A method for identifying distribution network topology based on user power consumption data

Abstract

A method for identifying distribution network topology based on user power consumption data includes the following steps: Step 1, using the average A observations per day as the standard for the power data and voltage data of the line and the user, performing data alignment processing and related preprocessing to complete the missing time dimension; obtaining complete and available voltage and power data; Step 2, calculating and comparing the line power values; Step 3, using the voltage data to perform similarity calculations to screen "suspected users"; Step 4, performing mixed integer linear programming on the power data; Step 5, after the mixed integer programming calculation in Step 4, using a genetic algorithm to identify the remaining users from all users to fill this gap. The present invention is based on the operating data generated during the current distribution network operation, and while obtaining the voltage data, it is also necessary to obtain the power and power data. When the similarity of the voltage curve is limited, the power curve and the power value are used for further identification.

Description

Technical Field

[0001] The invention belongs to the field of electric power information technology and relates to a method for identifying distribution network topology based on user power consumption data. Background Art

[0002] The relationship between distribution network lines and transformers is very important basic data. When there are errors in it, it seriously affects the development of various distribution network businesses, including line loss calculation, fault repair, power outage plan release, etc. However, with the continuous increase in power load, the power facilities have been changed and adjusted (such as relocation, expansion, cutover, and point layout). In the process of many years of operation, power supply companies will cause the meter files to be inconsistent with the actual situation due to various technical reasons, irregular management, or adjustment of rights and responsibilities, and the difficulty in determining the ownership of the station area of ​​ground cable power supply users. Other problems such as the disorder of distribution network relationships.

[0003] The traditional method of verifying the correctness of the distribution network line transformer relationship by manual inspection has two major limitations: on the one hand, there are many distribution network assets, and relying on manual methods is time-consuming, labor-intensive and ineffective; on the other hand, the distribution network line transformer relationship is dynamically changing, and relying on manual methods is difficult to ensure real-time performance. Summary of the invention

[0004] The purpose of the present invention is to provide a method for identifying the topology of a distribution network based on user power consumption data, so as to provide a solution to the two major limitations in the background technology.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] A method for identifying distribution network topology based on user power consumption data comprises the following steps:

[0007] Step 1: The power data P and voltage data U of the line and the user are aligned and preprocessed based on an average of A observations per day. At the same time, the line and user data whose data length is less than the average A observations per day are aligned, that is, the missing time dimension is completed; all data are traversed, and the missing data is completed by linear interpolation. For users and lines whose number of null values ​​exceeds B% of the total data, they are recorded and deleted to obtain complete and available voltage and power data; the processed user data is classified according to different substations, and the lines are classified according to different substations and then classified according to different buses.

[0008] Step 2: Calculate and compare the line power values.

[0009] Step 3: Use voltage data to perform similarity calculations and screen “suspected users”.

[0010] Step 4: Perform mixed integer linear programming on the power data; use the "suspected users" obtained in step 3 as the user set U and the line power data to perform mixed integer programming calculations, find the optimal linear combination within the feasible domain, and obtain a preliminary identification result.

[0011] Step 5: After the mixed integer programming calculation in step 4, use the genetic algorithm to identify the remaining users from all users to fill the gap.

[0012] Compared with the prior art, the present invention has the following advantages: the present invention is based on the operation data generated during the operation of the current distribution network, and obtains the voltage data, as well as the power and electricity data. When the similarity of the voltage curve is limited, the power curve and the electricity value are used for further identification.

[0013] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 It is the algorithm flow chart of the present invention. DETAILED DESCRIPTION

[0016] The exemplary embodiments disclosed in the present invention will be described in more detail below with reference to the accompanying drawings. These embodiments are intended to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art. Although the exemplary embodiments disclosed in the present invention are shown in the accompanying drawings, it should be understood that the present invention should not be limited by the embodiments described herein.

[0017] A method for identifying distribution network topology based on user power consumption data, such as Figure 1 , including the following steps:

[0018] Step 1: The power data P and voltage data U of the line and the user are aligned and preprocessed based on the average A observations per day. At the same time, the line and user data whose data length is less than the average A observations per day are aligned, that is, the missing time dimension is completed; all data are traversed, and the missing data is completed by linear interpolation. For users and lines whose number of null values ​​exceeds B% of the total data, they are recorded and deleted to obtain complete and available voltage and power data; the processed user data is classified according to different substations, and the lines are classified according to different substations and then classified according to different buses;

[0019] Step 2: Calculate and compare the line power values;

[0020] Step 3: Use voltage data to perform similarity calculation and screen “suspected users”;

[0021] Step 4: Perform mixed integer linear programming on the power data; use the "suspected users" obtained in step 3 as the user set U and the line power data to perform mixed integer programming calculations, find the optimal linear combination within the feasible domain, and obtain a preliminary identification result;

[0022] Step 5: After the mixed integer programming calculation in step 4, use the genetic algorithm to identify the remaining users from all users to fill the gap.

