CN117993274A - Power distribution network fault diagnosis method, device, equipment and readable storage medium - Google Patents
Power distribution network fault diagnosis method, device, equipment and readable storage medium Download PDFInfo
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The application belongs to the technical field of operation analysis of power distribution networks, and particularly relates to a power distribution network fault diagnosis method, a device, equipment and a readable storage medium, wherein the method comprises the following steps: when the power distribution network is detected to be faulty, current operation data of the physical entity are collected; based on current operation data of a physical entity, acquiring a fault diagnosis result of the power distribution network by utilizing a pre-constructed fault diagnosis model; the fault diagnosis model is obtained by constructing a digital twin body constructed by a physical entity. The technical scheme provided by the application shortens the fault diagnosis time of the power distribution network, improves the fault recognition efficiency of the power distribution network, and improves the accuracy and reliability of fault recognition, thereby reducing the probability of false operation and false judgment and protecting the driving of the power distribution network for safe and stable operation.
Description
Technical Field
The invention belongs to the technical field of operation analysis of power distribution networks, and particularly relates to a power distribution network fault diagnosis method, device, equipment and readable storage medium.
Background
With the rapid expansion of the scale of AC/DC transmission, the access quantity of distributed power supplies is continuously increased, and the complexity of the power distribution network is increased while a new generation of power distribution network mainly comprising new energy power production, transmission and consumption is formed. The power distribution network is used as a 'tie' between a power transmission system and power users, and an accurate and rapid power distribution network fault diagnosis mode can effectively improve the stability and reliability of the running state of the power distribution network, and is also a key element for ensuring smooth fault removal and power restoration.
For the current middle-low voltage distribution network, the operation mode mainly adopts an operation mode that the neutral point is not directly grounded, the operation mode is also called a small current grounding system, and 70% -80% of faults in the small current grounding system are single-phase grounding faults.
The current fault diagnosis method mainly comprises the following steps: expert system method, petri network, and fault diagnosis on main feeder line by relay protection function. The method has the advantages of quick diagnosis response and high diagnosis accuracy when applied to fault diagnosis of the traditional power distribution network. However, after a large number of distributed power supplies are connected, the fault diagnosis method of the traditional power distribution network is not applicable any more, and the speed, sensitivity and accuracy of fault diagnosis are affected to different degrees. Because of the rapid increase of the topology quantity of the power grid, the change of the operation mode, the intermittence and uncertainty of the distributed power supply, the current fault diagnosis method has the problems of reduced fault diagnosis performance and prolonged diagnosis time, and can possibly generate refusal operation, misoperation and misjudgment, thereby seriously threatening the safe operation of the power grid.
Disclosure of Invention
To overcome at least some of the problems with the related art, the present application provides a power distribution network fault diagnosis method, apparatus, device, and readable storage medium.
According to a first aspect of an embodiment of the present application, there is provided a power distribution network fault diagnosis method, including:
when the power distribution network is detected to be faulty, current operation data of the physical entity are collected;
Based on the current operation data of the physical entity, acquiring a fault diagnosis result of the power distribution network by utilizing a pre-constructed fault diagnosis model;
The fault diagnosis model is obtained by constructing a digital twin body constructed for the physical entity.
Preferably, the physical entity includes:
The power distribution network comprises a transformer, a feeder line, primary equipment and power distribution network automation equipment which are arranged in a medium-low voltage power distribution network.
Preferably, the construction of the digital twin comprises:
Step 11: collecting historical operation data of a physical entity, historical data on a physical entity association platform and historical power distribution network fault diagnosis results;
Step 12: classifying historical operation data of the physical entity and historical data on the physical entity association platform respectively by utilizing a correlation classification method to obtain first data and second data;
step 13: constructing a power distribution network data set by using the first data and the second data;
step 14: the digital twin is constructed based on the distribution network dataset.
Preferably, the physical entity associates historical data on a platform, including: historical PMS data, historical GIS data and historical SCADA data;
The historical SCADA data includes: fault switch information, relay protection device information, fault recording and SOE data.
Preferably, the step 12 includes:
The historical operation data of the physical entity is classified according to the correlation of the operation state and the fault type to obtain first data;
and carrying out correlation classification on the historical data on the physical entity correlation platform according to the time sequence to obtain second data.
Preferably, the step 14 includes:
Based on the distribution network data set, establishing three-dimensional mapping of physical entities in a virtual space to obtain the digital twin body;
the virtual space belongs to the digital twin.
