Fault diagnosis method, device and equipment of power distribution network and storage medium

Abstract

The application discloses a power distribution network fault diagnosis method, device and equipment and a storage medium. The method comprises the following steps: determining a suspicious node according to electrical operation data of each node in the power distribution network; screening structure data of the suspicious node from structure data of each node in the power distribution network based on the suspicious node, and determining an initial fault node according to the structure data and the electrical operation data of the suspicious node; extracting features of the electrical operation data of the initial fault node by using a preset time window, obtaining electrical feature data of the initial fault node, and determining a fault time of the initial fault node according to the electrical feature data; screening the electrical operation data by using the fault time, obtaining first electrical operation data, and determining a target fault node by using the first electrical operation data and a preset target power; and determining a fault diagnosis result of the power distribution network according to the target fault node. The technical scheme of the embodiment of the application can improve the efficiency and accuracy of power distribution network fault diagnosis.

Description

TECHNICAL FIELD

[0001] The embodiment of the application relates to the technical field of power distribution networks, and particularly relates to a power distribution network fault diagnosis method, device, equipment and storage medium. BACKGROUND

[0002] With the vigorous development of the power industry, as a key terminal of the power system, the power distribution network shoulders the heavy responsibility of stably and reliably supplying power to users, and its safe and stable operation is directly related to the continuity and quality of power supply. Therefore, effective fault diagnosis of the power distribution network has become an indispensable link to ensure its stable operation.

[0003] At present, the fault diagnosis method of the power distribution network mainly depends on manual experience analysis. However, this method is not only inefficient, but also difficult to guarantee accuracy.

[0004] Therefore, it is urgent to propose a new method to solve the above problems. SUMMARY

[0005] The application provides a power distribution network fault diagnosis method, device, equipment and storage medium, which can improve the efficiency and accuracy of power distribution network fault diagnosis.

[0006] In a first aspect, the embodiment of the application provides a power distribution network fault diagnosis method, comprising:

[0007] determining a suspected node according to electrical operation data of each node in the power distribution network;

[0008] filtering out structure data of the suspected node from structure data of each node in the power distribution network based on the suspected node, and determining an initial fault node according to the structure data of the suspected node and the electrical operation data;

[0009] performing feature extraction on the electrical operation data of the initial fault node by using a preset time window to obtain electrical feature data of the initial fault node, and determining a fault time of the initial fault node according to the electrical feature data;

[0010] filtering out first electrical operation data by using the fault time to filter the electrical operation data, and determining a target fault node by using the first electrical operation data and a preset target power;

[0011] determining a fault diagnosis result of the power distribution network according to the target fault node.

[0012] The technical solution of this invention identifies suspicious nodes based on the electrical operation data of each node in the distribution network, saving time in determining the initial fault node and thus improving the efficiency of the entire fault detection process. Then, based on the suspicious nodes, structural data of the suspicious nodes is filtered from the structural data of each node in the distribution network. The initial fault node is then determined based on the structural data of the suspicious nodes and the electrical operation data, effectively narrowing the scope of fault investigation and improving fault detection efficiency. Next, feature extraction is performed on the electrical operation data of the initial fault node using a preset time window to obtain the electrical feature data of the initial fault node. The fault time of the initial fault node is then determined based on the electrical feature data, improving the accuracy of the determined fault time. Subsequently, the electrical operation data is filtered by the fault time to obtain first electrical operation data, focusing on key information during the fault occurrence period and avoiding interference from irrelevant data. Using the first electrical operation data and a preset target power to determine the target fault node enables a more accurate assessment of the actual impact of the fault on electrical operation parameters, improving the accuracy of fault diagnosis. Finally, based on the target fault node, the fault diagnosis result of the distribution network is determined. This allows for rapid and accurate determination of the fault diagnosis result, providing valuable fault information for staff and ensuring the reliable operation of the distribution network, thereby enhancing user satisfaction. Therefore, the technical solution of this invention solves the problems of low efficiency and difficulty in guaranteeing accuracy caused by the reliance on manual experience analysis in existing technologies.

[0013] Secondly, embodiments of the present invention also provide a fault diagnosis device for a power distribution network, the device comprising:

[0014] The first node determination module is used to determine the suspected node based on the electrical operation data of each node in the distribution network;

[0015] The second node determination module is used to filter out the structural data of the suspected node from the structural data of each node in the distribution network based on the suspected node, and determine the initial fault node based on the structural data of the suspected node and the electrical operation data.

[0016] The time determination module is used to extract features from the electrical operation data of the initial fault node using a preset time window, obtain the electrical feature data of the initial fault node, and determine the fault time of the initial fault node based on the electrical feature data.

[0017] The third node determination module is used to filter the electrical operation data by the fault time to obtain the first electrical operation data, and to determine the target fault node by using the first electrical operation data and the preset target power.

[0018] The fault diagnosis module is used to determine the fault diagnosis result of the distribution network based on the target fault node.

[0019] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising:

[0020] At least one processor; and a memory communicatively connected to said at least one processor;

[0021] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to implement the fault diagnosis method for the power distribution network as described in any of the first aspects.

[0022] Fourthly, embodiments of the present invention also provide a storage medium containing computer-executable instructions.

[0023] The computer-executable instructions, when executed by a computer processor, implement the fault diagnosis method for the power distribution network as described in any of the first aspects.

[0024] It should be noted that the aforementioned computer instructions may be stored, in whole or in part, on a computer-readable storage medium. This computer-readable storage medium may be packaged together with the processor of the power distribution network fault diagnosis device, or it may be packaged separately from the processor of the power distribution network fault diagnosis device; this application does not impose any limitations on this.

[0025] The descriptions of the second, third, and fourth aspects in this application can be referenced to the detailed description of the first aspect; and the beneficial effects described in the second, third, and fourth aspects can be referenced to the analysis of the beneficial effects of the first aspect, which will not be repeated here.

[0026] In this application, the name of the aforementioned fault diagnosis device for the power distribution network does not limit the equipment or functional module itself. In actual implementation, these devices or functional modules may appear under other names. As long as the function of each device or functional module is similar to that of this application, it falls within the scope of the claims of this application and its equivalents.

