Distribution network line checking method

Abstract

The invention belongs to the technical field of distribution network line checking, particularly relates to a distribution network line checking method, and more particularly relates to a distribution network line checking method based on a distribution network diagram modulus incidence relation. The method comprises the steps that an equivalent resistance method is used for calculating a distribution network line, equipment data, operation data, electric quantity data and topological data are collected, and the topological data are difficult to verify; performing distribution network line topology anomaly check on the topology data by using a frequency domain similarity grey correlation analysis method, and preprocessing the checked abnormal data; and carrying out graphic file and model file separation on the pre-processed abnormal data, and respectively checking and correcting. According to the invention, automatic identification of a transformer area topological relation can be realized, transformer area internal information can be comprehensively acquired, transformer area archive data is accurate, terminal fusion of a marketing and distribution system is realized, an intelligent transformer area with active and accurate service is created, line loss treatment efficiency and checking efficiency are greatly improved, power supply reliability is improved, and accurate data support is provided for production and regulation and control.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network line verification, and in particular relates to a distribution network line verification method, and more specifically to a distribution network line verification method based on a distribution network diagram analog-to-digital correlation relationship. Background Art

[0002] With the development and improvement of distribution network function design, the requirements for terminal communication access are increasing. Under this premise, distribution network line verification is becoming more and more intelligent, and the requirements for distribution network line verification are also becoming higher and higher.

[0003] Some distribution network line verification systems currently used generally use a single factor as the standard for verification during the verification process. At present, the existing technology has the following shortcomings:

[0004] (1) The distribution network has a complex grid structure, a low degree of automation, and insufficient operation and management experience. Some algorithms with high calculation accuracy cannot be used in the theoretical calculation of distribution network line losses because data is difficult to collect.

[0005] (2) A large amount of initial investment is required in the design of distribution network model lines. The model must be completely consistent with the actual object and requires continuous maintenance in the later stages.

[0006] However, the results obtained using the traditional grey relational analysis (GRA) method have large deviations. Therefore, it is necessary to further update and improve the existing distribution network line verification technology to meet the current high requirements of the distribution network. Summary of the invention

[0007] In view of the shortcomings of the above-mentioned prior art, the present invention provides a distribution network line calibration method. Its purpose is to achieve accurate calibration of distribution network lines, terminal integration of distribution and marketing systems, create intelligent substations with proactive and accurate services, and based on the full amount of substation data obtained, realize big data analysis and application, greatly improve the efficiency of line loss management, and provide data support for production, regulation, etc.

[0008] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0009] A distribution network line verification method comprises the following steps:

[0010] Step 1. Use the equivalent resistance method to calculate the distribution network line, collect equipment data, operation data, power data, and topology data, among which the topology data is difficult to verify;

[0011] Step 2. Use the grey correlation analysis method of frequency domain similarity to check the topology data for distribution network line topology anomalies, and pre-process the abnormal data after inspection;

[0012] Step 3. Separate the graphic files and model files of the abnormal data after pre-processing, and check and correct them respectively.

[0013] Furthermore, the equivalent resistance method is used to calculate the distribution network line, where: the distribution network equivalent resistance R dz By wiring equivalent resistance R Ldz And the equivalent resistance R Tdz Composition, as shown in formula (1):

[0014] R dz =R Ldz +R Tdz (1)

[0015] The calculation formula for wiring equivalent resistance is:

[0016]

[0017] Where: r i is the resistance of the ith segment among the n distribution segments in the entire distribution network, S j is the m number of nodes connected to the i-th wiring segment i The rated capacity of the jth distribution transformer among the distribution transformers, S i is the rated capacity of the i-th distribution transformer among the m distribution transformers in the entire distribution network;

[0018] The calculation formula of the equivalent resistance of the distribution transformer is:

[0019]

[0020] Where: U is the average voltage of the entire distribution network, S i is the rated capacity of the ith distribution transformer among the m distribution transformers in the entire distribution network, P ki is the rated load loss of the i-th distribution transformer among the m distribution transformers in the entire distribution network;

[0021] The power loss of the entire distribution network is:

[0022]

[0023] Where: P oi is the no-load loss of the ith distribution transformer in the entire distribution network with m distribution transformers, and T is the calculation period.

