N-1 Verification Method, System, Terminal and Storage Medium for 10kV Power Line

Abstract

The present invention relates to the field of power supply technology, and specifically provides an N-1 verification method, system, terminal and storage medium for a 10 kV line, including: collecting historical fault information, and using an association rule algorithm to mine the correlation between fault lines; simulating a fault scenario on a pre-constructed power grid topology based on the correlation between the fault lines; obtaining first simulation parameters of components of the power grid topology in the fault scenario and second simulation parameters when the fault scenario is eliminated; comparing the first simulation parameters and the second simulation parameters with the standard parameters of the components, and screening out abnormal parameters that do not match the standard parameters; recording the types of the abnormal parameters, the components to which they belong, and the corresponding fault scenarios, where the type is any one of the first simulation parameters or the second simulation parameters. The fault scenarios simulated by the present invention can be close to real fault situations, thereby obtaining more accurate verification results and improving the comprehensiveness of the verification results.

Description

Technical Field

[0001] The present invention belongs to the technical field of power supply, and particularly relates to an N-1 verification method, system, terminal and storage medium for a 10 kV line. Background Art

[0002] Transmission lines with voltages above 10 kV are important members of the power transmission and distribution network, and their stability is crucial for the quality of power transmission. The existing stability detection methods often use the N-1 verification method. The N-1 principle, also known as the single-fault safety inspection rule, is a technical requirement put forward from the perspective of the safe operation of the power grid. In a power system under normal operating conditions, any component (such as a line, generator, transformer, DC monopole, etc.) fails or is disconnected due to a fault, and the power system should be able to maintain stable operation and normal power supply, with other components not overloaded, and the voltage and frequency within the allowable range.

[0003] For the existing N-1 verification methods, after establishing the power grid topology, a line is randomly selected as the fault line for fault simulation. After traversing all lines, the final verification result is obtained. This method cannot fully restore the real fault scenario, and the obtained verification result does not quite match the stability under the actual fault scenario. Summary of the Invention

[0004] Aiming at the problem that the existing technology has a certain difference from the real fault scenario, resulting in inaccurate verification results, the present invention provides an N-1 verification method, system, terminal and storage medium for a 10 kV line to solve the above technical problems.

[0005] In a first aspect, the present invention provides an N-1 verification method for a 10 kV line, including:

[0006] Collecting historical fault information and using the association rule algorithm to mine the correlation between fault lines;

[0007] Simulating a fault scenario on a pre-constructed power grid topology based on the correlation between fault lines;

[0008] Obtaining the first simulation parameters of the components of the power grid topology in the fault scenario and the second simulation parameters when the fault scenario is eliminated;

[0009] Comparing the first simulation parameters and the second simulation parameters with the standard parameters of the components, and screening out the abnormal parameters that do not match the standard parameters;

[0010] Recording the type of the abnormal parameters, the components to which they belong, and the corresponding fault scenarios, where the type is any one of the first simulation parameters or the second simulation parameters.

[0011] In an optional embodiment, historical fault information is collected, and the association between fault lines is mined using the association rule algorithm, including:

[0012] Set the fault component name, fault type, position of the fault component in the power grid topology, fault occurrence time, and fault duration as tags;

[0013] Build a classifier chain, where the classifiers in the classifier chain correspond one-to-one with the tags, and each classifier performs binary classification on the tags;

[0014] Use the classifier chain to extract effective information from each fault message, where the effective information includes the fault component name, fault type, position of the fault component in the power grid topology, fault occurrence time, and fault duration;

[0015] Summarize all the effective information extracted from the historical fault information to obtain a data set;

[0016] Use the association rule algorithm to mine the association relationship between the positions of fault components at the same fault time from the data set;

[0017] Count the fault types and fault durations in the historical fault information.

[0018] In an optional embodiment, using the association rule algorithm to mine the association relationship between the positions of fault components at the same fault time from the data set, including:

[0019] Set a time difference threshold;

[0020] Calculate the fault occurrence time difference between the effective information;

[0021] Generate an association mark for the effective information with the fault occurrence time difference within the time difference threshold range;

[0022] Use the marked effective information to train the association rule algorithm to obtain the association relationship between the positions of fault components.

