CN113761927A - Power grid fault handling real-time auxiliary decision-making method, system, equipment and storage medium - Google Patents
Power grid fault handling real-time auxiliary decision-making method, system, equipment and storage medium Download PDFInfo
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Abstract
The invention provides a real-time auxiliary decision-making method, a system, equipment and a storage medium for power grid fault disposal, wherein the method comprises the following steps: acquiring fault information; and searching and reasoning fault information based on the power grid fault handling knowledge graph, and performing real-time auxiliary decision of the real-time power flow data by combining the real-time power flow data of the power grid equipment. By constructing a power grid fault disposal knowledge map and combining power grid dispatching prior knowledge, a power grid fault real-time auxiliary decision method is designed, a decision can be effectively provided for power grid fault disposal, and power grid dispatching work is supported. The power grid fault handling efficiency is improved, power grid power protection work is supported, and intelligent development of power business is promoted based on knowledge reasoning of a power grid fault handling knowledge graph and power grid real-time tide data and by combining with actual conditions of scheduling business.
Description
Technical Field
The invention belongs to the field of electric power artificial intelligence, and relates to application of an artificial intelligence technology in the field of power grid dispatching. In particular to a real-time assistant decision-making method, a system, equipment and a storage medium for power grid fault disposal.
Background
Named Entity Recognition (NER) is a basic and important technology of natural language processing, and aims to identify important information from text, which generally includes various information such as time, people, places and the like. The traditional named entity recognition is a scheme based on rules, dictionaries and an online knowledge base, and various new algorithms appear along with the continuous development of machine learning and deep learning, so that the work of steps such as manual completion of feature engineering and the like can be reduced by using the strong computing power and learning power of a computer. The named entity recognition model which is most commonly used in the NER field at present is formed by taking the BilSTM-CRF algorithm into consideration of the advantages that the BilSTM has bidirectional long-distance semantic dependence and the CRF considers the label constraint relation on the sequence labeling task.
The Chinese Knowledge map (Chinese Knowledge Graph) was originally originated from Google Knowledge Graph. Is essentially a semantic network. Its nodes represent entities (entries) or concepts (concepts), and edges represent various semantic relationships between entities/concepts. The knowledge graph is good at fusing multi-source heterogeneous information together, different entities are connected in a relationship, information is converted into knowledge, and the knowledge graph is an important means in field data research. Theories such as knowledge extraction, knowledge representation, knowledge fusion and the like are involved in the construction process of the knowledge graph, and technologies corresponding to the theories are gradually mature at present. The knowledge graph is mainly applied to the aspects of intelligent search, deep question answering, network social contact and the like, and is widely applied to many vertical fields such as finance, medical treatment, e-commerce and the like in recent years.
The power grid fault handling efficiency is not high, and the problem of daily power conservation work cannot be solved. The prior art does not provide an effective solution, and how to provide an auxiliary decision by combining a knowledge graph is a problem to be solved.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a power grid fault disposal real-time auxiliary decision method, a system, equipment and a storage medium.
In order to achieve the purpose, the invention adopts the following technical scheme:
a real-time auxiliary decision-making method for power grid fault handling comprises the following steps:
acquiring fault information;
and searching and reasoning fault information based on a preset power grid fault handling knowledge graph, and performing real-time auxiliary decision of the real-time power flow data by combining with the real-time power flow data of the power grid equipment.
As a further improvement of the present invention, the preset power grid fault handling knowledge graph is constructed by the following method:
analyzing and dividing the data into structured data, semi-structured data and unstructured data according to the field of the power grid;
respectively carrying out data entity identification on the structured data, the semi-structured data and the unstructured data, and extracting to obtain a power grid fault entity library consisting of an equipment topology information data general table, a fault plan information general table and a scheduling procedure information general table;
respectively constructing ontology models for the structured data, the semi-structured data and the unstructured data, and combining the three types of ontology models to complete the construction of the fault handling ontology model; and based on the power grid fault entity library, connecting the data map library, automatically eliminating repeated nodes and relations, and constructing a power grid fault disposal knowledge map.
As a further improvement of the present invention, the structured data includes power grid operation section data, and the topology structure of the power grid device and the operation state of the power grid device can be obtained by analyzing the power grid operation section data;
the semi-structured data comprises a grid fault handling plan;
the unstructured data includes grid dispatch control management rules.