[0027] These or other aspects of this application will become more readily apparent in the following description. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 A flowchart illustrating a fault diagnosis method for a power distribution network provided in an embodiment of the present invention;

[0030] Figure 2 A flowchart illustrating another method for fault diagnosis in a power distribution network provided in an embodiment of the present invention;

[0031] Figure 3 This is a schematic diagram of the structure of a fault diagnosis device for a power distribution network provided in an embodiment of the present invention;

[0032] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0033] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0034] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0035] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0036] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0037] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the figures. The process can correspond to a method, function, procedure, subroutine, subroutine, etc. Moreover, embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0038] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0039] In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0040] Figure 1 This is a flowchart illustrating a fault diagnosis method for a distribution network according to an embodiment of the present invention. This embodiment is applicable to situations requiring fault diagnosis of the distribution network. The method can be executed by a fault diagnosis device for the distribution network, which can be implemented in hardware or software. This device can be integrated into an electronic device, such as being installed in a computer; this embodiment of the present invention does not impose any limitations on this. Figure 1 As shown, the specific steps include the following:

[0041] Step 110: Determine the suspicious nodes based on the electrical operation data of each node in the distribution network.

[0042] Specifically, a distribution network refers to a power grid that receives electrical energy from the transmission network or regional power plants and distributes it locally or tiered according to voltage to various users through distribution facilities. A node refers to a connection point in the power network; for example, a node can be a substation, distribution transformer, switching equipment, or user access point. Electrical operating data refers to various electrical parameter data generated by each node in the distribution network during operation; for example, electrical operating data can be voltage, current, and power. A node of concern refers to a node that, based on preliminary assessment of electrical operating data, is likely to have a problem.

[0043] In practice, electrical operation data of each node can be obtained from the monitoring system of the distribution network first. Then, the obtained data is preprocessed (such as filtering noise and filling missing values) to improve data quality. Afterwards, it is determined whether the preprocessed electrical operation data of each node is within the preset electrical data operating range. For each node, if the electrical operation data is within the preset electrical data operating range, the node is determined to be a normal node; if the electrical operation data is not within the preset electrical data operating range, the node is determined to be a questionable node. For example, if a distribution network contains two nodes, node A and node B, and the electrical operation data is voltage values, with a preset electrical data operating range of 9.5 kV - 10.5 kV, and at time t1 the electrical operation data of node A is 10.2 kV and the electrical operation data of node B is 11.3 kV, then node B is the questionable node. For example: A distribution network contains two nodes, namely node C and node D. The electrical operating data are voltage and current values. The preset operating range of electrical data is 9.5 kV-10.5 kV and 5 A-10 A. At time t1, the electrical operating data of node C is 10.2 kV and 5 A, and the electrical operating data of node D is 11.3 kV and 8 A. Then the node in question is node D.

[0044] In this embodiment, the suspected node is determined based on the electrical operation data of each node in the distribution network, which saves the time of subsequently determining the initial fault node and thus improves the efficiency of the entire fault detection process.

[0045] Step 120: Based on the suspected nodes, filter out the structural data of suspected nodes from the structural data of each node in the distribution network, and determine the initial fault node based on the structural data and electrical operation data of the suspected nodes.

[0046] Specifically, structural data refers to information about the physical structure and connections of each node in a distribution network. For example, structural data includes the type of line connected to the node (e.g., overhead line or cable), line length, conductor type, type of connected equipment (transformer, switch, etc.), and its parameters (e.g., impedance). Initial fault nodes refer to nodes that are preliminarily identified as potentially faulty based on the structural and electrical operation data of suspected faulty nodes.

[0047] In practice, after identifying the suspected nodes, structural data of each node in the distribution network can be obtained from the distribution network management system. Then, based on the previously obtained suspected nodes, relevant structural data of these nodes is filtered from the distribution network's structural data to obtain the structural data of the suspected nodes. Next, the adjacent nodes of the suspected nodes are determined based on their structural data. Then, for each suspected node, it is determined whether all its adjacent nodes are also suspected nodes. If all of its adjacent nodes are suspected nodes, then the suspected node is determined to be the initial fault node. If not all of its adjacent nodes are suspected nodes, then the suspected node is determined not to be the initial fault node.

[0048] In this embodiment, using topology data to filter suspicious nodes can effectively narrow down the scope of fault investigation, thereby improving fault detection efficiency.

[0049] Step 130: Use a preset time window to extract features from the electrical operation data of the initial fault node to obtain the electrical feature data of the initial fault node, and determine the fault time of the initial fault node based on the electrical feature data.

[0050] Specifically, the preset time window refers to the time period for feature extraction pre-set according to actual conditions or needs. Electrical feature data refers to representative or key feature information extracted from the electrical operation data of the initial fault node. Fault time refers to the specific time when the initial fault node experienced a fault, determined based on the electrical feature data.

[0051] In practice, electrical operating data of the initial fault node within a preset time window can be obtained from the monitoring system of the distribution network. Then, the average value of the electrical operating data within the preset time window is calculated to obtain the electrical characteristic data of the initial fault node. For example, if the electrical operating data is voltage, the average value of all voltage values ​​of the initial fault node within the preset time window can be calculated to obtain the average voltage value within that time window, which is the electrical characteristic data of the initial fault node.

[0052] Then, the fault time of the initial fault node is determined based on the electrical characteristic data. Specifically, the difference between the electrical operating data and the electrical characteristic data (mean) at each moment within the preset time window can be calculated to obtain the electrical difference at each moment. Then, the moment when the electrical difference is greater than the preset electrical difference is determined as the fault time of the initial fault node.

[0053] In this embodiment, the accuracy of the determined fault time is improved through the above steps.

[0054] Step 140: Filter the electrical operation data by fault time to obtain the first electrical operation data, and use the first electrical operation data and the preset target power to determine the target fault node.

[0055] Specifically, the first electrical operating data refers to the fault-related electrical operating data filtered through the fault time. The preset target power refers to the expected power set in advance based on actual conditions or needs. The target fault node refers to the node where the fault ultimately occurs.

[0056] In practice, the power of each node corresponding to the fault time can be filtered from the electrical operation data according to the fault time to obtain the first electrical operation data. Then, based on the first electrical operation data and the preset target power of each node, the power deviation of the fault time is calculated. The fault time with the power deviation greater than the second preset power threshold is determined as the target fault time. Finally, the initial fault node corresponding to the target fault time is determined as the target fault node.

