Impedance calculation model based on low voltage topology distribution network

Abstract

The present invention relates to an impedance calculation model based on a low-voltage topology distribution network. The operating data of household meters and main meters are collected by time period every day, and the information of distribution transformers and low-voltage distribution cabinets, as well as the voltage and current data of user electrical appliances in each time period are read. The success rate is judged by extracting time periods at intervals, and the similarity between the voltage curve data of single-phase users and three-phase users passing through household meters and main meters is calculated. The substation and phase with the highest similarity are taken as the actual ownership relationship of the user topology. Based on the information of low-voltage distribution cabinets and combined with the voltage and current data of user electrical appliances, segmented impedance calculation is performed. The method uses segmented calculation for rapid response to obtain intuitive and effective impedance parameters. After verification, the accuracy and rationality of the impedance data obtained by calculation are greatly improved.

Description

Technical Field

[0001] The invention relates to an impedance calculation model based on a low-voltage topology distribution network, and belongs to the technical field of distribution networks. Background Art

[0002] Line impedance is a crucial parameter for sensing line status and monitoring the operational status of distribution substations. Therefore, it is a crucial indicator in the development of smart distribution networks. Impedance not only provides specific correlations with line aging, damage, and cracking, but also demonstrates a positive correlation with theoretical line losses. Most importantly, it can shorten fault location and cause diagnosis when a distribution network fault occurs. This provides an accurate parameter basis for analyzing line health and overall power system operation.

[0003] Currently, impedance calculation methods include empirical estimation, field measurement, and equivalent model estimation. However, in topologically structured distribution networks, impedance values ​​calculated using these methods are either inaccurate or costly, making it difficult to calculate accurate and reasonable impedance data in complex distribution networks. Furthermore, topologically structured distribution networks often suffer from mismatches between household-phase-transformer data and archived data due to missing archival documentation or untimely topological updates, resulting in significant impedance data errors. Summary of the Invention

[0004] Purpose of the invention: In response to the above-mentioned existing problems and shortcomings, the purpose of the present invention is to provide an impedance calculation model based on a low-voltage topology distribution network, which solves the topological structure in a complex environment through "household-phase-transformer" matching, and then obtains the branch and trunk impedance data of the entire circuit through a segmented algorithm, and finally calculates the abnormal impedance data of the entire circuit system; this calculation model not only saves costs but also can quickly respond to obtain intuitive and effective impedance parameters; at the same time, the accuracy and rationality of the impedance data obtained through verification and calculation are greatly improved, which plays an important role in the construction of smart grids.

[0005] Technical solution: To achieve the above-mentioned purpose, the present invention adopts the following technical solution:

[0006] An impedance calculation model based on a low-voltage topology distribution network includes the following steps:

[0007] Step 1: Data collection: Collect the operation data of household meter and total meter by time period every day, namely household meter U sub1 、U sub2 、U sub3 ... U subN and the total table U master1 、U master2 、U master3 ... U masterN；

[0008] Step 2: Select the analysis period: read the information of distribution transformers and low-voltage distribution cabinets, and the voltage and current data of user electrical appliances within each period;

[0009] Step 3: Determine the success rate: At intervals, extract the voltage and current data from step 2 several times to determine the success rate. If the acquisition success rate during the extraction period is higher than the data completeness threshold, proceed to step 4. If the acquisition success rate during the extraction period is lower than the data completeness threshold, reselect the analysis period.

[0010] Step 4: Compare similarities: The phases in the archive are A, B, and C, and there are six combinations: ABC, ACB, BAC, BCA, CAB, and CBA. N points are selected in different time periods throughout the day to calculate the similarity between the voltage curve data of single-phase users and three-phase users passing through the household meter and the main meter. The substation and phase with the highest similarity are taken as the actual affiliation of the user topology.

[0011] Step 5: Calculate impedance by segment: Based on the low-voltage distribution cabinet information in step 2 and the voltage and current data of the user's electrical appliances, perform segment impedance calculation;

[0012] Step 6: Calculate the user's loop impedance using a constrained binary linear regression method.

