CN115935825A - Power distribution network fault processing method and device based on digital twin model - Google Patents
Power distribution network fault processing method and device based on digital twin model Download PDFInfo
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Abstract
The invention provides a power distribution network fault processing method and device based on a digital twin model. According to the method, the digital twin model is constructed through the fault sample and the system function, so that the state monitoring and fault detection of each node in the power distribution network are realized. The method combines the measurement and control data, the protection data, the wave recording data and other diagnosis data of a plurality of adjacent nodes, comprehensively determines the fault condition of each node in the power distribution network, obtains fault information of fault feeders, fault types, fault positions and the like of fault points, and realizes the fault analysis and processing of the active power distribution network. The method and the device divide the scenes of the diagnostic data of each node in the active power distribution network, independently analyze the faults based on each divided test scene, and improve the effectiveness and the accuracy of the fault analysis of the active power distribution network.
Description
Technical Field
The invention relates to the technical field of power supply of a power grid, in particular to a method and a device for processing faults of a power distribution network based on a digital twin model.
Background
Along with the rapid increase of the electric load of the urban distribution network and the increasing improvement of the requirement on the reliability of electric power, the load scale is gradually increased, and the topological structure of the distribution network is more and more complex; in addition, with the application of distributed energy access and distributed power supply, the operation complexity of the power distribution network is greatly improved. For the power distribution network with a complex topological structure, if systematic faults occur, the consequences are very serious, even electrical accidents can be caused, and huge losses are brought to social life. The power failure accident in the power distribution network can never be avoided, the power failure accident caused by weather factors and the fault caused by trees cause the power failure of users, and the reliability index of the power distribution network is influenced. However, many power outages are caused by recurring defects or by gradual failure of the instrument, which may occur weeks to months before the power outage due to eventual failure occurs. Because of this, conventional power distribution network fault diagnosis techniques for handling faults have not been able to accommodate the need for safe, economical, and reliable operation of active power distribution networks.
In order to meet the requirements, research on intelligent fault diagnosis and prediction technology of the power distribution network is urgently needed. The equipment in the distribution network may have a tendency to malfunction due to some external factors during long-term operation, or some equipment may still operate normally after a plurality of disturbances or failures, but some defects may exist due to the accumulation effect. Therefore, fault prediction is needed for these potential safety hazards. The failure prediction can be based on the current operation condition of the equipment as a starting point, and predict whether the equipment has a failure trend in the future according to the existing operation environment condition and historical data of the equipment.
Factors such as power electronization of electrical equipment in an active power distribution network, access of a large number of distributed power supplies, and change of power flow from one direction to two directions lead to increase of difficulty in fault diagnosis. And the traditional method based on the current equipment operation condition and historical data prediction is not suitable for the active power distribution network. A new power distribution network fault analysis method is urgently needed to solve the problem of fault analysis of an active power distribution network.
Disclosure of Invention
The invention provides a power distribution network fault processing method and device based on a digital twin model, which can realize fault analysis and processing of an active power distribution network.
In a first aspect, the invention provides a power distribution network fault processing method based on a digital twin model, which comprises the following steps: acquiring a fault sample, wherein the fault sample takes diagnosis data of a plurality of nodes before a fault point and a plurality of nodes after the fault point as input, and takes a fault feeder line and a fault type of the fault point as output, and the diagnosis data comprises measurement and control data, protection data and wave recording data; constructing a digital twin model based on the fault samples and the system functions of all fault types; acquiring diagnostic data of each node in the power distribution network in real time, and inputting the diagnostic data of each node into a digital twin model to obtain fault information of a fault point in the power distribution network, wherein the fault information comprises a fault feeder line, a fault type and a fault position; and processing the fault of the power distribution network based on the fault information.