[0057] In this embodiment, the first electrical operation data filtered based on the fault time accurately focuses on the key information during the fault occurrence period, avoiding interference from irrelevant data. Then, the first electrical operation data is compared and analyzed with the preset target power, which improves the accuracy of fault diagnosis.

[0058] Step 150: Determine the fault diagnosis results of the distribution network based on the target fault node.

[0059] Specifically, fault diagnosis results refer to the final judgment on the fault status of the distribution network. For example, fault diagnosis results include whether a fault has occurred or not.

[0060] In practice, the fault diagnosis result of the distribution network can be determined based on whether a target fault node exists. Specifically, if the number of target fault nodes is not zero (i.e., a target fault node exists), the fault diagnosis result of the distribution network is determined to be a fault. In this case, the identified target fault node and its fault time can be sent to the staff's terminal to help them quickly locate the fault point and promptly initiate emergency repairs, reducing inconvenience and economic losses to users' production and lives. If the number of target fault nodes is zero (i.e., no target fault node exists), the fault diagnosis result of the distribution network is determined to be a fault-free fault. In this case, the fault diagnosis result of no fault can be sent to the staff's terminal to help them eliminate potential fault hazards and avoid wasting unnecessary time and effort on inspections in fault-free areas, thereby improving the overall operation and maintenance management level and operational efficiency of the distribution network.

[0061] In this embodiment, the fault diagnosis result of the distribution network is determined based on the target fault node. This can quickly and accurately determine the fault diagnosis result of the distribution network, providing valuable fault information for staff, thereby ensuring the reliable operation of the distribution network and enhancing user satisfaction.

[0062] In this embodiment of the invention, suspected nodes are identified based on the electrical operation data of each node in the distribution network, saving time in determining the initial fault node and thus improving the efficiency of the entire fault detection process. Then, based on the suspected nodes, structural data of suspected nodes are filtered from the structural data of each node in the distribution network. The initial fault node is then determined based on the structural data and electrical operation data of the suspected nodes, effectively narrowing the scope of fault investigation and improving fault detection efficiency. Next, features are extracted from the electrical operation data of the initial fault node using a preset time window to obtain the electrical feature data of the initial fault node. The fault time of the initial fault node is then determined based on the electrical feature data, improving the accuracy of the determined fault time. Subsequently, the electrical operation data is filtered by the fault time to obtain first electrical operation data, focusing on key information during the fault occurrence period and avoiding interference from irrelevant data. Using the first electrical operation data and a preset target power to determine the target fault node allows for a more accurate assessment of the actual impact of the fault on electrical operation parameters, improving the accuracy of fault diagnosis. Finally, based on the target fault node, the fault diagnosis result of the distribution network is determined. This allows for rapid and accurate determination of the fault diagnosis result, providing valuable fault information for staff and ensuring the reliable operation of the distribution network, thereby enhancing user satisfaction. Therefore, the technical solution of this invention solves the problems of low efficiency and difficulty in guaranteeing accuracy caused by the reliance on manual experience analysis in existing technologies.

[0063] Figure 2 This is a flowchart of another fault diagnosis method for a distribution network provided by an embodiment of the present invention. This embodiment is a specific implementation based on the above embodiments. In this embodiment, the method may further include:

[0064] Step 210: Calculate the calculated power of each node based on the voltage and current of each node, and obtain the total power of the distribution network based on the power of each node.

[0065] Specifically, electrical operating data includes voltage, current, and power. Calculated power refers to the power calculated based on voltage and current. Total power refers to the sum of the power at all nodes in the distribution network.

[0066] In practice, the voltage, current, and power of each node at different times can be obtained from the distribution network monitoring system. Then, for each node, the average voltage is calculated based on its voltage value at each time. Similarly, the average current is calculated from the current values ​​at each time, and the average power is determined based on the power values ​​at each time. Subsequently, the average voltage and average current of the node are multiplied to obtain the calculated power of that node. After obtaining the calculated power of all nodes, the sum of the average power of each node is calculated to obtain the total power of the distribution network.

[0067] In this embodiment, the above steps provide a data foundation for determining the fault weight of each node.

[0068] Step 211: Determine the fault weight of each node based on the calculated power, power and total power of each node.

[0069] Specifically, fault weight refers to an indicator used to measure the likelihood of each node failing.

[0070] In the specific implementation, for each node, the average power of the node can be determined first as the power of the node. Then, the fault weight of the node can be determined based on the calculated power, power and total power of the node. The specific calculation formula is as follows: Fault weight of the node = |Calculated power of the node - Power of the node| / Total power.

[0071] In this embodiment, by determining the fault weight of each node, the accuracy of identifying suspected nodes is greatly improved. This enables the entire fault diagnosis process to locate potential fault points more efficiently and accurately, laying a solid foundation for ensuring the stable operation of the distribution network. It also reduces the waste of time and resources caused by inaccurate fault diagnosis and enhances the reliability and scientific nature of distribution network fault diagnosis and handling.

[0072] Step 212: Identify nodes with fault weights greater than zero as suspected nodes.

[0073] In practice, after obtaining the fault weight of each node, nodes with fault weights greater than zero can be identified as suspected nodes.

[0074] It should be noted that if there are no nodes with a fault weight greater than zero, it can be directly determined that there is no target fault node.

[0075] Step 213: Based on the suspected nodes, filter out the structural data of suspected nodes from the structural data of each node in the distribution network, and determine the initial fault node based on the structural data and electrical operation data of the suspected nodes.

[0076] Further, the initial fault node is determined based on the structural data and electrical operation data of the suspected node, including: determining the adjacent nodes of the suspected node based on the structural data of the suspected node, and filtering the impedance between the adjacent nodes and the suspected node from the structural data of the suspected node based on the adjacent nodes and the suspected node to obtain the adjacent impedance; determining the power flow power of the suspected node based on the adjacent impedance, the power of the suspected node and the power of the adjacent nodes; and determining the suspected node whose power flow power is greater than a first preset power threshold as the initial fault node.

