Online detection method of out-of-tolerance electric meter based on step-by-step compensation and elimination of suspected electric meter

Abstract

The present application provides an online detection method for out-of-tolerance meters based on step-by-step compensation and elimination of suspected meters. When detecting smart meters, the line loss electricity, sub-meter electricity and sub-meter relative error of each substation meter in the target substation at each moment in a preset historical period can be first determined, and the line loss electricity, sub-meter electricity and sub-meter relative error of each substation meter at each moment in the preset historical period can be used to determine the substation meter with the largest average suspicion coefficient in each substation meter, and the substation meter with the largest average suspicion coefficient is used as the first suspected sub-meter set. The first suspected sub-meter set is obtained by comprehensive analysis using the sub-meter relative error, suspicion coefficient and line loss factors. Therefore, the result is more objective and comprehensive. Then, the present application can continue to screen the substation meters in the first suspected sub-meter set through a preset line loss judgment mechanism to obtain a second suspected sub-meter set, and the suspected sub-meters in this set are more in line with the actual situation.

Description

Technical Field

[0001] The present application relates to the technical field of smart meter detection, and in particular to an online detection method, device, storage medium and computer equipment for out-of-tolerance meters based on step-by-step compensation and elimination of suspected meters. Background Art

[0002] At present, all industries are striving to promote digital construction, and power grid companies have put into operation a large number of smart meters to collect massive amounts of electric energy data with high accuracy and good data quality. However, with the increase in the service life and the influence of factors such as environmental effects, equipment abnormalities, and human damage, the measurement accuracy of smart meters will decrease, thereby reducing the data quality.

[0003] At present, smart meters mainly perform error detection through periodic on-site manual inspection. This method is inefficient, has a small detection range, consumes a lot of manpower and material resources, and has high maintenance costs. It is difficult to meet the requirements of smart grid digital transformation and daily operation and maintenance needs. Summary of the invention

[0004] The purpose of this application is to solve at least one of the above-mentioned technical defects, especially the technical defects of the prior art that error detection is performed through periodic on-site manual detection. This method is inefficient, has a small detection range, consumes a lot of manpower and material resources, and has high maintenance costs, making it difficult to meet the requirements of the digital transformation of smart grids and the needs of daily operation and maintenance.

[0005] The present application provides an online detection method for out-of-tolerance electric meters based on step-by-step compensation and elimination of suspected electric meters, the method comprising:

[0006] Determine the line loss electricity, sub-meter electricity and sub-meter relative error of each sub-meter in the target substation at each moment in the preset historical period;

[0007] Using the line loss electricity, sub-meter electricity and sub-meter relative errors of each sub-meter at each moment in the preset historical period, determine the sub-meter with the largest average suspicion coefficient among the sub-meters, and use the sub-meter with the largest average suspicion coefficient as the first suspicious sub-meter set;

[0008] The station-based tables in the first set of suspected sub-tables are screened by a preset line loss judgment mechanism to obtain a second set of suspected sub-tables;

[0009] According to the relative errors of the sub-tables of each sub-table at each moment in the preset historical period, the sub-tables with the largest absolute values ​​of the relative errors of the sub-tables in each sub-table are selected to form a first error sub-table set, and it is determined whether the first error sub-table set has an intersection with the second suspect sub-table set;

[0010] If there is no intersection, the electric meters in the second suspected sub-meter set are directly regarded as out-of-tolerance electric meters;

[0011] If there is an intersection, the substation meters with the intersection are selected from the second suspicious sub-meter set to form a third suspicious sub-meter set, and the substation meters in the third suspicious sub-meter set are screened using the suspected meter step-by-step compensation elimination method to obtain the final out-of-tolerance meter.

[0012] Optionally, the step of screening the station-based tables in the first set of suspected sub-tables by using a preset line loss judgment mechanism to obtain a second set of suspected sub-tables includes:

[0013] The line loss quantity of each substation table in the first suspected sub-table set at each moment in the preset historical period is compared with the first line loss threshold, and the substation table in the first suspected sub-table set whose probability of line loss quantity exceeding the first line loss threshold in the preset historical period reaches the preset probability threshold is determined, and the substation tables that reach the preset probability threshold are formed into a second suspected sub-table set.

[0014] Optionally, the step-by-step compensation elimination method for suspected electric meters is used to screen the electric meters in the third set of suspected sub-meters to obtain the final out-of-tolerance electric meters, including:

[0015] The sub-meter ranked first in the third set of suspected sub-meters is used as the final suspected electric meter, and the sub-meter power of the final suspected electric meter is compensated by using the sub-meter relative error of the final suspected electric meter to obtain the sub-meter theoretical power;

[0016] The final suspected electric meter is removed from the multiple substation tables of the target substation, and after the theoretical electric quantity of the sub-meter is subtracted from the substation total table of the target substation, the line loss electric quantity of each substation table after the final suspected electric meter is removed in the target substation at each moment in the preset historical period is determined;

[0017] According to the line loss electricity of each substation table after excluding the final suspect electricity meter in the target substation at each moment in the preset historical period, calculate the average line loss electricity of each substation table after excluding the final suspect electricity meter in the preset historical period, and determine whether the average line loss electricity is greater than the second line loss threshold;

[0018] If it is greater than, then according to the sub-meter electricity of each substation table at each moment in the preset historical period after the final suspected electricity meter is removed in the target substation, and the total meter electricity after the sub-meter theoretical electricity is subtracted from the total meter of the target substation, the sub-meter relative errors of each substation table at each moment in the preset historical period after the final suspected electricity meter is removed in the target substation, and the substation tables in the target substation are updated, and the step of determining the substation table with the largest average suspicion coefficient in each substation table by using the line loss electricity, sub-meter electricity and sub-meter relative errors of each substation table at each moment in the preset historical period is returned to obtain a new third set of suspected sub-meters;

[0019] If the station area table ranked first in average suspicion coefficient in the new third suspect sub-table set overlaps with the station area table in the previous third suspect sub-table set, then determining whether the station area table ranked second in average suspicion coefficient in the new third suspect sub-table set overlaps with the station area table in the previous third suspect sub-table set;

[0020] If yes, the second-ranked substation meter is used as the final out-of-tolerance meter;

[0021] Otherwise, the first ranked substation meter is used as the final out-of-tolerance meter;

[0022] If it is not greater than, the final suspected meter will be regarded as an out-of-tolerance meter.

[0023] Optionally, determining the relative error of each sub-table in the target sub-table at each moment in a preset historical period includes:

[0024] Obtaining first power data of the total meter of the target area in a preset historical period and second power data of the sub-meters whose power is greater than a preset power threshold in the preset historical period;

[0025] According to the first power data and the second power data corresponding to each substation table, a relative error of each substation table at each moment in the preset historical period is determined.

[0026] Optionally, the step of obtaining first power data of a total meter in a target area in a preset historical period and second power data of each sub-meter whose power is greater than a preset power threshold in the preset historical period includes:

[0027] Determine the first original data of the total table of the target area in the preset historical period and the second original data of each area table in the preset historical period;

[0028] Performing a preprocessing operation on the first original data and the second original data to obtain first electric quantity data and second electric quantity data, wherein the preprocessing operation includes removing abnormal data, filling missing data, correcting clock deviation data, and correcting user variable data using user variable files;

[0029] According to the second electricity data of each substation table, the substation tables whose sub-meter electricity is not greater than the preset electricity threshold in the substation tables are eliminated to obtain the second electricity data of each sub-meter whose sub-meter electricity is greater than the preset electricity threshold in the preset historical period.

[0030] Optionally, determining the relative error of each sub-meter at each moment in the preset historical period according to the first power data and the second power data corresponding to each sub-meter includes:

[0031] Using a sliding window of preset length, the first electricity data of the total meter of the substation area in the preset historical period and the second electricity data of the substation area table in the preset historical period are divided into time periods to obtain the total meter electricity and the sub-meter electricity in each time period;

[0032] Based on the law of conservation of energy, a group of equations for solving relative errors of sub-meters corresponding to the total meter electricity and sub-meter electricity in multiple time periods is established, and after solving the group of equations for solving relative errors of sub-meters, a point estimate of the desired quantity is obtained, wherein the desired quantity includes the relative error of the sub-meter, the line loss rate, and the fixed loss;

[0033] The relative error of each table for each station is determined according to the point estimated value of the quantity to be determined.