As a further improvement of the present invention, the data entity identification of the structured data specifically includes:
matching connected topology nodes in power grid operation section data by using topology attributes among devices, indirectly supplementing missing relations by combining device names and belonged intervals, and automatically constructing a device topology relation summary table; and after primary wiring diagram verification with the power grid equipment, obtaining an equipment topology information data summary table.
As a further improvement of the present invention, the data entity identification of the semi-structured data specifically includes:
locking the normalized information according to the corresponding transformer substation or fault case name, and extracting the normalized information into a predetermined plan data table;
identifying key fields before and after the target information, locking the target information, removing invalid characters and matching key information by using a regularization expression method, and extracting the key information into a predetermined plan data table;
and automatically extracting the failure disposal plan information to obtain a failure plan information summary table in a mode of locking target information.
As a further improvement of the present invention, the data entity identification of the unstructured data specifically includes:
and extracting text features by using a language model to obtain a word granularity vector matrix, extracting context information by using a bidirectional long-time memory neural network, and extracting a global optimal sequence by combining a conditional random field model to obtain a scheduling named entity and a scheduling information summary table.
As a further improvement of the present invention, the searching and reasoning of the fault information based on the power grid fault handling knowledge graph, and the real-time auxiliary decision making of the real-time power flow data by combining the real-time power flow data of the power grid equipment specifically include:
searching the knowledge graph by using the fault information to obtain a fault point and a fault type, and pushing a power grid fault disposal plan of the corresponding fault information after confirming the fault information and a corresponding contact finger strategy;
judging whether the equipment has a trial sending condition or not based on the real-time tide data of the equipment and the trial sending condition in the knowledge graph; after the condition is judged, confirming the push trial delivery operation and calculating whether the trial delivery is successful by combining the load flow data:
if the trial delivery is successful, the fault handling is finished;
and if the trial transmission fails, judging whether switching operation is performed or not by combining the knowledge graph, confirming the switching operation deduced by the knowledge graph, judging whether the equipment resumes power transmission or not after the switching operation is finished, and if the equipment resumes power transmission and confirming that the fault handling is finished after the power transmission operation.
A real-time aid decision-making system for grid fault handling comprises:
the acquisition module is used for acquiring fault information;
and the auxiliary decision module is used for searching and reasoning fault information based on the power grid fault handling knowledge graph and performing real-time auxiliary decision of the real-time power flow data by combining the real-time power flow data of the power grid equipment.
As a further improvement of the present invention, the grid fault handling knowledge graph in the aid decision module is constructed by the following method:
analyzing and dividing the data into structured data, semi-structured data and unstructured data according to the field of the power grid;
respectively carrying out data entity identification on the structured data, the semi-structured data and the unstructured data, and extracting to obtain a power grid fault entity library consisting of an equipment topology information data general table, a fault plan information general table and a scheduling procedure information general table;
respectively constructing ontology models for the structured data, the semi-structured data and the unstructured data, and combining the three types of ontology models to complete the construction of the fault handling ontology model; and based on the power grid fault entity library, connecting the data map library, automatically eliminating repeated nodes and relations, and constructing a power grid fault disposal knowledge map.
As a further improvement of the present invention, the assistant decision module is specifically configured to: searching the knowledge graph by using the fault information to obtain a fault point and a fault type, and pushing a power grid fault disposal plan of the corresponding fault information after confirming the fault information and a corresponding contact finger strategy;
judging whether the equipment has a trial sending condition or not based on the real-time tide data of the equipment and the trial sending condition in the knowledge graph; after the condition is judged, confirming the push trial delivery operation and calculating whether the trial delivery is successful by combining the load flow data:
if the trial delivery is successful, the fault handling is finished;
and if the trial transmission fails, judging whether switching operation is performed or not by combining the knowledge graph, confirming the switching operation deduced by the knowledge graph, judging whether the equipment resumes power transmission or not after the switching operation is finished, and if the equipment resumes power transmission and confirming that the fault handling is finished after the power transmission operation.
An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the grid fault handling real-time aid decision method when executing the computer program.
A computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the grid fault handling real-time aid decision method.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, a power grid fault real-time auxiliary decision method is designed by combining preset power grid fault disposal knowledge maps with power grid dispatching priori knowledge, so that a decision can be effectively provided for power grid fault disposal, and power grid dispatching work is supported. The method is based on the knowledge reasoning of the preset power grid fault handling knowledge graph and the real-time power flow data of the power grid, combines the actual situation of the dispatching service, improves the efficiency of power grid fault handling, supports the power protection work of the power grid, and promotes the intelligent development of the power service.