Preferably, the construction of the fault diagnosis model includes:
performing fault characteristic data extraction on the power distribution network data set in the digital twin body to obtain first fault characteristic data;
And training the artificial neural network model by using the historical operation data of the physical entity and the first fault characteristic data as input layer training samples of the artificial neural network model and using the historical power distribution network fault diagnosis result as output layer training samples of the artificial neural network model to obtain the fault diagnosis model.
Preferably, the fault characteristic data includes:
The state of the breaker at the moment of failure, the three-phase voltage at the moment of failure, the three-phase current at the moment of failure, the zero-sequence voltage at the moment of failure and the zero-sequence current at the moment of failure.
Preferably, the obtaining, based on the current operation data of the physical entity, the fault diagnosis result of the power distribution network by using the fault diagnosis model includes:
extracting fault characteristic data of the data set in the digital twin body to obtain second fault characteristic data;
and taking the current operation data of the physical entity and the second fault characteristic data as the input of the fault diagnosis model to obtain the fault diagnosis result of the power distribution network output by the fault diagnosis model.
According to a second aspect of an embodiment of the present application, there is provided a power distribution network fault diagnosis apparatus, the apparatus including:
the acquisition module is used for acquiring current operation data of the physical entity when the power distribution network is detected to be faulty;
The acquisition module is used for acquiring a power distribution network fault diagnosis result by utilizing a pre-constructed fault diagnosis model based on the current operation data of the physical entity.
Preferably, the physical entity includes:
The power distribution network comprises a transformer, a feeder line, primary equipment and power distribution network automation equipment which are arranged in a medium-low voltage power distribution network.
Preferably, the apparatus further comprises: a first construction module for constructing the digital twin;
A first build module comprising:
The collection unit is used for collecting historical operation data of the physical entity, historical data on a physical entity association platform and historical power distribution network fault diagnosis results;
The classification unit is used for classifying the historical operation data of the physical entity and the historical data on the physical entity association platform by utilizing a correlation classification method to obtain first data and second data;
a first construction unit for constructing a distribution network dataset using the first data and the second data;
And the second construction unit is used for constructing the digital twin body based on the distribution network data set.
Preferably, the physical entity associates historical data on a platform, including: historical PMS data, historical GIS data and historical SCADA data;
The historical SCADA data includes: fault switch information, relay protection device information, fault recording and SOE data.
Preferably, the classifying unit is specifically configured to:
The historical operation data of the physical entity is subjected to correlation classification according to the operation state and the fault type to obtain first data;
and carrying out correlation classification on the historical data on the physical entity correlation platform according to the time sequence to obtain second data.
Preferably, the second construction unit is specifically configured to:
Based on the distribution network data set, establishing three-dimensional mapping of physical entities in a virtual space to obtain the digital twin body;
the virtual space belongs to the digital twin.
Preferably, the apparatus further comprises: the second construction module is used for constructing a fault diagnosis model by utilizing the digital twin body;
the second building block comprises:
the first extraction unit is used for extracting fault characteristic data of the distribution network data set in the digital twin body to obtain first fault characteristic data;
and the training unit is used for training the artificial neural network model by using the historical operation data of the physical entity and the first fault characteristic data as input layer training samples of the artificial neural network model and using the historical power distribution network fault diagnosis results as output layer training samples of the artificial neural network model to obtain the fault diagnosis model.
Preferably, the fault characteristic data includes:
The state of the breaker at the moment of failure, the three-phase voltage at the moment of failure, the three-phase current at the moment of failure, the zero-sequence voltage at the moment of failure and the zero-sequence current at the moment of failure.
Preferably, the acquiring module includes:
the second extraction unit is used for extracting fault characteristic data of the data set in the digital twin body to obtain second fault characteristic data;
The obtaining unit is used for obtaining the power distribution network fault diagnosis result output by the fault diagnosis model by taking the current operation data of the physical entity and the second fault characteristic data as the input of the fault diagnosis model.
According to a third aspect of an embodiment of the present application, there is provided a computer apparatus comprising: one or more processors;
the processor is used for storing one or more programs;
The above-described power distribution network fault diagnosis method is implemented when the one or more programs are executed by the one or more processors.
According to a fourth aspect of the embodiment of the present application, there is provided a computer readable storage medium having a computer program stored thereon, the computer program, when executed, implementing the above-mentioned power distribution network fault diagnosis method.
