A power distribution network fault section location method and system

Abstract

The application provides a power distribution network fault section positioning method and system, and relates to the technical field of power distribution network automation. The method comprises the following steps: acquiring power distribution network topology structure data, FTU operation data and fault state data collected by each FTU when a fault occurs; constructing a line section correlation model and a fault section initial judgment matrix according to the power distribution network topology structure data; determining a plurality of suspected fault sections according to the fault section initial judgment matrix, and generating theoretical fault state data corresponding to each suspected fault section based on the suspected fault sections and the line section correlation model, and further determining fault determination probabilities corresponding to each suspected fault section; determining a target fault section according to the fault determination probabilities corresponding to each suspected fault section, and outputting a positioning result corresponding to the target fault section. The technical problem of low fault section positioning accuracy of the prior art under the condition of FTU information distortion is solved.

Description

Technical Field

[0001] This application relates to the field of power distribution network automation technology, and in particular to a method and system for locating fault sections in a power distribution network. Background Technology

[0002] In the operation and maintenance of power distribution networks, fault location is a crucial step in ensuring power supply reliability. Existing technologies typically rely on fault current direction information reported by feeder terminal units (FTUs) and utilize matrix algorithms or graph theory models to identify fault sections. However, in practical engineering applications, due to communication interference, equipment aging, or abnormal data acquisition, the information reported by FTUs often suffers from distortion, such as missed or false alarms. This leads to traditional matrix algorithms based on deterministic logic generating multiple suspected fault sections or even missing real fault sections. Furthermore, existing probabilistic location methods struggle to accurately distinguish between real faults and false disturbances in environments with distorted information. Therefore, a method is urgently needed to address the technical problem of low accuracy in fault location under FTU information distortion conditions. Summary of the Invention

[0003] This application provides a method and system for locating fault sections in a distribution network, which solves the technical problem of low accuracy in locating fault sections under the condition of FTU information distortion in the prior art.

[0004] To achieve the above objectives, this application adopts the following technical solution: Firstly, a method for locating fault sections in a distribution network is provided, comprising: acquiring distribution network topology data, FTU operation data, and fault status data collected by each FTU at the time of a fault occurrence. The distribution network topology data includes the connection relationships between switching nodes in the distribution lines and corresponding line section information; the FTU operation data includes historical fault status reporting results of each FTU and corresponding correct reporting probability data; and the fault status data includes fault direction status information output by each FTU for the current fault event. A line section association model is constructed based on the distribution network topology data, and an initial fault section judgment matrix is ​​generated based on the line section association model and the fault status data. Multiple suspected fault sections are identified based on the initial fault section judgment matrix, and theoretical fault status data corresponding to each suspected fault section is generated based on the suspected fault sections and the line section association model. The fault determination probability corresponding to each suspected fault section is determined based on the theoretical fault status data, the fault status data, and the correct reporting probability data in the FTU operation data. The target fault section is determined based on the fault determination probability corresponding to each suspected fault section, and the location result corresponding to the target fault section is output.

[0005] In conjunction with the first aspect mentioned above, in one possible implementation, a line segment association model is constructed based on the distribution network topology data, and an initial fault segment judgment matrix is ​​generated based on the line segment association model and fault status data. This includes: determining the switching nodes in the distribution network and the line segments formed between adjacent switching nodes based on the distribution network topology data, and constructing a network description matrix based on the connection relationship between the switching nodes and the line segments; determining the detection direction corresponding to each FTU based on the network description matrix, and arranging the FTU fault direction status information in the fault status data according to the corresponding detection direction to generate an FTU reporting information matrix; performing matrix operations based on the FTU reporting information matrix and the network description matrix to obtain a fault segment discrimination matrix; and determining the initial fault segment judgment matrix based on the line segments corresponding to the matrix elements in the fault segment discrimination matrix that meet the preset judgment conditions.

[0006] In conjunction with the first aspect mentioned above, in one possible implementation, the fault direction status information is determined as follows: acquiring the zero-sequence current transient direction information and the zero-sequence voltage transient direction information collected by each FTU; determining the fault direction status information based on the directional relationship between the zero-sequence current transient direction information and the zero-sequence voltage transient direction information; wherein, when the directional relationship between the two satisfies the forward fault determination condition, the fault direction status information is determined and recorded as 1; when the reverse fault determination condition is satisfied, it is recorded as -1; otherwise, it is recorded as 0.

[0007] In conjunction with the first aspect mentioned above, in one possible implementation, multiple suspected fault sections are determined based on the initial judgment matrix of the fault section, and theoretical fault state data corresponding to each suspected fault section is generated based on the suspected fault sections and the line section association model. This includes: determining the line sections with positive judgment results based on the discrimination results corresponding to each line section in the initial judgment matrix of the fault section, and taking the line sections with positive judgment results as suspected fault sections; for each suspected fault section, determining the theoretical fault direction state of each FTU corresponding to the suspected fault section when a fault occurs based on the line section association model, and generating theoretical fault state data corresponding to the suspected fault section based on the theoretical fault direction state of each FTU.

[0008] In conjunction with the first aspect mentioned above, in one possible implementation, the fault determination probability corresponding to each suspected fault segment is determined based on the theoretical fault state data, fault state data, and correct reporting probability data in the FTU operating data. This includes: for each suspected fault segment, comparing the theoretical fault state data and fault state data corresponding to the suspected fault segment with the FTU state data one by one to determine the state matching result for each FTU, where the state matching result includes consistency and inconsistency; based on the state matching result, obtaining the corresponding state reporting probability from the correct reporting probability data in the FTU operating data, wherein when the state matching result is consistent, the correct reporting probability corresponding to the FTU is used as the state reporting probability; when the state matching result is inconsistent, obtaining the incorrect reporting probability corresponding to the FTU based on the correct reporting probability corresponding to the FTU, and using the incorrect reporting probability as the state reporting probability; and generating the fault determination probability corresponding to the suspected fault segment based on the state reporting probability corresponding to each FTU.

[0009] In conjunction with the first aspect mentioned above, in one possible implementation, the fault determination probability... Satisfy the following formula:

[0010] in, Indicates the first There are several suspected fault sections, and the fault determination probability corresponding to the current reported status of the i-th FTU; p represents the number of the suspected fault section. Indicates the FTU number; This represents the fault direction status data that the i-th FTU should theoretically output when the p-th suspected faulty section experiences a fault. This represents the i-th FTU fault status data actually received under the current fault condition; This represents the probability that the i-th FTU correctly reports the positive direction fault status in the event of a positive direction fault. This represents the probability that the i-th FTU correctly reports a non-positive direction state when no positive direction fault occurs.

[0011] In conjunction with the first aspect mentioned above, in one possible implementation, before determining the target fault segment based on the fault determination probability corresponding to each suspected fault segment, the method further includes correcting the fault determination probability: Based on the fault determination probability corresponding to each suspected fault segment, the nodes are arranged according to the suspected fault segment number and the FTU number to obtain a node-reported state probability matrix; based on the node-reported state probability matrix and the theoretical fault state data corresponding to the suspected fault segments, the probability weights corresponding to the FTUs theoretically outputting positive-direction fault states and theoretically outputting non-positive-direction fault states are determined respectively, and a judgment influence matrix is ​​generated based on the probability weights; matrix operations are performed based on the judgment influence matrix and the fault state matrix corresponding to the actually acquired FTU fault state data to obtain a fault judgment matrix; information distortion correction coefficients are determined based on the fault judgment matrix and the information distortion during the FTU fault state reporting process, and the fault judgment matrix is ​​corrected based on the information distortion correction coefficients to obtain the final fault determination probability corresponding to each suspected fault segment; the final fault determination probability is used as the fault determination probability.

