CN113937764A - Low-voltage distribution network high-frequency measurement data processing and topology identification method - Google Patents
Low-voltage distribution network high-frequency measurement data processing and topology identification method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
A high-frequency measurement data processing and topology identification method for a low-voltage distribution network. Firstly, obtaining the strength of voltage correlation of coupling nodes to which each load belongs under the same distribution transformer by using load measurement information provided by AMI user nodes, and performing correlation analysis, namely, the loads with the strength of correlation belong to the same feeder line, so as to determine the feeder line to which each load belongs; and finally, rapidly identifying or correcting the low-voltage distribution network topology through the correlation analysis result and the voltage distribution of the coupling nodes of the loads. The method is used for high-frequency measurement data processing and topology identification of the low-voltage distribution network.
Description
The technical field is as follows:
the invention relates to a low-voltage distribution network high-frequency measurement data processing and topology identification method.
Background art:
the correct distribution network topology is an important basis for ensuring the safety analysis and control decision of the distribution network. With the development of the technology, the intelligent monitoring equipment can accurately monitor the information of the high-voltage and medium-voltage power grids, but the low-voltage intelligent equipment is not completely deployed, and low-voltage users have the problems of frequent line change, difficulty in checking underground wiring, manual and private line change, large number of power distribution network lines and the like, so that the power distribution network topological structure is frequently wrong, power dispatching personnel cannot timely master correct topological relation, and the operation and management of a power distribution system are seriously influenced. The topology identification technology of the power distribution network can update the change of the switch state to form a new topology structure, and provides necessary network structure data for advanced applications in the power distribution network management system, such as state estimation, fault diagnosis, load flow calculation, reactive power optimization, power grid reconstruction and the like.
In recent years, high-proportion renewable energy sources are connected to the power grid on a large scale, the power generation output of the high-proportion renewable energy sources has strong randomness, and higher requirements are provided for uncertainty analysis and optimization of a power system. With the continuous progress of the construction of the smart power grid, an Advanced Meter Infrastructure (AMI) is configured more and more in the power distribution network, and the most intuitive change is the great increase of the measurement data granularity of the smart power meter at the user side, so that the measurement, transmission and storage of data have better accumulation, and effective data information is provided for the identification of the distribution network topological relation. On the other hand, data analysis and processing technology is rapidly developed in recent years, various industries in the world have already carried out a great deal of investment and developed fierce competition on the brand-new dimension of data acquisition, analysis and processing, and the high-frequency low-voltage distribution network measurement data provides opportunities for upgrading the efficiency of a distribution network and simultaneously brings great challenges to the performance of a distribution network data driving method.
At the present stage, how to efficiently process and analyze high-frequency measurement data of a low-voltage distribution network, and further improve the difficulty in identifying the topology of the distribution network caused by frequent updating of the distribution network, complex wiring and other factors, and a solution is urgently needed.
The invention content is as follows:
the invention aims to provide a low-voltage distribution network high-frequency measurement data processing and topology identification method for ensuring economical, safe and stable operation of a power grid.
The above purpose is realized by the following technical scheme:
a low-voltage distribution network high-frequency measurement data processing and topology identification method includes the steps that firstly, the strength of voltage correlation of coupling nodes of loads under the same distribution transformer is obtained by means of load measurement information provided by AMI user nodes, correlation analysis is conducted, namely the loads with the strength of correlation belong to the same feeder line, and then the feeder line to which each load belongs is determined; and finally, rapidly identifying or correcting the low-voltage distribution network topology through the correlation analysis result and the voltage distribution of the coupling nodes of the loads.
The method comprises three parts of high-frequency data processing, time sequence correlation analysis and topology quick identification, wherein the high-frequency data processing module has high computing capability, carries out computing analysis on the measurement data of user nodes of the level of minutes and even the level of seconds, and is internally provided with an effective sequence time window positioning algorithm and a high-frequency time sequence data time section synchronization algorithm;
the time sequence correlation analysis module is internally provided with a time sequence similarity measurement algorithm, and further performs correlation analysis on time sequences uploaded by different measurement units;
the topology fast identification module is responsible for sequencing the correlation of the analyzed measurement stages, and completes the topology identification of the target area by utilizing a weighted tree generation algorithm and a voltage amplitude relation.