By adopting the technical scheme, the invention has the following beneficial effects:
According to the invention, when the power distribution network is detected to be faulty, the current operation data of the physical entity is collected, and the fault diagnosis result of the power distribution network is obtained by utilizing the pre-constructed fault diagnosis model based on the current operation data of the physical entity, so that the time for fault diagnosis of the power distribution network is shortened, the fault recognition efficiency of the power distribution network is improved, the accuracy and reliability of fault recognition are also improved, the probability of false operation and false judgment is reduced, and the safe and stable operation of the power distribution network is protected; the digital twin body constructed by the physical entity is used for constructing a fault diagnosis model, so that the digital twin body can update and correct the data contained in the digital twin body in real time, and the accuracy of a diagnosis result output by the fault diagnosis model is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart illustrating a method of power distribution network fault diagnosis, according to an exemplary embodiment;
fig. 2 is a block diagram illustrating a power distribution network fault diagnosis apparatus according to an exemplary embodiment.
Detailed Description
The following describes the embodiments of the present invention in further detail with reference to the drawings.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As disclosed in the background art, with the rapid expansion of the ac/dc transmission scale, the number of distributed power supplies connected is continuously increased, and the complexity of the power distribution network is increased while a new generation of power distribution network mainly comprising new energy power production, transmission and consumption is formed. The power distribution network is used as a 'tie' between a power transmission system and power users, and an accurate and rapid power distribution network fault diagnosis mode can effectively improve the stability and reliability of the running state of the power distribution network, and is also a key element for ensuring smooth fault removal and power restoration.
For the current middle-low voltage distribution network, the operation mode mainly adopts an operation mode that the neutral point is not directly grounded, the operation mode is also called a small current grounding system, and 70% -80% of faults in the small current grounding system are single-phase grounding faults.
The current fault diagnosis method mainly comprises the following steps: expert system method, petri network, and fault diagnosis on main feeder line by relay protection function. The method has the advantages of quick diagnosis response and high diagnosis accuracy when applied to fault diagnosis of the traditional power distribution network. However, after a large number of distributed power supplies are connected, the fault diagnosis method of the traditional power distribution network is not applicable any more, and the speed, sensitivity and accuracy of fault diagnosis are affected to different degrees. Because of the rapid increase of the topology quantity of the power grid, the change of the operation mode, the intermittence and uncertainty of the distributed power supply, the current fault diagnosis method has the problems of reduced fault diagnosis performance and prolonged diagnosis time, and can possibly generate refusal operation, misoperation and misjudgment, thereby seriously threatening the safe operation of the power grid.
In order to improve the problems, the fault identification efficiency of the power distribution network is improved, and the accuracy and reliability of fault identification are improved.
The above-described scheme is explained in detail below.
Example 1
Fig. 1 is a flowchart illustrating a power distribution network fault diagnosis method according to an exemplary embodiment, which may be used in, but not limited to, a terminal, as shown in fig. 1, including the steps of:
Step 101: when the power distribution network is detected to be faulty, current operation data of the physical entity are collected;
Step 102: based on current operation data of a physical entity, acquiring a fault diagnosis result of the power distribution network by utilizing a pre-constructed fault diagnosis model;
the fault diagnosis model is obtained by constructing a digital twin body constructed by a physical entity.
According to the power distribution network fault diagnosis method provided by the embodiment of the invention, when the power distribution network is detected to be faulty, the current operation data of the physical entity is collected, the fault diagnosis result of the power distribution network is obtained by utilizing the pre-constructed fault diagnosis model based on the current operation data of the physical entity, so that the time for diagnosing the power distribution network fault is shortened, the power distribution network fault recognition efficiency is improved, the accuracy and the reliability of fault recognition are also improved, the probability of occurrence of misoperation and misjudgment is reduced, and the safe and stable operation of the power distribution network is protected. The digital twin body constructed by the physical entity is used for constructing a fault diagnosis model, so that the digital twin body can update and correct the data contained in the digital twin body in real time, and the accuracy of a diagnosis result output by the fault diagnosis model is improved.
Further, physical entities may include, but are not limited to:
The power distribution network comprises a transformer, a feeder line, primary equipment and power distribution network automation equipment which are arranged in a medium-low voltage power distribution network.
In some embodiments, the primary device may include, but is not limited to: a column breaker, a column load switch, an automatic sectionalizer, a ring network switch cabinet and the like; the power distribution automation devices may include, but are not limited to: reclosers, sectionalizers, feeder terminals, and the like.