[0034] Optionally, the step of using the line loss electricity, sub-meter electricity and sub-meter relative error of each sub-meter at each moment in the preset historical period to determine the sub-meter with the largest average suspicion coefficient among the sub-meters, and using the sub-meter with the largest average suspicion coefficient as the first set of suspected sub-meters includes:

[0035] The confidence interval corresponding to the relative error of each station differentiation table is calculated by using the T test in the significance test, and the average suspicion coefficient of each station differentiation table in the preset historical period is calculated according to the confidence interval corresponding to the relative error of each station differentiation table;

[0036] According to the line loss electricity and sub-meter electricity of each differentiated meter at each moment in the preset historical period, the correlation between the line loss electricity and sub-meter electricity of each differentiated meter in the preset historical period is calculated, and the average suspicion coefficient correction factor of each differentiated meter in the preset historical period is determined by using the correlation influence mechanism;

[0037] The average suspicion coefficient correction factor of each station division table is used to correct the average suspicion coefficient of the corresponding station division table, and the station division tables are sorted in descending order according to the corrected average suspicion coefficient, and the station division tables with the largest sorting are formed into the first suspicion division table set.

[0038] The present application also provides an out-of-tolerance meter online detection device based on step-by-step compensation and elimination of suspected meters, comprising:

[0039] A parameter determination module is used to determine the line loss electricity, sub-meter electricity and sub-meter relative error of each sub-meter in the target sub-station at each moment in a preset historical period;

[0040] A first suspected sub-meter determination module is used to determine the sub-meter with the largest average suspicion coefficient among the sub-meters by using the line loss electricity, sub-meter electricity and sub-meter relative error of each sub-meter at each moment in the preset historical period, and use the sub-meter with the largest average suspicion coefficient as the first suspected sub-meter set;

[0041] A second suspect sub-table determination module, configured to screen the sub-tables by station in the first suspect sub-table set by using a preset line loss judgment mechanism to obtain a second suspect sub-table set;

[0042] A sub-table judgment module is used to select the sub-table with the largest absolute value of the sub-table relative error from each sub-table at each moment in the preset historical period according to the sub-table relative error of each sub-table at each moment in the preset historical period to form a first error sub-table set, and determine whether the first error sub-table set has an intersection with the second suspect sub-table set;

[0043] A first out-of-tolerance electric meter determination module is used for directly taking the area electric meters in the second suspicious sub-meter set as out-of-tolerance electric meters if there is no intersection;

[0044] The second out-of-tolerance meter determination module is used to select the substation meters with intersection from the second suspected sub-meter set to form a third suspected sub-meter set if there is an intersection, and use the suspected meter step-by-step compensation elimination method to screen the substation meters in the third suspected sub-meter set to obtain the final out-of-tolerance meter.

[0045] The present application also provides a storage medium, in which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the online detection method for out-of-tolerance meters based on step-by-step compensation and elimination of suspected meters as described in any of the above embodiments.

[0046] The present application also provides a computer device, comprising: one or more processors, and a memory;

[0047] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the online detection method for out-of-tolerance meters based on step-by-step compensation and elimination of suspected meters as described in any of the above embodiments are performed.

[0048] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0049] The online detection method for out-of-tolerance meters based on step-by-step compensation and elimination of suspected meters provided in the present application can first determine the line loss electricity, sub-meter electricity and sub-meter relative error of each substation meter in the target substation at each moment in a preset historical period, and use the line loss electricity, sub-meter electricity and sub-meter relative error of each substation meter at each moment in the preset historical period to determine the substation meter with the largest average suspicion coefficient in each substation meter, and use the substation meter with the largest average suspicion coefficient as the first suspected sub-meter set, which is obtained by comprehensive analysis of the sub-meter relative error, suspicion coefficient and line loss factors. Therefore, the result is more objective and comprehensive. Then, the present application can continue to screen the substation meters in the first suspected sub-meter set through a preset line loss judgment mechanism to obtain a second suspected sub-meter set. The suspected sub-meters in the set are more in line with the actual situation. Finally, the present application can also select the sub-meter table with the largest absolute value of the relative error of the sub-meter in each sub-meter table according to the relative error of the sub-meters at each moment in the preset historical period to form a first error sub-meter set, and determine whether the first error sub-meter set and the second suspect sub-meter set have an intersection. If there is no intersection, the sub-meters in the second suspect sub-meter set are directly used as out-of-tolerance meters; if there is an intersection, the sub-meters with intersection are selected from the second suspect sub-meter set to form a third suspect sub-meter set, and the suspected meter step-by-step compensation elimination method is used to screen the sub-meters in the third suspect sub-meter set, thereby further compressing the suspicion coefficient space, so that the final out-of-tolerance meter is more accurate and reliable, and this process can reduce the workload of on-site detection personnel, improve the detection rate, and reduce the false detection rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0051] Figure 1 A flow chart of an out-of-tolerance meter online detection method based on step-by-step compensation and elimination of suspected meters provided in an embodiment of the present application;

[0052] Figure 2 A schematic diagram of the structure of an out-of-tolerance meter online detection device based on distribution compensation and elimination of suspected meters provided in an embodiment of the present application;

[0053] Figure 3 A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0055] At present, all industries are striving to promote digital construction, and power grid companies have put into operation a large number of smart meters to collect massive amounts of electric energy data with high accuracy and good data quality. However, with the increase in the service life and the influence of factors such as environmental effects, equipment abnormalities, and human damage, the measurement accuracy of smart meters will decrease, thereby reducing the data quality.

[0056] At present, smart meters mainly perform error detection through periodic on-site manual detection. This method is inefficient, has a small detection range, consumes a lot of manpower and material resources, and has high maintenance costs. It is difficult to meet the requirements of smart grid digital transformation and daily operation and maintenance needs. Based on this, this application proposes the following technical solutions, see below for details:

[0057] In one embodiment, Figure 1 As shown, Figure 1 A flow chart of an online detection method for out-of-tolerance meters based on step-by-step compensation and elimination of suspected meters provided in an embodiment of the present application; the present application provides an online detection method for out-of-tolerance meters based on step-by-step compensation and elimination of suspected meters, the method may include:

[0058] S110: Determine the line loss electricity, sub-meter electricity and sub-meter relative error of each sub-meter in the target substation at each moment in a preset historical period.

[0059] In this step, when performing online detection on the smart meter, the target substation can be determined first, and then the line loss electricity, sub-meter electricity and sub-meter relative error of each substation meter in the target substation at each moment in the preset historical period can be determined. In this way, the substation meter with the largest average suspicion coefficient among the substation meters can be determined based on the line loss electricity, sub-meter electricity and sub-meter relative error.

[0060] Among them, the preset historical time period of the present application can be set according to the number of substation tables in the target substation and the accuracy of the detection results. When the number of substation tables in the target substation is large, in order to obtain more accurate detection results, the preset historical time period can be set to a longer time period. However, considering that the longer the time period, the greater the amount of data to be calculated, therefore, it can be set according to the actual situation, such as setting it to collect electricity data within the past month, or two months, three months, etc., without any restriction here.

[0061] It can be understood that the substation in this application refers to the power supply range or area of ​​(a) transformer, and the target substation is the area where the out-of-tolerance meter needs to be detected online in this application. The out-of-tolerance meter here refers to the metering error range of the electric energy meter that exceeds the accuracy of the electric energy meter. The out-of-tolerance electric energy meter has positive out-of-tolerance and negative out-of-tolerance. The out-of-tolerance electric energy meter is no longer accurate and can no longer be used, so online detection is required.

[0062] Furthermore, when considering the influence of line loss on the estimation of meter error, the existing methods often estimate line loss to reduce the influence of line loss randomness on the meter error solution equation, but line loss is time-varying and difficult to be accurately estimated. And according to the simulation experiment results, for the meter error solution equation, when the line loss and sub-meter electricity are strongly correlated, the shortage of line loss and the shortage of sub-meter electricity will be transferred to each other, and the sub-meter error obtained is inaccurate. Therefore, the present application can first determine the line loss and sub-meter electricity of each sub-meter in the target sub-meter area at each moment in the preset historical period, and then further screen the suspected meters obtained using the relative error of the sub-meters through the correlation between the two, thereby improving the recognition accuracy of out-of-tolerance meters.