[0023] In an optional embodiment, simulate a fault scenario on a pre-constructed power grid topology based on the association between fault lines, including:

[0024] Mark the lines in the power grid topology except for the single-radiation lines and the lines with a load of 0 as the lines to be inspected;

[0025] Randomly select a component from the lines to be inspected in the power grid topology as the target fault component;

[0026] Based on the position of the target fault component in the power grid topology and the association relationship between the positions of fault components, obtain the associated fault components of the target fault component;

[0027] Based on the statistical results of fault types and fault duration, randomly assign fault types and fault duration to the target fault components and associated fault components in the power grid topology.

[0028] In a second aspect, the present invention provides an N-1 verification system for a 10 kV line, including:

[0029] An information mining module, configured to collect historical fault information and mine the correlation between fault lines by using an association rule algorithm;

[0030] A scenario simulation module, configured to simulate a fault scenario on a pre-constructed power grid topology based on the correlation between fault lines;

[0031] A first acquisition module, configured to acquire first simulation parameters of components in the power grid topology in a fault scenario and second simulation parameters when the fault scenario is eliminated;

[0032] A second acquisition module, configured to compare the first simulation parameters and the second simulation parameters with the standard parameters of the components, and screen out abnormal parameters that do not match the standard parameters;

[0033] A result recording module, configured to record the types of abnormal parameters, the components to which they belong, and the corresponding fault scenarios, where the types are any one of the first simulation parameters or the second simulation parameters.

[0034] In an optional embodiment, the information mining module includes:

[0035] A label setting unit, configured to set the fault component name, fault type, the position of the fault component in the power grid topology, the fault occurrence time, and the fault duration as labels;

[0036] A classification establishment unit, configured to establish a classifier chain, where the classifiers in the classifier chain correspond to the labels one by one, and each classifier performs binary classification on the labels;

[0037] An information extraction unit, configured to extract valid information from each fault information by using the classifier chain, where the valid information includes the fault component name, fault type, the position of the fault component in the power grid topology, the fault occurrence time, and the fault duration;

[0038] An information aggregation unit, configured to aggregate all the valid information extracted from the historical fault information to obtain a data set;

[0039] An association mining unit, configured to mine the association relationship between the positions of fault components at the same fault time from the data set by using an association rule algorithm;

[0040] An information statistics unit, configured to count the fault types and fault duration in the historical fault information.

[0041] In an optional implementation, the association mining unit includes:

[0042] A threshold setting subunit, configured to set a time difference threshold;

[0043] A time calculation subunit, configured to calculate the time difference of fault occurrences between valid information;

[0044] An association marking subunit, configured to generate an association mark for valid information whose fault occurrence time difference is within the time difference threshold;

[0045] An algorithm training subunit, configured to train an association rule algorithm using the marked valid information to obtain the association relationship between the positions of fault components.

[0046] In an optional implementation, the scenario simulation module includes:

[0047] A line simplification unit, configured to mark lines in the power grid topology other than single-radiation lines and lines with a load of 0 as lines to be inspected;

[0048] A target selection unit, configured to randomly select a component from the lines to be inspected in the power grid topology as a target fault component;

[0049] An association acquisition unit, configured to acquire associated fault components of the target fault component based on the position of the target fault component in the power grid topology and the association relationship between the positions of fault components;

[0050] A parameter allocation unit, configured to randomly allocate a fault type and a fault duration for the target fault component and the associated fault components in the power grid topology based on the statistical results of the fault type and the fault duration.

[0051] In a third aspect, a terminal is provided, including:

[0052] A processor and a memory, where

[0053] The memory is configured to store a computer program,

[0054] The processor is configured to call and run the computer program from the memory, so that the terminal executes the method of the above terminal.

[0055] In a fourth aspect, a computer storage medium is provided. Instructions are stored in the computer-readable storage medium, and when it runs on a computer, the computer is made to execute the methods described in the above aspects.

[0056] The beneficial effects of the present invention are as follows. The N-1 verification method, system, terminal, and storage medium for a 10 kV line provided by the present invention can mine the correlation between faults from historical fault information. After randomly selecting a target faulty component, a fault scenario is simulated based on the correlation, and the fault scenario can be close to the actual fault situation, thus obtaining a more accurate verification result. In addition, by obtaining the component parameters when the fault occurs and recovers, and processing these data, the impact on the power grid topology when the fault occurs and disappears can be taken into account, further improving the comprehensiveness of the verification result.