It should be noted that the digital twin body is composed of four parts of a physical space, a data space, a knowledge space and a virtual space. The physical space is built by physical entities. The digital twin body can control physical space and issue instructions. The data placed in the physical space may be, but not limited to, data transmission by wired (ethernet), wireless network communication, etc., and the data is transmitted to the data space of the digital twin.
Further, the construction of the digital twin, comprising:
Step 11: collecting historical operation data of a physical entity, historical data on a physical entity association platform and historical power distribution network fault diagnosis results;
Specifically, the historical data on the physical entity association platform includes: historical PMS data, historical GIS data and historical SCADA data;
the historical SCADA data includes: fault switch information, relay protection device information, fault wave recording and SOE data;
The PMS data are operation data of a power distribution network production management system, the GIS data are geographic information data of the power distribution network, and the SOE data are time and event types of faults recorded by an event sequence recording system;
In some embodiments, the method may include, but is not limited to, collecting historical operation data of a physical entity one month ago, historical data on a physical entity association platform, and historical power distribution network fault diagnosis results; the time range of the data collected in step 11 may also be set by a person skilled in the art based on experimental data or expert experience, etc.;
Step 12: classifying historical operation data of the physical entity and historical data on a physical entity association platform respectively by utilizing a correlation classification method to obtain first data and second data;
Specifically, step 12 includes:
historical operation data of the physical entity is classified according to the correlation of the operation state and the fault type to obtain first data;
carrying out correlation classification on historical data on a physical entity correlation platform according to a time sequence to obtain second data;
it should be noted that, the "relevance classification" method according to the embodiments of the present invention is well known to those skilled in the art, and therefore, the specific implementation manner thereof is not described too much;
Step 13: constructing a power distribution network data set by using the first data and the second data;
Step 14: constructing a digital twin body based on the distribution network data set;
specifically, step 14 includes:
Based on a distribution network data set, establishing three-dimensional mapping of physical entities in a virtual space to obtain a digital twin body;
The virtual space belongs to a digital twin.
In some embodiments, the SCADA data may be collected by, but not limited to, a voltage transformer (mainly used for collecting three-phase voltage, zero-sequence voltage, power, etc.), a current transformer (mainly used for collecting three-phase current, zero-sequence current, power, etc.), a fault indicator (mainly used for collecting fault current, etc., such as circuit current), a signal collector (mainly used for collecting voltage, current, switching value, etc.), and other protection measurement and control devices installed at the feeder line of the medium-low voltage distribution network.
It should be noted that, the digital twin maps physical space things to information virtual space through digital technology, and establishes bidirectional real-time interaction. Since the digital twin is composed of four parts, namely a physical space, a data space, a knowledge space and a virtual space. Therefore, all the data collected in step 11 are stored in the data space of the digital twin; executing step 12 and step 13 in the knowledge space of the digital twin body to obtain a power distribution network data set; and finally, based on the distribution network data set, establishing three-dimensional mapping of the physical entity in the virtual space to obtain the digital twin body.
By establishing the digital twin body, the accuracy of the measurement signal is effectively improved, the basic characteristics of the physical entity are comprehensively reflected, the iterative updating and self-repairing of the data can be realized in the digital twin body, and the accurate depiction of the physical entity is completed. Thereby improving the accuracy and reliability of fault diagnosis by using the fault diagnosis model constructed by the digital twin body.
In addition, when a power distribution network fault occurs, the digital twin body can continuously and iteratively update the data stored in the data space of the power distribution network fault, so that the power distribution network data set is continuously supplemented and updated. The accuracy of data information acquisition is improved through the advantages of iterative updating and self-repairing of the word-utilizing twin technology.
Further, the construction of the fault diagnosis model comprises the following steps:
step 21: performing fault characteristic data extraction on a power distribution network data set in a digital twin body to obtain first fault characteristic data;
In particular, the fault signature data may include, but is not limited to: the state of the breaker at the moment of failure, the three-phase voltage at the moment of failure, the three-phase current at the moment of failure, the zero-sequence voltage at the moment of failure and the zero-sequence current at the moment of failure;
Step 22: and training the artificial neural network model by using the historical operation data of the physical entity and the first fault characteristic data as an input layer training sample of the artificial neural network model and using the historical power distribution network fault diagnosis result as an output layer training sample of the artificial neural network model to obtain a fault diagnosis model.