In one possible implementation, the digital twin model includes a neural network module and a system function module; inputting the diagnosis data of each node into a digital twin model to obtain fault information of a fault point in the power distribution network, wherein the fault information comprises the following steps: splitting the diagnostic data of each node to obtain a plurality of data slices; the method comprises the steps that each data slice comprises diagnostic data of a plurality of nodes in front of a central node and a plurality of nodes behind the central node, and the central node is any one of the nodes; inputting the plurality of data slices into a neural network module of a digital twin model to obtain a center node with a fault and a target fault type of the center node with the fault; determining a system function corresponding to the target fault type in the system functions of all fault types based on the target fault type; and determining fault information of fault points in the power distribution network based on the diagnostic data of the central nodes with faults and the system function corresponding to the target fault type.
In one possible implementation, constructing a digital twin model based on the fault samples and the system functions for each fault type includes: based on the fault sample, carrying out neural network training to obtain a neural network module of the digital twin model; constructing a system function module of a digital twin model based on the system functions of all fault types; and constructing a digital twin model based on the neural network module and the system function module.
In one possible implementation, the system function module for constructing the digital twin model based on the system functions of the fault types includes: for any position in the power distribution network, constructing a system function module of a digital twin model based on the system functions of all fault types and the normal value range and the fault value range of the calculated data of the position; the calculation data of the position is calculated after the diagnosis data of the position is input into the system function of each fault type.
In a possible implementation manner, the neural network module that performs neural network training based on the failure sample to obtain the digital twin model includes: dividing fault samples based on each test scene in the power distribution network to obtain fault samples of a plurality of test scenes; for a plurality of test scenes, taking diagnosis data of a plurality of nodes before a fault point and a plurality of nodes after the fault point as input, taking a fault feeder line and a fault type of the fault point as output, and respectively carrying out neural network training to obtain a plurality of sub-neural network modules; a neural network module of the digital twin model is determined based on the plurality of sub-neural network modules.
In one possible implementation manner, processing the fault of the power distribution network based on the fault information includes: and if the fault type is arc discharge or low-current grounding, disconnecting the upstream protection device of the fault point.
In one possible implementation, the digital twin model further comprises an auxiliary decision module; based on the fault information, the fault of the power distribution network is processed, and the method comprises the following steps: generating a plurality of decision information corresponding to the fault information based on the auxiliary decision module; outputting decision information; receiving confirmation information input by power workers; the confirmation information comprises decision information confirmed by power workers; and processing the fault of the power distribution network based on the confirmation information.
In a second aspect, an embodiment of the present invention provides a power distribution network fault processing apparatus based on a digital twin model, including: the communication module is used for acquiring fault samples, the fault samples take the diagnosis data of a plurality of nodes before a fault point and a plurality of nodes after the fault point as input, the fault feeder line and the fault type of the fault point as output, and the diagnosis data comprises measurement and control data, protection data and wave recording data; the processing module is used for constructing a digital twin model based on the fault samples and the system functions of all fault types; the communication module is also used for acquiring the diagnostic data of each node in the power distribution network in real time; the processing module is further used for inputting the diagnostic data of each node into the digital twin model to obtain fault information of a fault point in the power distribution network, wherein the fault information comprises a fault feeder line, a fault type and a fault position; and processing the fault of the power distribution network based on the fault information.
In one possible implementation, the digital twin model includes a neural network module and a system function module; the processing module is specifically used for splitting the diagnostic data of each node to obtain a plurality of data slices; the diagnostic data of a plurality of nodes in front of a central node and a plurality of nodes behind the central node are used for each data slice, and the central node is any one of the nodes; inputting the plurality of data slices into a neural network module of a digital twin model to obtain a center node with a fault and a target fault type of the center node with the fault; determining a system function corresponding to the target fault type in the system functions of all fault types based on the target fault type; and determining fault information of a fault point in the power distribution network based on the diagnostic data of the center node with the fault and a system function corresponding to the target fault type.
In a possible implementation manner, the processing module is specifically configured to perform neural network training based on the fault sample to obtain a neural network module of the digital twin model; constructing a system function module of a digital twin model based on the system functions of all fault types; and constructing a digital twin model based on the neural network module and the system function module.
In a possible implementation manner, the processing module is specifically configured to construct a system function module of the digital twin model for any position in the power distribution network based on the system functions of each fault type and the normal value range and the fault value range of the calculation data of the position; the calculated data of the position is calculated after the diagnostic data of the position is input into the system function of each fault type.