[0057] In addition, the design principle of the present invention is reliable, the structure is simple, and it has a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0059] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present invention.

[0060] Figure 2 It is a schematic block diagram of the system according to an embodiment of the present invention.

[0061] Figure 3 It is a schematic structural diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0062] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0064] The following explains the key terms that appear in the present invention.

[0065] The Apriori algorithm is a frequent item set algorithm for mining association rules. Its core idea is to mine frequent item sets through two stages: candidate set generation and downward closure detection of episodes. Moreover, the algorithm has been widely applied to various fields such as commerce and network security.

[0066] The N-1 verification method for 10kV lines provided by the embodiments of the present invention is executed by a computer device. Correspondingly, the N-1 verification system for 10kV lines runs in the computer device.

[0067] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present invention. Among them, Figure 1 The execution subject can be an N-1 verification system for 10kV lines. According to different requirements, the order of steps in this flowchart can be changed, and some can be omitted.

[0068] Such as Figure 1 shown, the method includes:

[0069] Step 110, collect historical fault information and use the association rule algorithm to mine the correlation between fault lines;

[0070] Step 120, simulate a fault scenario on the pre-constructed power grid topology based on the correlation between fault lines;

[0071] Step 130, obtain the first simulation parameters of the components of the power grid topology in the fault scenario and the second simulation parameters when the fault scenario is eliminated;

[0072] Step 140, compare the first simulation parameters and the second simulation parameters with the standard parameters of the components, and screen out the abnormal parameters that do not match the standard parameters;

[0073] Step 150, record the type of the abnormal parameters, the components to which they belong, and the corresponding fault scenarios, where the type is any one of the first simulation parameters or the second simulation parameters.

[0074] For the convenience of understanding the present invention, the principle of the N-1 verification method for 10kV lines of the present invention is further described below in combination with the process of performing N-1 verification on 10kV lines in the embodiments.

[0075] Specifically, the N-1 verification method for 10kV lines includes:

[0076] S1. Collect historical fault information and use the association rule algorithm to mine the correlation between fault lines.

[0077] S101. In the plant, equivalent nodes are formed according to the closed switches and all the branches connected to them (equivalent node analysis); then the formed busbars are connected into electrical islands according to the connection relationship between the plant and the station tie lines (electrical island analysis). The topology analysis method adopts the tree search method, which is the most widely used topology analysis method in current network topology analysis. It performs network topology analysis by searching for the adjacent nodes of the node, which is divided into depth first search (DFS) and breadth first search (BFS). The breadth first search method only visits each vertex once, while the depth first search method requires backtracking, so some nodes will be visited multiple times, which increases the algorithm overhead. This implementation adopts breadth first search to construct the power grid topology.

[0078] S102: After obtaining the power grid topology, actual component data is collected through the GIS system, and based on the corresponding relationship between the actual components and the components in the power grid topology, the actual component data is bound to the component names of the corresponding components in the power grid topology, and the bound data is saved in the database.

[0079] Fault information from the past year is retrieved from the database as historical fault information, which includes the name of the fault component, the fault type, the location of the fault component in the power grid topology, the fault occurrence time, and the fault duration.

[0080] Since the formats of historical fault information records are different, it is necessary to extract valuable information from them, that is, to extract the fault component name, fault type, location of the fault component in the power grid topology, fault occurrence time and fault duration. The extraction method is as follows:

[0081] The fault component name, fault type, fault component location in the power grid topology, fault occurrence time and fault duration are set as labels; a classifier chain is established, and the classifiers in the classifier chain correspond to the labels one by one, and each classifier performs binary classification on the labels; the classifier chain is used to extract valid information from each fault information, and the valid information includes the fault component name, fault type, fault component location in the power grid topology, fault occurrence time and fault duration. Among them, the label of the classifier chain contains the logistic regression model of each of the five labels.

[0082] S103, summarizing all valid information extracted from historical fault information to obtain a data set; using an association rule algorithm to mine the association relationship between the locations of faulty components at the same fault time from the data set.

