CN113327033A - Power distribution network fault diagnosis method and system - Google Patents
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Abstract
The invention discloses a power distribution network fault diagnosis method and system, relates to the technical field of power distribution networks, and particularly relates to a power distribution network fault diagnosis method and system, which comprises the following steps: acquiring target power grid data; analyzing the target power grid data and a preset key characteristic set to obtain a key characteristic quantity value; classifying the key characteristic quantity to obtain preliminary fault data; screening the preliminary fault data to obtain target fault data; and analyzing the target fault data to obtain target power grid operation state data. Target fault data are screened from a plurality of data, and the target fault data are analyzed to obtain power grid running state data, so that the power grid running state is visually fed back to a user, the user can conveniently and rapidly identify the power grid fault state, and the technical problem that the running state of a power distribution network cannot be obtained in time due to huge and complex data in the prior art is solved.
Description
Technical Field
The invention relates to the technical field of power distribution networks, in particular to a power distribution network fault diagnosis method and system.
Background
With the development of intelligent power distribution technology, an active power distribution network containing a distributed power source is evolved from a traditional passive power grid, and with the development of power distribution network automation, the types of power supply equipment of the power grid are more and more, data and information data related to the power grid are more and more complex, and the data volume acquired by a terminal acquisition device in the power distribution network is exponentially increased.
The statistics of data needs more planning personnel to carry out statistics, analysis, because the distribution network data volume is big, the model is many, and the work load that leads to when the operation and maintenance personnel to carry out statistics analysis is big, the cycle length, also is difficult to discover the problem of data wherein, to the distribution network trouble, is difficult in time to obtain feedback information, can't in time acquire the running state of distribution network.
Disclosure of Invention
With the development of intelligent power distribution technology, more and more data related to a power grid are provided, the data statistics is usually long in period, the existing data problems are difficult to find, the faults of the power distribution network are difficult to be fed back in time, and the running state of the power distribution network cannot be obtained in time, and the following invention contents are provided aiming at the problems:
the power distribution network fault diagnosis method comprises the following steps: acquiring target power grid data; analyzing the target power grid data and a preset key characteristic set to obtain a key characteristic quantity value; classifying the key characteristic quantity to obtain preliminary fault data; screening the preliminary fault data to obtain target fault data; and analyzing the target fault data to obtain target power grid operation state data.
Further, the analyzing the key characteristic quantity further comprises the following steps:
inputting the key characteristic quantity value into a preset fault diagnosis neural network;
the fault diagnosis neural network classifies the key feature quantity values.
Further, the step of screening the preliminary fault data to obtain target fault data further includes:
and screening the preliminary fault data by the fault diagnosis neural network to obtain target fault data.
Further, the fault diagnosing neural network comprises a fault diagnosing neural network and an LSTM model, wherein:
an attention mechanism model of the fault diagnosis neural network is preset by key characteristic quantity value input values;
and screening the preliminary fault data according to the LSTM model of the fault diagnosis neural network to obtain target fault data.
And further, after the target power grid operation state data is obtained, sending corresponding feedback information according to the target power grid operation state data.
Further, analyzing the target fault data specifically includes:
matching the target fault data with the index classification keywords;
establishing an index table according to the index classification key words;
and storing the target fault data into a database according to the index table.
Further, the target grid data includes: voltage data, current data, and temperature data.
Further, the key feature set includes: load transfer capacity, distributed energy consumption capacity, power supply reliability and harmonic qualification rate.
Distribution network fault diagnostic, includes:
a data acquisition module: acquiring target power grid data;
a feature quantity processing module: analyzing the target power grid data and preset key characteristic quantities to obtain key characteristic quantity values in a key characteristic set;
a data input module: inputting the key characteristic quantity value into a preset fault diagnosis neural network;
a data classification module: classifying each key characteristic quantity according to an attention mechanism model of the fault diagnosis neural network to obtain preliminary fault data;
and a fault evaluation module: evaluating the preliminary fault data according to an LSTM module of the fault diagnosis neural network to obtain target fault data;
and receiving the target fault data obtained by the data classification module, and sending the running state data to the application terminal.
Further, the central node analyzes the target fault data to obtain the target power grid operation state data.
According to the power distribution network fault method and system provided by the invention, the target fault data can be screened from a plurality of data, and the power distribution network running state data is obtained after the target fault data is analyzed, so that the power distribution network running state is visually fed back to a user, the user can conveniently and quickly identify the power distribution network fault state, and the technical problem that the running state of the power distribution network cannot be obtained in time due to huge and complex data in the prior art is solved.
Drawings
FIG. 1 is a flow chart of a power distribution network fault diagnosis method according to the present invention;
FIG. 2 is a flowchart illustrating step S3 according to the present invention;
fig. 3 is a schematic structural diagram of the power distribution network fault diagnosis system according to the present invention.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
First, several terms and techniques involved in the present disclosure are resolved:
particle Swarm Optimization algorithm (PSO): is a random population-based optimization technique, the particle swarm algorithm simulates the clustering behavior of insects, herds, birds, and fish, etc., which find food in a cooperative manner, with each member of the population constantly changing its search pattern by learning its own experience and that of other members.
