CN-115659553-B - Low-voltage power supply network topology identification method and system

CN115659553BCN 115659553 BCN115659553 BCN 115659553BCN-115659553-B

Abstract

The invention discloses a low-voltage power supply network topology identification method and system, and belongs to the technical field of power supply networks. The method comprises the steps of establishing a general structure of a low-voltage power supply network topology, dividing ammeter nodes in the low-voltage power supply network topology into branch nodes and user nodes, acquiring a branch node connection relation according to power data of the branch nodes, acquiring a user node connection position according to the power data of the branch nodes and the power data of the user nodes, and correcting an abnormal result of the user node connection position according to the voltage data of the branch nodes and the voltage data of the user nodes to acquire the low-voltage power supply network topology. The low-voltage power supply network topology structure can be accurately obtained, and the method is high in applicability and reliability.

Inventors

ZHAO XUTONG
KONG MING
LI HAO
LI LIN
LI CHANGJIN
CUI HAI
HU XUANZHENG

Assignees

国网山东省电力公司济南供电公司
国家电网有限公司

Dates

Publication Date: 20260505
Application Date: 20220728

Claims (8)

1. A method of topology identification of a low voltage power supply network, comprising: The method comprises the steps of establishing a general structure of a low-voltage power supply network topology, dividing ammeter nodes in the low-voltage power supply network topology into branch nodes and user nodes, acquiring a branch node connection relation according to power data of the branch nodes, acquiring possible father nodes of each branch node according to the power data of the branch nodes, carrying out wavelet transformation feature extraction on the branch nodes and the possible father nodes of the branch nodes, decomposing the power data through wavelet transformation to obtain wavelet coefficients containing detail components and approximate components, carrying out threshold filtering on the decomposed wavelet coefficients, namely reserving large wavelet coefficients in the detail components, filtering small wavelet coefficients, setting the coefficients of the approximate components to zero, and obtaining a power sequence only containing abrupt features through reconstruction to obtain characteristic power data; Acquiring a user node connection position according to the power data of the branch node and the power data of the user node, wherein the acquiring the user node connection position comprises acquiring the power data of a branch section according to the power data of the branch node and the power data of the user node, acquiring the user node connection position according to the power data of the branch section and the power data of the user node, acquiring the relation between the power data of the branch section and the power data of the user node according to power conservation, and acquiring the user node connection position 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; Correcting abnormal results of the connection positions of the user nodes through paired sample t test according to the voltage data of the branch nodes and the voltage data of the user nodes to obtain a topological structure of the low-voltage power supply network, wherein the topological structure comprises defined variables , wherein, A voltage sequence for a user node; The voltage sequence of the adjacent upstream branch node of the user is given, n is the sampling point, Obeying normal distribution, for variables Mean of (2) Checking to determine whether the user is less than or equal to zero, if yes, the user is considered to be positioned below the current branch section, and providing the original assumption H0: Alternative hypothesis H1: Constructing test statistics Wherein: 、 Respectively are samples Mean and variance of (1), looking up t distribution table Is used as a reference to the value of (a), At the level of significance, if And if not, accepting the original assumption, and considering that the user is positioned in the current branch section.
2. A method of identifying a topology of a low voltage power supply network as recited in claim 1, wherein said obtaining branch node connection relationships based on said branch node power data further comprises: Obtaining the similarity of the possible parent nodes for the branch node power data and the power data of the possible parent nodes; and acquiring the father node of each branch node according to the similarity.
3. A method of identifying a topology of a low voltage power supply network as recited in claim 2, According to the characteristic power data, obtaining the correlation coefficient of each possible father node; and acquiring a father node of the branch node according to the correlation coefficient.
4. A method of identifying a topology of a low voltage power supply network as claimed in claim 3, wherein the formula for the wavelet transform is defined as: Wherein, the Representing the original input; Representing a mother wavelet function; is a scale factor; Is a translation parameter, m and n are positive integers, and T is the number of sampling points.
5. A method of identifying a topology of a low voltage power supply network as claimed in claim 1, wherein the relationship between the power data of the branch section and the power data of the user node is: Wherein, the Segment power at time t for branch segment L; at t for user R a power value at the moment; Aggregate for all users on segment L; For power loss on section L and meter measurement error.
6. A low voltage network topology identification system employing a low voltage network topology identification method as claimed in any one of claims 1 to 5, comprising: The system comprises an initial topology acquisition module, a control module and a control module, wherein the initial topology acquisition module is used for establishing a general structure of a low-voltage power supply network topology and dividing ammeter nodes in the low-voltage power supply network topology into branch nodes and user nodes; The branch node connection relation acquisition module acquires a branch node connection relation according to the power data of the branch node; The user node connection position acquisition module is used for acquiring the 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 topology 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 topology structure.
7. An electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the method of any one of claims 1-5.
8. A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of any of claims 1-5.

