CN115659553A - Low-voltage power supply network topology identification method and system - Google Patents
Low-voltage power supply network topology identification method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for identifying topology of a low-voltage power supply network, and belongs to the technical field of power supply networks. The method comprises the following steps: establishing a general structure of a low-voltage power supply network topology, and dividing electric meter nodes in the low-voltage power supply network topology into branch nodes and user nodes; acquiring a branch node connection relation according to the power data of the branch node; acquiring a user node connection position according to the power data of the branch node and the power data of the user node; and correcting the abnormal result of the connection position of the user node according to the voltage data of the branch node and the voltage data of the user node, and acquiring the topological structure of the low-voltage power supply network. The topological structure of the low-voltage power supply network can be accurately obtained, and the method is high in applicability and reliability.
Description
Technical Field
The application relates to the technical field of power supply networks, in particular to a method and a system for identifying topology of a low-voltage power supply network.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The low-voltage power supply network refers to a network from a public transformer of a 10kV/400V transformer area to a user electric meter, and is an important infrastructure for supporting national economy and social development. The topological information of the low-voltage power supply network has important significance for accurate load modeling, power supply network line loss calculation, fault point troubleshooting, power supply reliability improvement and the like. The low-voltage power supply network construction in China has the problems that planning is not uniform enough, the reconstruction and extension engineering quantity is large, topology information mainly depends on manual maintenance and the like for years, so that the actual network topology structure changes frequently and is inconsistent with the system maintenance structure, difficulties are brought to power supply management, power failure position judgment, line loss calculation and other work of a transformer area, and the reliability of power supply management of the transformer area and power supply to users is seriously influenced. Therefore, it is necessary to research a power supply network topology automatic identification method to realize automatic identification and management of the power supply network topology.
At present, a power supply network topology identification method which is widely applied is a signal injection method. According to the method, signal sensing equipment is installed at a user electric meter, voltage or current characteristic signals are injected into a power supply area or a superior node, the connection relation between the electric meters is judged by analyzing the sensing result of the sensing equipment on the characteristic signals, and topology identification is further completed. The signal injection method has clear principle, has good power supply network topology identification capability, is easy to interfere, has higher requirements on signal processing, and needs to add signal injection equipment and detection equipment, thereby increasing the cost and the engineering quantity.
Many scholars develop researches on power supply network topology identification methods based on electricity meter measurement data, and the researches mainly include similarity algorithms, linear programming algorithms, cluster analysis, artificial intelligence algorithms and the like. In the document "Smart Meter Data analysis for Distribution Network Connectivity Verification [ J ]. IEEE Transactions on Smart Grid", the topological structure is judged by calculating the Pearson correlation coefficient of the node voltage sequence, and the method is simple and easy to implement, but the problem of low reliability exists only by depending on the node voltage Data. In the document "identification Topology of Low Voltage Distribution Networks Based on Smart Meter Data [ J ]. IEEE Transactions on Smart Grid", a linear relationship between adjacent sub-nodes is established by adopting principal component analysis and electric energy conservation, and a network Topology structure is identified layer by layer. The method can not accurately identify the network topological structure under the condition that the hierarchical relationship of the nodes is unknown and the user accesses the nodes. In the document, "low-voltage distribution network topological structure verification method based on discrete Frechet distance and clipping nearest neighbor method", a k nearest neighbor clustering algorithm is used for classifying low-voltage distribution areas and judging the distribution areas to which users belong. The method is suitable for the network with simple topology, and the application effect of the network with complex topology structure needs to be verified. The method adopts an artificial intelligence algorithm to identify the topological Structure of the Power supply Network in the literatures ' intelligent Power Distribution Network online Topology identification based on LightGBM and DNN ', ' Distribution Network connection relation identification technology based on integrated deep neural Network, ' Structure Learning in Power Distribution Networks [ J ]. IEEE Transactions on controls of Network systems, ' A Data-Driven Parameter and Topology Estimation Framework in Distribution Networks ], and the method needs a large amount of Data for Learning, has complex algorithm and general applicability to new Topology.
Disclosure of Invention
In recent years, advanced Metering Infrastructure (AMI) is rapidly developed, and the smart meter is used as an AMI terminal device to undertake tasks such as acquisition and uploading of electric quantity information. The intelligent electric meter can collect and upload multiple measurement data such as electric energy, active power, voltage, current and the like, the data not only comprise the power utilization information of each node on each time section, but also comprise the topology information of a power supply network, and the power utilization information and the network topology have a specific mathematical relationship. Therefore, the connection relation between the nodes is reversely deduced according to the measurement data of the intelligent electric meter, and the topological structure of the power supply network can be obtained.