[0024] Furthermore, the gray correlation analysis method using frequency domain similarity is used to perform a topology abnormality check on the topology data of the distribution network line, including: assuming that each distribution line is distributed by a different bus, and calculating the voltage at the node i to obtain the following formula (5):

[0025] U i =U j +IZ ij =U j+I(R+jX) (5)

[0026] Among them: U i represents the voltage at node i, U j represents the voltage at node j, Z ij represents line impedance, R represents line resistance, X represents line reactance, and I represents line current.

[0027] The scalar value of the bus voltage in the site is represented by the measurement data on the user side and the metering point of the public transformer, and the line voltage at node i is obtained from formula (6):

[0028]

[0029] Among them, R represents line resistance, X represents line reactance, P represents active power, and Q represents reactive power.

[0030] Furthermore, the abnormal data after inspection is preprocessed by combining and optimizing the new algorithm RF-GRA based on random forest RF and traditional GRA. First, the steps of RF preprocessing the electric energy metering data are as follows:

[0031] The distance between any electric energy metering data detection point and the isolated center point is calculated as follows:

[0032] ||xy n ||=min(||xy n ||) (7)

[0033] Where: Any sample x and any center point y in the feature data subsample described by formula (7) n The shortest distance between

[0034] By calculating the average value of high-voltage metering data and integrating the distance data between the monitoring point and the center point, the rule of the monitoring abnormal point equation group is obtained:

[0035]

[0036] H(k)=ln(k)+γ (9)

[0037] S(x,p)=2 -H(x) / c(p) (10)

[0038] Where: S represents the abnormal exponential function, p represents the center point, c represents the average distance from the electric energy metering data to the center point p, γ represents the Euler constant, H represents the harmonic series, k = 1, 2, 3...M;

[0039] From formulas (8), (9) and (10), we can see that S is abnormal only when the function value approaches 1, and the monitoring point is judged as abnormal data.

[0040] Furthermore, the calculation steps of the GRA are as follows:

[0041] Collect normal data through RF screening and analyze the data to form n data sequences;

[0042] In the frequency domain, the calculation steps of the grey relational analysis method are as follows:

[0043] (1) Collect and analyze data to form n data sequences;

[0044] X i =[x i (1),x i (2),…x i (m)],i=1,2,…n (11)

[0045] Where: X i Represents a data set, and m is the data sequence number in each column;

[0046] (2) Determine the reference sequence, which includes: ideal comparison criteria and selection of reference sequence according to the evaluation objectives;

[0047] X o =[x o (1),x o (2),…x o (m)] T (12)

[0048] Among them, X0 represents the data in the reference sequence;

[0049] (3) Dimensionless processing of data;

[0050]

[0051] Where: X j (k) represents the dimensionless processed data, x′ i (k) represents the kth data, x′ i (1) represents the first data, i = 0, 1, 2, ..., n; k = 1, 2, ..., m;

[0052] (4) Calculate the absolute value, compare the minimum value in each data column, and take the minimum value min|x from the minimum values ​​in N data columns o (k)-x i (k)|, compare the maximum value in each data column, and take max|x from the maximum value in N data columns. o (k)-x i (k)|;

[0053] (5) Calculate the correlation coefficient, and calculate the correlation coefficient η of each comparison sequence and reference sequence respectively:

[0054]

[0055] Where: k = 1, 2, ..., m, resolution factor ρ is 0.5;

[0056] (6) Calculate the degree of association. The number of degrees of association is the same as the number of comparison series. The calculation formula of the degree of association β is as follows:

[0057]

[0058] Where: η(k) represents the comparison series, i = 1, 2, ..., n;

[0059] Through the final grey correlation degree, the grade standard is tested according to the grey correlation model, so as to analyze the accuracy of distribution network line topology identification.

[0060] Furthermore, the discriminant of the grade standard according to the grey relational model is as follows:

[0061]

[0062] In the time domain, assuming that the Pearson correlation coefficient is r, its expression is:

[0063]

[0064] Where: m is the number of data in each column, X and Y are two data series, Represents the average of two data series;

[0065] The power spectrum is defined as:

[0066]

[0067] Where: x(n) represents a random signal, e jw represents a complex random variable, and N represents the number of voltage curve data.