[0083] Set the time difference threshold; calculate the time difference between fault occurrences among valid information; generate an association mark for the valid information whose time difference between fault occurrences is within the time difference threshold; use the marked valid information to train the association rule algorithm to obtain the association relationship between the positions of fault components.

[0084] For example, set the time difference threshold to 200S, convert the fault occurrence time of the valid information into date and occurrence moment, and convert the time unit of the occurrence moment into seconds. First, screen out the valid information corresponding to the fault occurrence times with the same date and divide them into the same group. Calculate the difference between the occurrence moments of the members within the group, and mark the valid information with a difference within 200 as associated.

[0085] Training method of the association rule algorithm (Apriori algorithm):

[0086] Input: data set D, support threshold a

[0087] Output: the largest frequent k-item set

[0088] (1) Scan the entire data set to obtain all the data that has appeared, which serves as the candidate frequent 1-item set. k = 1, and the frequent 0-item set is an empty set.

[0089] (2) Mine the frequent k-item set:

[0090] a) Scan the data to calculate the support of the candidate frequent k-item set;

[0091] b) Remove the data sets in the candidate frequent k-item set with support lower than the threshold to obtain the frequent k-item set. If the obtained frequent k-item set is empty, directly return the set of frequent k - 1-item sets as the algorithm result, and the algorithm ends. If the obtained frequent k-item set has only one item, directly return the set of frequent k-item sets as the algorithm result, and the algorithm ends;

[0092] c) Based on the frequent k-item set, connect to generate the candidate frequent k + 1-item set.

[0093] (3) Let k = k + 1, and transfer to step 2.

[0094] S104. Count the fault types and fault durations in the historical fault information.

[0095] Extract the fault types and fault durations from the valid information, perform summary and deduplication processing to obtain the statistical results.

[0096] S2. Simulate the fault scenario on the pre-constructed power grid topology based on the correlation between the fault lines.

[0097] Mark the lines in the power grid topology except the single - radiation lines and the lines with a load of 0 as the lines to be inspected; randomly select components from the lines to be inspected in the power grid topology as the target fault components; based on the positions of the target fault components in the power grid topology and the correlation relationships between the positions of the fault components, obtain the associated fault components of the target fault components; based on the statistical results of the fault types and fault duration, randomly assign fault types and fault duration to the target fault components and the associated fault components in the power grid topology.

[0098] Specifically, exclude the data that obviously cannot pass the verification and input them into the result table. In addition, screen the data that can obviously pass the verification and input them into the result table. The data that obviously cannot pass the verification includes the single - radiation lines obtained through topology and wiring mode analysis. Such lines have no transfer path. Once a fault occurs in the outlet section of the substation bus, it will inevitably lead to a large - area power outage in the area supplied by this outgoing line, and the line is directly judged as a line that cannot pass the verification. The data that can obviously pass the verification mainly refers to the situation where the line load is 0. In this case, it is directly determined that the line passes the N - 1 verification. The N - 1 verification pre - processing module effectively reduces the number of components to be verified, simplifies the verification process, and improves the verification speed.

[0099] Traverse the lines to be inspected and simulate fault scenarios for them. The method of simulating fault scenarios is to input the positions of the target fault components in the power grid topology into the association rule algorithm obtained in step S1 that can detect the association relationships of fault positions to obtain the associated fault components of the target fault components. Then, use a random function to randomly select a fault type and a fault duration from the statistical results in step S1, and assign the randomly selected fault type and fault duration to the target fault components or the associated fault components. After that, simulate the fault type and the fault duration for the corresponding components in the power grid topology, and the fault scenario can be simulated.

[0100] S3. Obtain the first simulation parameters of the components in the power grid topology in the fault scenario and the second simulation parameters when the fault scenario is eliminated.

[0101] Construct a power flow calculation model, and input the component state change situations when the fault scenario occurs and the component state change situations when the fault scenario disappears into this model, then the first simulation parameters and the second simulation parameters can be obtained.

[0102] Specifically, construct an optimal power flow calculation model. In this embodiment, the optimal power flow calculation model adopts the Newton algorithm, and trains the Newton algorithm based on the actual operation data of the power grid until the algorithm converges.