In order to solve the defects of the prior art, the application provides a method, a system, electronic equipment and a computer readable storage medium for identifying the topology of a low-voltage power supply network; the general structure of the low-voltage power supply network topology is established, meter nodes in the topology are divided into two categories, namely branch meter nodes and user meter nodes, wavelet transformation is carried out on an active power curve to extract characteristic quantity, the connection relation of the branch nodes is judged, then the connection position of the user nodes in the network is judged based on a 0-1 integer quadratic programming method, and the primary identification of the topology is completed. On the basis, the idea of checking hypothesis is adopted to check and correct the recognition result, and finally, the accurate network topology structure is obtained
In a first aspect, the present application provides a method for identifying a topology of a low voltage power supply network;
a low-voltage power supply network topology identification method comprises the following steps:
establishing a general structure of a low-voltage power supply network topology, and dividing electric meter nodes in the low-voltage power supply network topology into branch nodes and user nodes;
acquiring a branch node connection relation according to the power data of the branch node;
acquiring a user node connection position according to the power data of the branch node and the power data of the user node;
and correcting the abnormal result of the connection position of the user node according to the voltage data of the branch node and the voltage data of the user node, and acquiring the topological structure of the low-voltage power supply network.
In a second aspect, the present application provides a low voltage supply network topology identification system;
a low voltage power supply network topology identification system comprising:
the system comprises an initial topological structure acquisition module, a low-voltage power supply network topology acquisition module and a low-voltage power supply network topology management module, wherein the initial topological structure acquisition module is used for establishing a general structure of the low-voltage power supply network topology and dividing electric meter nodes in the low-voltage power supply network topology into branch nodes and user nodes;
the branch node connection relation acquisition module is used for acquiring the branch node connection relation according to the power data of the branch node;
a user node connection position obtaining module, configured to obtain a user node connection position according to the power data of the branch node and the power data of the user node;
and the low-voltage power supply network topological structure acquisition module is used for checking and correcting an abnormal result of the connection position of the user node according to the voltage data of the branch node and the voltage data of the user node, and acquiring the topological structure of the low-voltage power supply network.
In a third aspect, the present application provides an electronic device;
an electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor, perform the method described above.
In a fourth aspect, the present application provides a computer-readable storage medium;
a computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described above.
Compared with the prior art, the beneficial effects of this application are:
1. when the connection relation of the branch nodes is judged, the power mutation characteristics of the branch nodes are extracted by using the wavelet transformation idea, so that the characteristics of the branch nodes are amplified, the branch nodes have good separability, and the accuracy of similarity judgment is improved;
2. when the connection position of the user node is judged, a 0-1 integer quadratic programming method is used, the influence of high randomness of the power utilization behavior of the user is overcome, the judgment result is checked and corrected by using the idea of a test hypothesis, and the accuracy of the method is ensured;
3. different identification methods are pertinently adopted for the branch nodes and the user nodes without losing a general ground-oriented complex topological structure, so that the applicability and the reliability of the method are ensured; the automatic identification of the topological structure of the low-voltage power supply network is used as the basis and the premise of intelligent construction of the low-voltage power distribution network, is favorable for further improving the service quality of the intelligent power grid, and has important significance for lean management, loss reduction and energy conservation of a transformer area.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate exemplary embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a schematic flow chart diagram provided by an embodiment of the present application;
FIG. 2 is a flowchart of topology identification provided by an embodiment of the present application;
FIG. 3 is a schematic diagram of a wavelet decomposition provided in an embodiment of the present application;
fig. 4 is a schematic diagram illustrating a flow of determining a connection relationship between branch nodes according to an embodiment of the present application;
FIG. 5 is a schematic view of a branch section provided in an embodiment of the present application;
fig. 6 is a schematic diagram of an abnormal user according to an embodiment of the present application;
FIG. 7 is a schematic diagram of a topology provided by an embodiment of the present application;
fig. 8 is a schematic diagram of a branch node relationship determination process provided in the embodiment of the present application;
fig. 9 is a schematic diagram of a calculation result of a branch node correlation analysis according to an embodiment of the present application.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
The embodiment provides a low-voltage network topology identification method;
as shown in fig. 1, a method for identifying a low-voltage network topology includes:
establishing a general structure of a low-voltage power supply network topology, and dividing electric meter nodes in the low-voltage power supply network topology into branch nodes and user nodes;
acquiring the connection relation of the branch nodes according to the power data of the branch nodes;
acquiring a user node connection position according to the power data of the branch node and the power data of the user node;
and correcting the abnormal result of the connection position of the user node according to the voltage data of the branch node and the voltage data of the user node to obtain the topological structure of the low-voltage power supply network.