[0068] A distribution network line verification device, comprising:

[0069] The calculation module is used to calculate the distribution network line using the equivalent resistance method, collect equipment data, operation data, power data, and topology data, among which the topology data is difficult to verify;

[0070] The inspection and preprocessing module is used to use the gray correlation analysis method of frequency domain similarity to inspect the topology data for abnormal distribution network line topology, and preprocess the abnormal data after inspection;

[0071] The verification and correction module is used to separate the graphic files and model files of the abnormal data after pre-processing, and to verify and correct them respectively.

[0072] Furthermore, the distribution network line verification device is used to implement the steps of any of the distribution network line verification methods.

[0073] A computer device comprises a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the computer program, the steps of any one of the distribution network line verification methods are implemented.

[0074] A computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the distribution network line verification methods.

[0075] The present invention has the following beneficial effects and advantages:

[0076] The present invention provides a distribution network line verification method, which involves the compliance calculation of the outgoing line switches, terminals, and distribution transformer electrical operation data on the distribution line of the distribution system, and carries out data matching and abnormal statistical methods between the distribution transformer and the automatic switch. It can realize the automatic identification of the topological relationship of the substation area, comprehensively obtain the internal information of the substation area, fundamentally solve the problem of inaccurate archival data in the substation area, realize the terminal integration of the distribution system, create an intelligent substation area with active and accurate services, and realize big data analysis and application based on the full amount of data obtained in the substation area, greatly improve the efficiency of line loss management, and provide accurate data support for production, regulation, etc.

[0077] The present invention utilizes the distribution network line verification method of distribution transformer backtracking to perform logical integrity verification on topological data required by the distribution network equivalent resistance method, effectively identifying erroneous data, thereby improving the accuracy of calculation results.

[0078] The present invention finds that the frequency domain grey correlation analysis algorithm has strong robustness and high accuracy, can identify users with recognition errors, improves accuracy, and has high application value.

[0079] The present invention also provides a new direction for the verification of distribution network lines, ensures the efficiency of verification, improves the reliability of power supply, and has certain advantages in convergence speed.

[0080] The present invention can realize the compliance calculation of electrical operation data of outgoing line switches, terminals and distribution transformers on distribution lines, and carry out data matching and abnormal statistical methods between distribution transformers and automatic switches. However, the prior art has only a few studies on the verification of distribution network line calculation data. Therefore, the present invention can provide a new direction for distribution network line verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0082] Figure 1 It is the topological structure diagram of power distribution A of the present invention;

[0083] Figure 2 It is the automatic calibration system of the distribution network line of the present invention;

[0084] Figure 3 It is the distribution network line model of the present invention;

[0085] Figure 4 It is the T-connection relationship of the six measuring points of the present invention;

[0086] Figure 5 is the modulation equivalent circuit diagram of the present invention;

[0087] Figure 6 It is a schematic diagram of voltage modulation signal of the present invention;

[0088] Figure 7 It is a wiring circuit verification flow chart of the present invention;

[0089] Figure 8 It is a diagram of the verification iterative convergence comparison process of the present invention.

[0090] In the figure: first metering point 1, second metering point 2, third metering point 3, fourth metering point 4, fifth metering point 5, sixth metering point 6. DETAILED DESCRIPTION

[0091] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0092] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0093] Refer to the following Figure 1-Figure 8 The technical solutions of some embodiments of the present invention are described.

[0094] Example 1

[0095] The present invention provides an embodiment, which is a distribution network line verification method, involving the compliance calculation of the outgoing line switches, terminals, and distribution transformer electrical operation data on the distribution line of the distribution system, and conducting data matching and abnormal statistical methods for distribution transformers and automation switches. Figure 1As shown, Figure 1 This is the topological structure diagram of power distribution A of the present invention.

[0096] The present invention is a distribution network line verification method, which is based on the modular relationship of the distribution network diagram, and the specific steps are as follows:

[0097] Step S1: The distribution network line is calculated by using the equivalent resistance method, and a distribution network line verification method using distribution transformer backtracking is proposed to effectively identify erroneous data and improve the accuracy of the distribution network calculation structure;

[0098] The equivalent resistance method can be further divided into an equivalent resistance method based on distribution transformer capacity and an equivalent resistance method based on distribution transformer quantity.