[0013] Step 7: The trunk line impedance and branch line impedance obtained in step 6 are added together as the user's loop impedance, and the impedance abnormality threshold is used as the impedance abnormality judgment standard;

[0014] Step 8: The judgment data set obtained in step 7 is used to obtain the impedance data and abnormal impedance data of the entire substation through a statistical program.

[0015] Furthermore, in step 3, the voltage and current data in step 2 are obtained 96 times at intervals of 15 minutes to determine the success rate.

[0016] Furthermore, the data completeness threshold in step 3 is 90%.

[0017] Furthermore, the similarity algorithm in step 4 is the Pearson algorithm.

[0018] Furthermore, the segmented impedance calculation in steps 5 and 6 establishes the following equations and regression equations:

[0019]

[0020] in, For users who are currently calculating loop impedance;

[0021] For users The voltage value of the distribution transformer in the same phase;

[0022] For users Mains impedance;

[0023] For users The branch line impedance;

[0024] For users The current value, For users Mains current;

[0025] td is the dth moment in the corresponding period;

[0026] Then calculate the user's loop impedance by regression, the formula is as follows:

[0027]

[0028] Wherein, UT: the transformer phase voltage in phase with user U2, V;

[0029] U2: voltage of U2 user, V;

[0030] N: represents the number of voltage points taken in the corresponding period;

[0031] ti: represents the i-th moment of the corresponding period;

[0032] : represents the trunk impedance of user u2;

[0033] : Indicates the branch line impedance of user u2.

[0034] Furthermore, the impedance abnormality threshold in step 7 is 0.2±0.05.

[0035] Beneficial effects: Compared with the existing technology, the present invention has the following advantages: it reduces the cost to obtain accurate and reasonable impedance data, which plays an important role in the stable operation of the entire power system and the analysis of the entire line anomaly; at the same time, it also provides a complete intelligent calculation method for the low-voltage distribution network under the complex topology structure. This method relies on the technology of big data (pyspark+python), and through the re-matching of "household-phase-transformer" of single-phase and three-phase users (using the Pearson similarity method), the voltage and current data of users and distribution transformers are monitored in segments every day, and then the equation group is obtained by using the Kirchhoff voltage law equation to calculate the circuit trunk and branch line data in segments [xx]. Finally, the abnormal impedance and the cause of the abnormality are quickly obtained through the judgment criteria of the impedance anomaly and the impedance statistics of the entire low-voltage distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is an overall calculation flow chart of an embodiment of the present invention;

[0037] Figure 2 Schematic diagram of the household-phase-change matching matrix process of an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The present invention is further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, modifications of various equivalent forms of the present invention made by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0039] like Figure 1 As shown, an impedance calculation model based on a low-voltage topology distribution network is presented. The following examples of low-voltage power topology identification, impedance calculation, and abnormality judgment in a residential area in Jiangning District, Nanjing are used to illustrate the technical solution of the present invention in detail.

[0040] Step 1: Collect the current and voltage at the transformer outlet of the community at a 15-minute period, and attach the data time stamp to obtain the transformer operation data set UT1, UT2, UT3, ... UT96 with time stamp information; collect the current and voltage at the meter inlet of the community at a 15-minute period, and attach the data time stamp to obtain the meter operation data set U1, U2, U3, ... U96 with time stamp information;

[0041] Step 2: For the data sets U1 to U96 and UT1 to UT96, the Pearson calculation method is used to compare the corresponding time scale values ​​to extract the new time series feature data set such as Figure 2 ;

[0042] Step 3: Take one week as the time window and calculate the impedance from the main meter to the household meter every day (core algorithm of impedance calculation in process 1). Use Kirchhoff voltage equations and parameterized regression equations to find the corresponding branch line (ZX_MEAN) and trunk line (GX_MEAN) impedance mean. The overall process is as follows: Figure 1 ;

[0043] Step 4: Through the big data statistical program and the judgment standard of impedance abnormality (threshold 0.2), the abnormal impedance and the abnormal cause are counted, such as Figure 2 shown.

[0044] Calculation algorithm: Impedance calculation part:

[0045]

[0046] For users The voltage value of the distribution transformer in the same phase;

[0047] For users Mains impedance (result to be calculated);

[0048] For users The branch line impedance (the result to be calculated);

[0049] For users The current value;

[0050] For users of mains current.

