A Low-Voltage Distribution Area Topology Identification Method Based on Adaptive Sliding Window

Abstract

This invention relates to the field of distribution network automation technology, specifically to a low-voltage distribution area topology identification method based on an adaptive sliding window, comprising the following steps: S1: broadcast time synchronization; S2: acquire positive active total energy data and perform data preprocessing; S3: calculate 96-point energy data based on positive active total energy; S4: perform quartile calculation on the 96-point energy data in the assessment table to obtain the upper and lower quartiles of the energy consumption data; S5: determine the energy consumption data of all measuring devices based on the adaptive window algorithm; S6: calculate the daily energy consumption P of each circuit breaker. i Remove all P i For circuit breakers with a power consumption of less than 1 kW·h, the meter box topology and remaining topology are then determined. This application uses adaptive sliding window technology to obtain the power consumption data used for data analysis. For time periods with low power consumption, the time interval is extended to obtain larger power consumption data, which to some extent avoids the influence of low power consumption and solves the problem of low identification accuracy of devices with low power consumption.

Description

Technical Field

[0001] This invention relates to the field of distribution network automation technology, specifically to a method for identifying the topology of low-voltage distribution areas based on an adaptive sliding window. Background Technology

[0002] Traditional low-voltage transformer area topology identification mainly relies on manual inspections. This method is time-consuming, labor-intensive, inefficient, and unable to make judgments on complex topology situations.

[0003] In response to the above situation, there has been considerable research on the automated identification of low-voltage distribution area topologies, such as signal injection technology. However, this technology has high hardware requirements, is costly, and has a certain impact on the power grid. In recent years, some data analysis-based low-voltage distribution area topology technologies have been proposed. Compared with other methods, data analysis-based methods only require marketing data from various devices and do not require any other equipment. However, existing data analysis methods have significant drawbacks. For example, a topology identification method for low-voltage distribution area meter networks disclosed in publication number CN112087055A has low accuracy in identifying meters with low electricity readings. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a low-voltage transformer area topology identification method based on an adaptive sliding window.

[0005] The technical solution adopted by this invention to solve its technical problem is as follows: a low-voltage transformer area topology identification method based on an adaptive sliding window includes the following steps:

[0006] S1: Broadcast time synchronization;

[0007] S2: Obtain the total positive active power data and perform data preprocessing;

[0008] S3: Calculate 96 points of power data based on total positive active power;

[0009] S4: Perform quartile calculations on the 96 points of electricity consumption data in the assessment form to obtain the upper and lower quartiles of the electricity consumption data;

[0010] S5: Determine the power consumption data of all measuring devices based on the adaptive window algorithm;

[0011] S6: Calculate the daily power consumption P of each circuit breaker. i Remove all P i For circuit breakers with a capacity of <1 kW·h, the meter box topology and remaining topology are then determined.

[0012] In S1, the time of the assessment table, intelligent circuit breaker and all user tables in the transformer area are synchronized by broadcasting the time synchronization command.

[0013] The clock synchronization error in S1 is less than 1 second.

[0014] In S2, the total positive active power of the test meters, smart circuit breakers and all user meters within the transformer area are collected at a 1-minute granularity, and the data is preprocessed.

[0015] The data preprocessing method in S2 is as follows: if there is a power outage record, all data within this time period is deleted.

[0016] In step S3, the total positive active power collected from the assessment table is subtracted every 15 minutes to obtain 96 points of power data at a 15-minute granularity.

[0017] In S4, the upper quartile is denoted as Q. H This indicates that 1 / 4 of all observed values ​​are greater than it; the lower quartile is denoted as Q. L This indicates that 1 / 4 of the observed values ​​are smaller than this value.

[0018] S5 includes the following sub-steps:

[0019] S51: Obtain the total positive active power of the granularity in the assessment table for 1 minute, and record the power consumption of the first 15 minutes as Q1. If Q H ≥Q1≥Q L Then let the size of the first window be I1 = 15; if Q1 L If the window is expanded to include a power consumption greater than Q, then the window is expanded. L Record the window size I1 at this moment; if Q1 > Q H If the window is narrowed down to a value less than Q, then the power consumption within that window is less than Q. H Alternatively, if the window size is 1, record the current window size I1, and calculate the electricity consumption of the first data point for all smart circuit breakers and meters according to this window.