The high-frequency data processing and topology identification method for the low-voltage distribution network comprises the following steps that firstly, an effective time sequence time window is selected as a data analysis window; pre-processing the data prior to use; data pre-processing differs for different tasks and for different dataset properties; calculating the correlation relation among the acquired time sequence voltage amplitude information data of the intelligent electric meters of the user nodes of the low-voltage distribution network, and ensuring that data points in the time sequences of the user nodes are in one-to-one correspondence; traverse each of the target areasAnd the user starts to intercept the data when all the user data have no continuous missing problem, otherwise, skips the time interval until the required number of the voltage data of the same time segment are intercepted, and the data is formed into U-U1,…,Ui,…,Un500 and 1000 time sections are used as input for identifying the topology of the low-voltage distribution station area;
step two: carrying out time section synchronization processing on the obtained time sequence voltage amplitude information data of the user intelligent electric meter in the low-voltage distribution transformer area; and filling the user measurement data with different sampling moments by adopting a spline interpolation method, so that the time sections of the user data are kept synchronous.
According to the method for processing the high-frequency measurement data and identifying the topology of the low-voltage distribution network, the correlation of the time series is analyzed, and the correlation of each time series is analyzed by adopting a distance measurement method or an information entropy method as the measurement data among different users are synchronous time section series;
taking the mutual approximate entropy as an example, firstly, the information entropy is defined as follows:
H(x)＝E[I(xi)]＝E[log(2,1/P(xi))]＝-∑P(x)log(2,P(xi)) (1)
the calculation formula of the mutual approximation entropy is:
the method for processing high-frequency measurement data and identifying topology of the low-voltage distribution network is characterized in that the topology is quickly identified, and an information entropy method (Chow-Liu) algorithm is utilized according to correlation information
Constructing a maximum weight spanning tree by using a Krussal algorithm; one edge is constructed at a time according to the descending order of the weight, and if all the weights are greater than 0, a connection result is obtained; similarly, if the distance measurement method is adopted, the related information is the similarity measurement among a group of time series { X, Y },
respectively calculating the cross correlation among all users by using a calculation formula of the correlation information to obtain a cross correlation matrix; sequentially calculating mutual information among users and storing the mutual information into a user cross-correlation set theta; define Θ as the set that makes up the maximum weighted spanning tree,
sorting the sets of cross-correlation Θ in descending order and eliminating the same numerical value;
traversing theta, if a group of nodes (i, j) is detected in theta, continuing, otherwise, adding the group of nodes (i, j) into theta, and stopping if the number of node pairs in theta reaches n-2;
taking the node pairs corresponding to the first n-2 cross-correlation values and putting the node pairs into a set theta, and finally generating a pairwise combined node set of the low-voltage distribution network topology;
defining each branch, corresponding Vi，VjRepresenting the time sequence voltages of branch i and branch j, and i, j is e {1,2, …, N }, N is the total number of all branches connected to a certain phase line; if the number of branches connected to phase A is 6, a 6 × 6 symmetric matrix is obtained according to the voltage correlation coefficients of the 6 branches, where
Or
And a isi,j＝aj,iSince both values are the correlation coefficients between the branch i and the branch j time sequence voltages; the obtained matrix representing the correlation coefficient among the time sequence voltages of each branch circuit is in the form of
Completing directed acyclic Bayesian network learning by using the matrix according to the strength of the correlation and the prior cross-correlation information, selecting a branch i closest to the electrical distance of the transformer as an initial point, setting the upstream-downstream relation of each node by using voltage amplitude information, and simultaneously setting the closer the amplitude in a discontinuous plane is to the root node; all 6 branches are connected into the line, and the result is the network topology logic structure of the users belonging to phase a.
The high-frequency measurement data processing and topology identification method for the low-voltage distribution network obtains network topology structures of users belonging to a phase B and a phase C in the same way, and is connected by using reference buses with large correlation coefficients among voltages of buses used as references according to obtained results to obtain correlation coefficients among time sequence voltages of buses respectively belonging to a phase A, a phase B and a phase C used as references, and integrates three groups of reconstructed topology networks of the phase A, the phase B and the phase C to form a complete topology network structure so as to finish identification of low-voltage distribution network area topology.
Has the advantages that:
1. the correct topological structure of the power distribution network is an important basis for ensuring the safety analysis and control decision of the power distribution network, but the topological structure of the power distribution network is often wrong due to the fact that intelligent equipment on a low-voltage side is not completely deployed and low-voltage users have the problems of frequent line change, artificial and private line change and the like, and operation and management of the power distribution system are further seriously influenced. According to the invention, the high-frequency data processing algorithm of the distribution network is provided by fully combining engineering practice, and on the basis, the rapid identification of the low distribution network topology is realized by utilizing time series correlation analysis and Bayesian network learning algorithm, so that powerful support is provided for ensuring the economic, safe and stable operation of the power grid.
Description of the drawings:
figure 1 is an illustration of an actual low voltage distribution network topology sample of the present invention.