Description

Low-voltage power supply network topology identification method and system Technical Field The application relates to the technical field of power supply networks, in particular to a low-voltage power supply network topology identification method and system. Background The statements in this section merely relate to the background of the present disclosure and may not necessarily constitute prior art. The low-voltage power supply network refers to a network from a10 kV/400V transformer district public transformer to a user ammeter, and is an important infrastructure for supporting national economy and social development. The topology information of the low-voltage power supply network has important significance for accurate load modeling, power supply network line loss calculation, fault point investigation, power supply reliability improvement and the like. The problems of insufficient unification of planning, large reconstruction and expansion engineering quantity, main dependence on manual maintenance of topology information and the like exist in the low-voltage power supply network construction of China for many years, so that the actual network topology structure is changed frequently and is inconsistent with the system maintenance structure, difficulties are brought to work such as power supply management of a transformer area, power failure position judgment and line loss calculation, and the reliability of power supply management of the transformer area and power supply to users are seriously affected. Therefore, a method for automatically identifying the power supply network topology needs to be researched to realize the automatic identification and management of the power supply network topology. The current widely applied power supply network topology identification method is a signal injection method. According to the method, signal sensing equipment is installed at a user ammeter, voltage or current characteristic signals are injected into a power supply station area or an upper node, and the connection relation between the ammeter is judged by analyzing the sensing result of the sensing equipment on the characteristic signals, so that topology identification is completed. The signal injection method has clear principle, 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 causing the increase of cost and engineering quantity. Many students develop a power supply network topology identification method based on ammeter measurement data, and the method mainly comprises a similarity algorithm, a linear programming algorithm, a cluster analysis algorithm, an artificial intelligence algorithm and the like. In literature "Smart Meter Data Analytics 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 has the problem of low reliability only by relying on the node voltage data. In literature "Identifying Topology of Low Voltage Distribution Networks Based on Smart Meter Data[J]. IEEE Transactions on Smart Grid", a linear relation between adjacent hierarchical 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 topology structure under the condition that the node hierarchy relation is unknown and the user access exists between the nodes. And classifying the low-voltage area by using a k nearest neighbor clustering algorithm in literature 'low-voltage power distribution network topological structure verification method based on discrete Fre chet distance and clipping nearest neighbor method', and judging the area to which the user belongs. The method is suitable for the network with simple topology, and the application effect of the network with complex topological structure is to be verified. The documents LightGBM and DNN-based intelligent power distribution network online topology identification and the integrated deep neural network-based power distribution network contact relation identification technology "、"Structure Learning in Power Distribution Networks[J]. IEEE Transactions on Control of Network Systems."、"A Data-Driven Parameter and Topology Joint Estimation Framework in Distribution Grids" identify the power supply network topology structure by adopting an artificial intelligent algorithm, and the method needs a large amount of data to learn, is complex in algorithm and has general adaptability to new topology. Disclosure of Invention In recent years, advanced measurement systems (ADVANCED METERING Infrastructure, AMI) are rapidly developed, and smart meters serve as terminal devices