[0103] S4. Compare the first simulation parameters and the second simulation parameters with the standard parameters of the components, and screen out the abnormal parameters that do not match the standard parameters.

[0104] The first simulation parameter and the second simulation parameter are both specific voltage values of each component. Based on the standard voltage values of each component, the corresponding specific voltage values are compared, and the specific voltage values that do not match the standard voltage values are marked as abnormal parameters.

[0105] S5. Record the type of the abnormal parameter, the component to which it belongs, and the corresponding fault scenario, where the type is any one of the first simulation parameter or the second simulation parameter.

[0106] Record the component and the fault scenario corresponding to the abnormal parameter in the first simulation parameter; record the component and the fault scenario corresponding to the abnormal parameter in the second simulation parameter.

[0107] In some embodiments, the 10kV line N-1 verification system 200 may include multiple functional modules composed of computer program segments. The computer programs of each program segment in the 10kV line N-1 verification system 200 may be stored in the memory of the computer device and executed by at least one processor to execute (see details in Figure 1 the description) the functions of the 10kV line N-1 verification.

[0108] In this embodiment, according to the functions it executes, the 10kV line N-1 verification system 200 may be divided into multiple functional modules, as Figure 2 shown. The functional modules may include: an information mining module 210, a scenario simulation module 220, a first acquisition module 230, a second acquisition module 240, and a result recording module 250. The module referred to in the present invention means a series of computer program segments that can be executed by at least one processor and can complete fixed functions, and are stored in the memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0109] The information mining module 210 is used to collect historical fault information and mine the correlation between faulty lines by using the association rule algorithm;

[0110] The scenario simulation module 220 is used to simulate fault scenarios on a pre-constructed power grid topology based on the correlation between faulty lines;

[0111] The first acquisition module 230 is used to acquire the first simulation parameter of the components of the power grid topology in the fault scenario and the second simulation parameter when the fault scenario is eliminated;

[0112] The second acquisition module 240 is used to compare the first simulation parameter and the second simulation parameter with the standard parameters of the components, and screen out the abnormal parameters that do not match the standard parameters;

[0113] The result recording module 250 is used to record the type of abnormal parameters, the components to which they belong, and the corresponding fault scenarios, where the type is any one of the first analog parameter or the second analog parameter.

[0114] Optionally, as an embodiment of the present invention, the information mining module includes:

[0115] The label setting unit is used to set the fault component name, fault type, the position of the fault component in the power grid topology, the fault occurrence time, and the fault duration as labels;

[0116] The classification establishment unit is used to establish a classifier chain, where the classifiers in the classifier chain correspond one-to-one with the labels, and each classifier performs binary classification on the labels;

[0117] The information extraction unit is used to extract effective information from each fault message by using the classifier chain, and the effective information includes the fault component name, fault type, the position of the fault component in the power grid topology, the fault occurrence time, and the fault duration;

[0118] The information summary unit is used to summarize all the effective information extracted from the historical fault messages to obtain a data set;

[0119] The association mining unit is used to mine the association relationship between the positions of fault components at the same fault time from the data set by using the association rule algorithm;

[0120] The information statistics unit is used to count the fault types and fault durations in the historical fault messages.

[0121] Optionally, as an embodiment of the present invention, the association mining unit includes:

[0122] The threshold setting subunit is used to set the time difference threshold;

[0123] The time calculation subunit is used to calculate the fault occurrence time difference between the effective information;

[0124] The association marking subunit is used to generate an association mark for the effective information whose fault occurrence time difference is within the time difference threshold;

[0125] The algorithm training subunit is used to train the association rule algorithm by using the marked effective information to obtain the association relationship between the positions of fault components.

[0126] Optionally, as an embodiment of the present invention, the scenario simulation module includes:

[0127] The line simplification element is used to mark the lines in the power grid topology except for the single-radiation lines and the lines with a load of 0 as the lines to be inspected;

[0128] A target selection unit, configured to randomly select a component from the lines to be inspected in the power grid topology as a target fault component;

[0129] An associated acquisition unit, configured to acquire the associated fault components of the target fault component based on the position of the target fault component in the power grid topology and the association relationship between the positions of the fault components;

[0130] A parameter allocation unit, configured to randomly allocate a fault type and a fault duration for the target fault component and the associated fault components in the power grid topology based on the statistical results of the fault type and the fault duration.