[0099] In the calculation of distribution network line loss, the basic idea of ​​the equivalent resistance method is that the loss of the entire distribution network is equal to the total root mean square current I eff Flowing through a resistor of equal value R dz The loss generated is equal to the sum of all variable losses of distribution lines and load losses of all distribution transformers in the distribution network.

[0100] Distribution network equivalent resistance R dz By wiring equivalent resistance R Ldz And the equivalent resistance R Tdz Composition, as shown in formula (1):

[0101] R dz =R Ldz +R Tdz (1)

[0102] The calculation formula for wiring equivalent resistance is:

[0103]

[0104] Where: r i is the resistance of the ith segment among the n distribution segments in the entire distribution network, S j is the m number of nodes connected to the i-th wiring segment i The rated capacity of the jth distribution transformer among the distribution transformers, S i is the rated capacity of the i-th distribution transformer among the m distribution transformers in the entire distribution network.

[0105] The calculation formula of the equivalent resistance of the distribution transformer is:

[0106]

[0107] Where: U is the average voltage of the entire distribution network, S i is the rated capacity of the ith distribution transformer among the m distribution transformers in the entire distribution network, P ki is the rated load loss of the i-th distribution transformer among the m distribution transformers in the entire distribution network.

[0108] The power loss of the entire distribution network is:

[0109]

[0110] Where: P oi is the no-load loss of the ith distribution transformer in the entire distribution network with m distribution transformers, and T is the calculation period.

[0111] Step S2: A practical method for checking the topology anomaly of distribution network lines is proposed. The traditional gray correlation analysis method of frequency domain similarity is used to improve the accuracy of distribution line verification. In the distribution network, from the output bus in the substation to each distribution transformer on the line, the voltage distribution can be regarded as a tree structure, and each distribution transformer is regarded as an end node, which is an impedance branch connecting the bus and the end node. Assuming that each distribution line is distributed by different buses, the line model is as follows Figure 3 shown.

[0112] By calculating the voltage at node i, we can get formula (5) as follows:

[0113] U i =U j +IZ ij =U j +I(R+jX) (5)

[0114] Among them: U i represents the voltage at node i, U j represents the voltage at node j, Z ij represents line impedance, R represents line resistance, X represents line reactance, and I represents line current.

[0115] In practical applications, dedicated transformers and public transformers with a capacity of less than 315kVA are measured by high-voltage power supply and low-voltage instrumentation to convert the voltage on the low-voltage side to the line voltage, and then convert the line voltage on the low-voltage side to the high-voltage side. The line impedance data can be used to query the data of the power production management system (PMS), and the current of a specific part of the user-side line can be queried through the historical data of the power consumption information collection system. In this way, the scalar value of the bus voltage in the site can be represented by the measurement data on the user side and the metering point of the public transformer.

[0116] The line voltage at node i can be obtained from formula (6):

[0117]

[0118] Among them, R represents line resistance, X represents line reactance, P represents active power, and Q represents reactive power.

[0119] The present invention calculates the correlation between the bus voltage in the station and the terminal node voltage data through actual measurement data, and selects the T connection relationship of 6 metering points from the measurement data as follows: Figure 4 As shown, the figure includes the first metering point 1 to the sixth metering point 6. Figure 4 In the figure, the two substations A and B are powered by different substations. The two 10KV lines fed from the two substations are erected on the same tower and are distinguished by an interconnection switch in the figure. Therefore, the power supply of the sixth metering point 6 is easily confused.

[0120] To this end, the present invention realizes automatic sampling of substation voltage through carrier technology. The principle of carrier technology is to use outbound signal modulation to turn on the thyristor in the modulation circuit at ΔT / 2 (30 degrees before the zero point) before the voltage crosses the zero point, and the instantaneous current generated is coupled into the current i of the power frequency voltage. e , causing a voltage drop e mod , distortion occurs at the zero crossing point of the 10KV voltage E. The coding of voltage distortion signal uses two adjacent periodic voltage waveforms to carry information, and uses the difference in modulation position to represent "1" or "0". The modulation method of inbound signal is similar to that of outbound signal, except that the modulation of inbound signal is to add the distorted signal to the current at the moment when the voltage crosses zero. The modulation equivalent circuit and the schematic diagram of voltage modulation signal are shown in the figure. Figure 5 and Figure 6 shown.