[0063] S120: Using the line loss electricity, sub-meter electricity and sub-meter relative errors of each substation meter at each moment in the preset historical period, determine the substation meter with the largest average suspicion coefficient among the substation meters, and use the substation meter with the largest average suspicion coefficient as the first suspicious sub-meter set.

[0064] In this step, after determining the line loss electricity, sub-meter electricity and sub-meter relative error of each substation meter in the target substation at each moment in the preset historical period through S110, the present application can use the line loss electricity, sub-meter electricity and sub-meter relative error of each substation meter at each moment in the preset historical period to determine the substation meter with the largest average suspicion coefficient among the substation meters, and use the substation meter with the largest average suspicion coefficient as the first suspected sub-meter set.

[0065] Specifically, in order to further screen the suspected meters obtained using the sub-meter relative error through the correlation between line loss electricity and sub-meter electricity, the present application can calculate the average suspicion coefficient of each differentiation meter in the preset historical period based on the confidence interval of the sub-meter relative error of each differentiation meter at each moment in the preset historical period. In this way, the average suspicion coefficient can be corrected by using the correlation between line loss electricity and sub-meter electricity, so as to obtain a more accurate identification result.

[0066] It can be understood that since the confidence interval obtained in the present application is the confidence interval corresponding to the relative error of each station area table at each moment in the preset historical period, when calculating the average suspicion coefficient of each station area table in the preset historical period, the suspicion coefficient of each station area table at each moment in the preset historical period can be calculated first, and then the final average suspicion coefficient can be obtained based on the suspicion coefficient at each moment.

[0067] Furthermore, when the present application utilizes the correlation between line loss electricity and sub-meter electricity to correct the average suspicion coefficient, it can first utilize the correlation influence mechanism to determine the average suspicion coefficient correction factor of each sub-meter in a preset historical period, so that the average suspicion coefficient can be corrected by the average suspicion coefficient correction factor, and each sub-meter table can be sorted in descending order according to the corrected average suspicion coefficient. Since there may be multiple sub-meter tables with the largest ranking, when there are multiple sub-meter tables with the largest corrected average suspicion coefficients, all sub-meter tables with the largest rankings can be combined into a first suspected sub-meter set to improve the recognition accuracy of out-of-tolerance meters.

[0068] Among them, the correlation influence mechanism of the present application refers to the influence on the average suspicion coefficient of a station-divided meter determined according to the correlation between the line loss electricity and the sub-meter electricity of a station-divided meter within a preset time period. For example, when the line loss electricity of a station-divided meter is negatively correlated with the electricity of a sub-meter, this situation is consistent with the performance of electricity theft, so the average suspicion coefficient of the station-divided meter should be increased; and when the line loss electricity and the sub-meter electricity of a station-divided meter are negatively correlated, when the sub-meter relative error of the station-divided meter is estimated using the multivariate linear regression method or its improved algorithm, the sub-meter electricity and the line loss electricity have the same status in mathematics, so the error will be shared between the relative error of the sub-meter and the line loss rate, resulting in deviations in the calculation results of both, and at this time the average suspicion coefficient of the station-divided meter should be reduced.

[0069] S130: Screening the station sub-tables in the first set of suspected sub-tables through a preset line loss judgment mechanism to obtain a second set of suspected sub-tables.

[0070] In this step, the sub-meter table with the largest average suspicion coefficient among the sub-meter tables is determined through S120, and the sub-meter table with the largest average suspicion coefficient is used as the first suspected sub-meter set. Since the average suspicion coefficient is the average value of the suspicion coefficients in the preset historical time period, but the duration of the relative error of the sub-meter is generally not too long, and the on-site detection is based on the meter error in the current time period to determine whether it is out of tolerance, therefore, this approach will introduce the influence of the suspicion coefficients at the historical moment, resulting in an error in the average suspicion coefficient of the current time period.

[0071] Based on this, after obtaining the first set of suspected sub-meters, the present application can screen the substation tables in the first set of suspected sub-meters through a preset line loss judgment mechanism, and form the substation tables in the first set of suspected sub-meters whose line loss electricity exceeds a certain threshold into a second set of suspected sub-meters, so as to further improve the recognition accuracy of out-of-tolerance meters.

[0072] S140: According to the relative errors of each sub-table at each moment in the preset historical period, the sub-table with the largest absolute value of the relative error is selected from each sub-table to form a first error sub-table set, and it is determined whether the first error sub-table set has an intersection with the second suspect sub-table set; if there is no intersection, execute S150; if there is an intersection, execute S160.

[0073] In this step, after the station sub-tables in the first suspect sub-table set are screened through S130 and the second suspect sub-table set is obtained, since the second suspect sub-table only considers the average suspicion coefficient and is not further screened in combination with the sub-table relative error, the present application can use the sub-table relative error to further screen the second suspect sub-table set to further improve the recognition accuracy.

[0074] Specifically, the present application can calculate the absolute value of the relative error of the sub-meter of each sub-meter in the preset historical period at each moment in the preset historical period based on the relative error of the sub-meter of each sub-meter in the second suspected sub-meter set at each moment in the preset historical period, and then select the sub-meter with the largest absolute value of the relative error of the sub-meter in each sub-meter to form a first error sub-meter set. Then, the present application can compare the first error sub-meter set with the second suspected sub-meter set to determine whether there is an intersection between the two. If there is no intersection, it means that the current second suspected sub-meter set does not contain the sub-meter with the largest absolute value of the relative error of the sub-meter. At this time, the sub-meter in the second suspected sub-meter set can be directly used as the out-of-tolerance meter. If there is an intersection, it means that the current second suspected sub-meter set contains the sub-meter with the largest absolute value of the relative error of the sub-meter. At this time, further screening operations are required.

[0075] S150: Directly regard the substation electric meters in the second suspicious sub-meter set as out-of-tolerance electric meters.

[0076] In this step, it can be known through S140 that when there is no intersection between the first error sub-meter set and the second suspected sub-meter set, it indicates that the current second suspected sub-meter set does not contain the sub-meter with the largest absolute value of the sub-meter relative error. At this time, the sub-meter in the second suspected sub-meter set can be directly used as the out-of-tolerance meter.

[0077] S160: Filter out the substation meters with intersection from the second set of suspected sub-meters to form a third set of suspected sub-meters, and use the suspected meter step-by-step compensation elimination method to filter the substation meters in the third set of suspected sub-meters to obtain the final out-of-tolerance meters.

[0078] In this step, it can be known through S140 that when there is an intersection between the first error sub-meter set and the second suspected sub-meter set, it indicates that the current second suspected sub-meter set contains the substation table with the largest absolute value of the sub-meter relative error. At this time, the substation tables with the intersection can be screened out from the second suspected sub-meter set to form a third suspected sub-meter set, and the suspected meter step-by-step compensation elimination method is used to screen the substation meters in the third suspected sub-meter set to obtain the final out-of-tolerance meter.

[0079] It can be understood that the step-by-step compensation and elimination method of the suspected meter in the present application refers to a method of gradually deleting suspected sub-meters by combining the relative error of the sub-meters, the suspicion coefficient and the line loss factor. The accuracy of the out-of-tolerance meter obtained by this method is relatively high.

[0080] In the above embodiment, when the smart meter is detected, the line loss electricity, sub-meter electricity and sub-meter relative error of each substation meter in the target substation at each moment in the preset historical period can be determined first, and the line loss electricity, sub-meter electricity and sub-meter relative error of each substation meter at each moment in the preset historical period can be used to determine the substation meter with the largest average suspicion coefficient in each substation meter, and the substation meter with the largest average suspicion coefficient is used as the first suspected sub-meter set. The first suspected sub-meter set is obtained by comprehensive analysis using the sub-meter relative error, suspicion coefficient and line loss factors. Therefore, the result is more objective and comprehensive. Then, the present application can continue to screen the substation meters in the first suspected sub-meter set through a preset line loss judgment mechanism to obtain a second suspected sub-meter set. The suspected sub-meters in this set are more in line with the actual Actual situation, finally, the present application can also select the substation table with the largest absolute value of the substation relative error in each substation table to form a first error sub-table set according to the sub-meter relative error of each substation table at each moment in a preset historical period, and determine whether the first error sub-table set and the second suspected sub-table set have an intersection. If there is no intersection, the substation meters in the second suspected sub-table set are directly used as out-of-tolerance meters; if there is an intersection, the substation tables with the intersection are screened from the second suspected sub-table set to form a third suspected sub-meter set, and the suspected meter step-by-step compensation elimination method is used to screen the substation meters in the third suspected sub-meter set, thereby further compressing the suspicion coefficient space, so that the final out-of-tolerance meter is more accurate and reliable, and this process can reduce the workload of on-site detection personnel, improve the detection rate, and reduce the false detection rate.