[0077] Specifically, the adjacent nodes of a suspected node refer to other nodes directly connected to the suspected node. Adjacent impedance refers to the impedance value between the suspected node and its adjacent nodes. Power flow refers to the power determined based on the adjacent impedance, the power of the suspected node, and the power of its adjacent nodes, used to identify the initial fault node. The first preset power threshold refers to a power threshold set in advance according to actual conditions or requirements.

[0078] In practice, after identifying the suspected node, its adjacent nodes can be determined based on its structural data. Then, based on the adjacent nodes and the suspected node, the impedance between them is selected from the suspected node's structural data to obtain the adjacent impedance. Finally, the power flow of the suspected node is calculated based on the adjacent impedance, the power of the suspected node, and the power of its adjacent nodes. The specific calculation formula is as follows:

[0079]

[0080] Among them, P flow P represents the power flow, and P represents the power of the node in question. j Z represents the power of the j-th adjacent node. ij represents the adjacent impedance, and n represents the total number of adjacent nodes.

[0081] Then, after obtaining the power flow power of each suspected node, suspected nodes with power flow power greater than the first preset power threshold can be identified as initial fault nodes.

[0082] In this embodiment, the above steps not only improve the accuracy of initial fault node determination, but also avoid unnecessary calculations on a large number of irrelevant nodes, thereby significantly improving the overall efficiency of fault diagnosis.

[0083] Step 214: Determine the voltage characteristic value of the initial fault node at each time according to the preset first time window and the voltage of the initial fault node at each time, and determine the current characteristic value of the initial fault node at each time according to the preset second time window and the current of the initial fault node at each time.

[0084] Specifically, the preset time window includes a preset first time window and a preset second time window. The preset first time window refers to a time window set in advance based on actual conditions or needs. The preset second time window refers to another time window set in advance based on actual conditions or needs. The preset first time window is shorter than the preset second time window. Electrical characteristic data includes current characteristic values ​​and voltage characteristic values. Voltage characteristic values ​​refer to values ​​that characterize voltage changes, determined based on the preset first time window and the voltage of the initial fault node at each moment. Current characteristic values ​​refer to values ​​that characterize current changes, determined based on the preset second time window and the current of the initial fault node at each moment.

[0085] In the specific implementation, after determining the initial fault node, the voltage characteristic value of the initial fault node at each time moment can be calculated based on the preset first time window and the voltage of the initial fault node at each time moment. The specific calculation formula is as follows:

[0086]

[0087] Among them, V e (t i T represents the voltage characteristic value at time i. s V(t) represents the preset first time window. i ) represents the voltage value at the i-th time.

[0088] Simultaneously, the current characteristic value of the initial fault node at each time moment can be calculated based on the preset second time window and the current of the initial fault node at each time moment. The specific calculation formula is as follows:

[0089]

[0090] Among them, I e (t i T represents the characteristic value of the current at time i. l Indicates a preset second time window, I(t) i ) represents the current value at the i-th time.

[0091] In this embodiment, the voltage and current at multiple moments are considered comprehensively through the above steps, rather than relying solely on the observation value at a single moment. This effectively avoids the influence of data randomness on the analysis results, and thus can more accurately capture the changing trend of voltage and current over time, thereby improving the accuracy of the subsequent determination of the fault time.

[0092] Step 215: Determine the voltage deviation of the initial fault node at each time step based on the voltage of the initial fault node at each time step and the corresponding voltage characteristic value, and determine the current deviation of the initial fault node at each time step based on the current of the initial fault node at each time step and the corresponding current characteristic value.

[0093] Specifically, voltage deviation refers to the difference between the actual voltage at the initial fault node and its corresponding voltage characteristic value, and is one of the important criteria for determining the fault time. Current deviation refers to the difference between the actual current at the initial fault node and its corresponding current characteristic value, and is also one of the important criteria for determining the fault time.

[0094] In practice, after obtaining the voltage and current characteristic values ​​of the initial fault node at each time step, the voltage deviation of the initial fault node at each time step can be determined based on the voltage of the initial fault node at each time step and the corresponding voltage characteristic values. The specific calculation formula is as follows:

[0095] ΔV fault (t i )=|V e (t i )-V(t i )|

[0096] Where, ΔV fault (t i ) represents the voltage deviation at the i-th time.

[0097] Simultaneously, the current deviation of the initial fault node at each time step can be determined based on the current of the initial fault node at each time step and the corresponding current characteristic value. The specific calculation formula is as follows:

[0098] ΔI fault (t i )=|I e (t i )-I(t i )|

[0099] Where, ΔI fault (t i ) represents the current deviation at the i-th time.

[0100] In this embodiment, the above steps can accurately quantify the degree to which the electrical parameters of the initial fault node deviate from the normal state, thereby improving the accuracy of the subsequently determined fault time.

[0101] Step 216: Determine the time when the voltage deviation is greater than the preset voltage threshold and the current deviation is greater than the preset current threshold as the fault time of the initial fault node.

[0102] Specifically, the preset voltage threshold refers to a threshold set in advance based on actual conditions or needs to determine whether the voltage deviation exceeds the limit. The preset current threshold refers to a threshold set in advance based on actual conditions or needs to determine whether the current deviation exceeds the limit.

[0103] In practice, after obtaining the current deviation and current deviation of the initial fault node at each time, the time when the voltage deviation is greater than the preset voltage threshold and the current deviation is greater than the preset current threshold can be determined as the fault time of the initial fault node.

[0104] It should be noted that if there is no time when the voltage deviation is greater than the preset voltage threshold and the current deviation is greater than the preset current threshold, then it can be directly determined that there is no target fault node.

[0105] In this embodiment, the accuracy of the determined fault time is improved through the above steps.

[0106] Step 217: Filter the electrical operation data by fault time to obtain the first electrical operation data, and use the first electrical operation data and the preset target power to determine the target fault node.

[0107] Further, step 217 may specifically include: filtering the power of each node corresponding to the fault time from the electrical operation data according to the fault time to obtain the first electrical operation data; calculating the power deviation of the fault time based on the first electrical operation data and the preset target power of each node; determining the fault time with a power deviation greater than a second preset power threshold as the target fault time, and determining the initial fault node corresponding to the target fault time as the target fault node.