[0121] The 22-hour voltage curve data of 6 metering points were obtained by carrier wave technology, and the correlation strength in time domain and frequency domain was compared using grey correlation analysis and Pearson correlation coefficient.

[0122] As a further preferred embodiment, in the actual application, the step S2 has a great influence on the judgment accuracy due to the abnormal data, and the data collected by the smart meter inevitably has some abnormal values, and there may be many missing values. The result obtained by using the traditional grey correlation analysis (GRA) has a large deviation, so it is necessary to consider how to pre-process the electric energy metering data.

[0123] The present invention combines and optimizes the new algorithm RF-GRA based on random forest (RF) and traditional GRA. The first step is to pre-process the electric energy metering data with RF:

[0124] The distance between any electric energy metering data detection point and the isolated center point is calculated as follows:

[0125] ||xy n ||=min(||xy n ||) (7)

[0126] Where: Any sample x and any center point y in the feature data subsample described by formula (7) n The shortest distance between.

[0127] By calculating the average value of high-voltage metering data and integrating the distance data between the monitoring point and the center point, the rule of the monitoring abnormal point equation group is obtained:

[0128]

[0129] H(k)=ln(k)+γ (9)

[0130] S(x,p)=2 -H(x) / c(p) (10)

[0131] Where: S represents the abnormal exponential function, p represents the center point, c represents the average distance from the electric energy metering data to the center point p, γ represents the Euler constant, H represents the harmonic series, k = 1, 2, 3...M 。

[0132] It can be seen from formulas (8), (9) and (10) that S is abnormal only when the function value approaches 1, and the monitoring point is judged as abnormal data.

[0133] Afterwards, the calculation steps of the GRA method are outlined as follows:

[0134] Normal data are collected through RF screening and analyzed to form n data sequences.

[0135] In the frequency domain, the calculation steps of the grey relational analysis method are summarized as follows:

[0136] (7) Collect and analyze data to form n data sequences.

[0137] X i =[x i (1),x i (2),…x i (m)],i=1,2,…n (11)

[0138] Where: X i Represents a data set, and m is the data sequence number in each column.

[0139] (8) Determine the reference sequence. The reference sequence can be an ideal comparison standard, or it can be selected based on the evaluation objectives.

[0140] X o =[x o (1),x o (2),…x o (m)] T (12)

[0141] Among them, X0 represents the data in the reference sequence.

[0142] (9) Dimensionless processing of data. The physical meaning of each data column may be different, and the data dimension is also different, which makes comparison inconvenient. Therefore, it is necessary to perform dimensionless processing on the data.

[0143]

[0144] Where: X j (k) represents the dimensionless processed data, x′ i (k) represents the kth data, x′ i (1) represents the first data, i = 0, 1, 2, ..., n; k = 1, 2, ..., m.

[0145] (10) Calculate the absolute value. Compare the minimum value in each data column, and then take the minimum value min|x from the minimum values ​​in N data columns. o (k)-x i (k)|, compare the maximum value in each data column, and then take max|x from the maximum value in N data columns o (k)-x i (k)|.

[0146] (11) Calculate the correlation coefficient. Calculate the correlation coefficient η of each comparison sequence and reference sequence respectively.

[0147]

[0148] Where: k = 1, 2,…, m, and the resolution coefficient ρ is 0.5.

[0149] (12) Calculate the correlation degree. There are as many correlation degrees as the number of comparison levels. The calculation formula of the correlation degree β is as follows:

[0150]

[0151] Where: η(k) represents the comparison series, i=1,2,…,n.

[0152] Through the final grey correlation degree, the grade standard is tested according to the grey correlation model, so as to analyze the accuracy of distribution network line topology identification. The discriminant formula for testing the grade standard according to the grey correlation model is as follows:

[0153]

[0154] Through the calculation of the above grey correlation analysis method, it is possible to calculate the similarity or difference of various data development trends according to different data factors of the distribution network line topology, and then achieve the measurement between different data factors.

[0155] In the time domain, assuming that the Pearson correlation coefficient is r, its expression is:

[0156]

[0157] Where: m is the number of data in each column, X and Y are two data series, Represents the average of two data series.