[0081] In one embodiment, in S130, the station-based tables in the first set of suspicious sub-tables are screened by a preset line loss judgment mechanism to obtain a second set of suspicious sub-tables, which may include:

[0082] S131: Compare the line loss quantity of each substation table in the first suspected sub-table set at each moment in the preset historical period with the first line loss threshold, determine the substation table in the first suspected sub-table set whose probability of line loss quantity exceeding the first line loss threshold in the preset historical period reaches a preset probability threshold, and form the substation tables that reach the preset probability threshold into a second suspected sub-table set.

[0083] In this embodiment, when the substation tables in the first suspected sub-table set are screened through the line loss judgment mechanism, the line loss electricity of each substation table in the first suspected sub-table set at each moment in the preset historical period can be compared with the first line loss threshold, and then the substation tables in the first suspected sub-table set whose line loss electricity exceeds the first line loss threshold in the preset historical period are determined. Then, the present application can select the substation tables whose probability of line loss electricity exceeding the first line loss threshold reaches the preset probability threshold from these substation tables, and form this part of the substation tables into a second suspected sub-table set.

[0084] Among them, the first line loss threshold and the preset probability threshold of the present application are parameters that can be manually adjusted. For example, the present application can set the first line loss threshold to 2% by default and the preset probability threshold to 80% by default based on field experience. If it is required to improve the field detection rate, the first line loss threshold can be reduced or the preset probability threshold can be increased. The specific settings can be made according to the actual situation and are not limited here.

[0085] In one embodiment, in S160, the suspected electric meter step-by-step compensation elimination method is used to screen the substation electric meters in the third suspected sub-meter set to obtain the final out-of-tolerance electric meters, which may include:

[0086] S161: taking the sub-meter ranked first in the third set of suspected sub-meters as the final suspected electric meter, and compensating the sub-meter power of the final suspected electric meter by using the sub-meter relative error of the final suspected electric meter to obtain the sub-meter theoretical power;

[0087] S162: removing the final suspected electricity meter from the multiple sub-meter tables of the target sub-station, and after deducting the theoretical electricity of the sub-meter from the sub-meter total table of the target sub-station, determining the line loss electricity of each sub-meter table after removing the final suspected electricity meter in the target sub-station at each moment in a preset historical period;

[0088] S163: Based on the line loss electricity of each substation table after the final suspected electricity meter is removed from the target substation at each moment in the preset historical period, calculate the average line loss electricity of each substation table after the final suspected electricity meter is removed in the preset historical period, and determine whether the average line loss electricity is greater than the second line loss threshold; if greater, execute S164; if not greater, execute S168.

[0089] S164: Based on the sub-meter electricity consumption of each substation after the final suspected meter is removed in the target substation at each moment in the preset historical period and the total meter electricity consumption obtained by subtracting the sub-meter theoretical electricity consumption from the substation total meter of the target substation, the sub-meter relative errors of each substation after the final suspected meter is removed in the target substation at each moment in the preset historical period and the substation substation in the target substation are updated, and the steps of S120 to S160 are returned to be executed to obtain a new third set of suspected sub-meters.

[0090] S165: If the station area table ranked first in average suspicion coefficient in the new third suspect sub-table set overlaps with the station area table in the previous third suspect sub-table set, determine whether the station area table ranked second in average suspicion coefficient in the new third suspect sub-table set overlaps with the station area table in the previous third suspect sub-table set; if so, execute S166; otherwise, execute S167.

[0091] S166: The second-ranked electricity meter is used as the final out-of-tolerance electricity meter.

[0092] S167: The first-ranked meter is used as the final out-of-tolerance meter.

[0093] S168: If it is not greater than, the final suspected electricity meter is regarded as an out-of-tolerance electricity meter.

[0094] In this embodiment, when the suspected meter step-by-step compensation and elimination method is used to screen the substation meters in the third suspected sub-meter set, the substation meter ranked first in the third suspected sub-meter can be first used as the final suspected meter, and the sub-meter relative error of the final suspected meter can be used to compensate for the sub-meter power of the final suspected meter to obtain the sub-meter theoretical power.

[0095] For example, in this application, the relative error solution equation of the sub-table is established based on the law of conservation of energy:

[0096]

[0097] Where J is the number of meters in the substation; y(i) is the total meter power of the substation at the measurement time i; x j (i) is the electricity consumption of the jth sub-meter at the measurement time i; e j (i) is the relative error of the j-th sub-meter at the measurement time i; e line (i) is the line loss rate at the measurement time i; e line (i)y(i) is the line loss at metering time i, which is proportional to the total meter power of the substation; e0(i) is the fixed loss at metering time i.

[0098] After the final suspected meter is determined, the present application can compensate the sub-meter power of the final suspected meter according to the relative error of the sub-meter of the final suspected meter. The sub-meter theoretical power obtained after compensation can be x j (i)(1-e j (i)).

[0099] After obtaining the theoretical power of the sub-meter, the present application can remove the final suspected meter from the target substation and subtract the theoretical power of the sub-meter from the substation total meter. Then, the present application can recalculate the line loss power of each substation in the target substation at each moment in the preset historical period, and determine the average line loss power of each substation in the preset historical period, and judge whether the average line loss power is greater than the second line loss threshold. If not, the final suspected meter is regarded as an out-of-tolerance meter; if greater, the total meter power of the total meter in the current target substation at each moment in the preset historical period and the average line loss power of each substation After recalculating the relative error of each sub-meter and the sub-meter electricity of each sub-meter at each moment in the preset historical period, the line loss electricity, sub-meter electricity and relative error of each sub-meter at each moment in the preset historical period are reused to determine the sub-meter with the largest average suspicion coefficient among the sub-meters, and the sub-meter with the largest average suspicion coefficient is used as the new first suspected sub-meter set; then the sub-meters in the new first suspected sub-meter set are screened by the preset line loss judgment mechanism to obtain a new second suspected sub-meter set. ; Then, according to the relative errors of the sub-meters of each sub-meter at each moment in the preset historical period, the sub-meters with the largest absolute value of the relative errors of the sub-meters in each sub-meter are selected to form a new first error sub-meter set, and it is determined whether the new first error sub-meter set and the new second suspect sub-meter set have an intersection; if there is no intersection, the sub-meters in the new second suspect sub-meter set are directly used as out-of-tolerance meters; if there is an intersection, the sub-meters with the intersection are selected from the new second suspect sub-meter set to form a new third suspect sub-meter set; then, the present application can determine whether the new third suspect sub-meter set has an intersection. Whether the substation table ranked first in average suspicion coefficient overlaps with the substation table in the previous third suspicion sub-table set, if there is no overlap, the substation table ranked first this time is taken as the final suspected meter and the process returns to execute steps S161 to S168. If there is overlap, continue to determine whether the substation table ranked second in average suspicion coefficient in the new third suspicion sub-table set overlaps with the substation table in the previous third suspicion sub-table set. If there is overlap, the substation table ranked second is taken as the final out-of-tolerance meter, otherwise, the substation table ranked first is taken as the final out-of-tolerance meter.

[0100] In one embodiment, determining the relative errors of the sub-tables of each sub-table in the target sub-table at each moment in a preset historical period in S110 may include:

[0101] S111: Obtain first power data of the total meter in the target area during a preset historical period and second power data of the sub-meters whose power is greater than a preset power threshold during the preset historical period.

[0102] S112: Determine the relative error of each sub-meter at each moment in the preset historical period according to the first power data and the second power data corresponding to each sub-meter.

[0103] In this embodiment, when detecting out-of-tolerance electricity meters, in order to improve the detection accuracy, the first electricity data of the substation total meter in the preset historical period and the second electricity data of each substation meter in the preset historical period can be combined for analysis. In this way, not only the sub-meter electricity of each substation meter at each moment in the preset historical period can be obtained, but also the line loss electricity of each substation meter can be obtained. Furthermore, the present application can also use the first electricity data of the substation total meter and the second electricity data of each substation meter to calculate the sub-meter relative error of each substation meter, and use this to detect out-of-tolerance electricity meters.