[0012] In conjunction with the first aspect mentioned above, in one possible implementation, the influence matrix is ​​determined to satisfy the following formula:

[0013] in, To determine the first in the influence matrix The element in row and column q is used to represent the element in the decision influence matrix. The impact weight of the status reported by the qth FTU corresponding to each suspected faulty section; The node reports the state probability matrix in the first position. The element in row and column q represents the element in the qth row. Under the assumption that a suspected faulty section has failed, the probability that the current reported status of the q-th FTU is as follows: Indicates the first When a suspected faulty section fails, the theoretical fault status data corresponding to the qth FTU; when When, it indicates that the q-th FTU theoretically outputs a positive direction fault state; when When, it indicates that the q-th FTU theoretically outputs a non-positive direction fault state; c and d represent the indices used to traverse all FTU numbers.

[0014] In conjunction with the first aspect mentioned above, in one possible implementation, the target fault section is determined based on the fault determination probability corresponding to each suspected fault section, and the location result corresponding to the target fault section is output. This includes: sorting the probabilities of each suspected fault section according to the fault determination probability corresponding to each suspected fault section to obtain a fault probability sorting result; determining the suspected fault section corresponding to the highest fault determination probability based on the fault probability sorting result, and identifying the suspected fault section as the target fault section; and generating a fault section location result based on the line section information corresponding to the target fault section.

[0015] Secondly, a distribution network fault section location system is provided, comprising: a data acquisition module, used to acquire distribution network topology data, FTU operation data, and fault status data collected by each FTU when a fault occurs, wherein the distribution network topology data includes the connection relationships between each switching node in the distribution line and the corresponding line section information, the FTU operation data includes the historical fault status reporting results of each FTU and the corresponding correct reporting probability data, and the fault status data includes the fault direction status information output by each FTU for the current fault event; and an initial judgment module, used to construct a line section association model based on the distribution network topology data, and based on the line section association model and the fault status... The system generates an initial fault segment judgment matrix; a theoretical state generation module identifies multiple suspected fault segments based on the initial fault segment judgment matrix and generates theoretical fault state data corresponding to each suspected fault segment based on the suspected fault segments and the line segment association model; a probability determination module determines and corrects the fault determination probability corresponding to each suspected fault segment based on the theoretical fault state data, fault state data, and correct reporting probability data in the FTU operation data, thus obtaining the final fault determination probability; and a fault location module determines the target fault segment based on the fault determination probability corresponding to each suspected fault segment and outputs the location result corresponding to the target fault segment.

[0016] This application provides a method and system for locating fault sections in a distribution network. By constructing an operational data model based on FTU reporting probability and deeply integrating it with real-time fault status data, it overcomes the shortcomings of traditional matrix algorithms that rely solely on topological logic and cannot cope with information distortion. By comparing theoretical fault status data with actual fault status data and combining a probability model to quantify the probability of faults in each suspected section, it automatically identifies and suppresses false fault characteristics caused by FTU false alarms and missed alarms. Furthermore, by introducing a judgment influence matrix and information distortion correction coefficients, it performs differentiated weighting processing on FTU reporting information with different confidence levels, enabling the location algorithm to adaptively perceive the degree of information distortion and maintain high location reliability even in the case of single-point or multi-point information anomalies, thereby improving the accuracy of distribution network fault location.

[0017] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0018] Figure 1 A system architecture diagram of a power distribution network fault location system provided in this application embodiment; Figure 2 A flowchart illustrating a method for locating fault sections in a distribution network, as provided in this application embodiment. Figure 1 ; Figure 3 A flowchart illustrating a method for locating fault sections in a distribution network, as provided in this application embodiment. Figure 2 ; Figure 4 A topology example diagram of a simple power distribution network provided in the embodiments of this application; Figure 5 A flowchart illustrating a method for locating fault sections in a distribution network, as provided in this application embodiment. Figure 3 ; Figure 6 A flowchart illustrating a method for locating fault sections in a distribution network, as provided in this application embodiment. Figure 4 ; Figure 7 A flowchart illustrating a method for locating fault sections in a distribution network, as provided in this application embodiment. Figure 5 ; Figure 8 A schematic diagram of the IEEE 13-node system model provided for an embodiment of this application. Detailed Implementation

[0019] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0020] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0021] Example 1: The distribution network fault location method provided in this application embodiment can be applied to, for example, Figure 1 In the distribution network fault location system shown, such as Figure 1 As shown, the system includes: a data acquisition module 101, used to acquire distribution network topology data, FTU operation data, and fault status data collected by each FTU when a fault occurs. The distribution network topology data includes the connection relationships between each switching node in the distribution line and the corresponding line segment information; the FTU operation data includes the historical fault status reporting results of each FTU and the corresponding correct reporting probability data; and the fault status data includes the fault direction status information output by each FTU for the current fault event. An initial judgment module 102 is used to construct a line segment association model based on the distribution network topology data, and generate an initial fault segment judgment based on the line segment association model and the fault status data. The system comprises: an initial judgment matrix; a theoretical state generation module 103, used to determine multiple suspected fault sections based on the initial judgment matrix of the fault section, and to generate theoretical fault state data corresponding to each suspected fault section based on the suspected fault sections and the line section association model; a probability judgment module 104, used to determine and correct the fault judgment probability corresponding to each suspected fault section based on the theoretical fault state data, fault state data and correct reporting probability data in the FTU operation data, to obtain the final fault judgment probability; and a fault location module 105, used to determine the target fault section based on the fault judgment probability corresponding to each suspected fault section, and to output the location result corresponding to the target fault section.

[0022] Example 2: To address the technical problem of low accuracy in fault location under FTU information distortion conditions in existing technologies, this application provides a method for fault location in distribution networks. Figure 2 A flowchart illustrating the distribution network fault section location method provided in this application embodiment. Figure 1 ,like Figure 2 As shown, the method includes: S201. Obtain data on the distribution network topology, FTU operation data, and fault status data collected by each FTU when a fault occurs.

[0023] The distribution network topology data refers to data describing the connection relationships and segment divisions of distribution network lines. This includes the numbering information of each switch node in the distribution network, the connection relationships between switch nodes, the segment information formed by adjacent switch nodes, and the associated node information for each segment. FTU operation data refers to the fault state detection results and corresponding reliability statistics of each fault terminal unit during its historical operation. The reliability statistics include the probability information of each FTU correctly outputting the corresponding state under different fault states, used to characterize the reliability of fault information reported by different FTUs. Fault state data refers to the fault direction state information generated by each FTU based on the changes in collected electrical quantities after the current fault event occurs, used to represent the fault direction relationship detected by the current FTU.