In a possible implementation manner, the processing module is specifically configured to divide the fault samples based on each test scenario in the power distribution network to obtain fault samples of multiple test scenarios; for a plurality of test scenes, taking the diagnostic data of a plurality of nodes before a fault point and a plurality of nodes after the fault point as input, taking a fault feeder line and a fault type of the fault point as output, and respectively carrying out neural network training to obtain a plurality of sub-neural network modules; a neural network module of the digital twin model is determined based on the plurality of sub-neural network modules.
In a possible implementation, the processing module is specifically configured to disconnect the upstream protection device of the fault point if the fault type is arc discharge or low-current grounding.
In one possible implementation, the digital twin model further comprises an auxiliary decision module; the processing module is specifically used for generating a plurality of decision information corresponding to the fault information based on the auxiliary decision module; the communication module is specifically used for outputting decision information; receiving confirmation information input by power workers; the confirmation information comprises decision information confirmed by power workers; and the processing module is specifically used for processing the fault of the power distribution network based on the confirmation information.
In a third aspect, an embodiment of the present invention provides a regulation and control device for an electric power system, including a memory and a processor, where the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to perform the steps of the method according to any one of the foregoing first aspect and possible implementation manners of the first aspect.
In a fourth aspect, an embodiment of the present invention provides an electric power system, where the electric power system includes the frequency control device according to any one of the foregoing possible implementations of the first aspect, and performs the steps of the method according to any one of the foregoing possible implementations of the first aspect, so as to implement frequency control of the electric power system.
In a fifth aspect, the present invention provides a computer-readable storage medium, where a computer program is stored, where the computer program is configured to, when executed by a processor, implement the steps of the method according to the first aspect and any possible implementation manner of the first aspect.
The invention provides a power distribution network fault processing method and device based on a digital twin model. Because the diagnosis data comprises measurement and control data, protection data and wave recording data, the invention can analyze the upstream and downstream and the trend of the power flow of each node in the power distribution network, thereby realizing the fault analysis and processing of each node in the active power distribution network.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the embodiments or the prior art description will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings may be obtained according to these drawings without inventive labor.
FIG. 1 is a schematic flow diagram of a fault handling method for a power distribution network based on a digital twin model according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a power distribution network fault processing device based on a digital twin model according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In the description of the present invention, "/" means "or" unless otherwise specified, for example, a/B may mean a or B. "and/or" herein is merely an association describing an associated object, and means that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. Further, "at least one" or "a plurality" means two or more. The terms "first", "second", and the like do not necessarily limit the number and execution order, and the terms "first", "second", and the like do not necessarily limit the difference.
In the embodiments of the present application, words such as "exemplary" or "for example" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present relevant concepts in a concrete fashion for ease of understanding.
Furthermore, the terms "including" and "having," and any variations thereof, as referred to in the description of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or modules is not limited to only those steps or modules recited, but may alternatively include other steps or modules not recited, or may alternatively include other steps or modules inherent to such process, method, article, or apparatus.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following description is made by way of specific embodiments with reference to the accompanying drawings.
Fig. 1 is a schematic flow diagram of a power distribution network fault processing method based on a digital twin model according to an embodiment of the present invention. The execution main body of the method is a power distribution network fault processing device. The method comprises steps S101-S105.
And S101, obtaining a fault sample.
In the embodiment of the application, the diagnosis data of a plurality of nodes before the fault point and a plurality of nodes after the fault point are used as input of the fault sample, and the fault feeder line and the fault type of the fault point are used as output.
The diagnostic data comprises measurement and control data, protection data and wave recording data.
Illustratively, the measurement and control data includes voltage data, current data, frequency data, and phase data of each node in the power distribution network.
Illustratively, the protection data includes protection action information and alarm information of each node in the power distribution network after a fault is judged by the protection device according to the measurement and control data. Such as the open/close state of the circuit breaker and the action state of the zero sequence protector.
Illustratively, the recording data is waveform data of electrical parameters of each node.