[0104] In one embodiment, obtaining the first power data of the total meter of the target area in the preset historical period and the second power data of the sub-meters whose power is greater than the preset power threshold in the preset historical period in S111 may include:

[0105] S1110: Determine the first original data of the total area table in the target area in a preset historical period and the second original data of each area table in the preset historical period.

[0106] S1111: Preprocess the first original data and the second original data to obtain first power data and second power data, wherein the preprocessing operation includes removing abnormal data, filling missing data, correcting clock deviation data, and correcting user variable data using user variable files.

[0107] S1112: Based on the second electricity data of each substation table, the substation tables whose sub-meter electricity is not greater than a preset electricity threshold are eliminated, and the second electricity data of each sub-meter whose sub-meter electricity is greater than the preset electricity threshold in the preset historical period are obtained.

[0108] In this embodiment, when determining the first power data of the total meter of the area in the target area in the preset historical period, the first original data of the total meter of the area in the preset historical period can be obtained first, and then the first original data can be preprocessed to obtain the first power data of the total meter of the area in the preset historical period. When determining the second power data of the area table in the preset historical period in which the power of each sub-meter in the target area is greater than the preset power threshold, the second original data of all the area tables in the target area in the preset historical period can be obtained first, and then the second original data can be preprocessed to eliminate the area tables in which the power of the sub-meter is not greater than the preset power threshold according to the second power data of each area table, so as to obtain the second power data of the area table in the preset historical period in which the power of each sub-meter is greater than the preset power threshold.

[0109] Furthermore, the prior art does not consider the data quality of the measured data when identifying out-of-tolerance meters, lacks comprehensive data repair methods, including the lack of consideration of factors such as data anomalies, missing data, clock deviations, and household change files, resulting in a low accuracy rate of the final recognition result. Therefore, before using the first original data and the second original data for data analysis, the present application may perform a preprocessing operation on the first original data and the second original data, and the preprocessing operation may include operations such as removing abnormal data, filling missing data, correcting clock deviation data, and using household change files to correct household change data, thereby effectively improving the recognition accuracy of out-of-tolerance meters.

[0110] Specifically, when the first original data and the second original data are preprocessed, the substation data of the target substation can be obtained first, and the substation data can include a meter household change file, and a household change means that a user belongs to substation A before a certain time, but belongs to substation B after the time. Therefore, the ownership relationship between users and target substations in different periods can be clarified through the meter household change file, so as to assist in correcting the total meter power of the substation total meter in the target substation. Of course, the substation data of the present application can also include the substation belonging area name, substation belonging line name, substation name, substation number, meter measurement point number, etc., which are not limited here.

[0111] After obtaining the station area data, the statistical discrimination method can be used to eliminate the abnormal data in the station area data, the first original data and the second original data. There are many statistical discrimination methods. The basic method is to give a confidence level (such as 95%, 99%), and then find the corresponding confidence interval, and then determine the data outside the confidence interval as an abnormal value and eliminate it. The present application can eliminate the abnormal data in the station area data, the first original data and the second original data respectively by setting the confidence interval.

[0112] After the abnormal data is removed, the present application can also fill in the missing data in the area data after the abnormal data is removed, the first original data, and the second original data. There are many methods for filling in missing data, which can specifically include: filling in fixed values, filling in means, filling in modes, filling in medians, interpolation filling, filling in adjacent values ​​before or after the missing position, and establishing a regression model with non-missing values ​​as features to obtain missing values. The present application can choose any missing data filling method to fill in the missing data in the area data after the abnormal data is removed, the first original data, and the second original data.

[0113] Next, since the first original data in the present application may include not only the clock deviation of the total meter, but also the total meter power of the total meter of the substation at each moment in the preset historical period, the second original data may include not only the clock deviation of the sub-meter, but also the sub-meter power of each substation at each moment in the preset historical period. Therefore, the present application may also use the clock deviation of the total meter and the clock deviation of the sub-meter of each substation to correct the total meter power of the substation at each moment in the preset historical period and the sub-meter power of each substation at each moment in the preset historical period after filling in the missing data, and obtain the second power data.

[0114] For example, the present application can compare the clock deviation of the total meter and the clock deviation of a certain sub-meter with the standard time. When the comparison result is that the total meter of the substation is 2 minutes slow and the sub-meter of the substation is 5 minutes fast, it is necessary to re-align the total meter power of the substation at each moment in the preset historical period and the sub-meter power of the substation at each moment in the preset historical period. Otherwise, the total meter data and the sub-meter data at the same time cannot correspond one to one.

[0115] Furthermore, since there are multiple users in a substation, and there is only one substation total meter in a substation, each user corresponds to a substation table. When a user has a household change, that is, a user belongs to substation A before a certain moment, but belongs to substation B after that moment, then the user's sub-meter electricity should be counted into the total meter electricity of substation A before that moment, and should be counted into the total meter electricity of substation B after that moment. If the household change relationship is not considered, a large error will be introduced. Therefore, the present application can continue to correct the total meter electricity of the corrected substation total meter at each moment in the preset historical period according to the meter household change file in the substation data after filling in the missing data, so as to obtain the final first electricity data.

[0116] Furthermore, when eliminating the sub-meter electricity of each sub-meter in each sub-meter table that is not greater than the preset electricity threshold, the present application can first determine the sub-meter electricity of each sub-meter at each moment in the preset historical period based on the second electricity data of each sub-meter in the preset historical period, and then the present application can use 1 / K of the median of the sub-meter electricity of all sub-meters at each moment in the preset historical period as the small electricity threshold at that moment, and at each moment, assign a judgment value of 1 to the sub-meter in each sub-meter whose electricity is less than the small electricity threshold at that moment, and assign a judgment value of 0 to the remaining sub-meters, and finally calculate the average value of the sum of the judgment values ​​of each sub-meter at all moments in the preset historical period, and eliminate the sub-meters whose average values ​​in each sub-meter table are greater than the preset probability threshold as sub-meters not greater than the preset electricity threshold, thereby obtaining the second electricity data of each sub-meter in the preset historical period for each sub-meter whose electricity is greater than the preset electricity threshold.

[0117] It can be understood that after assigning a judgment value of 1 or 0 to the substation table at each moment, the present application can make the average value of the sum of the judgment values ​​of each substation table at all moments in the preset historical period obtained by subsequent calculation be in the range of [0, 1], which is physically equivalent to the probability of the occurrence of a small electricity sub-meter. The greater the probability of the occurrence of a small electricity sub-meter, the greater the deviation of the calculation result. Therefore, the value range of the preset probability threshold in the present application can be set to [0.05, 0.1] to control the probability of the occurrence of a small electricity sub-meter. Furthermore, when determining the small electricity threshold, the present application can also set the value range of K according to the above analysis, thereby effectively improving the recognition accuracy of out-of-tolerance meters.

[0118] In one embodiment, determining the relative error of each sub-meter at each moment in the preset historical period according to the first power data and the second power data corresponding to each sub-meter in S112 may include:

[0119] S1121: Use a sliding window of preset length to divide the first electricity data of the area total meter in the preset historical period and the second electricity data of each area meter in the preset historical period into time periods to obtain the total meter electricity and sub-meter electricity in each time period.

[0120] S1122: Based on the law of conservation of energy, a group of equations for solving sub-meter relative errors corresponding to the total meter power and sub-meter power in multiple time periods are established, and after solving the group of equations for solving the sub-meter relative errors, point estimates of the quantities to be determined are obtained, wherein the quantities to be determined include the sub-meter relative errors, line loss rate, and fixed loss.

[0121] S1123: Determine the relative error of each station-specific table according to the point estimate of the quantity to be determined.

[0122] In this embodiment, after obtaining the first electricity data of the total meter in the target substation in the preset historical period and the second electricity data of the substation meters whose electricity is greater than the preset electricity threshold in the preset historical period, the present application can calculate the sub-meter relative error and the confidence interval of the sub-meter relative error of each substation meter at each moment in the preset historical period based on the first electricity data and the second electricity data corresponding to each substation meter, and judge the probability of the meter being out of tolerance through the confidence interval of the sub-meter relative error, thereby obtaining the suspected meter.