[0024] In one possible implementation, the system acquires distribution network model data stored in the distribution network operation and management system. Based on the distribution network model data, it extracts the positional relationships of each switch node and the line connection relationships between nodes, and divides the network into multiple line segments according to the transmission paths between adjacent switch nodes. Simultaneously, it acquires the status reporting records of each FTU device during historical operation, performs statistical analysis on these records, and determines the probability that each FTU correctly outputs the fault direction status under fault conditions and the probability that it correctly outputs the non-fault direction status under non-fault conditions, forming FTU operation data. Further, when a fault event is detected in the current distribution network, it collects the fault direction status information output by each FTU for that fault event, and associates and stores the distribution network topology data, FTU operation data, and current fault status data to form the input data for fault segment location.

[0025] S202. Construct a line segment association model based on the distribution network topology data, and generate an initial judgment matrix for fault segments based on the line segment association model and fault status data.

[0026] In one possible implementation, the system determines the set of switch nodes in the distribution lines and the set of line segments composed of adjacent switch nodes based on the acquired distribution network topology data. It then establishes a line segment association model based on the connection direction relationship between each line segment and each switch node. During the construction process, a matrix is ​​used to describe the association relationship between line segments and FTU nodes, where the elements in the matrix indicate whether an association exists between the line segment and the corresponding FTU node and the direction of that association. Further, the current fault status data is arranged according to the detection direction corresponding to each FTU, forming an FTU fault status matrix. This matrix is ​​then input into the line segment association model, and the fault judgment result corresponding to each line segment is obtained through association relationship matching, generating an initial judgment matrix for the fault segment.

[0027] It should be noted that the initial fault segment judgment matrix in this step is used to quickly narrow down the fault range, rather than directly determining the final fault location. Because false alarms, missed alarms, or communication anomalies may occur during actual operation, the initial judgment result may simultaneously include multiple candidate line segments, requiring further probability calculations to determine the actual fault segment.

[0028] Based on the above steps, this step uses the distribution network topology to constrain the current FTU fault status information, converts the discrete FTU detection results into line section-level fault judgment results, realizes the mapping from node detection information to line section fault range, and improves the spatial correlation capability of fault analysis.

[0029] S203. Based on the initial judgment matrix of the fault section, determine multiple suspected fault sections, and generate theoretical fault status data corresponding to each suspected fault section based on the suspected fault sections and the line section association model.

[0030] Among them, suspected fault sections refer to line sections that meet the fault judgment conditions but whose actual fault location cannot yet be determined, as selected based on the initial fault section judgment matrix. Theoretical fault state data refers to the fault direction state information that each FTU should theoretically output, derived from the correlation relationship of line sections, under the assumption that a suspected fault section actually experiences a fault and all FTUs are operating normally.

[0031] In one possible implementation, the system reads the segment discrimination results from the initial fault segment judgment matrix, extracts the segment segments that meet the preset fault judgment conditions, and forms a set of suspected fault segments. For each segment in the suspected fault segment set, a fault hypothesis model is established, assuming that a fault has occurred in the current segment. Based on the segment association model, the directional relationship between the fault location and each FTU detection node is determined, and the theoretical fault state value that each FTU should output is derived based on the directional relationship. The theoretical fault state values ​​corresponding to all FTUs are arranged according to the FTU number to obtain the theoretical fault state data corresponding to the suspected fault segment.

[0032] It should be noted that this step does not directly use the current FTU's actual transmitted state to determine the fault location. Instead, it establishes a corresponding theoretical state model for each candidate fault segment and provides a basis for subsequent fault determination based on FTU reliability probability by comparing the differences between the theoretical state and the actual state.

[0033] As an example, when the initial judgment matrix determines that line segments A, B, and C may all be faulty, it is assumed that line segment A is faulty. Based on the line topology, the theoretical output state of each FTU is derived as [1,1,0,-1]. Then, corresponding theoretical state sequences are generated for line segments B and C respectively, forming theoretical fault state data for the three suspected faulty segments.

[0034] Based on the above steps, this step establishes corresponding theoretical fault states for different suspected fault sections, so that each candidate section has a reference basis for comparison with the current actual detection results, thereby avoiding misjudgment caused by relying solely on the results reported by a single FTU and improving the positioning accuracy in complex fault environments.

[0035] S204. Based on the theoretical fault status data, fault status data, and correct reporting probability data in the FTU operation data corresponding to each suspected fault section, determine the fault judgment probability corresponding to each suspected fault section.

[0036] In one possible implementation, for each suspected faulty section, the theoretical fault state data corresponding to the suspected faulty section is matched with the actually acquired fault state data to determine the consistency between the theoretical fault state and the actual fault state for each FTU. Specifically, when the actual fault state of an FTU is consistent with the theoretical fault state, the current reported state of that FTU is determined to be the correct reported state corresponding to the theoretical fault state; when the actual fault state is inconsistent with the theoretical fault state, the current reported state of that FTU is determined to be the abnormal reported state corresponding to the theoretical fault state.

[0037] Furthermore, based on the theoretical fault state type corresponding to each FTU, the correct reporting probability data corresponding to the current theoretical fault state is obtained from the FTU operating data. Based on the consistency between the theoretical fault state and the actual fault state, the state reliability data of each FTU for the current suspected fault section is determined. Specifically, for an FTU whose theoretical fault state is a fault-direction state, its state reliability is determined based on the corresponding correct reporting probability for the fault-direction state; for an FTU whose theoretical fault state is a non-fault-direction state, its state reliability is determined based on the corresponding correct reporting probability for the non-fault-direction state. In cases where the theoretical fault state and the actual fault state are inconsistent, the probability of the FTU making an abnormal report is determined based on the complementary probability of the corresponding correct reporting probability.

[0038] Based on this, the status reliability data of each FTU for the current suspected faulty section are collected to determine the initial fault judgment result for the current suspected faulty section, so as to characterize the probability of the suspected faulty section failing under the current reported status of the FTU and the probability of each FTU reporting correctly.

[0039] Furthermore, based on the number of state differences between the actual fault state data and the theoretical fault state data, the degree of information distortion corresponding to the current suspected fault segment is determined, and the initial fault judgment result is corrected based on the degree of information distortion to obtain the corrected fault judgment result corresponding to the current suspected fault segment.

[0040] S205. Determine the target fault section based on the fault determination probability corresponding to each suspected fault section, and output the location result corresponding to the target fault section.

[0041] In one possible implementation, the suspected fault sections are ranked according to their fault determination probabilities to obtain a fault probability ranking result; the suspected fault section with the highest fault determination probability is determined based on the fault probability ranking result, and the suspected fault section is identified as the target fault section; the fault section location result is generated based on the line section information corresponding to the target fault section.

[0042] Furthermore, for each suspected faulty segment, the theoretical fault state data corresponding to that segment is compared with the currently acquired fault state data for each FTU. When the actual output state of an FTU matches the theoretical output state, the current state of that FTU is considered to conform to the fault hypothesis of the suspected faulty segment, and the correct reporting probability corresponding to that FTU is used as the state confidence probability. When the actual output state of an FTU does not match the theoretical output state, it is considered that the FTU may have false alarms or missed alarms, and the false reporting probability is determined based on the correct reporting probability corresponding to that FTU. Further, the state probabilities corresponding to each FTU are fused to obtain the fault determination probability of the corresponding suspected faulty segment. Subsequently, the suspected faulty segments are sorted according to their corresponding fault determination probabilities, and the suspected faulty segment with the highest probability is determined as the target faulty segment, generating the corresponding location result.