It can be appreciated that the artificial neural network reduces the ambiguity of the operating parameters to some extent, and is not affected by the type of fault and the operating mode, since an accurate mathematical model does not need to be established. Therefore, the artificial neural network is utilized to extract the transient high-frequency fault characteristic quantity of the power distribution network, so that the time for fault identification is effectively shortened, and the performance of fault diagnosis of the power distribution network is improved.
In some embodiments, but not limited to, training is performed by adopting a three-layer network in the BP artificial neural network, the learning mode of the BP network is divided into forward propagation output and reverse correction weight, forward propagation output and reverse correction are performed in the middle layer by the BP artificial neural network, a trained neural network module is obtained through continuous training and learning, and a fault diagnosis model based on digital twin is constructed on the basis, and is output as fault diagnosis information.
The fault diagnosis of the power distribution network can be performed more rapidly and accurately by establishing a fault diagnosis model through the digital twin body and combining with the artificial neural network, so that the time for fault identification and fault processing is effectively shortened, and the safe and stable operation of the power distribution network is ensured.
Further, step 102 includes:
Step 1021: extracting fault characteristic data of the data set in the digital twin body to obtain second fault characteristic data;
Step 1022: and taking the current operation data and the second fault characteristic data of the physical entity as the input of a fault diagnosis model to obtain a power distribution network fault diagnosis result output by the fault diagnosis model.
According to the power distribution network fault diagnosis method provided by the embodiment of the invention, when the power distribution network is detected to be faulty, the current operation data of the physical entity is collected, the fault diagnosis result of the power distribution network is obtained by utilizing the pre-constructed fault diagnosis model based on the current operation data of the physical entity, so that the time for diagnosing the power distribution network fault is shortened, the power distribution network fault recognition efficiency is improved, the accuracy and the reliability of fault recognition are also improved, the probability of occurrence of misoperation and misjudgment is reduced, and the safe and stable operation of the power distribution network is protected. The digital twin body constructed by the physical entity is used for constructing a fault diagnosis model, so that the digital twin body can update and correct the data contained in the digital twin body in real time, and the accuracy of a diagnosis result output by the fault diagnosis model is improved.
Example two
In order to cooperate with the power distribution network fault diagnosis method, an embodiment of the present invention provides a power distribution network fault diagnosis device, referring to fig. 2, the device includes:
the acquisition module is used for acquiring current operation data of the physical entity when the power distribution network is detected to be faulty;
the acquisition module is used for acquiring a fault diagnosis result of the power distribution network by utilizing a pre-constructed fault diagnosis model based on the current operation data of the physical entity;
The fault diagnosis model is obtained by constructing a digital twin body constructed by a physical entity.
Further, the physical entity includes:
The power distribution network comprises a transformer, a feeder line, primary equipment and power distribution network automation equipment which are arranged in a medium-low voltage power distribution network.
Further, the device further comprises: a first construction module for constructing a digital twin;
A first build module comprising:
The collection unit is used for collecting historical operation data of the physical entity, historical data on a physical entity association platform and historical power distribution network fault diagnosis results;
the classification unit is used for classifying the historical operation data of the physical entity and the historical data on the physical entity association platform by utilizing a correlation classification method to obtain first data and second data;
a first construction unit for constructing a distribution network data set using the first data and the second data;
And the second construction unit is used for constructing the digital twin body based on the distribution network data set.
Further, the historical data on the physical entity association platform includes: historical PMS data, historical GIS data and historical SCADA data;
The historical SCADA data includes: fault switch information, relay protection device information, fault recording and SOE data.
Further, the classification unit is specifically configured to:
historical operation data of the physical entity is classified according to the correlation of the operation state and the fault type to obtain first data;
and carrying out correlation classification on the historical data on the physical entity correlation platform according to the time sequence to obtain second data.
Further, the second construction unit is specifically configured to:
Based on a distribution network data set, establishing three-dimensional mapping of physical entities in a virtual space to obtain a digital twin body;
The virtual space belongs to a digital twin.
Further, the device further comprises: the second construction module is used for constructing a fault diagnosis model by utilizing the digital twin body;
a second build module comprising:
The first extraction unit is used for extracting fault characteristic data of the distribution network data set in the digital twin body to obtain first fault characteristic data;
The training unit is used for training the artificial neural network model by using the historical operation data of the physical entity and the first fault characteristic data as input layer training samples of the artificial neural network model and using the historical power distribution network fault diagnosis results as output layer training samples of the artificial neural network model to obtain a fault diagnosis model.