Attention Mechanism (Attention Mechanism): the neural network attention mechanism may enable the neural network to have the ability to focus on a subset of its inputs (or features): selecting a particular input; attention may be applied to any type of input regardless of its shape; in situations where computing power is limited, the attention mechanism is a resource allocation scheme that is the primary means to solve the information overload problem, allocating computing resources to more important tasks. The attention mechanism has wide application in image segmentation, classification, natural language processing and the like.
Long Short-Term Memory (LSTM) is a time Recurrent Neural Network (RNN) that can learn Long-Term dependency problems, with a form of a chain of repeating neural network modules.
The embodiment of the application provides a power distribution network fault diagnosis method, as shown in fig. 1 and 2, the power distribution network fault diagnosis method comprises the following steps:
step S1, acquiring target power grid data;
the data of the target power grid comprise voltage data, current data, temperature data and the like, the data can be generated in the operation process of the power grid, the data of the target power grid are collected in real time in the fault diagnosis process of the power distribution network, and the target power grid is monitored in real time, so that the safe operation of the target power grid is guaranteed.
Because the acquired data of the front distribution network has error data, in order to ensure a more accurate diagnosis result, the data of the front distribution network needs to be cleaned to remove the error data, the data of the current distribution network is cleaned, and the error data is removed to obtain the cleaned data of the current distribution network.
Step S2, analyzing the target power grid data and a preset key feature set to obtain a key feature quantity value;
the key feature set includes: the load is transferred and is supplied ability, distributed energy absorption ability, power supply reliability, harmonic qualification rate etc. wherein, load is transferred and is supplied the ability and can be expressed as: the proportion of the successfully transferred load to the total load affected; the distributed energy consumption capability may be expressed as: calculating the ratio of the maximum consumption capacity to the maximum load of the distributed energy; the power reliability may be expressed as: the ratio of the effective time length of supplying power to the user to the actual power supply time length in the preset time; the harmonic yield can be expressed as: the node voltage total harmonic distortion rate and the current total harmonic distortion rate are in proportion to the number of contacts within an allowable deviation range, and specifically, may be a total number of nodes in which the node voltage total harmonic distortion rate and the current total harmonic distortion rate do not satisfy a standard in a year (or a quarter or a month) or a current time section. Specifically, for example, the load transferring capability may be expressed as a percentage, and the larger the value of the load transferring capability, the better the corresponding health state; the harmonic yield can also be expressed as a percentage, and the larger the value of the harmonic yield, the better the corresponding health status.
Step S3, classifying the key characteristic quantity to obtain preliminary fault data, which comprises the following steps:
the method for constructing the fault diagnosis neural network specifically comprises the following steps:
acquiring historical power distribution network data;
and constructing a fault diagnosis neural network according to historical power distribution network data.
Preferably, the historical power distribution network data comprises normal power distribution network data and abnormal power distribution network data, and the fault diagnosis neural network is constructed according to the historical power distribution network data, and comprises the following steps:
and performing model training on the normal power distribution network data and the abnormal power distribution network data through a PSO algorithm to construct a fault diagnosis neural network. Specifically, the abnormal power distribution network data refers to power distribution network data with faults, and model training is performed on the normal power distribution network data and the abnormal power distribution network data together, so that the accuracy of the fault diagnosis neural network can be improved.
Step S301, inputting the key characteristic quantity value into a preset fault diagnosis neural network;
the method comprises the steps of obtaining current power distribution network data to be diagnosed, analyzing the current power distribution network data and a preset key characteristic quantity set to obtain each key characteristic quantity value in the key characteristic quantity set, inputting each key characteristic quantity value into a preset fault diagnosis neural network, and classifying each key characteristic quantity value according to an attention mechanism module to obtain preliminary fault data.
And step S302, the fault diagnosis neural network classifies the key characteristic quantity values.
Evaluating the preliminary fault data according to the LSTM module to obtain target fault data
Step S4, screening the preliminary fault data to obtain target fault data;
and evaluating the preliminary fault data according to the LSTM module to obtain target fault data, so that the fault of the power distribution network can be diagnosed in real time, and the health state of the power distribution network can be known in time through the target fault data. The accuracy of target fault data evaluation can be improved through the LSTM module; the fault types can be classified through the attention mechanism module; in addition, due to the application of the neural network, various algorithms can be integrated, and a plurality of modeling costs of machine learning are reduced, namely the cost for constructing a plurality of fault diagnosis neural networks is reduced.
And step S5, analyzing the target fault data to obtain target power grid operation state data.
Target fault data are analyzed and calculated, and virtualized resources are used for analyzing and calculating, so that the calculation efficiency and accuracy are improved. After the target fault data are analyzed and calculated, the target power grid operation state data are obtained, and the target power grid operation state data are sent to the corresponding application terminals 102, so that when a fault occurs, related responsible persons can timely obtain the target power grid operation state data through the application terminals 102, and corresponding feedback can be timely made according to the target power grid operation state data.