[0043] This application constructs a fault segment location process for scenarios with uncertain information by integrating distribution network topology information, historical FTU operational reliability information, and current fault status information. Compared to methods that rely solely on real-time FTU reporting results for fault judgment, this application first uses a line segment association model to perform topology constraint analysis on FTU fault direction information to generate initial fault segment judgment results. Furthermore, it establishes corresponding theoretical fault state models for multiple suspected fault segments. By comparing the differences between the theoretical state and the actual reported state, and combining the correct reporting probabilities of different FTUs, it performs a probabilistic evaluation of each suspected fault segment, thereby reducing location errors caused by FTU false alarms, missed alarms, or communication anomalies.

[0044] Example 3: In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 3 As shown, the above S202 can be specifically implemented through the following S301 to S304, which are explained in detail below: S301. Based on the distribution network topology data, determine the switching nodes in the distribution network and the line segments formed between adjacent switching nodes, and construct a network description matrix based on the connection relationship between the switching nodes and the line segments.

[0045] The network description matrix is ​​a matrix model used to describe the topological relationship between line segments and switching nodes in a distribution network. Each row in the network description matrix corresponds to a line segment, and each column corresponds to a switching node. The matrix elements are used to represent the positional relationship of the corresponding line segment relative to the switching node, including the line segment being directly connected to the switching node and located upstream of the switching node, the line segment being directly connected to the switching node and located downstream of the switching node, and the two not being directly connected.

[0046] In one possible implementation, the system acquires the set of switch nodes from the distribution network topology data, determines adjacent switch nodes based on the connection relationships between them, and divides the transmission lines between adjacent switch nodes into corresponding line segments. Further, based on the switch nodes connected to both ends of each line segment and the power supply direction of the distribution network, the directional relationship of each line segment relative to each switch node is determined, and an initial matrix is ​​established using the line segment number as the matrix row index and the switch node number as the matrix column index. For each line segment, all switch nodes are traversed. When a line segment is directly connected to a switch node and that switch node is located upstream of the line segment, the corresponding matrix element is set to a first preset value; when a line segment is directly connected to a switch node and that switch node is located downstream of the line segment, the corresponding matrix element is set to a second preset value; when a line segment does not have a direct connection with a switch node, the corresponding matrix element is set to a third preset value, ultimately forming a network description matrix.

[0047] As an example, suppose the number of network nodes is n and the number of segments is m. For example, consider the network description matrix. middle position of element The value is determined according to the following formula:

[0048] Furthermore, Figure 4 A topology example diagram of a simple power distribution network provided in the embodiments of this application, such as... Figure 4 As shown, S1~S9 represent switching nodes with FTUs, (1)~(8) represent the sections between nodes, and S is the main power supply. According to the rules, its network description matrix D is as follows:

[0049] It should be noted that the values ​​of the elements in the network description matrix are not limited to a fixed form and can be configured according to the rules for determining the fault direction in the distribution network, as long as they can represent the spatial connection relationship between the line segment and the switching node where the FTU is located. By introducing the directional relationship between the line segment and the switching node, it is possible to deduce the line segment that may be faulty based on the FTU detection direction.

[0050] S302. Determine the detection direction corresponding to each FTU based on the network description matrix, and arrange the FTU fault direction status information in the fault status data according to the corresponding detection direction to generate the FTU reporting information matrix.

[0051] The fault direction status information is determined as follows: the transient direction information of zero-sequence current and the transient direction information of zero-sequence voltage collected by each FTU are acquired; the fault direction status information is determined according to the directional relationship between the transient direction information of zero-sequence current and the transient direction information of zero-sequence voltage; when the directional relationship between the two meets the forward fault judgment condition, the fault direction status information is recorded as 1; when the reverse fault judgment condition is met, it is recorded as -1; otherwise, it is recorded as 0.

[0052] In one possible implementation, the system determines the detection direction corresponding to each FTU's switch node based on the directional relationship between each line segment and switch node in the network description matrix, i.e., determining the correspondence between the FTU's detection direction and the power supply direction of the distribution line. Further, it acquires the fault direction status information collected by each FTU after the current fault event occurs. This fault direction status information includes a first status value indicating a detected fault in the forward direction, a second status value indicating a detected fault in the reverse direction, and a third status value indicating that no corresponding fault direction was detected. Based on the FTU's numbering order, the fault direction status information output by all FTUs is arranged to form an FTU reporting information matrix.

[0053] It should be noted that since the installation orientation of FTUs in different locations may vary, directly using the original reported status for judgment may result in the same fault manifesting differently in different FTUs. Therefore, this step determines the detection direction of each FTU by combining the network description matrix, and uniformly arranges the fault direction status so that the information output by different FTUs can be compared under the same directional reference system.

[0054] As an example, when the network's FTU is set to a positive direction, it has three operating states when detecting faults: it reports "1" when the direction of the detected zero-sequence current and zero-sequence voltage transient change is opposite; it reports "-1" when the direction of the detected zero-sequence current and zero-sequence voltage transient change is the same; and it reports "0" when no zero-sequence current is detected. An n×1 dimensional reporting information matrix is ​​generated based on the FTU's reported information.

[0055] S303. Perform matrix operations based on the FTU-reported information matrix and the network description matrix to obtain the fault segment discrimination matrix.

[0056] In one possible implementation, the FTU-reported information matrix is ​​used as the fault state input, and the network description matrix is ​​used as the topology constraint parameter. Matrix operations are then performed on both. Specifically, based on the directional relationship between each line segment and each FTU node in the network description matrix, the reported states of the corresponding FTUs are matched directionally. Multiple FTU state results corresponding to the same line segment are then aggregated to obtain the fault identification result for each line segment. All identification results corresponding to all line segments are arranged according to the line segment number to form a fault segment identification matrix.

[0057] It should be noted that matrix operations are not simply a count of FTU faults, but rather a means of determining whether a particular line segment meets the fault characteristics by using the directional constraints in the network description matrix to map the fault status of FTUs at different locations to the corresponding line segments.

[0058] As an example, the fault segment discrimination matrix Satisfy the following formula:

[0059] in, This is the network description matrix. For reporting information matrix.

[0060] Based on the above steps, this step converts the FTU node-level detection results into line section-level discrimination results, realizing the mapping from fault detection information to fault location range, and can reduce the impact of irrelevant FTU states on fault judgment by utilizing distribution network topology constraints.

[0061] S304. Determine the initial judgment matrix of the fault section based on the line sections corresponding to the matrix elements in the fault section judgment matrix that meet the preset judgment conditions.

[0062] The preset judgment criteria are rules used to filter potentially faulty line segments from the fault segment discrimination matrix. For example, they filter matrix elements whose discrimination results are greater than a preset threshold or that meet positive fault discrimination criteria. The initial fault segment judgment matrix stores candidate faulty line segment information after topological constraints, which is used to generate a set of suspected faulty segments later.

[0063] In one possible implementation, the system reads the discrimination results corresponding to each line segment in the fault segment discrimination matrix and compares each discrimination result with preset discrimination conditions. When a matrix element corresponding to a certain line segment meets the fault discrimination requirements, the line segment corresponding to that matrix element is marked as a candidate fault segment; when it does not meet the fault discrimination requirements, the line segment is excluded. Further, all line segments that meet the conditions are arranged in numerical order to generate an initial fault segment discrimination matrix.