[0108] Specifically, power deviation refers to the degree of power deviation determined based on the actual power and the preset target power. The second preset power threshold refers to a power threshold set in advance based on actual conditions or needs to determine the target failure time.

[0109] In practice, the power of each node corresponding to the fault time can be filtered from the electrical operation data first to obtain the first electrical operation data. Then, the power deviation of the fault time can be calculated based on the first electrical operation data and the preset target power of each node. The specific calculation formula is as follows:

[0110]

[0111] Wherein, ΔP fault (t i P represents the power deviation at time i, N represents the total number of nodes, and P a (t i P represents the power of the a-th node at time i. a-expected (t i ) represents the preset target power of the a-th node at the i-th time.

[0112] Then, the fault time when the power deviation is greater than the second preset power threshold can be determined as the target fault time, and the initial fault node corresponding to the target fault time can be determined as the target fault node. Alternatively, if there is no fault time when the power deviation is greater than the second preset power threshold, the number of target fault nodes can be determined to be zero, i.e., there are no target fault nodes.

[0113] In this embodiment, the accuracy of identifying the target fault node is improved through the above steps.

[0114] Step 218: Determine the fault diagnosis results of the distribution network based on the target fault node.

[0115] Step 219: Determine whether the fault diagnosis result indicates that a fault has occurred.

[0116] If no fault occurs, proceed to step 221; if no fault occurs, proceed to step 220.

[0117] In practice, after obtaining the fault diagnosis result, it can be determined whether the fault diagnosis result indicates that a fault has occurred. If a fault has occurred, the target fault node and the structural data of the distribution network are input into the pre-trained distribution network fault type model to obtain the fault type of the distribution network; if no fault has occurred, the fault diagnosis result is determined to be that no fault has occurred.

[0118] Step 220: Determine that the fault diagnosis result is no fault has occurred.

[0119] In practice, after determining that the fault diagnosis result is not a fault, it can be determined that the fault diagnosis result is no fault. At this time, the fault diagnosis result of no fault can be sent to the staff's terminal, so as to avoid the staff wasting unnecessary time and energy to check in areas without faults.

[0120] Step 221: Input the target fault node and the structural data of the distribution network into the pre-trained distribution network fault type model to obtain the fault type of the distribution network.

[0121] Specifically, a pre-trained distribution network fault type model refers to a model obtained by training based on the historical electrical operation data and corresponding structural data of each distribution network. It can determine the fault type of the distribution network based on the target fault node and the structural data of the distribution network.

[0122] In practice, after identifying the target fault node, the target fault node and the structural data of the distribution network can be input into a pre-trained distribution network fault type model to obtain the fault type of the distribution network (such as short-circuit fault, open-circuit fault, ground fault, or overload fault). The obtained fault type can then be sent to the staff's terminal to provide highly targeted guidance during fault handling. This allows staff to clearly understand the specific type of fault, enabling them to select appropriate handling methods and tools effectively, avoiding blind troubleshooting and ineffective operations, thereby improving the efficiency and accuracy of fault handling.

[0123] Furthermore, the training process of the distribution network fault type model includes: acquiring historical electrical operation data and corresponding structural data of each distribution network; performing data augmentation processing on the historical electrical operation data of each distribution network to obtain historical enhanced electrical operation data of each distribution network, and extracting multi-dimensional features from the historical electrical operation data and historical enhanced electrical operation data of each distribution network to obtain multi-dimensional historical electrical feature data of each distribution network; training the self-supervised model using the historical electrical operation data, structural data, historical enhanced electrical operation data, and multi-dimensional historical electrical feature data of each distribution network, and calculating the loss function; and optimizing the model based on the backpropagation algorithm until the preset optimization conditions are met to obtain the distribution network fault type model.

[0124] Specifically, data augmentation refers to a data processing technique that expands the amount of data through specific methods (such as adding noise, changing the time series order of data, scaling or shifting data, etc.) to generate more diverse data samples. Historical augmented electrical operation data refers to historical electrical operation data after data augmentation. Multi-dimensional historical electrical feature data refers to data obtained by extracting features from historical electrical operation data and historical augmented electrical operation data in multiple dimensions (such as time and space dimensions). A self-supervised model refers to a machine learning model that learns by automatically generating supervisory signals from the data. Loss functions include transform-invariant loss function and cross-entropy loss function. The transform-invariant loss function is a special loss function that, in this embodiment, is mainly used to measure the differences between historical electrical operation data and data-augmented historical electrical operation data in multiple dimensions. The model is trained by minimizing these differences, making it robust to data changes. That is, the model can maintain stable output when facing data changes (such as changes during the data augmentation process), learning the essential characteristics of the data rather than being disturbed by superficial changes. The transformation invariance loss function includes the square norm of the differences between historical electrical operation data and historical enhanced electrical operation data across multiple dimensions. In other words, it measures the degree of change in data after enhancement by calculating the square norm of the differences between historical electrical operation data and historical enhanced electrical operation data across different dimensions. For example, the transformation invariance loss function could be... Where L1 represents the transformation invariance loss function, M represents the number of samples, and T1(x) i T2(x) represents the i-th historical electrical operation data. i ) represents the historical enhanced electrical operation data corresponding to the i-th historical electrical operation data. This represents the historical electrical feature data obtained after feature extraction of the i-th historical electrical operation data in one dimension. This represents the historical electrical feature data obtained after feature extraction of the historical enhanced electrical operation data corresponding to the i-th historical electrical operation data in one dimension. This represents the historical electrical feature data obtained after feature extraction of the i-th historical electrical operation data in another dimension. This represents the historical electrical feature data obtained after feature extraction of the historical enhanced electrical operation data corresponding to the i-th historical electrical operation data in another dimension. This represents the historical electrical feature data obtained after feature extraction of the i-th historical electrical operation data in another dimension. Let λ1, λ2, and λ3 represent the historical electrical feature data obtained after feature extraction of the historical enhanced electrical operation data corresponding to the i-th historical electrical operation data in another dimension. The cross-entropy loss function includes the negative log-likelihood between the pseudo-label probability distribution and the predicted probability distribution.