Further, obtaining the connection relationship of the branch nodes according to the power data of the branch nodes includes:
acquiring possible father nodes of each branch node according to the power data of the branch nodes;
acquiring the similarity of the possible father nodes for the power data of the branch nodes and the power data of the possible father nodes;
and acquiring the father node of each branch node according to the similarity.
Furthermore, wavelet transformation feature extraction is carried out on the branch node and a possible father node of the branch node, and feature power data are obtained;
obtaining a correlation coefficient of each possible father node according to the characteristic power data;
and acquiring the father node of the branch node according to the correlation coefficient.
Further, the formula of the wavelet transform is defined as:
wherein f (t) represents the original input;
representing a mother wavelet function; a =2 m Is a scale factor; b = n · 2 m As translation parameters; m and n are positive integers; t is the number of sampling points.
Further, obtaining the connection position of the user node according to the power data of the branch node and the power data of the user node includes:
acquiring power data of a branch section according to the power data of the branch node;
and acquiring the connection position of the user node according to the power data of the branch section and the power data of the user node.
Further, according to power conservation, obtaining the relation between the power data of the branch section and the power data of the user node;
and acquiring the connection position of the user node through 0-1 integer quadratic programming according to the relation between the power data of the branch section and the power data of the user node.
Further, the relationship between the power data of the branch section and the power data of the user node is as follows:
wherein the content of the first and second substances,
is the section power of the branch section L at the time t;
the power value of the user R at the time t is shown;
set for all users on segment L; β is the power loss over the section L and the error of the meter measurement.
Next, a method for identifying topology of a low-voltage power supply network disclosed in the present embodiment will be described in detail with reference to fig. 1 to 6.
A method for identifying topology of low-voltage power supply network includes obtaining average value of power sequence of each branch node, finding out possible father nodes of each branch node according to sequence of average value from large to small, carrying out wavelet transformation feature extraction on power sequence of each branch node and all possible father nodes, obtaining correlation coefficient between each branch node and each possible father node, obtaining father node of each branch node when correlation coefficient is maximum, and finishing judgment of connection relation of branch nodes. On the basis, the section power of each branch section is solved, the column vector reflecting the connection position of the user is solved by using the equivalent relation that the section power is equal to the sum of the section user powers under the ideal condition and adopting a 0-1 integer quadratic programming method, and then the judgment result is checked and corrected by using a hypothesis test method to finish the judgment of the connection position of the user node, so that the topological structure of the whole power supply network is obtained; comprises the following steps:
s1, establishing a general structure of a low-voltage power supply network topology, and dividing electric meter nodes in the low-voltage power supply network topology into branch nodes and user nodes.
The general structure of the low-voltage power supply network topology comprises a regular topology structure and a complex topology structure; the regular topological structure is mostly seen in urban buildings and newly-built communities, radial wiring is adopted, a 400V low-voltage bus forms a plurality of branch lines through a primary power distribution device, and each branch line distributes electric energy to different end users through a secondary power distribution device; complex topologies, which are common in rural or urban-rural junctions, include various forms, and unlike regular topologies, complex topology subscriber nodes are not only located at the end of a power line, but may also be located in a branch section between any two branch nodes. The reason for the difference in topological structure between urban and rural power supply networks is the difference in distribution of users. Urban users are relatively concentrated, power supply lines are heavily loaded, current is large, and line voltage drop is relatively obvious, so that the users are connected to the tail ends of the power supply lines in parallel, and the voltage quality of each user is guaranteed. Rural customers tend to be more dispersed, have light loads, low currents, and insignificant line drops, thus allowing the customers to connect irregularly to each branch segment.
The regular topology is a simple form of a complex topology without loss of generality, and in the embodiment, the complex topology is taken as a research object. For the sake of analysis, the present embodiment is simplified and assumed as follows:
(1) Since the branch meters and the user meters are distinguished in an actual system, the types and the number of the meters are assumed to be known;
(2) In reality, three-phase users may exist, but in theoretical analysis, the three-phase users can be regarded as three single-phase users, so that the theoretical analysis process is simplified, and all the users are assumed to be single-phase users;
(3) Suppose the user's facies are known;
(4) The power reverse transmission phenomenon existing in a power supply network is not considered temporarily, and if photovoltaic exists at the user side, night data can be adopted to shield the influence;
(5) Assuming that the smart meter normally works in the sampling period, no user with zero power exists.