[0158] The above formula can realize the calculation of linear relationships between different data amounts, improve the correlation calculation ability between data, and enable direct data relationships between data with different macro-thinking data correlations.

[0159] When there is noise in two discrete time series, by directly comparing the similarity of the two series, the results will be disturbed by the noise.

[0160] The solution used by the present invention is to transform the discrete time series into the frequency domain to reduce noise, and compare the similarity of the power spectra to measure the similarity of two discrete time series.

[0161] The power spectrum is defined as:

[0162]

[0163] Where: x(n) represents a random signal, e jw represents a complex random variable, and N represents the number of voltage curve data.

[0164] Through the analysis of the above formula, frequency domain estimation can be divided into classical frequency domain estimation and modern frequency domain estimation, and modern frequency domain estimation is superior to classical frequency domain estimation. The present invention adopts the autocorrelation method to realize the frequency domain analysis of the parameters of the autoregression (AR) model. The power spectrum estimation of the AR model is one of the modern frequency domain estimation methods. The accurate estimation of the AR model parameters can be solved by the Yule-Walker equation. Compared with the similarity of the voltage curve, only two sets of voltage measurement data of finite length are known. The method first obtains the estimated value of the autocorrelation function, and then obtains the estimated value of the AR model parameters.

[0165] Step S3: Separate the graphic files and model files from the pre-processed data, and perform verification on them respectively, and design the distribution network line verification process. Experiments show that the iterative convergence of the distribution network line verification process is better.

[0166] As a further preference, in step S3, data analysis must be performed first. Data generally exists in the diagram model of the distribution network, and the data needs to be separated in the diagram model in order to obtain effective information from the separated data. The verified graphic file and the verified model file are collectively referred to as the diagram model file. After the diagram model file is stored in the library, data verification is performed. There are three ways of data verification, namely: model data verification, application verification and diagram model consistency verification.

[0167] When the system is in an offline environment, the basic attributes of various types of equipment in the model file of the distribution network diagram are checked during the import phase: each basic attribute is located at the location of a specific model file. Then, in an offline environment, the graphics and models in the distribution network are respectively checked for association, a network topology is generated, and the connection status of the topology at that time is checked based on the network topology. After that, in an offline environment, the model file and the graphic file of the distribution network are cross-checked, the differences between the model file and the graphic file are obtained and corrected, and the consistency between the distribution network diagrams is ensured. The distribution network diagram includes an attribute verification module, a network topology verification module, and a diagram model cross-verification module.

[0168] like Figure 7 Show the people, Figure 7 It is a wiring line verification flow chart of the present invention.

[0169] According to the verification process in the above figure, in the process of distribution network line verification, the detailed description of the verification step is as follows: First, it is necessary to determine whether the selected distribution network line can meet the verification conditions. When the conditions are met, STU will immediately query, create a corresponding topological model, and then generate and expand the corresponding file, and upload the expanded file to the system responsible for verification. After STU completes the query and can successfully upload the topological description file of the distribution network, the process designed by the present invention can classify and organize the received files, store them in the database of the system, and randomly select data to match the model, build a topological model based on STU, and ensure that the model has a high degree of consistency in time and regional levels. After the model is established, it is uploaded to the system for verification, and the verification results are sent to the administrator of the verification system. After manual judgment and comparison, the verified image model information is fed back to the distribution network line designed by the present invention. After all updates are completed, the obtained model can complete the difference verification in the image model, and thus complete the "distribution network line verification" design based on the distribution network image model number association relationship.

[0170] The design of the present invention is compared with the single factor calibration, and the calibration iterative convergence process of the two systems is obtained as follows: Figure 8 As shown, Figure 8 This is a comparison diagram of the verification iterative convergence process of the present invention. The iterative convergence characteristics of the traditional single-factor verification system are poor in the early stage, but the difference is not great. When the number of iterations is between 10-30, the gap between the two systems gradually increases. In comparison, the convergence speed of the present invention has certain advantages, and the verification results are more accurate.

[0171] Example 2

[0172] The present invention further provides an embodiment, which is a distribution network line verification method, which is implemented by a distribution network line automatic verification system, such as Figure 2 As shown, Figure 2 It is a schematic diagram of the automatic calibration system for distribution network lines of the present invention.