FIG. 2 is a frame diagram of the high-frequency measurement data processing and topology identification of the low-voltage distribution network.
The specific implementation mode is as follows:
the technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the present invention.
Example 1:
the method is characterized in that the method firstly obtains the strength of the voltage correlation of the coupling node to which each load belongs under the same distribution transformer by means of load measurement information provided by AMI user nodes, and performs correlation analysis, namely the loads with the strength of correlation belong to the same feeder line, so as to determine the feeder line to which each load belongs; and finally, rapidly identifying or correcting the low-voltage distribution network topology through the correlation analysis result and the voltage distribution of the coupling nodes of the loads. Considering that the low-voltage distribution network is a typical tree structure, the topology is shown in fig. 1, in which the root node a, B, C, D, E is a branch node, and {1,2, …,17} is a leaf node (user node) in fig. 1.
Example 2:
the method for processing high-frequency measurement data and identifying topology of the low-voltage distribution network in embodiment 1 includes three parts, namely high-frequency data processing, time sequence correlation analysis and topology quick identification, the high-frequency data processing module has high computing capability, can perform computing analysis on minute-level and even second-level user node measurement data, and is internally provided with an effective sequence time window positioning algorithm and a high-frequency time sequence data time section synchronization algorithm.
The time series correlation analysis module is internally provided with a time series similarity measurement algorithm, and further can perform correlation analysis on the time series uploaded by different measurement units.
The topology fast identification module is responsible for sequencing the correlation of the analyzed measurement stages, and completes the topology identification of the target area by utilizing a weighted tree generation algorithm and a voltage amplitude relation.
Example 3:
the method for processing the high-frequency measurement data and identifying the topology of the low-voltage distribution network in the embodiment 2 comprises the step of processing the high-frequency data, wherein in the step I, an effective time sequence time window is selected as a data analysis window. In view ofData has problems of missing values, duplicate values, etc., and data preprocessing is required before use. Data pre-processing typically differs for different tasks and for different data set attributes. For the collected time series voltage amplitude information data of the low-voltage distribution network user node intelligent electric meter, in order to effectively calculate the correlation relationship between the time series voltage amplitude information data and the collected time series voltage amplitude information data, the data points in the time series of each user node need to be ensured to be in one-to-one correspondence. Therefore, each user in the target area needs to be traversed, when all user data have no continuous missing problem, data is intercepted, otherwise, the time interval is skipped until the required number of voltage data of the same segment is intercepted, and the formed data is U ═ U { (U {)1,…,Ui,…,Un(500) and 1000 time sections) as input for identifying the topology of the low-voltage distribution station area, wherein n represents the number of users under the ammeter, and U is the number of users under the ammeteriRepresenting the voltage vector at one user i.
Step two: and carrying out time section synchronous processing on the obtained time sequence voltage amplitude information data of the user intelligent electric meter in the low-voltage distribution transformer area. In actual engineering, although the data of different measurement nodes have the same acquisition frequency, the sampling time of the data may have offset, which affects the accuracy of subsequent correlation measurement, the sampling interval of high-frequency data is relatively short, and the voltage change of the power grid has inertia, so that the user measurement data with different sampling times can be filled by adopting a spline interpolation method, and the time sections of the user data are kept synchronous.
Example 4:
the method for processing the high-frequency measurement data and identifying the topology of the low-voltage distribution network in the embodiment 2 is characterized in that the correlation of time series is analyzed, and because the measurement data among different users are synchronous time section sequences, the correlation of each time series can be analyzed by adopting a distance measurement method or an information entropy method.
Taking the mutual approximate entropy as an example, firstly, the information entropy is defined as follows:
H(x)＝E[I(xi)]＝E[log(2,1/P(xi))]＝-∑P(x)log(2,P(xi)) (1)
wherein i is 1,2i) Probability distribution of x voltage amplitude of ith user; e (-) represents expectation; h represents information entropy; and I is mutual information.
The calculation formula of the mutual approximation entropy is:
in the formula, P (x, y) is the voltage amplitude joint probability distribution of users x and y respectively; p (X) is the probability distribution of the voltage amplitude of user X, p (Y) is the probability distribution of the voltage amplitude of user Y, X represents the voltage data set of user X, and Y represents the voltage data set of user Y.
Example 5:
Chow-Liu algorithm based on correlation information by using information entropy method
The maximum weight spanning tree is constructed using the Kruskal algorithm. One edge is constructed at a time in descending order of weight, and if all weights are greater than 0, a concatenated result is obtained. Similarly, if the distance measurement method is adopted, the related information is the similarity measurement among a group of time series { X, Y },
and respectively calculating the cross correlation among all users by using a calculation formula of the correlation information to obtain a cross correlation matrix. And sequentially calculating mutual information among the users and storing the mutual information into a user cross-correlation set theta. Define Θ as the set that makes up the maximum weighted spanning tree,
the set of cross-correlations Θ is sorted in descending order and the same values are discarded.