S2, acquiring a branch node connection relation according to the power data of the branch node; since a user is connected to a branch section between two branch nodes in a complex topology structure, the electric energy of a certain branch node is not equal to the sum of the electric energy of the connected sub-branch nodes, and the power and the current are the same. This makes the equivalence relation between the upper and lower branch nodes unclear, and methods such as regression analysis and linear programming are not suitable for judging the connection relation of the branch nodes. The characteristics of the power consumption of the user determine that a plurality of power value abrupt change points exist in a power curve. For the branch nodes of the same level or without direct connection relation, the power mutation of each node has randomness, but for the branch nodes with direct connection relation between the upper level and the lower level, the power mutation of the two nodes has similarity. The method takes power mutation as a characteristic, analyzes whether similar mutation characteristics exist in possible father nodes of the branch node, finds out the father node corresponding to the child node and further determines the connection relation of the branch nodes. Specifically, the method comprises the following steps:
s201, extracting wavelet transformation characteristics of the branch nodes and the possible father nodes of the branch nodes to obtain characteristic power data;
specifically, the wavelet transform has good localization properties in both time domain and frequency domain, and thus is widely applied to the fields of signal decomposition and reconstruction, signal-noise separation, data feature extraction and the like. This embodiment adopts Discrete Wavelet Transform (DWT), which is defined as:
wherein f (t) represents the original input;
representing a mother wavelet function; a =2 m Is a scale factor; b = n · 2 m Is a translation parameter; m and n are positive integers; t is the number of sampling points.
The power sequence is decomposed by DWT, assuming the decomposition level is 3 layers, the decomposition process is as shown in fig. 3.
A3 is a low-frequency component subjected to three-level decomposition, reflects the overall variation trend of the power sequence and is called as an approximate component; d1, D2 and D3 are high-frequency components in the decomposition process, reflect the detail characteristics of different frequencies of the power sequence and are called as detail components. The power abrupt change belongs to a high-frequency component in the power sequence and exists in a detail component obtained by wavelet decomposition, and the embodiment extracts the detail component as an abrupt change characteristic of the power sequence.
And (3) expressing the decomposition result by a row vector, as shown in formula (2):
wherein, a 3t Are approximate wavelet coefficients; d st Are detail wavelet coefficients.
The wavelet coefficient represents the matching degree of the original data sequence and the wavelet basis function at a certain moment, and the larger the wavelet coefficient is, the more components with the same frequency as the wavelet basis function are contained in the original data sequence. By filtering the wavelet coefficients, the larger wavelet coefficients are reserved, and the smaller wavelet coefficients are filtered, so that the power curve of a non-abrupt point can be smoothed while power abrupt change is reserved, and the feature extraction of the power sequence is completed.
To D s Performing feature extraction to obtain processed detail wavelet coefficient line phasor
The feature extraction process is as follows:
wherein λ is a wavelet coefficient threshold.
Will D s The wavelet coefficient of middle or more than lambda is retained, and the wavelet coefficient of less than lambda is set to zero so as to implement characteristic extraction of detail componentAnd (6) taking.
Since the approximate component reflects the overall variation trend of the power sequence and has no power abrupt change characteristic, A is 3 All the wavelet coefficients in the wavelet are set to be zero to obtain all-zero row vectors
The wavelet coefficient row vector after feature extraction is as follows:
the detail wavelet component after the feature extraction is finished
And the approximate wavelet component after the all-zero processing
Performing wavelet reconstruction to finally obtain a characteristic power sequence after characteristic extraction
S202, acquiring a correlation coefficient of each possible father node according to the characteristic power data; specifically, before judging the power sequence similarity of the branch nodes, feature extraction is carried out on the sub-branch nodes and all possible parent nodes.
Since the power sequences of different user nodes may annihilate each other in a certain time period, the power fluctuation characteristics of the upper branch nodes are not obvious, and therefore, the power data of the nodes to be analyzed can be initially selected. Dividing the power sampling value P of the node into a plurality of time periods, respectively calculating the power value variance of each time period, and if the variance value of a certain time period is smaller, indicating that the power change characteristic is not obvious, the method is not suitable for similarity analysis. Therefore, only the power sequence with strong fluctuation can be selected to participate in the calculation according to the variance result.
Finding out the time when the power mutation occurs in the sub-branch node and the corresponding frequency spectrum components, only reserving the frequency spectrum components at the corresponding time for all possible father nodes, neglecting all the frequency spectrum components at the rest times, and the extraction process is shown as the formula (5):
wherein the content of the first and second substances,
the characteristic wavelet coefficient row vector of the branch node to be identified;
characteristic wavelet coefficient row vectors of possible father nodes of branch nodes to be identified; f m Is F n Possible parent node, let F n There are M possible parent nodes, then M =1,2, …, M.