[0123] Specifically, when calculating the relative error of each sub-meter at each moment in a preset historical period for each sub-meter, the present application may first set the sliding window size to D (assuming D ≥ (J+2)), and then use the sliding window to divide the first electricity data of the total meter in the preset historical period and the second electricity data of each sub-meter in the preset historical period into time periods, thereby obtaining the total meter electricity and sub-meter electricity in each time period. Next, the present application may establish a sub-meter relative error solution equation group corresponding to the total meter electricity and sub-meter electricity in multiple time periods based on the law of conservation of energy, and after solving the sub-meter relative error solution equation group, obtain the point estimate of the quantity to be determined, which includes the sub-meter relative error, line loss rate and fixed loss. Finally, the present application may determine the sub-meter relative error of each sub-meter according to the point estimate of the quantity to be determined, and use the T test in the significance test to calculate the confidence interval corresponding to the sub-meter relative error of each sub-meter.

[0124] Furthermore, based on the law of conservation of energy, the relative error solution equation of the sub-table is established as follows:

[0125]

[0126] Where J is the number of meters in the substation; y(i) is the total meter power of the substation at the measurement time i; x j (i) is the electricity consumption of the jth sub-meter at the measurement time i; e j (i) is the relative error of the j-th sub-meter at the measurement time i; e line (i) is the line loss rate at the measurement time i; e line (i)y(i) is the line loss at metering time i, which is proportional to the total meter power of the substation; e0(i) is the fixed loss at metering time i.

[0127] Since the first electricity data and the second electricity data in the present application are divided into D time periods, the present application can construct a group of equations for solving the relative errors of the sub-meters at D moments through the above-mentioned equations for solving the relative errors of the sub-meters. In the solving equations, the total meter electricity of the substation area and the meter electricity of the substation area are known quantities, and the relative errors of the sub-meters, the line loss rate and the fixed loss are quantities to be determined. Therefore, the matrix representation of the group of multivariate linear regression equations is:

[0128] Y=XB (2)

[0129] Where Y is the total power vector of the area meter, Y = [y(1), y(2), ..., y(D)] T ; X is the electricity matrix of the substation, X=[x(1),x(2),...,x(D)] T , where x(D)=[x1(D),x2(D),...,x J (D), y(D), 1]; B is the matrix of the quantity to be determined, B = [B(1), B(2), ..., B(D)], B(D) = [1-e1(D), 1-e2(D), ..., 1-e J (D),e line (D),e0(D)] T .

[0130] According to the matrix transformation, the point estimate of the unknown quantity B is:

[0131]

[0132] In one embodiment, in S120, the line loss electricity, sub-meter electricity, and sub-meter relative error of each sub-meter table at each moment in the preset historical period are used to determine the sub-meter table with the largest average suspicion coefficient among the sub-meter tables, and the sub-meter table with the largest average suspicion coefficient is used as the first suspect sub-meter set, which may include:

[0133] S121: Calculate the confidence interval corresponding to the relative error of each station classification table by using the T test in the significance test, and calculate the average suspicion coefficient of each station classification table in the preset historical period according to the confidence interval corresponding to the relative error of each station classification table.

[0134] S122: Calculate the correlation between the line loss electricity and the sub-meter electricity of each differentiated meter in the preset historical period based on the line loss electricity and the sub-meter electricity of each differentiated meter at each moment in the preset historical period, and determine the average suspicion coefficient correction factor of each differentiated meter in the preset historical period using the correlation influence mechanism.

[0135] S123: using the average suspicion coefficient correction factor of each station division table to correct the average suspicion coefficient of the corresponding station division table, and sorting each station division table in descending order according to the corrected average suspicion coefficient, and forming the station division table with the largest sorting into a first suspicion division table set.

[0136] In this embodiment, when determining the first set of suspected sub-tables, the present application can use the T test in the significance test to calculate the confidence interval corresponding to the sub-table relative error of each station table. Specifically, from formula (3), it can be seen that the point estimate of the quantity to be determined B is Affected by the measured data of total meter power and meter power in the substation area, due to the systematic error and random error in the measured data, the point estimate of the quantity B to be determined is There are also errors. The confidence interval centered on the point estimate can contain the point estimate with a certain degree of confidence. Therefore, this application can use the confidence interval to measure the point estimate. Specifically, from the T test in the significance test, we can know that under the confidence level of (1-α) The confidence interval for is:

[0137]

[0138] Among them, t can be queried in the T test critical value table through the confidence level (1-α) and the degree of freedom (D-1). α / 2 The value of s D The result of repeated calculations The standard deviation of .

[0139] Then, from equations (2) and (4), it is easy to get the confidence interval of the relative error of the score table (θ L ,θ U )for:

[0140]

[0141] Since the confidence interval obtained in the present application is the confidence interval corresponding to the relative error of each station area table at each moment in the preset historical period, when calculating the average suspicion coefficient of each station area table in the preset historical period, the suspicion coefficient of each station area table at each moment in the preset historical period can be calculated first, and then the final average suspicion coefficient can be obtained based on the suspicion coefficient at each moment.

[0142] When calculating the suspicion coefficient of each station table at each moment in the preset historical period, the credible range (θ L0 ,θ U0 ), and then calculate the corresponding suspicion coefficient. The specific calculation formula is:

[0143]

[0144] In this application, the smaller the set credible range is, the higher the on-site precision rate of the suspected sub-meter is. Of course, it can also be set according to on-site experience, and the credible range is generally set to (-2, 2). After obtaining the suspected coefficients of each sub-meter of each substation area at each moment within the preset historical period, the sum of the suspected coefficients of the corresponding sub-meter of the substation area can be divided by the total metering time to obtain the average suspected coefficient.

[0145] Furthermore, in order to avoid the situation where the calculation result of the relative error of the sub-meter is inaccurate due to the influence of the correlation between the line loss power and the sub-meter power when solving the relative error of the sub-meter of each substation area, this application can calculate the correlation between the line loss power and the sub-meter power of each sub-meter of each substation area at each moment within the preset historical period, and then this application can use the correlation influence mechanism to determine the average suspected coefficient correction factor of each sub-meter of each substation area within the preset historical period, so that the average suspected coefficient can be corrected by the average suspected coefficient correction factor, so as to improve the recognition accuracy of out-of-tolerance electric meters.

[0146] For example, when the line loss power of a certain sub-meter of a substation area is negatively correlated with the power of a certain sub-meter, this situation is consistent with the manifestation of electricity theft behavior. Therefore, the average suspected coefficient of this sub-meter of the substation area should be increased. At this time, the average suspected coefficient correction factor of this sub-meter of the substation area within the preset historical period can be set to a value greater than 1; when the line loss power of a certain sub-meter of a substation area is negatively correlated with the sub-meter power, when using the multiple linear regression method or its improved algorithm to estimate the relative error of the sub-meter of this sub-meter of the substation area, the sub-meter power and the line loss power have the same status mathematically. Therefore, the error will be分摊 between the relative error of this sub-meter and the line loss rate, resulting in the deviation of the calculation results of both. At this time, the average suspected coefficient of this sub-meter of the substation area should be decreased, such as setting the average suspected coefficient correction factor of this sub-meter of the substation area within the preset historical period to a value less than 1.

[0147] Based on the above considerations, the calculation formula of the average suspected coefficient correction factor λ in this application is as follows:

[0148]

[0149] Among them, c is the average suspected coefficient of the sub-meter of the substation area, a1 and a2 are correction coefficients, 0 < a1 < 1, 1 < a2 < 1.5. For example, take a1 = 0.5 and a2 = 1.2.

[0150] The following is a description of the online detection device for out-of-tolerance meters based on step-by-step compensation and elimination of suspected meters provided in an embodiment of the present application. The online detection device for out-of-tolerance meters based on step-by-step compensation and elimination of suspected meters described below and the online detection method for out-of-tolerance meters based on step-by-step compensation and elimination of suspected meters described above can be referenced to each other.

[0151] In one embodiment, Figure 2 As shown, Figure 2 A schematic diagram of the structure of an out-of-tolerance meter online detection device based on the distribution compensation elimination of suspected meters provided in an embodiment of the present application; the present application also provides an out-of-tolerance meter online detection device based on the step-by-step compensation elimination of suspected meters, which may include a parameter determination module 210, a first suspected sub-meter determination module 220, a second suspected sub-meter determination module 230, a sub-meter judgment module 240, a first out-of-tolerance meter determination module 250, and a second out-of-tolerance meter determination module 260, which specifically include the following:

[0152] The parameter determination module 210 is used to determine the line loss electricity, sub-meter electricity and sub-meter relative error of each sub-meter in the target substation at each moment in a preset historical period.