Analyzing the target fault data specifically further comprises:
matching the target fault data with the index classification keywords;
establishing an index table according to the index classification key words;
and storing the target fault data into a database according to the index table.
Specifically, the index sort key may be a data unit or a data range, where the data range may be an integer data range, a decimal data range, or a percentage data range; taking the data range as an example for explanation, the index table may be proposed according to the range of the harmonic qualification rate, the index table may be proposed according to the range of the power supply reliability rate, and the index table may be proposed according to the range of the load transfer supply capacity or the range of the distributed energy consumption capacity. And storing the target fault data into a database through the established index table.
Further, the present application also provides a power distribution network fault diagnosis system, as shown in fig. 3, including: the data acquisition module 301: acquiring target power grid data;
the feature amount processing module 302: analyzing the target power grid data and preset key characteristic quantities to obtain key characteristic quantity values in a key characteristic set;
data input module 303: inputting the key characteristic quantity value into a preset fault diagnosis neural network;
the data classification module 304: classifying each key characteristic quantity according to an attention mechanism model of the fault diagnosis neural network to obtain preliminary fault data;
the fault evaluation module 305: evaluating the preliminary fault data according to an LSTM module of the fault diagnosis neural network to obtain target fault data;
and receiving the target fault data obtained by the data classification module, and sending the running state data to the application terminal.
And the fault evaluation module analyzes the target fault data to obtain the target power grid operation state data.
The power distribution network fault diagnosis system can further comprise a communication module and a human-computer interaction module (not shown), wherein the power distribution network fault diagnosis system further has edge computing capability, and the communication module supports multiple communication modes such as ZigBee, LoRa, RS485, 4G, 5G and optical fibers, so that the power distribution network fault diagnosis system can communicate with a power distribution terminal through the communication module.
The power distribution network fault diagnosis method provided by the embodiment of the disclosure can be applied to a terminal, a server side and software running in the terminal or the server side. In some embodiments, the terminal may be a smartphone, tablet, laptop, desktop computer, smart watch, or the like; the server side can be configured into an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and cloud servers for providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, network service, cloud communication, middleware service, domain name service, security service, CDN (content delivery network) and big data and artificial intelligence platforms; the software may be an application for implementing a power distribution network fault diagnosis method, and the like, but is not limited to the above form.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or 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 place, or may be distributed on a plurality of 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 application 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 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, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.
Claims (10)
1. The power distribution network fault diagnosis method is characterized by comprising the following steps:
acquiring target power grid data;
analyzing the target power grid data and a preset key characteristic set to obtain a key characteristic quantity value;
classifying the key characteristic quantity to obtain preliminary fault data;
screening the preliminary fault data to obtain target fault data;
and analyzing the target fault data to obtain target power grid operation state data.
2. The method for diagnosing faults in a power distribution network according to claim 1, wherein analyzing the key characteristic quantities further comprises the steps of:
inputting the key characteristic quantity value into a preset fault diagnosis neural network;
the fault diagnosis neural network classifies the key feature quantity values.
3. The method according to claim 1, wherein the screening of the preliminary fault data to obtain target fault data further comprises:
and screening the preliminary fault data by the fault diagnosis neural network to obtain target fault data.
4. The power distribution network fault diagnosis method according to claim 2 or claim 3, wherein the fault diagnosis neural network comprises a fault diagnosis neural network and an LSTM model, wherein:
an attention mechanism model of the fault diagnosis neural network is preset by key characteristic quantity value input values;
and screening the preliminary fault data according to the LSTM model of the fault diagnosis neural network to obtain target fault data.
5. The power distribution network fault diagnosis method according to claim 1, wherein after the target power grid operation state data is obtained, corresponding feedback information is sent according to the target power grid operation state data.
6. The power distribution network fault diagnosis method according to claim 1, wherein analyzing the target fault data specifically comprises:
matching the target fault data with the index classification keywords;
establishing an index table according to the index classification key words;
and storing the target fault data into a database according to the index table.
7. The power distribution network fault diagnosis method according to claim 1, wherein the target grid data includes: voltage data, current data, and temperature data.
8. The power distribution network fault diagnosis method according to claim 1, wherein the set of key features comprises: load transfer capacity, distributed energy consumption capacity, power supply reliability and harmonic qualification rate.
9. Distribution network fault diagnostic, its characterized in that includes:
a data acquisition module: acquiring target power grid data;
a feature quantity processing module: analyzing the target power grid data and preset key characteristic quantities to obtain key characteristic quantity values in a key characteristic set;
a data input module: inputting the key characteristic quantity value into a preset fault diagnosis neural network;
a data classification module: classifying each key characteristic quantity according to an attention mechanism model of the fault diagnosis neural network to obtain preliminary fault data;
and a fault evaluation module: evaluating the preliminary fault data according to an LSTM module of the fault diagnosis neural network to obtain target fault data;
and receiving the target fault data obtained by the data classification module, and sending the running state data to the application terminal.
10. The system according to claim 9, wherein the fault evaluation module analyzes the target fault data to obtain the target grid operating state data.
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