Will be provided with
All the approximate wavelet coefficients in the sequence are set to zero, wavelet reconstruction is respectively carried out, and finally a characteristic power sequence is obtained
And
the Pearson correlation coefficient rho is used as a measurement index of the similarity of the branch node characteristic power sequences, and the expression is as follows:
wherein cov (X, Y) is the covariance of X and Y; v (X) is the variance of X; the value of rho is between-1 and 1, the larger the value is, the higher the positive correlation between X and Y is, and the more likely the two nodes are parent-child nodes.
And S3, acquiring a father node of each branch node according to the correlation coefficient. Specifically, for any branch node, the power value at each time is not greater than the power value of the parent node at the corresponding time, that is:
wherein, F 1 Is F 2 A parent node of (2);
are respectively a branch node F 1 And F 2 The power value at the sampling instant t.
Then:
namely:
n branch nodes are arranged, and the power sequence mean values of all the branch nodes are sequenced from large to small to obtain an array:
wherein the content of the first and second substances,
is the average of the branch nodes Fn.
For branch node F 1 The mean of its power sequence is maximum, determined as the root node, F 2 As root node F 1 As shown in fig. 4 (a).
For branch node F 3 ，F 1 And F 2 It is possible to be its parent node as shown in fig. 4 (b). To at the judgment of F 3 And F 1 、F 2 Has better discrimination on the similarity of F 1 Is updated, minus the child node F 2 Of power, i.e.
With F 3 As object, pair F 1 、F 2 、F 3 Power sequence of
Carrying out feature extraction to obtain a feature power sequence
Respectively obtain
And
pearson's correlation coefficient
If it is
Then F 3 Is F 1 A child node of (1); otherwise, F 3 Is F 2 The child node of (1).
Similarly, for branch node F n ，F 1 …F n-1 It is possible to be its parent node as shown in fig. 4 (c). To F 1 …F n-1 Is updated to obtain a new power sequence
With F n As an object, pair
And updated F 1 …F n-1 Power sequence of
Carrying out feature extraction to obtain a feature power sequence
And
respectively obtain
And
pearson's correlation coefficient
Selecting the maximum value
Then F n Is F k The child node of (1). And so on until the judgment of the connection relation of all the branch nodes is completed, as shown in fig. 4 (d).
S3, acquiring a user node connection position according to the power data of the branch node and the power data of the user node; the method comprises the following steps:
s301, acquiring power data of a branch section according to the power data of the branch node; the line between two branch nodes and the line connected with the tail end branch node are collectively called as a branch section, and after the judgment of the connection relation of the branch nodes is completed, the power of all the branch sections can be obtained according to the power data of each branch node. For a branch segment with branch nodes at the beginning and end, the segment power is equal to the power of the branch node at the head end minus the power of all branch nodes at the end. As shown in FIG. 5, the segment power of the L1 segment at time t is
Segment power of L2 segment
S302, obtaining the connection position of the user node according to the power data of the branch section and the power data of the user node. Specifically, since the user phase is known, when determining the user node connection position, the A, B, C is separately determined for three phases. The default branch sector power is the power of a certain phase, and the sector users are the users of the phase. According to the power conservation, the relationship between the branch section power and the user power is shown in equation (11):
wherein the content of the first and second substances,
is the section power of the branch section L at the time t;
the power value of the user R at the time t is shown;
set for all users on segment L; β is the power loss over the section L and the error of the meter measurement.
Introducing a variable x of 0-1 to represent the connection relationship between the user node and the branch section, wherein if the user R is connected to the section L, x is R,L =1, whereas x R,L And =0. Equation (11) can be converted to:
wherein the content of the first and second substances,
is the set of all users of a certain phase under a cell.
Defining a matrix:
and N is the total number of the user nodes in the distribution area, and T is the sampling point number of the electric meter.
The matrix expression of formula (12) is as follows:
and by trying different values of X, finding a group of values of X which enables beta to be minimum, and judging the connection position of the user node can be completed. The following optimization model was constructed for solving X:
equation (15) is a 0-1 integer quadratic programming problem that is solved to obtain the optimal solution for X to find the connected users on segment L. And solving respectively for each branch section, so that the branch section positioning of all users can be completed.
The power characteristics of different users are considered to be possible to have special conditions with high similarity in a certain time period, and then the topology identification result is influenced. When the above calculation is performed, the power sequence used in each calculation is not less than 100 data points or the corresponding time is not less than 24 hours, and the high similarity of the power characteristics of different users is avoided as much as possible by using longer time sequence data, changing the length of the data sequence calculated in each calculation and the like. In addition, multiple groups of data can be used for carrying out multiple calculations, the judgment result of the topological structure is continuously corrected, and finally the accurate topological structure can be obtained.
And S4, correcting the abnormal result of the connection position of the user node according to the voltage data of the branch node and the voltage data of the user node, and acquiring the topological structure of the low-voltage power supply network.