Further, the fault signature data includes:
The state of the breaker at the moment of failure, the three-phase voltage at the moment of failure, the three-phase current at the moment of failure, the zero-sequence voltage at the moment of failure and the zero-sequence current at the moment of failure.
Further, the acquisition module includes:
the second extraction unit is used for extracting fault characteristic data of the data set in the digital twin body to obtain second fault characteristic data;
The acquisition unit is used for taking the current operation data of the physical entity and the second fault characteristic data as the input of the fault diagnosis model to obtain a power distribution network fault diagnosis result output by the fault diagnosis model.
According to the power distribution network fault diagnosis device provided by the embodiment of the invention, when the power distribution network is detected to be faulty through the acquisition module, the current operation data of the physical entity is acquired, and the acquisition module acquires the power distribution network fault diagnosis result based on the current operation data of the physical entity by utilizing the pre-constructed fault diagnosis model, so that the time for power distribution network fault diagnosis is shortened, the power distribution network fault recognition efficiency is improved, the accuracy and reliability of fault recognition are improved, the probability of misoperation and misjudgment is reduced, and the safe and stable operation and the driving are protected for the power distribution network; the digital twin body constructed by the physical entity is used for constructing a fault diagnosis model, so that the digital twin body can update and correct the data contained in the digital twin body in real time, and the accuracy of a diagnosis result output by the fault diagnosis model is improved.
It can be understood that the above-provided device embodiments correspond to the above-described method embodiments, and corresponding specific details may be referred to each other, which is not described herein again.
Example III
Based on the same inventive concept, the invention also provides a computer device comprising a processor and a memory, the memory being for storing a computer program comprising program instructions, the processor being for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processor, digital signal processor (DIGITAL SIGNAL Processor, DSP), application specific integrated circuit (Application SpecificIntegrated Circuit, ASIC), off-the-shelf Programmable gate array (Field-Programmable GATEARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular to load and execute one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding functions, to implement the steps of a power distribution network fault diagnosis method in the above embodiments.
Example IV
Based on the same inventive concept, the present invention also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a computer device, for storing programs and data. It is understood that the computer readable storage medium herein may include both built-in storage media in a computer device and extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium herein may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the steps of a power distribution network fault diagnosis method in the above embodiments.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (20)
1. A method for diagnosing faults in a power distribution network, the method comprising:
when the power distribution network is detected to be faulty, current operation data of the physical entity are collected;
Based on the current operation data of the physical entity, acquiring a fault diagnosis result of the power distribution network by utilizing a pre-constructed fault diagnosis model;
The fault diagnosis model is obtained by constructing a digital twin body constructed for the physical entity.
2. The method of claim 1, wherein the physical entity comprises:
The power distribution network comprises a transformer, a feeder line, primary equipment and power distribution network automation equipment which are arranged in a medium-low voltage power distribution network.
3. The method of claim 1, wherein the construction of the digital twins comprises:
Step 11: collecting historical operation data of a physical entity, historical data on a physical entity association platform and historical power distribution network fault diagnosis results;
Step 12: classifying historical operation data of the physical entity and historical data on the physical entity association platform respectively by utilizing a correlation classification method to obtain first data and second data;
step 13: constructing a power distribution network data set by using the first data and the second data;
step 14: the digital twin is constructed based on the distribution network dataset.
4. A method according to claim 3, wherein the physical entity associates historical data on a platform, comprising: historical PMS data, historical GIS data and historical SCADA data;
The historical SCADA data includes: fault switch information, relay protection device information, fault recording and SOE data.
5. A method according to claim 3, wherein said step 12 comprises:
The historical operation data of the physical entity is subjected to correlation classification according to the operation state and the fault type to obtain first data;
and carrying out correlation classification on the historical data on the physical entity correlation platform according to the time sequence to obtain second data.
6. A method according to claim 3, wherein said step 14 comprises:
Based on the distribution network data set, establishing three-dimensional mapping of physical entities in a virtual space to obtain the digital twin body;
the virtual space belongs to the digital twin.
7. The method of claim 1, wherein the constructing of the fault diagnosis model comprises:
performing fault characteristic data extraction on the power distribution network data set in the digital twin body to obtain first fault characteristic data;
And training the artificial neural network model by using the historical operation data of the physical entity and the first fault characteristic data as input layer training samples of the artificial neural network model and using the historical power distribution network fault diagnosis result as output layer training samples of the artificial neural network model to obtain the fault diagnosis model.