S102, constructing a digital twin model based on the fault samples and the system functions of the fault types.
In some embodiments, the digital twin model includes a neural network module and a system function module;
in some embodiments, the digital twin model further comprises an aid decision module.
In some embodiments, a system function is used to determine if a node has failed.
It should be noted that different fault types have different system functions. For example, the system functions of soliton discharge and low current ground are different.
As a possible implementation manner, the distribution network fault processing apparatus may construct a digital twin model based on steps S1021 to S1023.
And S1021, carrying out neural network training based on the fault sample to obtain a neural network module of the digital twin model.
As a possible implementation manner, the distribution network fault processing apparatus may obtain the neural network module of the digital twin model based on steps A1-A3.
A1, dividing fault samples based on each test scene in the power distribution network to obtain fault samples of a plurality of test scenes.
And A2, for a plurality of test scenes, respectively carrying out neural network training by taking the diagnostic data of a plurality of nodes before and after the fault point as input and taking the fault feeder line and the fault type of the fault point as output to obtain a plurality of sub-neural network modules.
And A3, determining a neural network module of the digital twin model based on the plurality of sub-neural network modules.
And S1022, constructing a system function module of the digital twin model based on the system functions of all fault types.
As a possible implementation manner, for any position in the power distribution network, the power distribution network fault processing apparatus may construct the system function module of the digital twin model based on the system function of each fault type and the normal value range and the fault value range of the calculation data of the position.
The calculated data of the position is calculated after the diagnostic data of the position is input into the system function of each fault type.
And S1023, constructing a digital twin model based on the neural network module and the system function module.
As a possible implementation manner, the power distribution network fault processing apparatus may use the input of the neural network module as the input of the digital twin model, use the output of the neural network module as the input of the system function module, and use the output of the system function module as the output of the digital twin model to construct the digital twin model.
S103, acquiring the diagnostic data of each node in the power distribution network in real time.
In some embodiments, the diagnostic data for each node includes measurement and control data, protection data, and recording data for each node
As a possible implementation manner, the power distribution network fault processing apparatus may periodically detect the diagnostic data of each node to achieve real-time acquisition.
And S104, inputting the diagnostic data of each node into a digital twin model to obtain fault information of a fault point in the power distribution network.
In the embodiment of the application, the fault information includes a fault feeder, a fault type and a fault position.
As a possible implementation manner, the power distribution network fault processing apparatus may determine fault information of a fault point in the power distribution network based on steps S1041-S1044.
And S1041, splitting the diagnostic data of each node to obtain a plurality of data slices.
And each data slice comprises diagnostic data of a plurality of nodes in front of a central node and a plurality of nodes behind the central node, and the central node is any one of the nodes.
S1042, inputting the plurality of data slices into a neural network module of the digital twin model to obtain a center node with a fault and a target fault type of the center node with the fault.
And S1043, based on the target fault type, determining a system function corresponding to the target fault type in the system functions of the fault types.
S1044 is that fault information of fault points in the power distribution network is determined based on the diagnosis data of the center nodes with the faults and the system functions corresponding to the target fault types.
And S105, processing the fault of the power distribution network based on the fault information.
As a possible implementation, if the fault type is arc discharge or low current grounding, the distribution network fault handling device may disconnect the upstream protection device of the fault point.
As a possible implementation manner, the power distribution network fault processing apparatus may output the decision information through the auxiliary decision module to process the fault of the power distribution network.
For example, the power distribution network fault processing apparatus may implement fault processing on the power distribution network through steps S1051 to S1054.
S1051, generating a plurality of decision information corresponding to the fault information based on the auxiliary decision module.
And S1052, outputting the decision information.
And S1053, receiving confirmation information input by the power worker.
In some embodiments, the confirmation information includes decision information confirmed by the power personnel.
And S1054, processing the fault of the power distribution network based on the confirmation information.
The invention provides a power distribution network fault processing method and device based on a digital twin model. Because the diagnosis data comprises measurement and control data, protection data and wave recording data, the invention can analyze the upstream and downstream and the trend of the power flow of each node in the power distribution network, thereby realizing the fault analysis and processing of each node in the active power distribution network.