Traversing theta, if a group of nodes (i, j) is detected in theta, continuing, otherwise adding the group of nodes (i, j) into theta, and if the number of node pairs in theta reaches n-2 stops.
And (4) taking the node pairs corresponding to the first n-2 cross-correlation values and putting the node pairs into a set theta, and finally generating a pairwise combined node set of the low-voltage distribution network topology.
Defining each branch, corresponding Vi，VjRepresenting the timing voltages of branch i and branch j, and i, j e {1,2, …, N }, N being the total number of all branches connected to a phase line. Assuming that there are 6 branches connected to the phase A, a 6 × 6 symmetric matrix can be obtained according to the voltage correlation coefficients of the 6 branches, where
Or
(ensure agreement with the foregoing), and ai,j＝aj,iSince both values are the correlation coefficient between the branch i and branch j timing voltages. The matrix form of the obtained correlation coefficient between the time sequence voltages of the branches is as the following formula 3.
By utilizing the matrix, directed acyclic Bayesian network learning can be completed according to the strength of the correlation and the prior cross-correlation information. And then, selecting a branch i which is closest to the electrical distance of the transformer as an initial point, and setting the upstream and downstream relation of each node by using the voltage amplitude information, wherein the larger the amplitude in the discontinuous surface is, the closer the branch i is to the root node. All 6 branches are connected to the line, and the result is the network topology logic structure of the users belonging to phase a. And according to the obtained result, the reference buses with large correlation coefficients among the voltages of the buses used as the reference are connected, the correlation coefficients among the time sequence voltages of the buses respectively used as the reference and belonging to the phase A, the phase B and the phase C are obtained, the topological networks reconstructed by the three groups of the phase A, the phase B and the phase C are integrated, a complete topological network structure is formed, and the identification of the low-voltage distribution station topology is completed.
Claims (6)
1. The method is characterized in that the method firstly obtains the strength of the voltage correlation of the coupling node to which each load belongs under the same distribution transformer by means of load measurement information provided by AMI user nodes, and performs correlation analysis, namely the loads with the strength of correlation belong to the same feeder line, so as to determine the feeder line to which each load belongs; and finally, rapidly identifying or correcting the low-voltage distribution network topology through the correlation analysis result and the voltage distribution of the coupling nodes of the loads.
2. The method for processing the high-frequency measurement data and identifying the topology of the low-voltage distribution network according to claim 1, wherein the method comprises three parts of high-frequency data processing, time sequence correlation analysis and topology quick identification, the high-frequency data processing module has high computing capability, is used for computing and analyzing the measurement data of the user nodes of the level of minutes or even the level of seconds, and is internally provided with an effective sequence time window positioning algorithm and a high-frequency time sequence data time section synchronization algorithm;
the time sequence correlation analysis module is internally provided with a time sequence similarity measurement algorithm, and further performs correlation analysis on time sequences uploaded by different measurement units;
the topology fast identification module is responsible for sequencing the correlation of the analyzed measurement stages, and completes the topology identification of the target area by utilizing a weighted tree generation algorithm and a voltage amplitude relation.
3. The method for processing the high-frequency measurement data and identifying the topology of the low-voltage distribution network according to claim 2, wherein the high-frequency data processing comprises the step I of selecting an effective time sequenceAn inter window as a data analysis window; pre-processing the data prior to use; data pre-processing differs for different tasks and for different dataset properties; calculating the correlation relation among the acquired time sequence voltage amplitude information data of the intelligent electric meters of the user nodes of the low-voltage distribution network, and ensuring that data points in the time sequences of the user nodes are in one-to-one correspondence; traversing each user in the target area, intercepting data when all user data have no continuous missing problem, otherwise skipping the time interval until the required number of voltage data of the same time interval is intercepted, and forming data as U ═ U { (U {)1,…,Ui,…,Un500 and 1000 time sections are used as input for identifying the topology of the low-voltage distribution station area;
step two: carrying out time section synchronization processing on the obtained time sequence voltage amplitude information data of the user intelligent electric meter in the low-voltage distribution transformer area; and filling the user measurement data with different sampling moments by adopting a spline interpolation method, so that the time sections of the user data are kept synchronous.