Due to the influence of factors such as line loss, asynchronous data acquisition of the electric meters and the like, the power of a line section is not strictly equal to the sum of the power of users in the section, and the power of the line section and the power of users in the section have a certain difference value which is generally smaller than the power of most users. However, under a cell, individual users may have low power levels during the sampling period, or power characteristics of different users are highly similar, which may lead to misjudgment of the connection location of the users. Therefore, the results of 0-1 integer quadratic programming need to be checked and corrected.
Because the topology identification method is carried out based on the power data of the electric meter, the voltage data is adopted when the abnormal result is corrected, so that the value of the measurement data of the electric meter is comprehensively exerted, and the reliability of the topology identification method is improved. The voltage of a single-supply radial power supply network has the following characteristics:
(1) Under the premise of not considering the photovoltaic access of the user side, the node voltage is gradually decreased from the head end to the tail end under the same time section.
(2) The similarity of the user voltages under the same branch is higher than that under different branches, and the closer the electrical distance between two nodes is, the closer the voltages are.
According to the characteristics, the voltage of the user node under the same time section is close to and slightly lower than the voltage of the adjacent upstream branch node. In fact, due to the measurement error of the electric meter, the user voltage at the individual sampling moment is higher than that of the adjacent upstream branch node, but the characteristic that the voltage sequence in a period of time is lower than that of the adjacent upstream branch node is not influenced.
In order to compare the voltage level of the user with the voltage level of the adjacent upstream branch node within a period of time, the voltage sequences of the user and the adjacent upstream branch node within a period of time are used as samples, and paired sample t test is carried out to judge whether the user and the adjacent upstream branch node have significant difference. The method comprises the following specific steps:
s401, defining variable Z i ＝X i -Y i I =1,2, …, n. Wherein, X i A sequence of user node voltages; y is i A sequence of voltages for the subscriber's adjacent upstream branch nodes; n is the number of sample points, i.e. the number of samples. Z i Subject to a normal distribution of the signals,
for variable Z i Mean value of (a) z And checking to judge whether the number is less than or equal to zero, and if so, determining that the user is positioned below the current branch section.
S402, proposing an original hypothesis H0: mu.s z Less than or equal to 0; let us assume H1 z >0。
S403, constructing test statistic
In the formula:
S Z respectively is a sample Z 1 ,Z 2 ,…,Z n Mean and variance of.
S404, looking up a t distribution table to obtain t α The value of (n-1). Alpha is a significance level, and is generally 0.05. If t>t α (n-1), rejecting the original hypothesis, and considering that the user is not located in the current branch section; otherwise, the original assumption is accepted, and the user is considered to be located in the current branch section.
As shown in fig. 6, if the user R is determined to be connected to both the branch section Lm and the branch section Ln through the examination, the paired sample t test is performed on the user R and the upstream branch nodes Fm and Fn according to the steps (1) to (4), and the branch node satisfying the original assumption is the real upstream branch node of the user. Further, if the paired sample t tests of the user R, the Fm and the Fn all meet the original assumption, that is, the user voltage level is not higher than the branch nodes Fm and Fn, respectively calculating Euclidean distances between the user R and the voltage sequences of the branch nodes Fm and Fn, and determining the branch section where the user R is located, wherein the branch node with the minimum Euclidean distance to the user R is the real superior branch node of the branch node.
And correcting the connection positions of all abnormal users to finish the accurate judgment of the connection positions of the user nodes. And (3) positioning A, B, C three-phase users respectively according to the method, so that the identification of the whole low-voltage power supply network topology can be completed.
In order to verify the effectiveness of the method for identifying the topology of the low-voltage power supply network, in this embodiment, the power supply network shown in fig. 7 is taken as a research object, and both the network structure and the data information of the smart meters are from an actual 400V power supply system in a place in cigarette benches city of Shandong province, and include 12 branch meters and 114 user meters. The minimum sampling time interval of the electric meter is 15min, sampling is carried out 96 times every day, the sampling time is from 13 days in 9 months in 2020 to 20 days in 9 months in 2020, and 672 sampling points are counted. And 482 effective sampling points are finally obtained after the bad data are eliminated.
Firstly, judging the connection relation of the branch nodes, wherein the judging process is as follows:
(1) Taking the three-phase total power value of all effective sampling points of the intelligent electric meter as the power sequence of each branch node
(2) For 12 branch nodes F 1 …F 12 Respectively obtaining the average values of the power sequences, and sorting the power sequences from large to small according to the average values, wherein the obtained sorting result is as follows:
and judging the connection relation of the branch nodes in turn according to the sequence.