[0020] S52: Take the electricity consumption within the window from I1 to (I1+15) and record it as Q2. If Q H ≥Q2≥Q L Then let the size of the first window be I2 = 15; if Q2 L If the window is expanded to include a power consumption greater than Q, then the window is expanded. L Record the window size i2 at this moment; if Q2 > Q H If the window is narrowed down to a value less than Q, then the power consumption within that window is less than Q. H Alternatively, if the window size is 1, record the current window size I2, and calculate the electricity consumption of the second data point for all smart circuit breakers and meters according to this window.

[0021] S53: Repeat steps S51-S52 to calculate the power consumption of other windows in turn until the last one, and record the number of windows as n;​​

[0022] S54: Obtain the electricity data of n points in the assessment form, all smart circuit breakers, and electricity meters within the transformer area.

[0023] S6 includes the following sub-steps:

[0024] S61: The daily power consumption P of each circuit breaker is obtained by solving the total positive active power of the circuit breaker at the 1-minute granularity. i ;

[0025] S62: Remove all P i Circuit breakers with a capacity of less than 1 kWh are connected to the subordinate electricity meters via a 485 line, and the meter box topology is determined through the meter search function.

[0026] S63: Determine the remaining topology using a bottom-up energy conservation algorithm.

[0027] S63 includes the following sub-steps:

[0028] S631: Use the n-point electricity data from all the meters to fit the n-point electricity data from each smart circuit breaker, and solve for the fitting coefficients using ridge regression. The optimization objective of the ridge regression model is:

[0029]

[0030] Where y is the power consumption vector of the circuit breaker, X is the power consumption matrix of all meters, and λ is the regularization coefficient. Here is the coefficient matrix, and β is the line loss correction coefficient;

[0031] S632: If the coefficient of circuit breaker j corresponding to meter i is greater than α, then meter i is assigned to circuit breaker j and meter i is deleted from the remaining list, where the value of α is 0.5 to 1.5;

[0032] S633: Determine the upstream circuit breaker of the electricity meter, and use the n-point electricity data of the determined circuit breaker and the unassigned electricity meter to fit the n-point electricity data of the unassigned smart circuit breaker, and determine the affiliation through S632.

[0033] S634: Repeat step S633 until all assignments are completed.

[0034] Compared with existing technologies, this invention has the following advantages: This application obtains the electricity data used for data analysis through adaptive sliding window technology. For time periods with low electricity consumption, the time interval is extended to obtain larger electricity data, thus avoiding the impact of low electricity consumption to a certain extent. For meter boxes with low electricity consumption, the meter box circuit breaker and the associated meter are connected via a 485 line. The meter box topology is sorted out through the meter search function, solving the problem of low identification accuracy of devices with low electricity consumption. Other devices are sorted out through a bottom-up electricity conservation method. This invention only requires the total positive active power of the metering devices within the transformer area at a 1-minute granularity, without the need for other devices. Attached Figure Description

[0035] Figure 1 This is a flowchart of the present invention;

[0036] Figure 2 The result is the breakdown of the total positive active power at the particle size level over 1 minute.

[0037] Figure 3 This is a topology diagram. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] Reference Figures 1-3 The low-voltage distribution area topology identification method based on adaptive sliding window includes the following steps:

[0040] S1: Broadcast Time Synchronization; In S1, the time synchronization of the monitoring tables, smart circuit breakers, and all user tables within the distribution area is performed via broadcast time synchronization command. The clock synchronization error in S1 is less than 1 second.

[0041] S2: Acquire total positive active power data and perform data preprocessing; S2 involves collecting the total positive active power data of the transformer substation's assessment meters, smart circuit breakers, and all user meters at a 1-minute granularity, and performing data preprocessing. The data preprocessing method in S2 is as follows: if there are power outage / once records, all data within this time period is deleted.

[0042] S3: Calculate 96 points of power data based on the total positive active power; in S3, the total positive active power of the collected assessment table is subtracted every 15 minutes to obtain 96 points of power data with a 15-minute granularity.

[0043] S4: Perform quartile calculations on the 96 points of electricity consumption data in the assessment form to obtain the upper and lower quartiles of the electricity consumption data; in S4, the upper quartile is denoted as Q. H This indicates that 1 / 4 of all observed values ​​are greater than it; the lower quartile is denoted as Q. L This indicates that 1 / 4 of the observed values ​​are smaller than this value.