[0064] It should be noted that the initial fault segment judgment matrix obtained in this step is only used to determine the fault candidate range and is not directly used as the final fault location result. In the actual operation of the distribution network, due to the possibility of missed reports, false reports, or communication anomalies by the FTU, multiple line segments may simultaneously meet the initial judgment conditions, requiring further analysis based on the FTU reliability probability to determine the actual fault segment.

[0065] It should also be noted that if the number of FTUs with distorted information is small, the fault information matrix actually received by the main station may be the same as the fault information matrix correctly reported by FTUs in a certain segment. In this case, the actual faulty segment may be missed. Therefore, the reported information of each non-suspected faulty segment is checked. The checking method is to assume that a non-suspected faulty segment is the actual faulty segment, compare the reported information matrix of the segment where all FTUs are correctly reported with the actual reported information matrix, and count the number of FTUs with data discrepancies, which is set as the number of data discrepancies.

[0066] Based on the above steps, this step filters the fault segment identification results, narrowing down a large number of line segments in the complex distribution network to a limited candidate range, reducing the calculation objects of subsequent probability analysis, while retaining the locations where real faults may exist, thus improving the processing efficiency of the fault location process.

[0067] This application implements a mapping from FTU node-level fault detection information to line segment-level fault identification results. By constructing a network description matrix, the actual connection structure of the distribution network can be fully utilized to constrain fault direction information, reducing the impact of unrelated FTU states on fault identification. Simultaneously, by filtering the fault segment identification results, multiple possible fault segments are obtained, providing a candidate range for further analysis based on the reliability probability reported by FTUs. Compared to methods that rely solely on the reported state of a single FTU for fault location, this approach is adaptable to complex distribution network topologies and uncertainties in fault information, improving the accuracy and reliability of fault candidate segment determination.

[0068] Example 4: In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 5 As shown, the above S203 can be implemented through the following S501 and S502, which are explained in detail below: S501. Based on the discrimination results corresponding to each line segment in the initial judgment matrix of the fault section, determine the line segments with positive discrimination results and regard the line segments with positive discrimination results as suspected fault sections.

[0069] In one possible implementation, the system obtains the matrix elements corresponding to each line segment in the initial fault segment judgment matrix and reads the judgment results corresponding to each line segment sequentially according to the line segment number. The judgment result for each line segment is compared with preset fault judgment rules. If the judgment result for a line segment is greater than zero, it indicates that the line segment can be interpreted as a possible fault location based on the current FTU fault status information, and the line segment is added to the suspected fault segment set. If the judgment result for a line segment is less than or equal to zero, it is considered that the line segment does not match the current FTU fault status information and is not considered a candidate fault location. After traversing all line segments, all line segments that meet the conditions are arranged according to their line numbers to form the suspected fault segment set.

[0070] It should be noted that suspected fault sections do not represent line sections that have been confirmed to have faults, but rather candidate areas initially selected based on the current FTU reporting status and the distribution network topology. In actual operation, due to the possibility of FTUs missing or falsely reporting fault status or communication anomalies, the same fault event may result in positive values ​​for multiple line sections. Therefore, it is necessary to further distinguish multiple suspected fault sections by combining the historical reporting reliability probability of the FTUs.

[0071] Based on the above steps, this step filters the initial judgment results to extract the line sections that may be faulty from the entire power distribution network, reducing the scope of subsequent fault analysis. At the same time, it retains multiple potential fault locations caused by FTU information anomalies, so that subsequent probabilistic judgments can cover actual fault situations.

[0072] S502. For each suspected fault section, determine the theoretical fault direction status of each FTU when the suspected fault section occurs based on the line section association model, and generate the theoretical fault status data corresponding to the suspected fault section based on the theoretical fault direction status of each FTU.

[0073] The theoretical fault direction state refers to the fault direction state that each FTU should theoretically output, derived from the distribution network topology, under the assumption that a fault actually occurs in a suspected fault section and all FTUs can detect it normally. The theoretical fault state data is a set of data formed by arranging the theoretical fault direction states of all FTUs corresponding to the same suspected fault section according to the FTU number, and is used to represent the theoretically complete FTU state response when a fault occurs in the suspected fault section.

[0074] In one possible implementation, for each line segment in the set of suspected fault segments, a corresponding fault hypothesis is established, that is, the current suspected fault segment is assumed to be the actual fault location. For any suspected fault segment, the connection relationship between the line segment and the switching nodes where each FTU is located is obtained according to the line segment association model, and the fault propagation direction of the fault location relative to each FTU is determined by combining the line power supply direction. Further, based on the directional relationship between the fault location and the FTU, the theoretical fault direction state value that each FTU should output under the fault hypothesis is determined. For example, when an FTU is located upstream of the fault segment and meets the positive direction fault detection condition, the corresponding theoretical state is set to the positive direction state; when an FTU is located downstream of the fault segment and the detection direction is opposite, the corresponding theoretical state is set to the reverse direction state; when an FTU has no effective fault propagation association with the fault segment, the corresponding theoretical state is set to the non-fault state. Finally, all the theoretical fault direction states corresponding to all FTUs are arranged according to the FTU number to generate the theoretical fault state data corresponding to the suspected fault segment.

[0075] As an example, when S501 determines that line sections L2 and L4 are suspected fault sections, it is assumed that L2 and L4 have failed, respectively. For the L2 fault assumption, the theoretical states of FTU1, FTU2, and FTU3 are determined to be [1, -1, 0] based on the line topology; for the L4 fault assumption, the theoretical states of FTU1, FTU2, and FTU3 are determined to be [0, 1, -1], thus forming the theoretical fault state data for L2 and L4, respectively.

[0076] Based on the above steps, this step constructs a corresponding theoretical FTU state response for each suspected faulty section, so that each candidate fault location has an independent state reference model. This enables quantitative comparison of the actual FTU reporting results with different fault assumptions, thereby improving the fault location differentiation capability in the subsequent fault probability determination process.

[0077] Example 5: In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 6 As shown, the above S204 can be specifically implemented through the following S601 to S603, which are explained in detail below: S601. For each suspected fault section, compare the theoretical fault state data and the fault state data corresponding to the suspected fault section with the state of each FTU to determine the state matching result corresponding to each FTU.

[0078] In one possible implementation, for each suspected fault segment, the theoretical fault state data corresponding to that segment is obtained. Then, according to the FTU number, the theoretical fault direction states in the theoretical fault state data are matched one by one with the actual fault states output by the corresponding FTU under the current fault event. For the i-th FTU, it is determined whether its theoretical fault direction state and actual fault state are the same; if they are the same, a state consistency flag is generated; if they are different, a state inconsistency flag is generated. After traversing all FTUs, the state matching results for all FTUs corresponding to the suspected fault segment are obtained.

[0079] It should be noted that the state matching result is used to reflect the degree to which the current actual FTU reported state supports a certain fault hypothesis, rather than being used directly to determine the fault location. Since abnormal FTU reporting may occur during actual operation, even if there are inconsistencies in the states, the corresponding suspected faulty section cannot be directly ruled out; further judgment needs to be made based on the historical reporting reliability of the FTU itself.