[0153] The first suspected sub-meter determination module 220 is used to determine the sub-meter with the largest average suspicion coefficient among the sub-meters by using the line loss electricity, sub-meter electricity and sub-meter relative error of each sub-meter at each moment in the preset historical period, and use the sub-meter with the largest average suspicion coefficient as the first suspected sub-meter set.

[0154] The second suspect sub-table determining module 230 is configured to screen the station-based sub-tables in the first suspect sub-table set by using a preset line loss judgment mechanism to obtain a second suspect sub-table set.

[0155] The sub-table judgment module 240 is used to select the sub-table with the largest absolute value of the sub-table relative error among the various sub-tables to form a first error sub-table set according to the sub-table relative errors of each sub-table at each moment in the preset historical period, and determine whether the first error sub-table set has an intersection with the second suspected sub-table set.

[0156] The first out-of-tolerance electricity meter determination module 250 is configured to directly use the substation electricity meters in the second suspicious sub-meter set as out-of-tolerance electricity meters if there is no intersection.

[0157] The second out-of-tolerance meter determination module 260 is used to select the substation meters with intersection from the second suspected sub-meter set to form a third suspected sub-meter set if there is an intersection, and use the suspected meter step-by-step compensation elimination method to screen the substation meters in the third suspected sub-meter set to obtain the final out-of-tolerance meter.

[0158] In the above embodiment, when the smart meter is detected, the line loss electricity, sub-meter electricity and sub-meter relative error of each substation meter in the target substation at each moment in the preset historical period can be determined first, and the line loss electricity, sub-meter electricity and sub-meter relative error of each substation meter at each moment in the preset historical period can be used to determine the substation meter with the largest average suspicion coefficient in each substation meter, and the substation meter with the largest average suspicion coefficient is used as the first suspected sub-meter set. The first suspected sub-meter set is obtained by comprehensive analysis using the sub-meter relative error, suspicion coefficient and line loss factors. Therefore, the result is more objective and comprehensive. Then, the present application can continue to screen the substation meters in the first suspected sub-meter set through a preset line loss judgment mechanism to obtain a second suspected sub-meter set. The suspected sub-meters in this set are more in line with the actual Actual situation, finally, the present application can also select the substation table with the largest absolute value of the substation relative error in each substation table to form a first error sub-table set according to the sub-meter relative error of each substation table at each moment in a preset historical period, and determine whether the first error sub-table set and the second suspected sub-table set have an intersection. If there is no intersection, the substation meters in the second suspected sub-table set are directly used as out-of-tolerance meters; if there is an intersection, the substation tables with the intersection are screened from the second suspected sub-table set to form a third suspected sub-meter set, and the suspected meter step-by-step compensation elimination method is used to screen the substation meters in the third suspected sub-meter set, thereby further compressing the suspicion coefficient space, so that the final out-of-tolerance meter is more accurate and reliable, and this process can reduce the workload of on-site detection personnel, improve the detection rate, and reduce the false detection rate.

[0159] In one embodiment, the present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the online detection method for out-of-tolerance meters based on step-by-step compensation and elimination of suspected meters as described in any of the above embodiments.

[0160] In one embodiment, the present application also provides a computer device, including: one or more processors, and a memory.

[0161] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the online detection method for out-of-tolerance meters based on step-by-step compensation and elimination of suspected meters as described in any of the above embodiments are performed.

[0162] Indicatively, Figure 3 As shown, Figure 3 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 300 may be provided as a server. Figure 3The computer device 300 includes a processing component 302, which further includes one or more processors, and a memory resource represented by a memory 301, for storing instructions executable by the processing component 302, such as an application. The application stored in the memory 301 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 302 is configured to execute instructions to execute the online detection method of out-of-tolerance electric meter based on step-by-step compensation and elimination of suspected electric meter according to any of the above embodiments.

[0163] The computer device 300 may further include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate based on an operating system stored in the memory 301, such as Windows Server TM, Mac OS X TM, Unix TM, Linux TM, Free BSD TM, or the like.

[0164] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0165] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0166] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can refer to each other.

[0167] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An online detection method for out-of-tolerance electric meters based on step-by-step compensation and elimination of suspected electric meters, characterized in that: The method comprises: Determine the line loss electricity, sub-meter electricity and sub-meter relative error of each sub-meter in the target substation at each moment in the preset historical period; Using the line loss electricity, sub-meter electricity and sub-meter relative errors of each sub-meter at each moment in the preset historical period, determine the sub-meter with the largest average suspicion coefficient among the sub-meters, and use the sub-meter with the largest average suspicion coefficient as the first suspicious sub-meter set; The station-based tables in the first set of suspected sub-tables are screened by a preset line loss judgment mechanism to obtain a second set of suspected sub-tables; According to the relative errors of the sub-tables of each sub-table at each moment in the preset historical period, the sub-tables with the largest absolute values ​​of the relative errors of the sub-tables in each sub-table are selected to form a first error sub-table set, and it is determined whether the first error sub-table set has an intersection with the second suspect sub-table set; If there is no intersection, the electric meters in the second suspected sub-meter set are directly regarded as out-of-tolerance electric meters; If there is an intersection, the substation meters with the intersection are selected from the second set of suspected sub-meters to form a third set of suspected sub-meters, and the suspected electric meter step-by-step compensation elimination method is used to select the substation electric meters in the third set of suspected sub-meters to obtain the final out-of-tolerance electric meters; The method of using the line loss electricity, sub-meter electricity and sub-meter relative error of each sub-meter at each moment in the preset historical period to determine the sub-meter with the largest average suspicion coefficient in each sub-meter, and taking the sub-meter with the largest average suspicion coefficient as the first suspicious sub-meter set, includes: The confidence interval corresponding to the relative error of each station differentiation table is calculated by using the T test in the significance test, and the average suspicion coefficient of each station differentiation table in the preset historical period is calculated according to the confidence interval corresponding to the relative error of each station differentiation table; According to the line loss electricity and sub-meter electricity of each differentiated meter at each moment in the preset historical period, the correlation between the line loss electricity and sub-meter electricity of each differentiated meter in the preset historical period is calculated, and the average suspicion coefficient correction factor of each differentiated meter in the preset historical period is determined by using the correlation influence mechanism; The average suspicion coefficient correction factor of each station division table is used to correct the average suspicion coefficient of the corresponding station division table, and the station division tables are sorted in descending order according to the corrected average suspicion coefficient, and the station division tables with the largest sorting order are formed into a first set of suspicion division tables; The method of using the suspected electric meter step-by-step compensation elimination method to screen the electric meters in the third suspected sub-meter set to obtain the final out-of-tolerance electric meters includes: The sub-meter ranked first in the third set of suspected sub-meters is used as the final suspected electric meter, and the sub-meter power of the final suspected electric meter is compensated by using the sub-meter relative error of the final suspected electric meter to obtain the sub-meter theoretical power; The final suspected electric meter is removed from the multiple substation tables of the target substation, and after the theoretical electric quantity of the sub-meter is subtracted from the substation total table of the target substation, the line loss electric quantity of each substation table after the final suspected electric meter is removed in the target substation at each moment in the preset historical period is determined; According to the line loss electricity of each substation table after excluding the final suspect electricity meter in the target substation at each moment in the preset historical period, calculate the average line loss electricity of each substation table after excluding the final suspect electricity meter in the preset historical period, and determine whether the average line loss electricity is greater than the second line loss threshold; If it is greater than, then according to the sub-meter electricity of each substation table at each moment in the preset historical period after the final suspected electricity meter is removed in the target substation, and the total meter electricity after the sub-meter theoretical electricity is subtracted from the total meter of the target substation, the sub-meter relative errors of each substation table at each moment in the preset historical period after the final suspected electricity meter is removed in the target substation, and the substation tables in the target substation are updated, and the step of determining the substation table with the largest average suspicion coefficient in each substation table by using the line loss electricity, sub-meter electricity and sub-meter relative errors of each substation table at each moment in the preset historical period is returned to obtain a new third set of suspected sub-meters; If the station area table ranked first in average suspicion coefficient in the new third suspect sub-table set overlaps with the station area table in the previous third suspect sub-table set, then determining whether the station area table ranked second in average suspicion coefficient in the new third suspect sub-table set overlaps with the station area table in the previous third suspect sub-table set; If yes, the second-ranked substation meter is used as the final out-of-tolerance meter; Otherwise, the first ranked substation meter is used as the final out-of-tolerance meter; If it is not greater than, the final suspected meter will be regarded as an out-of-tolerance meter.