(3) First, F can be determined 4 Is F 1 As shown in FIG. 3 (a), and then sequentially judges F 2 To F 8 The power characteristic sequence correlation coefficient of the child node and the possible father node in each step is shown in attached table 1, the node with the maximum correlation coefficient with the child node is the father node of the child node, and the judgment process is shown in 8.
Fig. 9 shows in the form of a histogram the power signature sequence correlation coefficient between each branch node and the true parent node, the power signature sequence correlation coefficient between its next possible parent node, and the difference between the two in the topology identification process.
As shown in fig. 9, the characteristic power sequence between the child node and its true parent node shows strong correlation, the correlation coefficient is mostly about 0.8, the minimum correlation coefficient is also above 0.6, and the correlation coefficients between the child node and the true parent node and its next possible parent node have a certain difference, and the minimum difference is also above 0.2. The method has good reliability because the child nodes have obvious distinction degree with the true father nodes and the non-father nodes.
And secondly, judging the connection position of the user node, wherein the judgment is carried out according to the phase difference when the user connection position is judged because the user phase is known. Taking 38B-phase users as an example, the determination steps are as follows:
(1) Calculating power sequences of all branch sections of the B phase according to the connection relation of the identified branch nodes
And (4) showing.
(2) The 12 branch segments are calculated according to the 0-1 integer quadratic programming method described in section 3.1, respectively, to obtain 12 column vectors reflecting the connection positions of the user nodes, as shown in table 1.
TABLE 1 user node connection location
Table 1 Location of user nodes
Note: the value of "0" is omitted as blank in the table
(3) In the user connection position determination results shown in table 1, it was found that the connection position determination of the user B37 was abnormal, and it was determined that the user B was connected to both the zone L6 and the zone L12. Comparing the voltage sequences of the user B37 and the branch nodes F6 and F12 at the head ends of the branch sections L6 and L12, the voltage of B37 is generally higher than the voltage of F6 and similar to the voltage of F12. Further, assuming that the voltage level of B37 is lower than F6, a paired sample t test is performed on the B37, the significance level α =0.05 is taken, and t is obtained by looking up a t distribution table 0.05 (96) =1.661; through the calculation, the method has the advantages that,
so the original assumption is rejected, and the real situation is that the voltage level of B37 is higher than F6; assuming that the voltage level of B37 is lower than F12, a paired sample t-test is performed thereon,
so the assumption is true, in the real case that the voltage level of B37 is lower than F12. Since the voltage of the radial network decreases gradually from the head end to the tail end, the subscriber B37 is connected to the tail end of the branch node F12 at the branch section L12.
So far, the connection position of the B-phase user is judged, and the obtained topological structure is shown in fig. 3.
And (3) similarly, judging the connection positions of the users of the A phase and the C phase according to the steps, and after the step (2) is completed, checking the judgment results of the A phase and the C phase, wherein no abnormity occurs. The A, B, C three-phase user judgment results are integrated together to obtain the final power supply network topology structure which is the same as that shown in the attached figure 7.
To further verify the reliability of the method presented herein, 8 other different topologies were also analyzed, with the results shown in table 2.
TABLE 2 topology identification results
Table 1 Result of topology identification
The result shows that the accuracy of the method is more than 99%, and the connection relation of the branch nodes with 8 topologies can be correctly identified. 7. 8 the subscriber nodes of both topologies recognize that there are two errors each because there is an inactive subscriber in both topologies and the power remains small for the sample period. For such a situation, the sampling time can be prolonged, and other sampling time periods can be selected.
The identification effect of the topological structure shown in fig. 8 is compared with the current common regression analysis method, and the result is shown in table 3.
TABLE 3 comparison of topology identification results
Table 2 Comparison of topology identification results
Although the regression analysis method has higher accuracy in identifying the regular topological structure, the method in the embodiment has higher accuracy in identifying the complex topology structure. In addition, the regression analysis method needs to know the connection relation of the branch nodes when being applied, and the method of the embodiment can develop extensive identification under the condition that the topological structure is completely unknown. Therefore, the method provided by the embodiment has higher reliability and applicability.
Example two
The embodiment provides a low-voltage power supply network topology identification system;
a low voltage power supply network topology identification system comprising:
the system comprises an initial topological structure acquisition module, a low-voltage power supply network topology acquisition module and a low-voltage power supply network topology management module, wherein the initial topological structure acquisition module is used for establishing a general structure of the low-voltage power supply network topology and dividing electric meter nodes in the low-voltage power supply network topology into branch nodes and user nodes;
the branch node connection relation acquisition module is used for acquiring a branch node connection relation according to the power data of the branch node;
a user node connection position obtaining module, configured to obtain a user node connection position according to the power data of the branch node and the power data of the user node;
and the low-voltage power supply network topological structure acquisition module is used for checking and correcting an abnormal result of the connection position of the user node according to the voltage data of the branch node and the voltage data of the user node and acquiring the topological structure of the low-voltage power supply network.