[0044] S5: Determine the power consumption data of all measuring devices based on an adaptive window algorithm; S5 includes the following sub-steps:

[0045] S51: Obtain the total positive active power of the granularity in the assessment table for 1 minute, and record the power consumption of the first 15 minutes as Q1. If Q H ≥Q1≥Q L Then let the size of the first window be I1 = 15; if Q1 L If the window is expanded to include a power consumption greater than Q, then the window is expanded. L Record the window size I1 at this moment; if Q1 > Q H If the window is narrowed down to a value less than Q, then the power consumption within that window is less than Q. H Alternatively, if the window size is 1, record the current window size I1, and calculate the electricity consumption of the first data point for all smart circuit breakers and meters according to this window.

[0046] S52: Take the electricity consumption within the window from I1 to (I1+15) and record it as Q2. If Q H ≥Q2≥Q L Then let the size of the first window be I2 = 15; if Q2 L If the window is expanded to include a power consumption greater than Q, then the window is expanded. L Record the window size i2 at this moment; if Q2 > Q H If the window is narrowed down to a value less than Q, then the power consumption within that window is less than Q. H Alternatively, if the window size is 1, record the current window size I2, and calculate the electricity consumption of the second data point for all smart circuit breakers and meters according to this window.

[0047] S53: Repeat steps S51-S52 to calculate the power consumption of other windows in turn until the last one, and record the number of windows as n;

[0048] S54: Obtain the electricity data of n points in the assessment form, all smart circuit breakers, and electricity meters within the transformer area.

[0049] The results of the 1-minute particle size positive active total electrical energy partitioning are as follows: Figure 2 As shown.

[0050] S6: Calculate the daily power consumption P of each circuit breaker. i Remove all P i ​​For circuit breakers with a capacity of <1 kW·h, the meter box topology and remaining topology are then determined.

[0051] S6 includes the following sub-steps:

[0052] S61: The daily power consumption P of each circuit breaker is obtained by solving the total positive active power of the circuit breaker at the 1-minute granularity. i ;

[0053] S62: Remove all P i Circuit breakers with a capacity of less than 1 kWh are connected to the subordinate electricity meters via a 485 line, and the meter box topology is determined through the meter search function.

[0054] S63: Determine the remaining topology using a bottom-up energy conservation algorithm.

[0055] S63 includes the following sub-steps:

[0056] S631: Use the n-point electricity data from all the meters to fit the n-point electricity data from each smart circuit breaker, and solve for the fitting coefficients using ridge regression. The optimization objective of the ridge regression model is:

[0057]

[0058] Where y is the power consumption vector of the circuit breaker, X is the power consumption matrix of all meters, and λ is the regularization coefficient. Here is the coefficient matrix, and β is the line loss correction coefficient. The suggested value of β in this application is between 1 and 1.05. When the slip time is short, considering the measurement rounding, the suggested value can be increased to 0.95-1.1. In actual operation, it can be increased to multiple regression processes.

[0059] S632: If the coefficient of circuit breaker j corresponding to meter i is greater than α, then meter i is assigned to circuit breaker j and meter i is deleted from the remaining list, where the value of α is 0.5 to 1.5;

[0060] S633: Determine the upstream circuit breaker of the electricity meter, and use the n-point electricity data of the determined circuit breaker and the unassigned electricity meter to fit the n-point electricity data of the unassigned smart circuit breaker, and determine the affiliation through S632.

[0061] S634: Repeat step S633 until all assignments are completed.

[0062] The specific topology in this application is as follows: Figure 3 As shown, the system includes an energy controller, circuit breakers, and meters. Circuit breaker 9 consumes less than 1 kWh per day, therefore it is connected to its subordinate meters via a 485 line. The meter box topology is determined using the meter search function. The remaining topology is then structured from bottom to top as shown in the diagram.