[0125] In practice, the historical electrical operation data and corresponding structural data of each distribution network are first acquired. Then, data augmentation processing (such as translation and scaling) is performed on the historical electrical operation data of each distribution network to obtain the historical enhanced electrical operation data of each distribution network. Next, neural networks (such as convolutional neural networks or backpropagation neural networks) can be used to extract multi-dimensional features from the historical electrical operation data and historical enhanced electrical operation data of each distribution network to obtain multi-dimensional historical electrical feature data of each distribution network. Afterwards, a pre-defined pseudo-label generation strategy can be used to generate pseudo-labels for the historical electrical operation data and their corresponding structural data of each distribution network. For example, the pseudo-label generation strategy can be expressed by the following formula. Among them, P pse (t) represents the pseudo-fault label at time t, V i (t) and I i (t) represents the voltage and current at the i-th node at time t, P total (t) represents the total power of the distribution network at time t, α i The weight coefficient for each node.

[0126] After completing the above steps, historical electrical operation data, structural data, historical enhanced electrical operation data, and historical electrical feature data from multiple dimensions of each distribution network can be used to train the self-supervised model, combined with pre-generated pseudo-labels. During training, the loss function is calculated until it converges. Subsequently, the model can be optimized using a backpropagation algorithm (such as gradient descent) until preset optimization conditions are met (such as reaching a preset number of iterations), thus obtaining the distribution network fault type model.

[0127] In this embodiment, the above steps enable the distribution network fault type model to be used to obtain the fault type of the distribution network based on the target fault node and the structural data of the distribution network.

[0128] Therefore, the technical solution of this invention calculates the calculated power of each node based on the voltage and current of each node, and obtains the total power of the distribution network based on the power of each node. Based on the calculated power, total power, and total power of each node, the fault weight of each node is determined, which greatly improves the accuracy of identifying suspected nodes. This allows the entire fault diagnosis process to locate potential fault points more efficiently and accurately. Nodes with fault weights greater than zero are then identified as suspected nodes. Based on the suspected nodes, the structural data of suspected nodes is filtered from the structural data of each node in the distribution network. The initial fault node is determined based on the structural data and electrical operation data of the suspected nodes, effectively narrowing the scope of fault investigation and improving fault detection efficiency. Next, the voltage characteristic value of the initial fault node at each time moment is determined based on a preset first time window and the voltage of the initial fault node at each time moment. The current characteristic value of the initial fault node at each time moment is determined based on a preset second time window and the current of the initial fault node at each time moment. This effectively avoids the influence of data randomness on the analysis results, and thus can more accurately capture the changing trends of voltage and current over time, thereby improving the accuracy of the subsequently determined fault time. The voltage deviation of the initial fault node at each time point is determined based on its voltage and corresponding voltage characteristic values. Similarly, the current deviation is determined based on its current and corresponding current characteristic values, precisely quantifying the degree to which the electrical parameters of the initial fault node deviate from their normal state. The time when both the voltage and current deviations exceed a preset voltage threshold are defined as the fault time of the initial fault node, improving the accuracy of the determined fault time. Subsequently, electrical operating data is filtered using the fault time to obtain the first set of electrical operating data, focusing on key information during the fault occurrence period and avoiding interference from irrelevant data. Using the first set of electrical operating data and a preset target power to determine the target fault node allows for a more accurate assessment of the actual impact of the fault on electrical operating parameters, improving the accuracy of fault diagnosis. The fault diagnosis result of the distribution network is then determined based on the target fault node, enabling rapid and accurate determination of the fault diagnosis result. This provides valuable fault information for staff, ensuring the reliable operation of the distribution network and enhancing user satisfaction. Finally, it is determined whether the fault diagnosis result confirms a fault has occurred. If a fault occurs, the target fault node and the structural data of the distribution network are input into a pre-trained distribution network fault type model to obtain the fault type of the distribution network. At this time, the obtained fault type of the distribution network can be sent to the staff's terminal to provide highly targeted guidance to the staff in the fault handling process. This allows the staff to clearly understand the specific type of fault, and thus select appropriate handling methods and tools in a targeted manner, avoiding blind investigation and ineffective operation, thereby improving the efficiency and accuracy of fault handling.If no fault occurs, the fault diagnosis result is determined to be "no fault." In this case, the "no fault" fault diagnosis result can be sent to the staff's terminal, avoiding unnecessary time and effort spent by staff on inspections in fault-free areas. Therefore, the technical solution of this invention solves the problems of low efficiency and difficulty in guaranteeing accuracy caused by the reliance on manual experience analysis in existing technologies.

[0129] Figure 3 This is a schematic diagram of a fault diagnosis device for a power distribution network provided in an embodiment of the present invention. This device belongs to the same inventive concept as the fault diagnosis methods for power distribution networks in the above embodiments. For details not described in detail in the embodiments of the fault diagnosis device for power distribution networks, please refer to the embodiments of the fault diagnosis methods for power distribution networks described above.

[0130] like Figure 3 As shown, the device includes:

[0131] The first node determination module 310 is used to determine the suspicious node based on the electrical operation data of each node in the distribution network;

[0132] The second node determination module 320 is used to filter out the structural data of the suspected node from the structural data of each node in the distribution network based on the suspected node, and determine the initial fault node based on the structural data of the suspected node and the electrical operation data.

[0133] The time determination module 330 is used to extract features from the electrical operation data of the initial fault node using a preset time window, obtain the electrical feature data of the initial fault node, and determine the fault time of the initial fault node based on the electrical feature data.

[0134] The third node determination module 340 is used to filter the electrical operation data by the fault time to obtain the first electrical operation data, and use the first electrical operation data and the preset target power to determine the target fault node.

[0135] The fault diagnosis module 350 is used to determine the fault diagnosis result of the distribution network based on the target fault node.

[0136] Based on the above embodiments, the electrical operating data includes voltage, current, and power, and the first node determination module 310 is specifically used for:

[0137] The calculated power of each node is calculated based on the voltage and current of each node, and the total power of the distribution network is obtained based on the power of each node.

[0138] The fault weight of each node is determined based on the calculated power, power, and total power of each node.

[0139] The nodes with fault weights greater than zero are identified as the suspected nodes.