It should be noted that, the invention constructs a digital twin model aiming at a three-dimensional virtual space model of power distribution network physics-data, and sets a neural network module and a system function module. Establishing a numerical precision evaluation system based on model precision based on a neural network and a system function; and by combining an index system with the existing relevant standard guide rules and communication standards of the power distribution network, a standardized and normalized experimental verification scheme and method for scene generation capabilities of multi-source information perception, scene recognition, intelligent calculation, aid decision and the like are provided.
It should be noted that, aiming at the difference problem of different numerical solutions of a large-scale system, the invention utilizes characteristic values representing system equation characteristics to establish a model verification system, provides a quantitative verification technology of power distribution network twin simulation and a simulation algorithm sensitivity analysis verification method based on a leading characteristic value, and establishes a verification scheme and a method of typical comprehensive assistant decision support scenes such as distributed power supply inverter control, fault diagnosis, power supply recovery, power distribution network fault early warning and the like.
It should be noted that, the invention combines with typical engineering test environment, selects the scene set to be tested, designs the simulation test scheme, and establishes the effective mapping between the key features of the scene to be tested and the test result of the test scheme; carrying out feature substitution on non-key objects in the scheme by using a virtual simulation technology, a feature aggregation mode and the like; designing a reasonable monitoring scheme, and recording uncertain factors in the test process; and checking a model, a simulation method and a calculation result of the developed digital twin modeling and simulation verification platform of the power distribution network based on the experimental record, so as to realize scene verification of the typical demonstration project.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by functions and internal logic of the process, and should not limit the implementation process of the embodiments of the present invention in any way.
The following are embodiments of the apparatus of the invention, reference being made to the corresponding method embodiments described above for details which are not described in detail therein.
Fig. 3 shows a schematic structural diagram of a power distribution network fault processing device based on a digital twin model according to an embodiment of the present invention. The power distribution network fault processing apparatus 200 includes a communication module 201 and a processing module 202.
The communication module 201 is configured to obtain a fault sample, where the fault sample takes the diagnostic data of a plurality of nodes before and after a fault point as input, and takes a fault feeder line and a fault type of the fault point as output, and the diagnostic data includes measurement and control data, protection data, and wave recording data.
And the processing module 202 is used for constructing a digital twin model based on the fault samples and the system functions of the fault types.
The communication module 201 is further configured to obtain diagnostic data of each node in the power distribution network in real time.
The processing module 202 is further configured to input the diagnostic data of each node into the digital twin model to obtain fault information of a fault point in the power distribution network, where the fault information includes a fault feeder, a fault type, and a fault location; and processing the fault of the power distribution network based on the fault information.
In one possible implementation, the digital twin model includes a neural network module and a system function module; the processing module 202 is specifically configured to split the diagnostic data of each node to obtain a plurality of data slices; the diagnostic data of a plurality of nodes in front of a central node and a plurality of nodes behind the central node are used for each data slice, and the central node is any one of the nodes; inputting the plurality of data slices into a neural network module of a digital twin model to obtain a center node with a fault and a target fault type of the center node with the fault; based on the target fault type, determining a system function corresponding to the target fault type in the system functions of all fault types; and determining fault information of a fault point in the power distribution network based on the diagnostic data of the center node with the fault and a system function corresponding to the target fault type.
In a possible implementation manner, the processing module 202 is specifically configured to perform neural network training based on the fault sample to obtain a neural network module of the digital twin model; constructing a system function module of a digital twin model based on the system functions of all fault types; and constructing a digital twin model based on the neural network module and the system function module.
In a possible implementation manner, the processing module 202 is specifically configured to, for any position in the power distribution network, construct a system function module of a digital twin model based on a system function of each fault type and a normal value range and a fault value range of calculation data of the position; the calculated data of the position is calculated after the diagnostic data of the position is input into the system function of each fault type.
