drops from about 60V to about 20V. It can be judged that the B-phase voltage at this moment is faulty, and the fault ends until the B-phase voltage recovers to about 60V. Similarly, it can be judged that the fault start time of user 2 is when the B-phase voltage drops from about 60V to about 20V, and the fault ends when it recovers to about 60V. The fault start time of user 3 is when both the A-phase voltage and the C-phase voltage drop from about 60V to about 0V, and the fault ends until the A-phase voltage and the C-phase voltage recover to about 60V.

[0097] According to step 2 and step 3, it is determined whether there is a large deviation between the estimated power consumption calculated by user 1, user 2 and user 3 using the integrated current and the actual power consumption. The specific process is as follows:

[0098] Assuming the time window size g=200 and the rolling step size s=200, the power consumption estimated by the integral current method and the actual power consumption are calculated, and the difference between the two is calculated to obtain 5 paired sample data sets of estimated power consumption and actual power consumption.

[0099] Assume Δ0=0 and conduct hypothesis test. The null hypothesis is that the mean of the power consumption difference is equal to 0.

[0100] According to formula (6), the statistical test quantity is constructed The statistical test quantities of user 1, user 2 and user 3 are: Assume α = 0.05, and look up the table to get, t 0.05 / 2,5-1 =2.132, t 0.05,5-1 =1.660, so Therefore, it is believed that the estimated electricity consumption of User 1 and User 2 are significantly different from the actual values, and the estimated electricity consumption is significantly greater than the actual value, so they enter the correction and compensation process; User 3 has no significant difference and does not enter the correction and compensation process.

[0101] The correction and compensation process is as follows:

[0102] Assume that the voltage threshold U0 = 48V, and observe that the voltage values ​​of the three users' electric energy meters are all greater than U0 in the first 1000 moments, which is taken as T0. Assume that g in =100,s in =1,s out =50, and rolling Bootstrap sampling is performed on the time series data sets of the three users in t∈T0. The estimated power consumption and actual power consumption as well as the pairing correction coefficient using the integral current method are calculated, and a large paired sample set with a sample size of 8568 is obtained through integration.

[0103] Most of the correction coefficients of User 1 and User 2 are less than 1, indicating that the power consumption estimated by the integral current method is slightly greater than the actual power consumption. The average correction coefficient of User 1 is 0.973314, and the histogram is shown in the figure. Figure 1 As shown in Figure 2, the mean value of the correction coefficient of user 2 is 0.994467, and the histogram is as follows: Figure 2 The mean value of the correction coefficient of user 3 is 0.994878, and the histogram is shown in Figure 3 As shown in the figure, the correction coefficient is distributed on both ends of 1, indicating that the difference between the estimated electricity consumption of user 3 by the integral current method and the actual electricity consumption is not significant, which further verifies the conclusion of the hypothesis test, that is, there is no significant difference between the estimated electricity consumption of user 3 by the current integral method and the actual electricity consumption; it also verifies the effectiveness of the small sample test, indicating that the small sample test can timely determine whether there is a significant difference.

[0104] Calculation of power withdrawal during fault period after correction

[0105] The estimated power consumption in the normal time period before correction is calculated using the current integration method. The estimated power consumption in the normal time period before correction for user 1 is 205461.61960414643kWh, and the actual power consumption is 199910.8kWh. The estimated power consumption during the fault period after correction obtained using the above steps is 199978.6708233902kWh, which is closer to the actual power consumption; the estimated power consumption in the normal time period before correction for user 2 is 346274.6418675909kWh, and the actual power consumption is 344217.2kWh. The estimated power consumption during the fault period after correction obtained using the above steps is 344358.7042741375kWh, which is closer to the actual power consumption; the estimated power consumption during the fault period for user 3 is 235386.67786892565kWh, and no correction is required, which achieves the purpose of reducing the measurement error compensation work and improving work efficiency.

[0106] The system for calculating the error electric quantity under the electric energy metering fault of this embodiment is specifically described as follows: A system for calculating the error electric quantity under the electric energy metering fault can be used to implement the above-mentioned method for calculating the error electric quantity under the electric energy metering fault. Specifically, the system for calculating the error electric quantity under the electric energy metering fault includes a data acquisition and processing module, a sampling module, a judgment module and a compensation model.