It should be noted here that the initial topology obtaining module, the branch node connection relation obtaining module, the user node connection position obtaining module, and the low-voltage power supply network topology obtaining module correspond to the steps in the first embodiment, and the modules are the same as the corresponding steps in the implementation example and application scenarios, but are not limited to the disclosure in the first embodiment. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
EXAMPLE III
The embodiment also provides an electronic device, which includes a memory, a processor and computer instructions stored in the memory and executed on the processor, where the computer instructions, when executed by the processor, implement the method of the first embodiment.
Example four
The present embodiments also provide a computer-readable storage medium for storing computer instructions, which when executed by a processor, perform the method of the first embodiment.
In the foregoing embodiments, the descriptions of the embodiments have respective emphasis, and parts that are not described in detail in a certain embodiment may refer to related descriptions of other embodiments.
The proposed system can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the above-described modules is merely a logical functional division, and in actual implementation, there may be other divisions, for example, multiple modules may be combined or integrated into another system, or some features may be omitted, or not executed.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A low-voltage power supply network topology identification method is characterized by comprising the following steps:
establishing a general structure of a low-voltage power supply network topology, and dividing electric meter nodes in the low-voltage power supply network topology into branch nodes and user nodes;
acquiring a branch node connection relation according to the power data of the branch node;
acquiring a user node connection position according to the power data of the branch node and the power data of the user node;
and correcting the abnormal result of the connection position of the user node according to the voltage data of the branch node and the voltage data of the user node, and acquiring the topological structure of the low-voltage power supply network.
2. A method of identifying topology of a low voltage power supply network as claimed in claim 1, wherein said deriving branch node connections from power data of said branch nodes comprises:
acquiring possible father nodes of each branch node according to the power data of the branch nodes;
acquiring the similarity of the possible father nodes for the power data of the branch nodes and the power data of the possible father nodes;
and acquiring the father node of each branch node according to the similarity.
3. A method of identifying the topology of a low voltage supply network as claimed in claim 2, characterised by performing wavelet transform feature extraction on a branch node and a possible parent node of the branch node to obtain feature power data;
obtaining a correlation coefficient of each possible father node according to the characteristic power data;
and acquiring the father node of the branch node according to the correlation coefficient.
4. A method of identifying the topology of a low voltage power supply network as claimed in claim 3, characterized in that the formula of the wavelet transform is defined as:
wherein f (t) represents the original input;
representing a mother wavelet function; a =2 m Is a scale factor; b = n · 2 m As translation parameters; m and n are positive integers; t is the number of sampling points.
5. A method of identifying the topology of a low voltage power supply network as claimed in claim 1, wherein said deriving the connection location of a customer node from the power data of the branch node and the power data of the customer node comprises:
acquiring power data of a branch section according to the power data of the branch node;
and acquiring the connection position of the user node according to the power data of the branch section and the power data of the user node.
6. A method of identifying the topology of a low voltage supply network as claimed in claim 5, characterised by obtaining the relationship between the power data of the branch sections and the power data of the user nodes based on conservation of power;
and acquiring the connection position of the user node through 0-1 integer quadratic programming according to the relation between the power data of the branch section and the power data of the user node.
7. A method of identifying the topology of a low voltage power supply network as claimed in claim 6, wherein the relationship between the power data of the branch section and the power data of the customer nodes is:
wherein, P t L The section power of the branch section L at the time t; p t R Power value for user R at time t；
Set for all users on segment L; β is the power loss over the section L and the error of the meter measurement.
8. A low-voltage power supply network topology identification system is characterized by comprising:
the system comprises an initial topological structure acquisition module, a low-voltage power supply network topology acquisition module and a low-voltage topological structure acquisition module, wherein the initial topological structure acquisition module is used for establishing a general structure of the low-voltage power supply network topology and dividing electric meter nodes in the low-voltage power supply network topology into branch nodes and user nodes;
the branch node connection relation acquisition module is used for acquiring the branch node connection relation according to the power data of the branch node;
a user node connection position obtaining module, configured to obtain a user node connection position according to the power data of the branch node and the power data of the user node;
and the low-voltage power supply network topological structure acquisition module is used for checking and correcting an abnormal result of the connection position of the user node according to the voltage data of the branch node and the voltage data of the user node to acquire the low-voltage power supply network topological structure.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executable on the processor, the computer instructions when executed by the processor performing the method of any of claims 1-7.
10. A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
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