[0023] Example

[0024] The method for identifying the distribution network topology based on user power consumption data includes the following steps:

[0025] Step 1: The power data P and voltage data U of the lines and users are aligned and preprocessed based on an average of 96 observations per day. At the same time, the line and user data whose data length is less than 96 observations per day are aligned, that is, the missing time dimension is completed. Traverse all the data, and use linear interpolation to complete the missing data caused by collection dropouts, poor signals, etc. For users and lines with more than 50% of the total data, they are recorded and deleted to obtain complete and available voltage and power data. The processed user data is classified according to different substations, and the lines are classified according to different substations and then classified according to different buses.

[0026] Step 2: Calculate and compare the line power values.

[0027] Specifically, calculate the line power data P l Get the line power value The formula is: The calculation results and the line power data Q in the original file l For comparison, The data is retained effectively, and the lines with ratios outside this range are recorded and deleted from the calculated data.

[0028] Step 3: Use voltage data to perform similarity calculations and screen “suspected users”.

[0029] Specifically, read all line voltage values, calculate the similarity ρ between the line voltage data and all user voltage data, and take out the users with ρ≥0.9 as the "suspected users" under the current line to provide prior information for subsequent calculations. The calculation method is as follows:

[0030]

[0031]

[0032] where ρ l,u is the correlation coefficient of the line and user power curves, Cov(l,u) is the covariance value of the line and user power, E(l*u) is the expectation of the product of line power and user power, and Var(l) is the variance value of the line power data. The calculation results are shown in Table 1.

[0033] Table 1 - Similarity table between line voltage data and user voltage data

[0034] Line1 voltage data similarity User1 User2 User3 ... User Featureday1 0.4188 0.4209 0.3856 ... 0.3914 Featureday2 0.8074 0.8021 0.7942 ... 0.8993 ... ... ... ... ... ... Featuredayn 0.5361 0.5369 0.7182 ... 0.6947

[0035] Step 4: Perform mixed integer linear programming on the power data.

[0036] Specifically, the "suspected users" obtained in step 3 are used as the user set U and the line power data for mixed integer programming calculations to find the optimal linear combination within the feasible domain and obtain preliminary identification results. Linear programming is a mathematical theory and method for studying the extreme value problem of linear objective functions under linear constraints, while mixed integer programming is a linear programming in which some decision variables are integers and some are non-negative numbers.

[0037] The specific calculation steps are as follows:

[0038] Step 4.1: Create two sets of non-negative variables u 1 ,u 2 ,…,u n ,v 1 ,v 2 ,…,v n , the variables must satisfy the following relationship: minS = u 1 +u 2 +…+u n -v 1 -v 2 -…-v n .

[0039] Step 4.2: Use the user set power and line power data as x and y respectively to establish a set of equations. There are k users in the user set and each user has n observations. The details are as follows:

[0040]

[0041] where x nk represents the nth observation value of user k; a k is the relationship between user k and the current line; y n represents the nth observation value of the current line; u 1 ,u 2 ,…,u n ,v 1 ,v 2 ,…,v n are two sets of constrained variables that are always non-negative.

[0042] Step 4.3: In the feasible domain of 4.2, solve the optimal solution matrix [a] that satisfies step 4.1. Matrix [a] is the preliminary identification result of the connection relationship between users and lines.

[0043] Table 2- Line user connection relationship table

[0044] Line1 Line2 Line3 ... Linen User1 0 1 0 ... 0 User2 1 0 0 ... 0 ... ... ... ... ... ... User 0 0 1 ... 0

[0045] Step 5. After the mixed integer programming calculation in step 4, only the association relationship between a part of users and the corresponding lines is obtained. Because the selected user set only includes the "suspected users" taken out by calculating the "voltage similarity" in step 3, the mixed integer programming can only identify some users associated with the line. After the power curves of these users are superimposed, there is still a certain gap between them and the line power curve. It is necessary to use a genetic algorithm to identify the remaining users among all users to fill this gap.

[0046] The specific calculation process is as follows:

[0047] Step 5.1: Use the total power value P of the line l Subtract the user power P identified in step 4 u Get the new line power value

[0048] Step 5.2: Find the user power value that 50% is greater than , delete these users from the user set.

[0049] Step 5.3: Set the number of evolution populations and the number of iterations, randomly match all users under the same substation to all lines under the same substation, and randomly generate line-user combinations.

[0050] Step 5.4: Calculate the curve similarity of the population combination. The calculation method is as follows:

[0051]

[0052] Step 5.5: After determining the evaluation indicators, start "gene crossover".