[0131] Figure 3 FIG. 10 is a schematic structural diagram of a terminal 300 provided by an embodiment of the present invention. The terminal 300 can be used to execute the N-1 verification method for a 10 kV line provided by the embodiment of the present invention.

[0132] Among them, the terminal 300 may include: a processor 310, a memory 320, and a communication module 330. These components communicate through one or more buses. Those skilled in the art can understand that the structure of the server shown in the figure does not constitute a limitation to the present invention. It can be a bus structure, a star structure, and may also include more or fewer components than those shown in the figure, or combine some components, or different component arrangements.

[0133] Among them, the memory 320 can be used to store the execution instructions of the processor 310. The memory 320 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can execute some or all of the steps in the above method embodiments.

[0134] The processor 310 is the control center of the storage terminal, connecting various parts of the entire electronic terminal through various interfaces and circuits. By running or executing software programs and / or modules stored in the memory 320, and by invoking data stored in the memory, it performs various functions of the electronic terminal and / or processes data. The processor may be composed of an integrated circuit (IC), for example, it may be composed of a single packaged IC, or it may be composed of multiple packaged ICs with the same or different functions connected together. For example, the processor 310 may only include a central processing unit (CPU). In the embodiments of the present invention, the CPU may be a single operation core or may include multiple operation cores.

[0135] The communication module 330 is used to establish a communication channel so that the storage terminal can communicate with other terminals. It receives user data sent by other terminals or sends user data to other terminals.

[0136] The present invention also provides a computer storage medium. Among them, the computer storage medium can store a program, and when the program is executed, it can include some or all of the steps in the embodiments provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0137] Therefore, the present invention mines the correlation between faults from historical fault information, and then after randomly selecting a target fault component, based on the correlation, it simulates a fault scenario. The fault scenario can be close to the real fault situation, and thus a more accurate verification result can be obtained. In addition, by obtaining the component parameters when the fault occurs and recovers, and processing these data, it is possible to pay attention to the impact on the power grid topology when the fault occurs and disappears, further improving the comprehensiveness of the verification result. The technical effects that can be achieved in this embodiment can be seen in the above description and will not be elaborated here.

[0138] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solutions in the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disc, etc., which can store program codes, and includes several instructions to enable a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0139] For the same or similar parts among the various embodiments in this specification, reference can be made to each other. In particular, for the terminal embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for the relevant parts, reference can be made to the descriptions in the method embodiments.

[0140] In several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of systems or modules can be in electrical, mechanical or other forms.

[0141] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they can be located in one place, or can be distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0142] In addition, in each embodiment of the present invention, the functional modules can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0143] Although the present invention has been described in detail by referring to the accompanying drawings and in conjunction with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, those of ordinary skill in the art can make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions should all be within the scope covered by the present invention. / Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, and all should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for N-1 verification of a 10 kV line, characterized in that, Including: Collect historical fault information and use the association rule algorithm to mine the correlation between fault lines; Simulate fault scenarios on a pre-constructed power grid topology based on the correlation between fault lines; Obtain the first simulation parameters of the components in the power grid topology in the fault scenario and the second simulation parameters when the fault scenario is eliminated; Compare the first simulation parameters and the second simulation parameters with the standard parameters of the components, and screen out the abnormal parameters that do not match the standard parameters; Record the type of abnormal parameters, the components to which they belong, and the corresponding fault scenarios, where the type is either the first simulation parameter or the second simulation parameter; Collect historical fault information and use the association rule algorithm to mine the correlation between fault lines, including: Set the fault component name, fault type, the position of the fault component in the power grid topology, the fault occurrence time, and the fault duration as labels; Establish a classifier chain, where the classifiers in the classifier chain correspond one-to-one with the labels, and each classifier performs binary classification on the labels; Use the classifier chain to extract effective information from each fault message, where the effective information includes the fault component name, fault type, the position of the fault component in the power grid topology, the fault occurrence time, and the fault duration; Summarize all the effective information extracted from the historical fault information to obtain a data set; Use the association rule algorithm to mine the association relationship between the positions of fault components at the same fault time from the data set; Count the fault types and fault durations in the historical fault information; Simulate fault scenarios on a pre-constructed power grid topology based on the correlation between fault lines, including: Mark the lines in the power grid topology except for the single-radiation lines and the lines with a load of 0 as the lines to be inspected; Randomly select components from the lines to be inspected in the power grid topology as the target fault components; Based on the position of the target fault component in the power grid topology and the association relationship between the positions of fault components, obtain the associated fault components of the target fault component; Based on the statistical results of the fault type and the fault maintenance time, randomly assign the fault type and the fault maintenance time to the target fault component and the associated fault components in the power grid topology.