[0063] The above description is merely an optional embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A low-voltage distribution area topology identification method based on an adaptive sliding window, characterized in that, Includes the following steps: S1: Broadcast time synchronization; S2: Obtain the total positive active power data and perform data preprocessing; S3: Calculate 96 points of power data based on total positive active power; S4: Perform quartile calculations on the 96 points of electricity consumption data in the assessment form to obtain the upper and lower quartiles of the electricity consumption data; in S4, the upper quartile is denoted as... This indicates that 1 / 4 of all observed values ​​are greater than it; the lower quartile is denoted as . This indicates that 1 / 4 of the observed values ​​are smaller than this value; S5: Determine the power consumption data of all measuring devices based on an adaptive window algorithm; S5 includes the following sub-steps: S51: Obtain the total positive active power of the granularity in the assessment form for 1 minute, and record the power consumption of the first 15 minutes as follows. ,like Then record the size of the first window. ;like If the window is expanded to include a power consumption greater than [a certain value], then [the power consumption within the window is increased]. Record the window size at this time. ;like If the window is reduced to a value less than 1, then the power consumption within that window is reduced to 1. Alternatively, if the window size is 1, record that window size. All smart circuit breakers and meters will calculate the electricity consumption of the first data point according to this window; S52: Take to The electricity consumption within the window is recorded as follows: ,like Then record the size of the first window. ;like If the window is expanded to include a power consumption greater than [a certain value], then [the power consumption within the window is increased]. Record the window size at this time. ;like If the window is reduced to a value less than 1, then the power consumption within that window is reduced to 1. Alternatively, if the window size is 1, record that window size. And calculate the electricity consumption of the second data point for all smart circuit breakers and meters according to this window; S53: Repeat steps S51-S52 to calculate the power consumption of other windows in turn, until the last one. Record the number of windows as . ; S54: Obtain the assessment forms for the transformer substation, all smart circuit breakers, and electricity meters. Point battery level data; S6: Calculate the daily power consumption of each circuit breaker Take out all The circuit breaker, then determine the meter box topology and the remaining topology; S6 includes the following sub-steps: S61: The daily power consumption of each circuit breaker is obtained by solving the total positive active power of the circuit breaker at the 1-minute granularity. S62: Retrieve all The circuit breaker is connected to the subordinate electricity meter via a 485 line, and the meter box topology is determined through the meter search function. S63: Determine the remaining topology using a bottom-up energy conservation algorithm.

2. The low-voltage distribution area topology identification method based on adaptive sliding window according to claim 1, characterized in that, In S1, the time of the assessment table, intelligent circuit breaker and all user tables in the transformer area are synchronized by broadcasting the time synchronization command.

3. The low-voltage transformer area topology identification method based on an adaptive sliding window according to claim 1 or 2, characterized in that, The clock synchronization error in S1 is less than 1 second.

4. The low-voltage distribution area topology identification method based on an adaptive sliding window according to claim 1 or 2, characterized in that, In S2, the total positive active power of the test meters, smart circuit breakers and all user meters within the transformer area are collected at a 1-minute granularity, and the data is preprocessed.

5. The low-voltage distribution area topology identification method based on adaptive sliding window according to claim 4, characterized in that, The data preprocessing method in S2 is as follows: if there is a power outage record, all data within this time period is deleted.

6. The low-voltage distribution area topology identification method based on adaptive sliding window according to claim 5, characterized in that, In step S3, the total positive active power collected from the assessment table is subtracted every 15 minutes to obtain 96 points of power data at a 15-minute granularity.

7. The low-voltage distribution area topology identification method based on adaptive sliding window according to claim 1, characterized in that, S63 includes the following sub-steps: S631: Use all electricity meters The point power data is used to fit the data of each smart circuit breaker. The point electricity data was used to solve for the fitting coefficients using ridge regression, where the optimization objective of the ridge regression model was: in, This is the power consumption vector of the circuit breaker. A power consumption matrix for all meters. The regularization coefficient is... The coefficient matrix, This is the line loss correction coefficient; S632: If the meter Corresponding circuit breaker The coefficient is greater than Then the electricity meter Assigned to circuit breaker And delete the meter from the remaining list. ,in The value ranges from 0.5 to 1.5; S633: Determine the upstream circuit breaker of the electricity meter, and connect the identified circuit breaker with the unassigned electricity meter. The point power data was used to fit the data of the unassigned smart circuit breakers. The power consumption data is collected and its ownership is determined via S632. S634: Repeat step S633 until all assignments are completed.

Images