[0053] The evaluation index randomly selects 100 pairs of combinations from the top 80% of the combinations, and randomly selects 20% of the elements in each pair of combinations to interchange, generating 200 new combinations. The new combinations replace the 200 combinations with the lowest evaluation index, completing a step of "gene crossover".

[0054] Step 5.6: After "gene crossover", start "gene mutation".

[0055] The combinations with the top 5% evaluation indicators are retained, and several combinations are randomly selected from the remaining combinations. 20% of the elements in them are randomly replaced to complete a step of "gene mutation".

[0056] Step 5.7: Repeat steps 5.4 to 5.6 until the preset number of iterations is completed, and the combination with the highest evaluation index at this time is output, which is the line change relationship finally identified and modified by the algorithm, and is updated in the line user connection relationship table 2.

[0057] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described above in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0058] Therefore, the above detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Claims

1. A method for identifying distribution network topology based on user power consumption data, characterized in that: The following steps are involved: Step 1: The power data P and voltage data U of the line and the user are aligned and preprocessed based on the average A observations per day. At the same time, the line and user data whose data length is less than the average A observations per day are aligned, that is, the missing time dimension is completed; all data are traversed, and the missing data is completed by linear interpolation. For users and lines whose number of null values ​​exceeds B% of the total data, they are recorded and deleted to obtain complete and available voltage and power data; the processed user data is classified according to different substations, and the lines are classified according to different substations and then classified according to different buses; Among them, the value of A is 96; the value of B is 50; Step 2: Calculate and compare the line power values; Where: Calculate line power data P l Get the line power value in, The calculation results and the line power data Q in the original file l Make a comparison; The data is retained effectively, and the lines with ratios outside the interval [0.9-1.1] are recorded and deleted from the calculated data; Step 3: Use voltage data to perform similarity calculation and screen "suspected users"; Among them: read all line voltage values, calculate the similarity ρ between line voltage data and all user voltage data, and take out users with ρ≥0.9 as "suspected users" under the current line to provide prior information for subsequent calculations. The calculation method is as follows: Among them, ρ l，u is the correlation coefficient of the line and user power curves, Cov(l,u) is the covariance value of the line and user power, E(l*u) is the expectation of the product of line power and user power, Var(l) is the variance value of the line power data; Step 4: Perform mixed integer linear programming on the power data; use the "suspected users" obtained in step 3 as the user set U and the line power data to perform mixed integer programming calculations, find the optimal linear combination within the feasible domain, and obtain a preliminary identification result; Step 4.1: Create two sets of non-negative variables u1, u2, ..., u n , v1, v2, …, v n , the variables must satisfy the following relationship: minS = u1 + u2 + ... + u n -v1-v2-…-v n ; Step 4.2: Take the user set power and line power data as x and y respectively. There are k users in the user set and each user has n observations. Establish the equation system as follows: Among them, x nk represents the nth observation value of user k; a k is the relationship between user k and the current line; y n represents the nth observation value of the current line; u1, u2, ..., u n , v1, v2, …, v n are two sets of constrained variables that are always non-negative; Step 4.3: Solve the optimal solution matrix [a] that satisfies step 4.1 in the feasible domain of step 4.

2. Matrix [a] is the preliminary identification result of the connection relationship between users and lines; Step 5: After the mixed integer programming calculation in step 4, use the genetic algorithm to identify the remaining users from all users to fill the gap; Step 5.1: Use the total power value P of the line l Subtract the user power P identified in step 4 u Get the new line power value Step 5.2: Find the user concentration power value greater than Delete these users from the user set; Step 5.3: Set the number of evolution populations and the number of iterations, randomly match all users under the same substation to all lines under the same substation, and randomly generate line-user combinations; Step 5.4: Calculate the curve similarity of the population combination. The curve similarity calculation formula is as follows: Step 5.5: After determining the evaluation indicators, start "gene crossover"; The evaluation index randomly selects several pairs of combinations from the top 80% of the combinations, and randomly selects 20% of the elements in each pair of combinations to exchange them, generating several new combinations, and then replaces the combinations with the lower evaluation index with the new combinations to ensure that the total population remains unchanged, thus completing a step of "gene crossover"; Step 5.6: After "gene crossover", start "gene mutation"; Keep the combinations with the top 5% evaluation indicators, randomly select a number of combinations from the remaining combinations, and randomly replace 20% of the elements in each of them to complete the "gene mutation" step; Step 5.7: Repeat steps 5.4 to 5.6 until the preset number of iterations is completed, and the combination with the highest evaluation index at this time is output, which is the line change relationship finally identified and modified by the algorithm, and is updated in the line user connection relationship table.

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