2. The method according to claim 1, characterized in that, Use the association rule algorithm to mine the association relationship between the positions of fault components at the same fault time from the data set, including: Set the time difference threshold; Calculate the fault occurrence time difference between the effective information; Generate association marks for the effective information with the fault occurrence time difference within the time difference threshold; Use the marked effective information to train the association rule algorithm to obtain the association relationship between the positions of fault components.

3. An N-1 verification system for a 10 kV line, characterized in that, Including: An information mining module for collecting historical fault information and using the association rule algorithm to mine the correlation between fault lines; A scenario simulation module for simulating fault scenarios on a pre-constructed power grid topology based on the correlation between fault lines; A first acquisition module for obtaining the first simulation parameters of the components in the power grid topology in the fault scenario and the second simulation parameters when the fault scenario is eliminated; A second acquisition module for comparing the first simulation parameters and the second simulation parameters with the standard parameters of the components and screening out the abnormal parameters that do not match the standard parameters; A result recording module, configured to record the type of abnormal parameters, the components to which they belong, and the corresponding fault scenarios, where the type is any one of the first analog parameter or the second analog parameter; The information mining module includes: A label setting unit, configured to set the faulty component name, fault type, the location of the faulty component in the power grid topology, the fault occurrence time, and the fault duration as labels; A classification establishment unit, configured to establish a classifier chain, where the classifiers in the classifier chain correspond to the labels one by one, and each classifier performs binary classification on the labels; An information extraction unit, configured to extract valid information from each fault message by using the classifier chain, where the valid information includes the faulty component name, fault type, the location of the faulty component in the power grid topology, the fault occurrence time, and the fault duration; An information summarization unit, configured to summarize all the valid information extracted from the historical fault messages to obtain a data set; An association mining unit, configured to use an association rule algorithm to mine the association relationship between the locations of faulty components at the same fault time from the data set; An information statistics unit, configured to count the fault types and fault durations in the historical fault messages; The scenario simulation module includes: A line simplification element, configured to mark the lines in the power grid topology other than the single-radiation lines and the lines with a load of 0 as lines to be inspected; A target selection unit, configured to randomly select a component from the lines to be inspected in the power grid topology as the target faulty component; An association acquisition unit, configured to acquire the associated faulty components of the target faulty component based on the location of the target faulty component in the power grid topology and the association relationship between the locations of faulty components; A parameter allocation unit, configured to randomly allocate a fault type and a fault duration to the target faulty component and the associated faulty components in the power grid topology based on the statistical results of the fault type and the fault duration; 4. The system according to claim 3, wherein The association mining unit includes: A threshold setting subunit, configured to set a time difference threshold; A time calculation subunit, configured to calculate the fault occurrence time difference between the valid information; An association marking subunit, configured to generate an association mark for the valid information with a fault occurrence time difference within the time difference threshold; An algorithm training subunit, configured to use the marked valid information to train the association rule algorithm to obtain the association relationship between the locations of faulty components; 5. A terminal, characterized in that, It includes: A memory, configured to store the N-1 check program for the 10kV line; A processor, configured to implement the steps of the N-1 check method for the 10kV line as described in any one of claims 1-2 when executing the N-1 check program for the 10kV line.

6. A computer-readable storage medium storing a computer program, characterized in that, The N-1 check program for the 10kV line is stored on the readable storage medium, and when the N-1 check program for the 10kV line is executed by the processor, the steps of the N-1 check method for the 10kV line as described in any one of claims 1-2 are implemented.

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