[0051] The mains current consists of two parts, namely the daily average voltage higher than the current calculation user (a) and the daily average voltage not exceeding the current calculation user (b) superimposed:

[0052]

[0053] For users who are currently calculating loop impedance;

[0054] Represents Same phase, and the average daily voltage is higher than the user users;

[0055] Represents a user Current;

[0056] Represents a user Current;

[0057] Indicates that the same area In the same phase, all daily average voltages exceed the user The user collection of

[0058] Indicates that the same area Same phase, all daily average voltages do not exceed the user User collection

[0059] Selection of calculation formula.

[0060] For one day Current data of 96, 48 or 24 points.

[0061] A. When the household meter current in a day The record is not less than , choose binary regression equation.

[0062] B. When the household meter current in a day The record is not less than , select a univariate regression equation. That is, Iun is 0.

[0063] Solve the system of equations:

[0064] The above formula can be used to obtain the following equations:

[0065]

[0066] or

[0067]

[0068] Use curve fitting (scipy.optimize.curve_fit()) to solve the equations. Add constraints (0, 5] for ZZX and ZGX. For three-phase users, calculate the loop impedances for phases A, B, and C separately.

Claims

1. A method for calculating impedance based on a low-voltage topology distribution network, characterized by: The steps include: Step 1: Data collection: Collect the operation data of household meter and total meter by time period every day, namely household meter U sub1 、U sub2 、U sub3 ... U subN and the total table U master1 、U master2 、U master3 ... U masterN； Step 2: Select the analysis period: read the information of distribution transformers and low-voltage distribution cabinets, and the voltage and current data of user electrical appliances within each period; Step 3: Determine the success rate: At intervals, extract the voltage and current data from step 2 several times to determine the success rate. If the acquisition success rate during the extraction period is higher than the data completeness threshold, proceed to step 4. If the acquisition success rate during the extraction period is lower than the data completeness threshold, reselect the analysis period. Step 4: Compare similarities: The phases in the archive are A, B, and C, and there are six combinations: ABC, ACB, BAC, BCA, CAB, and CBA. N points are selected in different time periods throughout the day to calculate the similarity between the voltage curve data of single-phase users and three-phase users passing through the household meter and the main meter. The substation and phase with the highest similarity are taken as the actual affiliation of the user topology. Step 5: Calculate impedance in sections: Based on the low-voltage distribution cabinet information in step 2 and the voltage and current data of the user's electrical appliances, write the user's loop impedance equations under multiple sections to form an equation group; The segmented impedance calculation in step 5 is established by establishing the following equations: , in, un For users who are currently calculating loop impedance; For users un The voltage value of the distribution transformer in the same phase; For users un Mains impedance; For users un The branch line impedance; For users un The current value, For users un Mains current; td is the dth moment in the corresponding period; Then calculate the user's loop impedance by regression, the formula is as follows: , Wherein, UT: the transformer phase voltage in phase with user U2, V; U2: voltage of U2 user, V; N: represents the number of voltage points taken in the corresponding period; ti: represents the i-th moment of the corresponding period; : represents the trunk impedance of user u2; : Indicates the branch line impedance of user u2 Step 6: Calculate the user's loop impedance using a constrained binary linear regression method. Step 7: The trunk line impedance and branch line impedance obtained in step 6 are added together as the user's loop impedance, and the impedance abnormality threshold is used as the impedance abnormality judgment standard; Step 8: The judgment data set obtained in step 7 is used to obtain the impedance data and abnormal impedance data of the entire substation through a statistical program.

2. The impedance calculation method based on the low-voltage topology distribution network according to claim 1 is characterized in that: In step 3, the voltage and current data in step 2 are obtained 96 times at 15-minute intervals to determine the success rate.

3. The impedance calculation method based on the low-voltage topology distribution network according to claim 1 is characterized in that: The data completeness threshold in step 3 is 90%.

4. The impedance calculation method based on a low-voltage topology distribution network according to claim 1, characterized in that: The similarity algorithm in step 4 is the Pearson algorithm.

5. The impedance calculation method based on the low-voltage topology distribution network according to claim 1 is characterized in that: The impedance abnormality threshold in step 7 is 0.2±0.05.

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