[0080] Based on the above steps, this step compares the theoretical state under different fault assumptions with the actual detection state, so that each suspected fault section can form a matching relationship with the current fault information, providing a state basis for subsequent fault probability calculation in combination with FTU reliability probability.

[0081] S602. Based on the state matching result, obtain the corresponding state reporting probability from the correct reporting probability data in the FTU operation data.

[0082] Specifically, when the state matching result is consistent, the correct reporting probability corresponding to the FTU is used as the state reporting probability; when the state matching result is inconsistent, the incorrect reporting probability corresponding to the FTU is obtained based on the correct reporting probability corresponding to the FTU, and the incorrect reporting probability is used as the state reporting probability.

[0083] In one possible implementation, based on the state matching results of each FTU, the correct reporting probability data for that FTU is obtained. For FTUs with consistent state matching results, the correct reporting probability corresponding to that FTU is used as the current state reporting probability. For FTUs with inconsistent state matching results, the corresponding incorrect reporting probability is determined based on the correct reporting probability of that FTU. Specifically, this can be obtained through the complementary relationship between the incorrect and correct probabilities, and the incorrect reporting probability is used as the current state reporting probability. After traversing all FTUs, the state reporting probabilities of each FTU corresponding to the current suspected faulty section are obtained.

[0084] It should be noted that the status reporting probability for different FTUs is not the same, but is determined based on the historical operating data of each FTU. This makes the FTUs with higher reliability more credible in the fault determination process and avoids the fault location result being affected by the reporting result of a single abnormal FTU.

[0085] Based on the above steps, this step dynamically selects the correct reporting probability or the incorrect reporting probability according to the FTU status matching, so that the current state of each FTU is converted into a probability parameter with reliability constraints, thereby improving the adaptability to FTU abnormal information during the fault judgment process.

[0086] S603. Generate the fault determination probability corresponding to the suspected fault section based on the status reporting probability of each FTU.

[0087] In one possible implementation, for each suspected faulty section, the reported probabilities of multiple FTU states corresponding to that section are obtained and fused according to the independent joint probability relationship of the states corresponding to each FTU. Specifically, the reported probabilities of all FTU states corresponding to the same suspected faulty section are jointly calculated to obtain the initial fault determination probability corresponding to that suspected faulty section. Furthermore, the initial fault determination probabilities corresponding to all suspected faulty sections are saved for subsequent correction based on the determination influence matrix and information distortion.

[0088] It should be noted that the probability fusion in this step is not a simple summation or averaging of the probabilities of each FTU state, but rather a comprehensive judgment result obtained when a suspected faulty section needs to meet the observation results of multiple FTUs at the same time by comprehensively considering the possibility of multiple FTUs simultaneously exhibiting the current state.

[0089] Preferably, the fault determination probability Satisfy the following formula:

[0090] in, Indicates the first There are several suspected fault sections, and the fault determination probability corresponding to the current reported status of the i-th FTU; p represents the number of the suspected fault section. Indicates the FTU number; This represents the fault direction status data that the i-th FTU should theoretically output when the p-th suspected faulty section experiences a fault. This represents the i-th FTU fault status data actually received under the current fault condition; This represents the probability that the i-th FTU correctly reports the positive direction fault status in the event of a positive direction fault. This represents the probability that the i-th FTU correctly reports a non-positive direction state when no positive direction fault occurs.

[0091] Based on the above steps, this step integrates the occurrence probabilities of multiple FTU states, converting the dispersed FTU detection information into probability evaluation results for specific line sections. This enables the fault location process to comprehensively utilize information from multiple detection nodes, improving the ability to distinguish between multiple suspected fault sections.

[0092] Example 6: In one possible implementation, combining Figure 2 ,like Figure 7 After S603 and before S205, the distribution network fault section location method provided in this application embodiment further includes, before determining the target fault section based on the fault determination probability corresponding to each suspected fault section, a correction of the fault determination probability, specifically implemented by the following S701 to S704: S701. Based on the fault determination probability corresponding to each suspected fault section, arrange them according to the suspected fault section number and FTU number to obtain the node reporting status probability matrix.

[0093] In one possible implementation, the system obtains the fault determination probability corresponding to each suspected fault segment, and establishes a matrix row index based on the suspected fault segment number and a matrix column index based on the FTU number. Further, the occurrence probability of each FTU state corresponding to each suspected fault segment is filled into the corresponding position in the matrix according to the corresponding FTU number, so that elements in the same row represent the state probabilities of different FTUs under the same suspected fault segment, and different rows represent the probability distribution corresponding to different suspected fault segments, ultimately generating a node-reported state probability matrix.

[0094] It should be noted that the node-reported state probability matrix does not only store the final failure segment probability, but also retains the probability correlation between different suspected failure segments and different FTUs, so that subsequent influence weight analysis can be performed based on the importance of different FTUs under different failure assumptions.

[0095] S702. Based on the node-reported state probability matrix and the theoretical fault state data corresponding to the suspected fault section, determine the probability weights of the FTUs corresponding to the theoretical output positive direction fault state and the probability weights of the FTUs corresponding to the theoretical output non-positive direction fault state, and generate a judgment influence matrix based on the probability weights.

[0096] In one possible implementation, for each suspected faulty section, the theoretical fault state data corresponding to that section is obtained and analyzed in conjunction with the FTU probability data of the corresponding row in the node-reported state probability matrix. First, based on the theoretical fault state data, the theoretical output state type of each FTU when a fault occurs in the suspected faulty section is determined. When the theoretical state of an FTU is a positive-direction fault state, the state probability corresponding to that FTU is extracted as the positive-direction state probability weight; when the theoretical state of an FTU is a negative-direction fault state, the corresponding state probability is extracted as the negative-direction state probability weight. Further, based on the theoretical state category of each FTU and its corresponding probability weight, all suspected faulty sections and FTU nodes are arranged to generate a judgment influence matrix.

[0097] It should be noted that the impact matrix is ​​not determined solely by the number of FTUs, but is dynamically generated based on the theoretical response states of the FTUs under specific fault section assumptions. When the same FTU fails in different suspected fault sections, its corresponding impact weight may also differ due to the different theoretical fault direction states.

[0098] Preferably, the influence matrix satisfies the following formula:

[0099] in, To determine the first in the influence matrix The element in row and column q is used to represent the element in the decision influence matrix. The impact weight of the status reported by the qth FTU corresponding to each suspected faulty section; The node reports the state probability matrix in the first position. The element in row and column q represents the element in the qth row. Under the assumption that a suspected faulty section has failed, the probability that the current reported status of the q-th FTU is as follows: Indicates the first When a suspected faulty section experiences a fault, the theoretical fault status data corresponding to the q-th FTU; when When, it indicates that the q-th FTU theoretically outputs a positive direction fault state; when When, it indicates that the q-th FTU theoretically outputs a non-positive direction fault state; c and d represent the indices used to traverse all FTU numbers.

[0100] Based on the above steps, this step combines the fault location assumption with the FTU state probability distribution relationship, enabling different FTU information to be processed differently according to their actual contribution to the current fault judgment, thereby improving the effectiveness of information utilization in the fault judgment process.