[0107] Among them, the data acquisition and processing module, according to the acquisition frequency Continuously collect data N times from the three-phase four-wire electric energy metering device to obtain historical operation data of the three-phase four-wire electric energy metering device in N consecutive fixed time periods t in the normal operation time period T, and pre-process the collected historical operation data;

[0108] A sampling module, capable of performing a first sampling without replacement in all the historical operation data collected by the data collection and processing module, and establishing a judgment data set L according to the result of the first sampling;

[0109] A judgment module, capable of constructing a test statistic t0 of a paired t test in the judgment data set L, and judging whether there is a large deviation between the estimated power consumption calculated by the current integration method and the actual power consumption through the test statistic t0;

[0110] The compensation model can correct and compensate the estimated power consumption calculated by the current integration method when the judgment module judges that there is a large deviation between the estimated power consumption calculated by the current integration method and the actual power consumption.

[0111] All relevant contents of each step involved in the embodiment of the method for calculating the error power under the electric energy metering failure mentioned above can be referred to the functional description of the functional module corresponding to the system for calculating the error power under the electric energy metering failure in the embodiment of the present invention, and will not be repeated here.

[0112] The division of modules in the embodiments of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present invention may be integrated into one processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or software modules.

[0113] The present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for calculating the error power under the electric energy metering fault in the above embodiment.

[0114] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0115] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for calculating the error amount of electric energy under electric energy metering failure, characterized by: The following steps are included Step 1: According to the collection frequency Continuously collect data from the three-phase four-wire electric energy metering device N times, where N is a natural number; obtain historical operation data of the three-phase four-wire electric energy metering device in N consecutive fixed time periods t in the normal operation time period T, pre-process the collected historical operation data in the N consecutive fixed time periods t and collect all the pre-processed historical operation data to form a historical operation data sequence; Step 2: Perform a first sampling without replacement in the historical running data sequence, and establish a judgment data set L according to the result of the first sampling. The specific steps are as follows: Step 2.1: Set the time window g and rolling step s of the first sampling, wherein g and s satisfy the following formula (1): In formula (1), T0 is the total time length of the historical operation data sequence; In the total time length T0 of the historical operation data sequence, an initial time T1 is selected b As the first sampling start time point of the first sampling, extract the historical operation data with a time length of g from the historical operation data sequence, substitute all the historical operation data extracted in the first sampling into the following formula (2) and use the current integration method to calculate the first sampling time period [T1 b ,T1 b +g]Estimated electricity consumption In formula (2), is the three-phase average voltage during the first sampling period; and are respectively the a-phase, b-phase and c-phase voltages of the three-phase four-wire electric energy metering device at the tth moment; is the three-phase average current during the first sampling period, and are respectively the a-phase, b-phase and c-phase currents of the three-phase four-wire electric energy metering device at the moment t; is the average power factor during the first sampling period; P t is the active power of the three-phase four-wire electric energy metering device at the tth moment; Q t is the reactive power of the three-phase four-wire electric energy metering device at the tth moment; The first sampling time period [T1 b ,T1 b +g] Actual power consumption In formula (3), In the [T1 b ,T1 b +g] the final amount of electricity of the three-phase four-wire electric energy metering device at the end of the time; In the [T1 b ,T1 b +g] the starting power of the three-phase four-wire electric energy metering device at the beginning; Step 2.2: Take T1 b +s is used as the starting time point of the second sampling of the first sampling, historical operation data with a time length of g is extracted from the historical operation data sequence, and all the historical operation data extracted in the second sampling are substituted into formulas (2) and (3) to respectively calculate the second sampling time period [T1 b +s,T1 b +g+s]Estimated electricity consumption and actual power consumption Repeat the above steps until the nth sampling is completed, and obtain the nth sampling time period [T1 b +(n-1)*s,T1 b +g+(n-1)*s] estimated power consumption and actual power consumption The n pairs of estimated power consumption and actual power consumption obtained from the first sampling to the nth sampling in the first sampling are collected to form a judgment data set L, as shown in the following formula (4): Step 3: Construct the test statistic t0 of the paired t test based on all the data in the judgment data set L. If the test statistic t0 satisfies the following formula (5): -t α / 2,n-1 <t0<t α / 2,n-1 (5) In formula (5), t α / 2,n-1 It is the upper quantile of the t distribution with n-1 degrees of freedom, indicating the critical value of the right tail at a significance level of α / 2, -t α / 2,n-1 It represents the critical value of the left tail at the significance level of α / 2; It is considered that the difference between the estimated power consumption calculated by the current integration method according to formula (2) and the actual power consumption calculated by formula (3) is not large, and the estimated power consumption during the fault period is calculated according to formula (2) as the error power under the power metering fault; If the test statistic t0 does not satisfy formula (5), it is considered that the difference between the estimated power consumption calculated by the current integration method according to formula (2) and the actual power consumption calculated by formula (3) is large, and correction compensation is required. The correction coefficient δ is calculated and the correction coefficient δ is multiplied by the estimated power consumption during the fault period calculated by formula (2) to obtain the error power under the electric energy metering fault after correction and compensation; The specific correction and compensation process in step 3 is as follows: Step 3.1: Perform a second sampling of external rolling with replacement and internal rolling without replacement in the historical running data sequence. The specific process is as follows: Step 3.1.1: Set the internal rolling time window g without replacement for the second sampling in and rolling step length s in , the g in and in Satisfies the following formula (7): Set the rolling step size s of the second sample to roll with replacement outside out , the out Satisfies the following formula (8), 1≤s out ≤g in (8); Step 3.1.2: Select an initial time in the total time length T0 of the historical operation data sequence As the first sampling start time point of the first internal no-replacement rolling of the second sampling, a time length of g is extracted from the historical running data sequence. in , substitute all the historical running data extracted in the first sampling into formulas (2) and (3) to calculate the first sampling time period of the first internal no-replacement rolling of the second sampling respectively. Estimated power consumption within and actual power consumption by As the second sampling start time point of the first internal no-replacement rolling of the second sampling, a time length of g is extracted from the historical running data sequence. in , substitute all the historical running data of the second sampling into formulas (2) and (3) to calculate the second sampling time period of the first internal no-replacement rolling in the second sampling. Estimated power consumption within and actual power consumption Repeat the above steps until the N0th sampling is completed, and obtain the N0th sampling time period of the first internal no-replacement rolling in the second sampling Estimated power consumption within and actual power consumption At this time, the first internal rolling sampling without replacement is completed, and a total of N0 paired estimated power consumption and actual power consumption data extracted by the first internal rolling sampling without replacement are collected; Step 3.1.3: After the first internal rolling sampling without replacement is completed, the second external sampling with replacement rolling step s out Increase the rolling time window g within the second sampling without replacement in The second sampling is repeated without replacement within the rolling new time window g. new , as shown in the following formula (9), g new =g in +s out (9) Roll the new time window g according to the internal no-replacement new , rolling step length s in The second internal rolling sampling without replacement of the second sampling is performed according to the principle of step 3.1.2 to obtain N1 paired estimated power consumption and actual power consumption data; repeat the above steps for a total of k-1 times to obtain N2, N3, N4, and so on. k The sample size is N = N0 + N1 + ... + N. k A large sample data set is used as a correction and compensation data set; Step 3.1.4: Substitute all data pairs in the correction compensation data set into the following formula (10) to calculate the correction coefficient δ: In formula (10), is the jth actual power consumption data in the correction and compensation data set; is the jth estimated power consumption data in the correction compensation data set; The estimated power consumption during the fault period is calculated according to formula (2), and the calculated result is multiplied by the result of formula (10) to obtain the error power under the corrected and compensated power metering fault.