[0140] Based on the above embodiments, the second node determination module 320 determines the initial fault node according to the structural data of the suspected node and the electrical operation data, including:

[0141] Based on the structural data of the suspected node, the adjacent nodes of the suspected node are determined, and based on the adjacent nodes and the suspected node, the impedance between the adjacent nodes and the suspected node is filtered from the structural data of the suspected node to obtain the adjacent impedance.

[0142] The power flow power of the suspected node is determined based on the adjacent impedance, the power of the suspected node, and the power of the adjacent node.

[0143] The suspected nodes whose power flow is greater than a first preset power threshold are identified as the initial fault nodes.

[0144] Based on the above embodiments, the preset time window includes a preset first time window and a preset second time window, wherein the preset first time window is smaller than the preset second time window, and the electrical characteristic data includes current characteristic values ​​and voltage characteristic values. The time determination module 330 is specifically used for:

[0145] The voltage characteristic value of the initial fault node at each time moment is determined based on the preset first time window and the voltage of the initial fault node at each time moment, and the current characteristic value of the initial fault node at each time moment is determined based on the preset second time window and the current of the initial fault node at each time moment.

[0146] The voltage deviation of the initial fault node at each time moment is determined based on the voltage of the initial fault node at each time moment and the corresponding voltage characteristic value, and the current deviation of the initial fault node at each time moment is determined based on the current of the initial fault node at each time moment and the corresponding current characteristic value.

[0147] The time when the voltage deviation is greater than a preset voltage threshold and the current deviation is greater than a preset current threshold is determined as the fault time of the initial fault node.

[0148] Based on the above embodiments, the third node determination module 340 is specifically used to: filter the power of each node corresponding to the fault time from the electrical operation data according to the fault time, and obtain the first electrical operation data;

[0149] The power deviation of the fault time is calculated based on the first electrical operation data and the preset target power of each node;

[0150] The fault time when the power deviation is greater than the second preset power threshold is determined as the target fault time, and the initial fault node corresponding to the target fault time is determined as the target fault node.

[0151] Based on the above embodiments, the device further includes:

[0152] The fault type determination module is used to input the structural data of the target fault node and the distribution network into a pre-trained distribution network fault type model after determining the fault diagnosis result of the distribution network based on the target fault node and if the fault diagnosis result is determined to be a fault, so as to obtain the fault type of the distribution network.

[0153] Based on the above embodiments, the training process of the distribution network fault type model includes:

[0154] Obtain historical electrical operation data and corresponding structural data for each power distribution network;

[0155] Data augmentation processing is performed on the historical electrical operation data of each distribution network to obtain the historical enhanced electrical operation data of each distribution network. Multi-dimensional feature extraction is performed on the historical electrical operation data and the historical enhanced electrical operation data of each distribution network to obtain the multi-dimensional historical electrical feature data of each distribution network.

[0156] The self-supervised model is trained using historical electrical operation data, structural data, historical enhanced electrical operation data, and historical electrical feature data from multiple dimensions of each distribution network, and a loss function is calculated. The loss function includes a transformation-invariant loss function and a cross-entropy loss function. The transformation-invariant loss function includes the squared norm of the difference between the historical electrical operation data and the historical enhanced electrical operation data in multiple dimensions, and the cross-entropy loss function includes the negative log-likelihood between the pseudo-label probability distribution and the predicted probability distribution.

[0157] The model is optimized using the backpropagation algorithm until the preset optimization conditions are met, thus obtaining the distribution network fault type model.

[0158] The fault diagnosis device for power distribution networks provided in this embodiment of the invention can execute the fault diagnosis method for power distribution networks provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0159] It is worth noting that in the embodiments of the above-mentioned fault diagnosis device for power distribution networks, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0160] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Figure 4 A block diagram of an exemplary electronic device 4 suitable for implementing embodiments of the present invention is shown. Figure 4 The electronic device 4 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0161] like Figure 4 As shown, electronic device 4 is represented in the form of a general-purpose computing electronic device. The components of electronic device 4 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0162] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0163] Electronic device 4 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 4, including volatile and non-volatile media, removable and non-removable media.

[0164] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 4 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 4 Not shown; usually referred to as a "hard drive"). Although Figure 4 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0165] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0166] Electronic device 4 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with electronic device 4, and / or with any device that enables electronic device 4 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 22. Furthermore, electronic device 4 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 20. Figure 4 As shown, network adapter 20 communicates with other modules of electronic device 4 via bus 18. It should be understood that, although... Figure 4 Not shown, it can be combined with electronic device 4 to use other hardware and / or software modules, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0167] Processing unit 16 executes various functional applications and page displays by running programs stored in system memory 28, such as implementing the power distribution network fault diagnosis method provided in this embodiment of the invention, which includes:

[0168] The nodes in question are identified based on the electrical operation data of each node in the distribution network;

[0169] Based on the suspected node, the structural data of the suspected node is filtered out from the structural data of each node in the distribution network, and the initial fault node is determined according to the structural data of the suspected node and the electrical operation data.

[0170] The electrical operation data of the initial fault node is feature extracted using a preset time window to obtain the electrical feature data of the initial fault node, and the fault time of the initial fault node is determined based on the electrical feature data.

[0171] The electrical operation data is filtered by the fault time to obtain the first electrical operation data, and the target fault node is determined by the first electrical operation data and the preset target power.

[0172] The fault diagnosis result of the distribution network is determined based on the target fault node.

[0173] Of course, those skilled in the art will understand that the processor can also implement the technical solution of the power distribution network fault diagnosis method provided in any embodiment of the present invention.

[0174] This invention provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the program implements, for example, the fault diagnosis method for a power distribution network provided in this invention. The method includes:

[0175] The nodes in question are identified based on the electrical operation data of each node in the distribution network;

[0176] Based on the suspected node, the structural data of the suspected node is filtered out from the structural data of each node in the distribution network, and the initial fault node is determined according to the structural data of the suspected node and the electrical operation data.

[0177] The electrical operation data of the initial fault node is feature extracted using a preset time window to obtain the electrical feature data of the initial fault node, and the fault time of the initial fault node is determined based on the electrical feature data.

[0178] The electrical operation data is filtered by the fault time to obtain the first electrical operation data, and the target fault node is determined by the first electrical operation data and the preset target power.