In a possible implementation manner, the processing module 202 is specifically configured to divide the fault samples based on each test scenario in the power distribution network to obtain fault samples of multiple test scenarios; for a plurality of test scenes, taking the diagnostic data of a plurality of nodes before a fault point and a plurality of nodes after the fault point as input, taking a fault feeder line and a fault type of the fault point as output, and respectively carrying out neural network training to obtain a plurality of sub-neural network modules; a neural network module of the digital twin model is determined based on the plurality of sub-neural network modules.
In one possible implementation, the processing module 202 is specifically configured to disconnect the upstream protection device of the fault point if the fault type is an arc discharge or a low current ground.
In one possible implementation, the digital twin model further comprises an auxiliary decision module; a processing module 202, specifically configured to generate multiple pieces of decision information corresponding to the fault information based on the assistant decision module; a communication module 201, specifically configured to output decision information; receiving confirmation information input by power workers; the confirmation information comprises decision information confirmed by power workers; the processing module 202 is specifically configured to process a fault of the power distribution network based on the confirmation information.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 3, the electronic apparatus 300 of this embodiment includes: a processor 301, a memory 302 and a computer program 303 stored in said memory 302 and executable on said processor 301. The processor 301, when executing the computer program 303, implements the steps in the above-described method embodiments, such as the steps 101 to 104 shown in fig. 1. Alternatively, the processor 301 implements the functions of each module/unit in each device embodiment described above, for example, the functions of the communication module 201 and the processing module 202 shown in fig. 2, when executing the computer program 303.
Illustratively, the computer program 303 may be partitioned into one or more modules/units, which are stored in the memory 302 and executed by the processor 301 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 303 in the electronic device 300. For example, the computer program 303 may be divided into the communication module 201 and the processing module 202 shown in fig. 2.
The Processor 301 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 302 may be an internal storage unit of the electronic device 300, such as a hard disk or a memory of the electronic device 300. The memory 302 may also be an external storage device of the electronic device 300, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 300. Further, the memory 302 may also include both an internal storage unit and an external storage device of the electronic device 300. The memory 302 is used for storing the computer programs and other programs and data required by the terminal. The memory 302 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other ways. For example, the above-described apparatus/terminal embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated module/unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signal, telecommunications signal, and software distribution medium, etc.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A power distribution network fault processing method based on a digital twin model is characterized by comprising the following steps:
acquiring a fault sample, wherein the fault sample takes diagnosis data of a plurality of nodes before a fault point and a plurality of nodes after the fault point as input, and takes a fault feeder line and a fault type of the fault point as output, and the diagnosis data comprises measurement and control data, protection data and wave recording data;
constructing a digital twin model based on the fault sample and the system function of each fault type;
acquiring diagnostic data of each node in the power distribution network in real time, and inputting the diagnostic data of each node into the digital twin model to obtain fault information of a fault point in the power distribution network, wherein the fault information comprises a fault feeder line, a fault type and a fault position;
and processing the fault of the power distribution network based on the fault information.
2. The power distribution network fault handling method based on the digital twin model as claimed in claim 1, wherein the digital twin model comprises a neural network module and a system function module;
inputting the diagnostic data of each node into the digital twin model to obtain fault information of fault points in the power distribution network, wherein the fault information comprises the following steps:
splitting the diagnostic data of each node to obtain a plurality of data slices; the diagnostic data of a plurality of nodes in front of a central node and a plurality of nodes behind the central node are used for each data slice, and the central node is any one of the nodes;
inputting the plurality of data slices into a neural network module of the digital twin model to obtain a center node with a fault and a target fault type of the center node with the fault;
based on the target fault type, determining a system function corresponding to the target fault type in system functions of all fault types;
and determining fault information of a fault point in the power distribution network based on the diagnostic data of the center node with the fault and a system function corresponding to the target fault type.
3. The method for processing the power distribution network fault based on the digital twin model as claimed in claim 1, wherein the step of constructing the digital twin model based on the fault samples and the system functions of each fault type comprises:
based on the fault sample, carrying out neural network training to obtain a neural network module of the digital twin model;
a system function module for constructing the digital twin model based on the system function of each fault type;
and constructing a digital twin model based on the neural network module and the system function module.