[0101] S703. Perform matrix operations based on the judgment influence matrix and the fault state matrix corresponding to the actual acquired FTU fault state data to obtain the fault judgment matrix.

[0102] In one possible implementation, the system obtains the fault direction status information of each FTU based on the current fault event and arranges them according to the FTU number to form a fault status matrix. Further, the fault status matrix is ​​input into the calculation process corresponding to the judgment influence matrix, so that each element in the judgment influence matrix is ​​weighted and combined according to the actual FTU status to obtain the comprehensive judgment result corresponding to different suspected fault sections. The judgment results corresponding to all suspected fault sections are arranged according to the section number to generate a fault judgment matrix.

[0103] It should be noted that this step introduces a judgment influence matrix, so that the actual FTU state no longer uses a uniform weight to participate in fault judgment, but is normalized according to the theoretical response relationship of FTU under different suspected fault sections, thereby reducing the adverse impact of some abnormal nodes on the judgment results.

[0104] As an example, the fault diagnosis matrix Satisfy the following formula:

[0105] in, To determine the influence matrix, This is the matrix of actually reported information.

[0106] Based on the above steps, this step integrates the actual detection status of the FTU with the influence of the fault section, thereby achieving a comprehensive evaluation of different candidate fault locations and improving the ability to distinguish between multiple suspected fault sections.

[0107] S704. Determine the information distortion correction coefficient based on the fault judgment matrix and the information distortion during the FTU fault status reporting process, and correct the fault judgment matrix based on the information distortion correction coefficient to obtain the final fault judgment probability corresponding to each suspected fault section.

[0108] Information distortion refers to discrepancies between the actual fault status reported by the FTU and the theoretical fault status of the corresponding suspected fault section, including the number of inconsistent statuses and the proportion of abnormal reports. The information distortion correction coefficient characterizes the degree to which the current FTU-reported information deviates from the theoretical state and is used to correct the fault judgment results. The final fault determination probability is used as the fault determination probability.

[0109] In one possible implementation, the system obtains the judgment results corresponding to each suspected fault segment in the fault judgment matrix, and compares the theoretical fault state data corresponding to each suspected fault segment with the actual FTU fault state data, counting the number of FTUs with discrepancies between the two to obtain the corresponding information distortion degree. An information distortion correction coefficient is determined based on the information distortion degree. When the FTU state difference corresponding to a suspected fault segment is small, a small correction effect is set; when the state difference is large, the credibility of the corresponding fault judgment result is reduced according to a preset correction rule. Further, the information distortion correction coefficient is applied to the corresponding elements in the fault judgment matrix, and the corrected results are normalized to obtain the final fault judgment probability corresponding to each suspected fault segment.

[0110] It should be noted that information distortion correction is not simply excluding FTUs with inconsistent states, but rather adjusting the fault judgment results according to the degree of abnormality while retaining the possibility of FTU abnormality, so that the system can adapt to situations such as equipment false alarms and communication abnormalities that exist in actual power distribution networks.

[0111] Preferably, the information distortion correction coefficient Satisfy the following formula:

[0112] Where t is the number of data differences, determined by S304.

[0113] As an example, the information distortion correction coefficient for each suspected faulty section is calculated, the relative probability of each suspected faulty section occurring is corrected for information distortion, the corresponding element of each suspected faulty section in the fault judgment matrix is ​​multiplied by the information distortion correction coefficient to obtain the distortion-corrected fault judgment matrix, and finally, the elements in the distortion-corrected fault judgment matrix are normalized to obtain the final fault judgment probability of each suspected faulty section.

[0114] Based on the above steps, this step dynamically corrects the fault judgment result by combining the degree of distortion of FTU state information, so that the final fault judgment probability not only considers the node detection result, but also adapts to abnormal detection information, thereby improving the accuracy of fault location results in the actual operating environment.

[0115] Example 7: Based on the technical solutions of embodiments 1 to 6 above, this application conducts simulation analysis. Figure 8 A schematic diagram of the IEEE 13-node system model provided in the embodiments of this application is shown below. Figure 8 As shown, T1~T13 are FTU numbers, and S1~S13 are the adjacent downstream segments of each FTU. The network description matrix D is shown in the following formula:

[0116] All FTUs have been pre-set to a positive direction. The reporting probabilities of each FTU are reasonably set according to the actual situation, as shown in Table 1.

[0117] Table 1. Reporting Probability of Each FTU in the IEEE 13-Node System

[0118] IEEE 13-node system When a fault occurs in a section, the fault section discrimination matrix can be obtained according to S303. Furthermore, the suspected faulty section can be identified as follows: and .in The correct fault information matrix reported by the section FTU is as follows , possibly because The fault information matrix received by the main station due to missed reports is as follows And it satisfies the equation t=1, therefore it will Add suspected faulty sections. After identifying the suspected faulty sections, write the fault information matrix actually received by the main station. and and The corresponding FTU correctly reports the fault information matrix. and . , and As shown in the formula below:

[0119] Based on the reporting probabilities of each case in Table 1, respectively with and By comparing the results, the reported state probability matrix N can be written out:

[0120] Further, from the formula for determining the influence matrix, we can obtain M:

[0121] Furthermore, the influence matrix M and the fault information matrix actually received by the main station are used. The fault diagnosis matrix J can be obtained as follows:

[0122] The information distortion correction coefficient for each suspected faulty section is calculated as follows: ,right Information distortion correction is performed to obtain the distortion correction fault judgment matrix. :

[0123] Finally, Normalizing each element yields the final failure probability for each suspected faulty section. Therefore, the faulty section was finally determined to be S3, which is consistent with the fault setting result.

[0124] Furthermore, suppose a fault occurs in the S5 segment of the IEEE 13-node system. If a false alarm occurs, the fault segment discrimination matrix can be obtained. At this point, the suspected faulty section can be identified as follows: and After identifying the suspected faulty sections, write down the matrix of fault information actually received by the main station. and and The corresponding FTU correctly reports the fault information matrix. and :

[0125] Similarly, the reported state probability matrix N is obtained:

[0126] And determine the influence matrix M:

[0127] The fault judgment matrix can be obtained by using the judgment influence matrix M and the fault information matrix actually received by the main station. :

[0128] Calculate the information distortion correction factor ,right Information distortion correction is performed to obtain the distortion correction fault judgment matrix. ,Will The failure probability of each suspected faulty section is obtained by normalizing each element. Therefore, the faulty section was ultimately determined to be... .

[0129] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0130] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A method for locating fault sections in a distribution network, characterized in that, include: The system acquires distribution network topology data, FTU operation data, and fault status data collected by each FTU when a fault occurs. The distribution network topology data includes the connection relationships between each switch node in the distribution line and the corresponding line section information. The FTU operation data includes the historical fault status reporting results of each FTU and the corresponding correct reporting probability data. The fault status data includes the fault direction status information output by each FTU for the current fault event. A line segment association model is constructed based on the distribution network topology data, and an initial judgment matrix for fault segments is generated based on the line segment association model and the fault status data. Multiple suspected fault sections are determined based on the initial judgment matrix of the fault section, and theoretical fault state data corresponding to each suspected fault section is generated based on the suspected fault sections and the line section association model. Based on the theoretical fault status data corresponding to each suspected fault section, the fault status data, and the correct reporting probability data in the FTU operation data, the fault judgment probability corresponding to each suspected fault section is determined. The target fault section is determined based on the fault determination probability corresponding to each suspected fault section, and the location result corresponding to the target fault section is output.