4. The method for processing the high-frequency measurement data and identifying the topology of the low-voltage distribution network according to claim 2, wherein in the time series correlation analysis, as the measurement data among different users are synchronous time section sequences, the correlation of each time series is analyzed by a distance measurement method or an information entropy method;
taking the mutual approximate entropy as an example, firstly, the information entropy is defined as follows:
H(x)＝E[I(xi)]＝E[log(2,1/P(xi))]＝-∑P(x)log(2,P(xi)) (1)
the calculation formula of the mutual approximation entropy is:
5. the method according to claim 2, wherein the method comprises the steps of measuring data of a low voltage distribution network with high frequency and identifying topologyThe characteristic is that the topology is rapidly identified by utilizing an information entropy method (Chow-Liu) algorithm according to the correlation information
Constructing a maximum weight spanning tree by using a Krussal algorithm; one edge is constructed at a time according to the descending order of the weight, and if all the weights are greater than 0, a connection result is obtained; similarly, if the distance measurement method is adopted, the related information is the similarity measurement among a group of time series { X, Y },
respectively calculating the cross correlation among all users by using a calculation formula of the correlation information to obtain a cross correlation matrix; sequentially calculating mutual information among users and storing the mutual information into a user cross-correlation set theta; define Θ as the set that makes up the maximum weighted spanning tree,
sorting the sets of cross-correlation Θ in descending order and eliminating the same numerical value;
traversing theta, if a group of nodes (i, j) is detected in theta, continuing, otherwise, adding the group of nodes (i, j) into theta, and stopping if the number of node pairs in theta reaches n-2;
taking the node pairs corresponding to the first n-2 cross-correlation values and putting the node pairs into a set theta, and finally generating a pairwise combined node set of the low-voltage distribution network topology;
defining each branch, corresponding Vi，VjRepresenting the time sequence voltages of branch i and branch j, and i, j is e {1,2, …, N }, N is the total number of all branches connected to a certain phase line; if the number of branches connected to phase A is 6, a 6 × 6 symmetric matrix is obtained according to the voltage correlation coefficients of the 6 branches, where
Or
And a isi,j＝aj,iSince both values are the correlation coefficients between the branch i and the branch j time sequence voltages; the obtained matrix representing the correlation coefficient among the time sequence voltages of each branch circuit is in the form of
Completing directed acyclic Bayesian network learning by using the matrix according to the strength of the correlation and the prior cross-correlation information, selecting a branch i closest to the electrical distance of the transformer as an initial point, setting the upstream-downstream relation of each node by using voltage amplitude information, and simultaneously setting the closer the amplitude in a discontinuous plane is to the root node; all 6 branches are connected into the line, and the result is the network topology logic structure of the users belonging to phase a.
6. The method for processing the high-frequency measurement data and identifying the topology of the low-voltage distribution network according to claim 5, wherein the network topology structures of users belonging to the phases B and C are obtained in the same way, the reference buses with large correlation coefficients among the voltages of the buses used as the reference are connected according to the obtained results, the correlation coefficients among the time sequence voltages of the buses respectively belonging to the phases A, B and C are obtained as the reference, and the topological networks reconstructed by the three groups of the phases A, B and C are integrated to form a complete topological network structure so as to complete the identification of the topology of the low-voltage distribution network area.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114814420A (en) * | 2022-04-20 | 2022-07-29 | 北京飞利信信息安全技术有限公司 | Low-voltage distribution network topology identification method and system based on frozen data |
| CN116317094A (en) * | 2022-09-07 | 2023-06-23 | 东南大学溧阳研究院 | Low-voltage distribution network topology identification method based on Internet of things equipment measurement data |
| WO2023156556A1 (en) * | 2022-02-17 | 2023-08-24 | Centrica Business Solutions Belgium N.V. | Synchronization of frequency measurements in a demand response system |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023156556A1 (en) * | 2022-02-17 | 2023-08-24 | Centrica Business Solutions Belgium N.V. | Synchronization of frequency measurements in a demand response system |
| CN114814420A (en) * | 2022-04-20 | 2022-07-29 | 北京飞利信信息安全技术有限公司 | Low-voltage distribution network topology identification method and system based on frozen data |
| CN114814420B (en) * | 2022-04-20 | 2024-02-02 | 北京飞利信信息安全技术有限公司 | Low-voltage distribution network topology identification method and system based on frozen data |
| CN116317094A (en) * | 2022-09-07 | 2023-06-23 | 东南大学溧阳研究院 | Low-voltage distribution network topology identification method based on Internet of things equipment measurement data |
| CN116317094B (en) * | 2022-09-07 | 2024-03-19 | 东南大学溧阳研究院 | Low-voltage distribution network topology identification method based on Internet of things equipment measurement data |
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