2. The method for calculating the error amount of electric energy under electric energy metering failure according to claim 1 is characterized in that: The historical operation data collected in step 1 includes the starting power of the three-phase four-wire power metering device at the beginning of the fixed time period t. At the end of the fixed time period t, the final power of the three-phase four-wire power metering device and the three-phase current data under the fixed time period t Three-phase voltage data Total power factor Three-phase power factor Total active power P t , three-phase AC active power P t i，i＝a,b,c , total reactive power Q t , three-phase AC reactive power The preprocessing includes data type conversion and standardization.

3. The method for calculating the error amount of electric energy under electric energy metering failure according to claim 1 is characterized in that: The calculation of the test statistic t0 in step 3 is shown in the following formula (6): In formula (6), μ is the mean of all estimated power consumption in the judgment data set L; μ′ is the mean of all actual power consumption in the judgment data set L; μ D is the difference between μ and μ′.

4. A system for calculating the amount of error electricity in an electric energy metering fault equipped with the method for calculating the amount of error electricity in an electric energy metering fault according to any one of claims 1 to 3, characterized in that: include: Data acquisition and processing module, according to the acquisition frequency Continuously collect data from the three-phase four-wire electric energy metering device N times, where N is a natural number; obtain historical operation data of the three-phase four-wire electric energy metering device in N consecutive fixed time periods t in the normal operation time period T, and pre-process the collected historical operation data; A sampling module, capable of performing a first sampling without replacement in all the historical operation data collected by the data collection and processing module, and establishing a judgment data set L according to the result of the first sampling; A judgment module, capable of constructing a test statistic t0 of a paired t test in the judgment data set L, and judging whether there is a large deviation between the estimated power consumption calculated by the current integration method and the actual power consumption through the test statistic t0; The compensation model can correct and compensate the estimated power consumption calculated by the current integration method when the judgment module judges that there is a large deviation between the estimated power consumption calculated by the current integration method and the actual power consumption.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for calculating the error power under the electric energy metering fault described in any one of claims 1-3 are implemented.

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