8. The method of claim 7, wherein the fault signature data comprises:
The state of the breaker at the moment of failure, the three-phase voltage at the moment of failure, the three-phase current at the moment of failure, the zero-sequence voltage at the moment of failure and the zero-sequence current at the moment of failure.
9. The method according to claim 1, wherein the obtaining, based on the current operation data of the physical entity, the fault diagnosis result of the power distribution network using the fault diagnosis model includes:
extracting fault characteristic data of the data set in the digital twin body to obtain second fault characteristic data;
and taking the current operation data of the physical entity and the second fault characteristic data as the input of the fault diagnosis model to obtain the fault diagnosis result of the power distribution network output by the fault diagnosis model.
10. A power distribution network fault diagnosis apparatus, the apparatus comprising:
the acquisition module is used for acquiring current operation data of the physical entity when the power distribution network is detected to be faulty;
the acquisition module is used for acquiring a fault diagnosis result of the power distribution network by utilizing a pre-constructed fault diagnosis model based on the current operation data of the physical entity;
The fault diagnosis model is obtained by constructing a digital twin body constructed for the physical entity.
11. The apparatus of claim 10, wherein the physical entity comprises:
The power distribution network comprises a transformer, a feeder line, primary equipment and power distribution network automation equipment which are arranged in a medium-low voltage power distribution network.
12. The apparatus of claim 10, wherein the apparatus further comprises: a first construction module for constructing the digital twin;
A first build module comprising:
The collection unit is used for collecting historical operation data of the physical entity, historical data on a physical entity association platform and historical power distribution network fault diagnosis results;
The classification unit is used for classifying the historical operation data of the physical entity and the historical data on the physical entity association platform by utilizing a correlation classification method to obtain first data and second data;
a first construction unit for constructing a distribution network dataset using the first data and the second data;
And the second construction unit is used for constructing the digital twin body based on the distribution network data set.
13. The apparatus of claim 12, wherein the physical entity associates historical data on a platform, comprising: historical PMS data, historical GIS data and historical SCADA data;
The historical SCADA data includes: fault switch information, relay protection device information, fault recording and SOE data.
14. The device according to claim 12, characterized in that said classification unit is specifically configured to:
The historical operation data of the physical entity is subjected to correlation classification according to the operation state and the fault type to obtain first data;
and carrying out correlation classification on the historical data on the physical entity correlation platform according to the time sequence to obtain second data.
15. The apparatus according to claim 12, wherein the second construction unit is specifically configured to:
Based on the distribution network data set, establishing three-dimensional mapping of physical entities in a virtual space to obtain the digital twin body;
the virtual space belongs to the digital twin.
16. The apparatus of claim 10, wherein the apparatus further comprises: the second construction module is used for constructing a fault diagnosis model by utilizing the digital twin body;
the second building block comprises:
the first extraction unit is used for extracting fault characteristic data of the distribution network data set in the digital twin body to obtain first fault characteristic data;
and the training unit is used for training the artificial neural network model by using the historical operation data of the physical entity and the first fault characteristic data as input layer training samples of the artificial neural network model and using the historical power distribution network fault diagnosis results as output layer training samples of the artificial neural network model to obtain the fault diagnosis model.
17. The apparatus of claim 16, wherein the fault signature data comprises:
The state of the breaker at the moment of failure, the three-phase voltage at the moment of failure, the three-phase current at the moment of failure, the zero-sequence voltage at the moment of failure and the zero-sequence current at the moment of failure.
18. The apparatus of claim 10, wherein the acquisition module comprises:
the second extraction unit is used for extracting fault characteristic data of the data set in the digital twin body to obtain second fault characteristic data;
The obtaining unit is used for obtaining the power distribution network fault diagnosis result output by the fault diagnosis model by taking the current operation data of the physical entity and the second fault characteristic data as the input of the fault diagnosis model.
19. A computer device, comprising: one or more processors;
the processor is used for storing one or more programs;
A digital twinning-based power distribution network fault diagnosis method according to any one of claims 1 to 9, when the one or more programs are executed by the one or more processors.
20. A computer readable storage medium, characterized in that a computer program is stored thereon, which computer program, when executed, implements the digital twin based power distribution network fault diagnosis method according to any of claims 1 to 9.
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