2. The method for online detection of out-of-tolerance electric meters based on step-by-step compensation and elimination of suspected electric meters according to claim 1 is characterized in that: The step of screening the station-based tables in the first set of suspicious sub-tables by using a preset line loss judgment mechanism to obtain a second set of suspicious sub-tables includes: The line loss quantity of each substation table in the first suspected sub-table set at each moment in the preset historical period is compared with the first line loss threshold, and the substation table in the first suspected sub-table set whose probability of line loss quantity exceeding the first line loss threshold in the preset historical period reaches the preset probability threshold is determined, and the substation tables that reach the preset probability threshold are formed into a second suspected sub-table set.

3. The method for online detection of out-of-tolerance electric meters based on step-by-step compensation and elimination of suspected electric meters according to claim 1 is characterized in that: The step of determining the relative error of each sub-table in the target sub-table at each moment in a preset historical period includes: Obtaining first power data of the total meter of the target area in a preset historical period and second power data of the sub-meters whose power is greater than a preset power threshold in the preset historical period; According to the first power data and the second power data corresponding to each substation table, a relative error of each substation table at each moment in the preset historical period is determined.

4. The method for online detection of out-of-tolerance electric meters based on step-by-step compensation and elimination of suspected electric meters according to claim 3 is characterized in that: The step of obtaining first power data of the total meter of the target area in the preset historical period and second power data of the sub-meters whose power is greater than the preset power threshold in the preset historical period includes: Determine the first original data of the total table of the target area in the preset historical period and the second original data of each area table in the preset historical period; Performing a preprocessing operation on the first original data and the second original data to obtain first electric quantity data and second electric quantity data, wherein the preprocessing operation includes removing abnormal data, filling missing data, correcting clock deviation data, and correcting user variable data using user variable files; According to the second electricity data of each substation table, the substation tables whose sub-meter electricity is not greater than the preset electricity threshold in the substation tables are eliminated to obtain the second electricity data of each sub-meter whose sub-meter electricity is greater than the preset electricity threshold in the preset historical period.

5. The method for online detection of out-of-tolerance electric meters based on step-by-step compensation and elimination of suspected electric meters according to claim 4 is characterized in that: The determining, based on the first power data and the second power data corresponding to each substation table, the sub-meter relative error of each substation table at each moment in the preset historical period includes: Using a sliding window of preset length, the first electricity data of the total meter of the substation area in the preset historical period and the second electricity data of the substation area table in the preset historical period are divided into time periods to obtain the total meter electricity and the sub-meter electricity in each time period; Based on the law of conservation of energy, a group of equations for solving relative errors of sub-meters corresponding to the total meter electricity and sub-meter electricity in multiple time periods is established, and after solving the group of equations for solving relative errors of sub-meters, a point estimate of the desired quantity is obtained, wherein the desired quantity includes the relative error of the sub-meter, the line loss rate, and the fixed loss; The relative error of each table for each station is determined according to the point estimated value of the quantity to be determined.

6. An online detection device for out-of-tolerance electric meters based on step-by-step compensation and elimination of suspected electric meters, characterized in that: include: A parameter determination module is used to determine the line loss electricity, sub-meter electricity and sub-meter relative error of each sub-meter in the target sub-station at each moment in a preset historical period; A first suspected sub-meter determination module is used to determine the sub-meter with the largest average suspicion coefficient among the sub-meters by using the line loss electricity, sub-meter electricity and sub-meter relative error of each sub-meter at each moment in the preset historical period, and use the sub-meter with the largest average suspicion coefficient as the first suspected sub-meter set; A second suspect sub-table determination module, configured to screen the sub-tables by station in the first suspect sub-table set by using a preset line loss judgment mechanism to obtain a second suspect sub-table set; A sub-table judgment module is used to select the sub-table with the largest absolute value of the sub-table relative error from each sub-table at each moment in the preset historical period according to the sub-table relative error of each sub-table at each moment in the preset historical period to form a first error sub-table set, and determine whether the first error sub-table set has an intersection with the second suspect sub-table set; A first out-of-tolerance electric meter determination module is used for directly taking the area electric meters in the second suspicious sub-meter set as out-of-tolerance electric meters if there is no intersection; A second out-of-tolerance electric meter determination module is used to select the substation electric meters with the intersection from the second suspect sub-meter set to form a third suspect sub-meter set if there is an intersection, and use the suspect electric meter step-by-step compensation elimination method to screen the substation electric meters in the third suspect sub-meter set to obtain the final out-of-tolerance electric meter; The first suspect sub-table determination module includes: The confidence interval corresponding to the relative error of each station differentiation table is calculated by using the T test in the significance test, and the average suspicion coefficient of each station differentiation table in the preset historical period is calculated according to the confidence interval corresponding to the relative error of each station differentiation table; According to the line loss electricity and sub-meter electricity of each differentiated meter at each moment in the preset historical period, the correlation between the line loss electricity and sub-meter electricity of each differentiated meter in the preset historical period is calculated, and the average suspicion coefficient correction factor of each differentiated meter in the preset historical period is determined by using the correlation influence mechanism; The average suspicion coefficient correction factor of each station division table is used to correct the average suspicion coefficient of the corresponding station division table, and the station division tables are sorted in descending order according to the corrected average suspicion coefficient, and the station division tables with the largest sorting order are formed into a first set of suspicion division tables; The second out-of-tolerance electric meter determination module uses a suspected electric meter step-by-step compensation elimination method to screen the substation electric meters in the third suspected sub-meter set to obtain a final out-of-tolerance electric meter, including: The sub-meter ranked first in the third set of suspected sub-meters is used as the final suspected electric meter, and the sub-meter power of the final suspected electric meter is compensated by using the sub-meter relative error of the final suspected electric meter to obtain the sub-meter theoretical power; The final suspected electric meter is removed from the multiple substation tables of the target substation, and after the theoretical electric quantity of the sub-meter is subtracted from the substation total table of the target substation, the line loss electric quantity of each substation table after the final suspected electric meter is removed in the target substation at each moment in the preset historical period is determined; According to the line loss electricity of each substation table after excluding the final suspect electricity meter in the target substation at each moment in the preset historical period, calculate the average line loss electricity of each substation table after excluding the final suspect electricity meter in the preset historical period, and determine whether the average line loss electricity is greater than the second line loss threshold; If it is greater than, then according to the sub-meter electricity of each substation table at each moment in the preset historical period after the final suspected electricity meter is removed in the target substation, and the total meter electricity after the sub-meter theoretical electricity is subtracted from the total meter of the target substation, the sub-meter relative errors of each substation table at each moment in the preset historical period after the final suspected electricity meter is removed in the target substation, and the substation tables in the target substation are updated, and the step of determining the substation table with the largest average suspicion coefficient in each substation table by using the line loss electricity, sub-meter electricity and sub-meter relative errors of each substation table at each moment in the preset historical period is returned to obtain a new third set of suspected sub-meters; If the station area table ranked first in average suspicion coefficient in the new third suspect sub-table set overlaps with the station area table in the previous third suspect sub-table set, then determining whether the station area table ranked second in average suspicion coefficient in the new third suspect sub-table set overlaps with the station area table in the previous third suspect sub-table set; If yes, the second-ranked substation meter is used as the final out-of-tolerance meter; Otherwise, the first ranked substation meter is used as the final out-of-tolerance meter; If it is not greater than, the final suspected meter will be regarded as an out-of-tolerance meter.

7. A storage medium, characterized in that: The storage medium stores computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to execute the steps of the method for online detection of out-of-tolerance meters based on step-by-step compensation and elimination of suspected meters as described in any one of claims 1 to 5.

8. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the online detection method for out-of-tolerance meters based on step-by-step compensation and elimination of suspected meters as described in any one of claims 1 to 5 are performed.

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