2. The method according to claim 1, characterized in that, The step of constructing a line segment association model based on the distribution network topology data, and generating an initial fault segment judgment matrix based on the line segment association model and the fault status data, includes: Based on the distribution network topology data, the switching nodes in the distribution network and the line segments formed between adjacent switching nodes are determined, and a network description matrix is ​​constructed based on the connection relationship between the switching nodes and the line segments. The detection direction corresponding to each FTU is determined based on the network description matrix, and the FTU fault direction status information in the fault status data is arranged according to the corresponding detection direction to generate an FTU reporting information matrix. Matrix operations are performed based on the FTU reported information matrix and the network description matrix to obtain the fault segment discrimination matrix; The initial judgment matrix for fault sections is determined based on the line sections corresponding to the matrix elements in the fault section discrimination matrix that meet the preset judgment conditions.

3. The method according to claim 2, characterized in that, The fault direction status information is determined in the following way: Acquire the transient direction information of zero-sequence current and zero-sequence voltage collected by each FTU; The fault direction status information is determined based on the directional relationship between the zero-sequence current transient direction information and the zero-sequence voltage transient direction information; wherein, when the directional relationship between the two satisfies the positive fault determination condition, the fault direction status information is recorded as 1; when the reverse fault determination condition is satisfied, it is recorded as -1; otherwise, it is recorded as 0.

4. The method according to claim 3, characterized in that, The step of determining multiple suspected fault sections based on the initial judgment matrix of the fault section, and generating theoretical fault state data corresponding to each suspected fault section based on the suspected fault sections and the line section association model, includes: Based on the discrimination results corresponding to each line segment in the initial judgment matrix of fault sections, the line segments with positive discrimination results are determined, and the line segments with positive discrimination results are regarded as suspected fault sections; For each suspected faulty section, the theoretical fault direction status of each FTU corresponding to the suspected faulty section when a fault occurs is determined according to the line section association model, and the theoretical fault status data corresponding to the suspected faulty section is generated according to the theoretical fault direction status of each FTU.

5. The method according to claim 1, characterized in that, The step of determining the fault determination probability corresponding to each suspected fault segment based on the theoretical fault state data corresponding to each suspected fault segment, the fault state data, and the correct reporting probability data in the FTU operation data includes: For each suspected fault section, the theoretical fault state data corresponding to the suspected fault section is compared with the fault state data for each FTU to determine the state matching result for each FTU. The state matching result includes consistency and inconsistency. Based on the state matching result, the corresponding state reporting probability is obtained from the correct reporting probability data in the FTU operation data. When the state matching result is consistent, the correct reporting probability corresponding to the FTU is used as the state reporting probability. When the state matching result is inconsistent, the incorrect reporting probability corresponding to the FTU is obtained based on the correct reporting probability corresponding to the FTU, and the incorrect reporting probability is used as the state reporting probability. The fault determination probability corresponding to the suspected faulty section is generated based on the status reporting probability of each FTU.

6. The method according to claim 5, characterized in that, The probability of fault determination Satisfy the following formula: in, Indicates the first There are several suspected fault sections, and the fault determination probability corresponding to the current reported status of the i-th FTU; p represents the number of the suspected fault section. Indicates the FTU number; This represents the fault direction status data that the i-th FTU should theoretically output when the p-th suspected faulty section experiences a fault. This represents the i-th FTU fault status data actually received under the current fault condition; This represents the probability that the i-th FTU correctly reports the positive direction fault status in the event of a positive direction fault. This represents the probability that the i-th FTU correctly reports a non-positive direction state when no positive direction fault occurs.

7. The method according to claim 5, characterized in that, Before determining the target fault section based on the fault determination probability corresponding to each suspected fault section, the method further includes correcting the fault determination probability: Based on the fault determination probability corresponding to each suspected fault section, the nodes are arranged according to the suspected fault section number and FTU number to obtain the node reported status probability matrix. Based on the node-reported state probability matrix and the theoretical fault state data corresponding to the suspected fault section, the probability weights corresponding to the FTUs that theoretically output positive direction fault states and the probability weights corresponding to the FTUs that theoretically output non-positive direction fault states are determined respectively, and a judgment influence matrix is ​​generated according to the probability weights. A matrix operation is performed based on the judgment influence matrix and the fault state matrix corresponding to the actual acquired FTU fault state data to obtain the fault judgment matrix; Based on the fault judgment matrix and the information distortion during the FTU fault status reporting process, the information distortion correction coefficient is determined, and the fault judgment matrix is ​​corrected based on the information distortion correction coefficient to obtain the final fault judgment probability corresponding to each suspected fault segment. The final fault determination probability is referred to as the fault determination probability.

8. The method according to claim 7, characterized in that, The determination influence matrix satisfies the following formula: in, To determine the first in the influence matrix line, number Column elements are used to represent the values ​​in the decision influence matrix. The suspected faulty section corresponds to the first... The impact weight of the status reported by each FTU; The node reports the state probability matrix in the first position. line, number Column element, representing the first column. Under the assumption that the suspected faulty section has failed, the first... The probability of the current reported status occurring for each FTU; Indicates the first When a suspected faulty section fails, the first... Theoretical fault state data corresponding to each FTU; when When, it indicates the first Each FTU theoretically outputs a positive fault status; when When, it indicates the first Each FTU theoretically outputs a non-positive direction fault state; c and d represent indices used to traverse all FTU numbers.

9. The method according to claim 7, characterized in that, The step of determining the target fault section based on the fault determination probability corresponding to each suspected fault section, and outputting the location result corresponding to the target fault section, includes: Based on the fault determination probability corresponding to each suspected fault section, the probabilities of each suspected fault section are sorted to obtain the fault probability sorting result. Based on the fault probability ranking results, the suspected fault section corresponding to the highest fault judgment probability is determined, and the suspected fault section is determined as the target fault section; The fault location result is generated based on the line section information corresponding to the target fault section.

10. A fault location system for a power distribution network, characterized in that, include: The data acquisition module is used to acquire distribution network topology data, FTU operation data, and fault status data collected by each FTU when a fault occurs. The distribution network topology data includes the connection relationship between each switch node in the distribution line and the corresponding line section information. The FTU operation data includes the historical fault status reporting results of each FTU and the corresponding correct reporting probability data. The fault status data includes the fault direction status information output by each FTU for the current fault event. The initial judgment module is used to construct a line segment association model based on the distribution network topology data, and generate an initial judgment matrix for fault segments based on the line segment association model and the fault status data. The theoretical state generation module is used to determine multiple suspected fault sections based on the initial judgment matrix of the fault section, and to generate theoretical fault state data corresponding to each suspected fault section based on the suspected fault sections and the line section association model. The probability determination module is used to determine and correct the fault determination probability corresponding to each suspected fault section based on the theoretical fault state data, the fault state data, and the correct reporting probability data in the FTU operation data, so as to obtain the final fault determination probability. The fault location module is used to determine the target fault section based on the fault determination probability corresponding to each suspected fault section, and output the location result corresponding to the target fault section.

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