[0173] The master station contains functions such as fault handling, system management, wiring diagram display, power flow calculation, topology verification, and big data analysis. The distribution network consists of Line 1 and Line 2, with nine switches. Among them, S0 and S8 are substation exit circuit breakers, S1, S2, S3, S4, S5, S6, and S7 are ordinary section switches, and S4 is a contact switch. Each switch is equipped with a feeder terminal unit (FTU) and a power line communication and sensing (PLSM) device. PLSM-M1 is the main device of Line 1, and PLSM-M2 is the main device of Line 2. Their corresponding terminals are called edge terminals. PLSN10 to PLSN12 are slave devices of Line 1, and PLSN20 to PLSN22 are slave devices of Line 2. PLSN-C12 is not only a slave device of Line 1, but also a slave device of Line 2. The master device contains the addresses of other slave devices on its own line.

[0174] Example 3

[0175] The present invention further provides an embodiment, which is a distribution network line verification device, comprising:

[0176] The calculation module is used to calculate the distribution network line using the equivalent resistance method, collect equipment data, operation data, power data, and topology data, among which the topology data is difficult to verify;

[0177] The inspection and preprocessing module is used to use the gray correlation analysis method of frequency domain similarity to inspect the topology data for abnormal distribution network line topology, and preprocess the abnormal data after inspection;

[0178] The verification and correction module is used to separate the graphic files and model files of the abnormal data after pre-processing, and to verify and correct them respectively.

[0179] The device described in the present invention is used to implement the steps of a distribution network line calibration method as described in any one of Examples 1 or 2, and the description is not repeated here.

[0180] Example 4

[0181] Based on the same inventive concept, an embodiment of the present invention further provides a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, the steps of any one of the distribution network line verification methods described in Embodiment 1 or 2 are implemented.

[0182] Example 5

[0183] Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any distribution network line verification method described in Embodiment 1 or 2 are implemented.

[0184] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0185] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0186] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0187] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0188] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distribution network line verification method, characterized by: The following steps are involved: Step 1. Use the equivalent resistance method to calculate the distribution network line, collect equipment data, operation data, power data, and topology data, among which the topology data is difficult to verify; Step 2. Use the grey correlation analysis method of frequency domain similarity to check the topology data for distribution network line topology anomalies, and pre-process the abnormal data after inspection; Step 3. Separate the graphic files and model files of the abnormal data after pre-processing, and check and correct them respectively.

2. A distribution network line verification method according to claim 1, characterized in that: The equivalent resistance method is used to calculate the distribution network line, where: the distribution network equivalent resistance R dz By wiring equivalent resistance R Ldz And the equivalent resistance R Tdz Composition, as shown in formula (1): R dz =R Ldz +R Tdz (1) The calculation formula for wiring equivalent resistance is: Where: r i is the resistance of the ith segment among the n distribution segments in the entire distribution network, S j is the m number of nodes connected to the i-th wiring segment i The rated capacity of the jth distribution transformer among the distribution transformers, S i is the rated capacity of the i-th distribution transformer among the m distribution transformers in the entire distribution network; The calculation formula of the equivalent resistance of the distribution transformer is: Where: U is the average voltage of the entire distribution network, S i is the rated capacity of the ith distribution transformer among the m distribution transformers in the entire distribution network, P ki is the rated load loss of the i-th distribution transformer among the m distribution transformers in the entire distribution network; The power loss of the entire distribution network is: Where: P oi is the no-load loss of the ith distribution transformer in the entire distribution network with m distribution transformers, and T is the calculation period.

3. A distribution network line verification method according to claim 1, characterized in that: The gray correlation analysis method using frequency domain similarity is used to check the topology data for abnormal distribution network line topology, including: assuming that each distribution line is distributed by different buses, and calculating the voltage at node i to obtain formula (5) as follows: U i =U j +IZ ij =U j +I(R+jX) (5) Among them: U i represents the voltage at node i, U j represents the voltage at node j, Z ij represents line impedance, R represents line resistance, X represents line reactance, and I represents line current; The scalar value of the bus voltage in the site is represented by the measurement data on the user side and the metering point of the public transformer, and the line voltage at node i is obtained from formula (6): Among them, R represents line resistance, X represents line reactance, P represents active power, and Q represents reactive power.