[0179] The fault diagnosis result of the distribution network is determined based on the target fault node.

[0180] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0181] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0182] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0183] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0184] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0185] Furthermore, the acquisition, storage, use, and processing of data in the technical solution of this invention all comply with the relevant provisions of national laws and regulations.

[0186] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A fault diagnosis method for a power distribution network, characterized in that, The method includes: The nodes in question are identified based on the electrical operation data of each node in the distribution network; Based on the suspected node, the structural data of the suspected node is filtered from the structural data of each node in the distribution network, and the initial fault node is determined according to the structural data of the suspected node and the electrical operation data; the structural data is information on the physical structure and connection relationship of each node in the distribution network. The electrical operation data of the initial fault node is feature extracted using a preset time window to obtain the electrical feature data of the initial fault node, and the fault time of the initial fault node is determined based on the electrical feature data. The electrical operation data is filtered by the fault time to obtain the first electrical operation data, and the target fault node is determined by the first electrical operation data and the preset target power. The fault diagnosis result of the distribution network is determined based on the target fault node.

2. The fault diagnosis method for a power distribution network according to claim 1, characterized in that, The electrical operating data includes voltage, current, and power. Suspicious nodes are identified based on the electrical operating data of each node in the distribution network, including: The calculated power of each node is calculated based on the voltage and current of each node, and the total power of the distribution network is obtained based on the power of each node. The fault weight of each node is determined based on the calculated power, power, and total power of each node. The nodes with fault weights greater than zero are identified as the suspected nodes.

3. The fault diagnosis method for a power distribution network according to claim 2, characterized in that, The initial fault node is determined based on the structural data of the suspected node and the electrical operation data, including: Based on the structural data of the suspected node, the adjacent nodes of the suspected node are determined, and based on the adjacent nodes and the suspected node, the impedance between the adjacent nodes and the suspected node is filtered from the structural data of the suspected node to obtain the adjacent impedance; The power flow power of the suspected node is determined based on the adjacent impedance, the power of the suspected node, and the power of the adjacent node. The suspected nodes whose power flow is greater than a first preset power threshold are identified as the initial fault nodes.

4. The fault diagnosis method for a power distribution network according to claim 2, characterized in that, The preset time window includes a preset first time window and a preset second time window, wherein the preset first time window is smaller than the preset second time window. The electrical feature data includes current feature values ​​and voltage feature values. Feature extraction is performed on the electrical operating data of the initial fault node using the preset time window to obtain the electrical feature data of the initial fault node. The fault time of the initial fault node is determined based on the electrical feature data, including: The voltage characteristic value of the initial fault node at each time moment is determined based on the preset first time window and the voltage of the initial fault node at each time moment, and the current characteristic value of the initial fault node at each time moment is determined based on the preset second time window and the current of the initial fault node at each time moment. The voltage deviation of the initial fault node at each time moment is determined based on the voltage of the initial fault node at each time moment and the corresponding voltage characteristic value, and the current deviation of the initial fault node at each time moment is determined based on the current of the initial fault node at each time moment and the corresponding current characteristic value. The time when the voltage deviation is greater than a preset voltage threshold and the current deviation is greater than a preset current threshold is determined as the fault time of the initial fault node.

5. The fault diagnosis method for a power distribution network according to claim 2, characterized in that, The electrical operating data is filtered based on the fault time to obtain first electrical operating data. The target fault node is then determined using the first electrical operating data and a preset target power, including: Based on the fault time, the power of each node corresponding to the fault time is filtered from the electrical operation data to obtain the first electrical operation data; The power deviation of the fault time is calculated based on the first electrical operation data and the preset target power of each node; The fault time when the power deviation is greater than the second preset power threshold is determined as the target fault time, and the initial fault node corresponding to the target fault time is determined as the target fault node.

6. The fault diagnosis method for a power distribution network according to claim 1, characterized in that, After determining the fault diagnosis result of the distribution network based on the target fault node, the method further includes: If the fault diagnosis result indicates that a fault has occurred, the target fault node and the structural data of the distribution network are input into a pre-trained distribution network fault type model to obtain the fault type of the distribution network.

7. The fault diagnosis method for a power distribution network according to claim 6, characterized in that, The training process of the distribution network fault type model includes: Obtain historical electrical operation data and corresponding structural data for each power distribution network; Data augmentation processing is performed on the historical electrical operation data of each distribution network to obtain the historical enhanced electrical operation data of each distribution network. Multi-dimensional feature extraction is performed on the historical electrical operation data and the historical enhanced electrical operation data of each distribution network to obtain the multi-dimensional historical electrical feature data of each distribution network. The self-supervised model is trained using historical electrical operation data, structural data, historical enhanced electrical operation data, and historical electrical feature data from multiple dimensions of each distribution network, and a loss function is calculated. The loss function includes a transformation-invariant loss function and a cross-entropy loss function. The transformation-invariant loss function includes the squared norm of the difference between the historical electrical operation data and the historical enhanced electrical operation data in multiple dimensions, and the cross-entropy loss function includes the negative log-likelihood between the pseudo-label probability distribution and the predicted probability distribution. The model is optimized using the backpropagation algorithm until the preset optimization conditions are met, thus obtaining the distribution network fault type model.

8. A fault diagnosis device for a power distribution network, characterized in that, The device includes: The first node determination module is used to determine the suspected node based on the electrical operation data of each node in the distribution network; The second node determination module is used to filter out the structural data of the suspected node from the structural data of each node in the distribution network based on the suspected node, and determine the initial fault node based on the structural data of the suspected node and the electrical operation data; the structural data is information on the physical structure and connection relationship of each node in the distribution network. The time determination module is used to extract features from the electrical operation data of the initial fault node using a preset time window, obtain the electrical feature data of the initial fault node, and determine the fault time of the initial fault node based on the electrical feature data. The third node determination module is used to filter the electrical operation data by the fault time to obtain the first electrical operation data, and to determine the target fault node by using the first electrical operation data and the preset target power. The fault diagnosis module is used to determine the fault diagnosis result of the distribution network based on the target fault node.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the fault diagnosis method for the power distribution network as described in any one of claims 1-7.

10. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, implement the fault diagnosis method for the power distribution network as described in any one of claims 1-7.

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