4. The method for processing the faults of the power distribution network based on the digital twin model, as claimed in claim 3, wherein the system function module for constructing the digital twin model based on the system functions of each fault type comprises:
for any position in the power distribution network, constructing a system function module of the digital twin model based on the system functions of all fault types, and the normal value range and the fault value range of the calculated data of the position; the calculated data of the position is calculated after the diagnostic data of the position is input into the system function of each fault type.
5. The method for processing the power distribution network fault based on the digital twin model according to claim 3, wherein the neural network module for performing neural network training based on the fault sample to obtain the digital twin model comprises:
dividing the fault samples based on each test scene in the power distribution network to obtain fault samples of a plurality of test scenes;
for the plurality of test scenes, taking the diagnostic data of a plurality of nodes before a fault point and a plurality of nodes after the fault point as input, taking a fault feeder line and a fault type of the fault point as output, and respectively carrying out neural network training to obtain a plurality of sub-neural network modules;
determining a neural network module of the digital twin model based on a plurality of sub-neural network modules.
6. The method for processing the fault of the power distribution network based on the digital twin model as claimed in claim 1, wherein the processing the fault of the power distribution network based on the fault information comprises:
and if the fault type is arc discharge or low-current grounding, disconnecting the upstream protection device of the fault point.
7. The power distribution network fault handling method based on the digital twin model as claimed in claim 1, characterized in that the digital twin model further comprises an auxiliary decision module;
the processing the fault of the power distribution network based on the fault information comprises:
generating a plurality of decision information corresponding to the fault information based on the assistant decision module;
outputting the decision information;
receiving confirmation information input by power workers; the confirmation information comprises decision information confirmed by the power staff;
and processing the fault of the power distribution network based on the confirmation information.
8. A distribution network fault processing device based on a digital twin model is characterized by comprising:
the system comprises a communication module, a fault analysis module and a fault analysis module, wherein the communication module is used for acquiring fault samples, the fault samples take the diagnosis data of a plurality of nodes before a fault point and a plurality of nodes after the fault point as input, and take a fault feeder line and a fault type of the fault point as output, and the diagnosis data comprises measurement and control data, protection data and wave recording data;
the processing module is used for constructing a digital twin model based on the fault samples and the system functions of all fault types;
the communication module is also used for acquiring the diagnostic data of each node in the power distribution network in real time;
the processing module is further used for inputting the diagnostic data of each node into the digital twin model to obtain fault information of a fault point in the power distribution network, wherein the fault information comprises a fault feeder line, a fault type and a fault position; and processing the fault of the power distribution network based on the fault information.
9. A control device for an electrical distribution network, characterized in that the control device comprises a memory, in which a computer program is stored, and a processor for invoking and executing the computer program stored in the memory to perform the method according to any one of claims 1 to 7.
10. An electric power system, characterized in that the electric power system comprises the distribution network fault processing device according to claim 8, and is used for executing the method according to any one of claims 1 to 7, and realizing fault analysis and processing of an active distribution network.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116526480A (en) * | 2023-07-05 | 2023-08-01 | 山西中控绿源科技有限公司 | Distributed power supply system and method based on intelligent energy management platform |
| CN117148048A (en) * | 2023-10-30 | 2023-12-01 | 国网江苏省电力有限公司南通供电分公司 | Power distribution network fault prediction method and system based on digital twin technology |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116526480A (en) * | 2023-07-05 | 2023-08-01 | 山西中控绿源科技有限公司 | Distributed power supply system and method based on intelligent energy management platform |
| CN116526480B (en) * | 2023-07-05 | 2023-10-13 | 北京中芯标准科技有限公司 | Distributed power supply system and method based on intelligent energy management platform |
| CN117148048A (en) * | 2023-10-30 | 2023-12-01 | 国网江苏省电力有限公司南通供电分公司 | Power distribution network fault prediction method and system based on digital twin technology |
| CN117148048B (en) * | 2023-10-30 | 2024-01-12 | 国网江苏省电力有限公司南通供电分公司 | Power distribution network fault prediction method and system based on digital twin technology |
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