4. A distribution network line verification method according to claim 1, characterized in that: The abnormal data after inspection is preprocessed by combining the new algorithm RF-GRA based on random forest RF and optimizing the traditional GRA. First, the steps of RF preprocessing the electric energy metering data are as follows: The distance between any electric energy metering data detection point and the isolated center point is calculated as follows: ||xy n ||=min(||xy n ||) (7) Where: Any sample x and any center point y in the feature data subsample described by formula (7) n The shortest distance between By calculating the average value of high-voltage metering data and integrating the distance data between the monitoring point and the center point, the rule of the monitoring abnormal point equation group is obtained: H(k)=ln(k)+γ (9) S(x,p)=2 -H(x) / c(p) (10) Where: S represents the abnormal exponential function, p represents the center point, c represents the average distance from the electric energy metering data to the center point p, γ represents the Euler constant, H represents the harmonic series, k = 1, 2, 3...M; From formulas (8), (9) and (10), we can see that S is abnormal only when the function value approaches 1, and the monitoring point is judged as abnormal data.

5. A distribution network line verification method according to claim 1, characterized in that: The calculation steps of the GRA are as follows: Collect normal data through RF screening and analyze the data to form n data sequences; In the frequency domain, the calculation steps of the grey relational analysis method are as follows: (1) Collect and analyze data to form n data sequences; X i =[x i (1),x i (2),…x i (m)],i=1,2,…n (11) Where: X i Represents a data set, and m is the data sequence number in each column; (2) Determine the reference sequence, which includes: ideal comparison criteria and selection of reference sequence according to the evaluation objectives; X o =[x o (1),x o (2),…x o (m)] T (12) Among them, X0 represents the data in the reference sequence; (3) Dimensionless processing of data; Where: X j (k) represents the dimensionless processed data, x′ i (k) represents the kth data, x′ i (1) represents the first data, i = 0, 1, 2, ..., n; k = 1, 2, ..., m; (4) Calculate the absolute value, compare the minimum value in each data column, and take the minimum value min|x from the minimum values ​​in N data columns o (k)-x i (k)|, compare the maximum value in each data column, and take max|x from the maximum value in N data columns. o (k)-x i (k)|; (5) Calculate the correlation coefficient, and calculate the correlation coefficient η of each comparison sequence and reference sequence respectively: Where: k = 1, 2, ..., m, resolution factor ρ is 0.5; (6) Calculate the degree of association. The number of degrees of association is the same as the number of comparison series. The calculation formula of the degree of association β is as follows: Where: η(k) represents the comparison series, i = 1, 2, ..., n; Through the final grey correlation degree, the grade standard is tested according to the grey correlation model, so as to analyze the accuracy of distribution network line topology identification.

6. A distribution network line verification method according to claim 5, characterized in that: The discriminant formula for testing the grade standard according to the grey relational model is as follows: In the time domain, assuming that the Pearson correlation coefficient is r, its expression is: Where: m is the number of data in each column, X and Y are two data series, Represents the average of two data series; The power spectrum is defined as: Where: x(n) represents a random signal, e jw represents a complex random variable, and N represents the number of voltage curve data.

7. A distribution network line verification device, characterized by: include: The calculation module is used to calculate the distribution network line using the equivalent resistance method, collect equipment data, operation data, power data, and topology data, among which the topology data is difficult to verify; The inspection and preprocessing module is used to use the gray correlation analysis method of frequency domain similarity to inspect the topology data for abnormal distribution network line topology, and preprocess the abnormal data after inspection; The verification and correction module is used to separate the graphic files and model files of the abnormal data after pre-processing, and to verify and correct them respectively.

8. A distribution network line verification device according to claim 1, characterized in that: The device is used to implement the steps of a distribution network line calibration method described in any one of claims 1-6.

9. A computer device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein: When the processor executes the computer program, the steps of a distribution network line calibration method described in any one of claims 1 to 6 are implemented.

10. A computer storage medium, characterized in that: The computer storage medium stores a computer program, and when the computer program is executed by the processor, the steps of a distribution network line calibration method described in any one of claims 1 to 6 are implemented.

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