Drawings
FIG. 1 is a flow chart of a power grid fault handling real-time aid decision method;
FIG. 2 is a flow chart of a grid fault handling real-time aid decision method of the preferred embodiment;
FIG. 3 is a schematic view of a data feature analysis process;
FIG. 4 is a schematic view of a knowledge graph ontology model;
FIG. 5 is a schematic diagram of a real-time aid decision service flow;
FIG. 6 is an example of aiding decision;
FIG. 7 is a schematic diagram of a power grid fault handling real-time decision-making aid system according to the present invention;
fig. 8 is a schematic structural diagram of an electronic device according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
As shown in fig. 1, the invention provides a real-time aid decision-making method for handling a power grid fault, which includes the following steps: acquiring fault information;
and searching and reasoning fault information based on the power grid fault handling knowledge graph, and performing real-time auxiliary decision of the real-time power flow data by combining the real-time power flow data of the power grid equipment.
According to the method, the power grid fault handling efficiency is improved based on the knowledge reasoning of the power grid fault handling knowledge graph and the real-time power flow data of the power grid and by combining the actual situation of the scheduling service, the power protection work of state network companies is supported, and the intelligent development of the power service is promoted.
The power grid fault handling knowledge graph is constructed by adopting the following method:
analyzing and dividing the data into structured data, semi-structured data and unstructured data according to the field of the power grid;
respectively carrying out data entity identification on the structured data, the semi-structured data and the unstructured data, and extracting to obtain a power grid fault entity library consisting of an equipment topology information data general table, a fault plan information general table and a scheduling procedure information general table;
respectively constructing ontology models for the structured data, the semi-structured data and the unstructured data, and combining the three types of ontology models to complete the construction of the fault handling ontology model; and based on the power grid fault entity library, connecting the data map library, automatically eliminating repeated nodes and relations, and constructing a power grid fault disposal knowledge map.
As a preferred embodiment, the searching and reasoning of the fault information based on the power grid fault handling knowledge graph and the real-time assistant decision-making of the real-time power flow data by combining the real-time power flow data of the power grid equipment specifically include:
searching the knowledge graph by using the fault information to obtain a fault point and a fault type, and pushing a power grid fault disposal plan of the corresponding fault information after confirming the fault information and a corresponding contact finger strategy;
judging whether the equipment has a trial sending condition or not based on the real-time tide data of the equipment and the trial sending condition in the knowledge graph; after the condition is judged, confirming the push trial delivery operation and calculating whether the trial delivery is successful by combining the load flow data:
if the trial delivery is successful, the fault handling is finished;
and if the trial transmission fails, judging whether switching operation is performed or not by combining the knowledge graph, confirming the switching operation deduced by the knowledge graph, judging whether the equipment resumes power transmission or not after the switching operation is finished, and if the equipment resumes power transmission and confirming that the fault handling is finished after the power transmission operation.
The present invention is described in detail below, and with reference to fig. 2, the method specifically includes the following steps:
the method comprises the following steps: power grid domain data analysis
In combination with the data characteristic analysis shown in fig. 3, the data for assisting the grid fault handling is various. The structural partitioning can be divided into structured data, semi-structured data, and unstructured data.
a) The power grid operation section data are structured data reflecting power grid real-time operation equipment information, and the topological structure of the power grid equipment and the operation state of the power grid equipment can be obtained through analysis of the power grid operation section data. The processing mode is direct extraction.
b) The grid fault handling plan is a necessary reference text in the fault handling process, and comprises important data such as substation outline, fault phenomenon and emergency handling measures. The disposal plan has good compilation habit, relatively uniform content and consistent editing format, and belongs to semi-structured data. The processing mode is keyword extraction.
c) The power grid dispatching control management regulation is a regulation and regulation on which the power grid dispatching control management work is based, and is important auxiliary decision reference data in the power grid fault handling work. The scheduling procedure data is unstructured and the data volume is small, and a traditional entity identification algorithm cannot meet the identification requirement, so that entity identification is carried out by adopting an entity identification technology based on transfer learning. The processing mode is deep learning.
And extracting the original data to obtain a power grid fault contact finger entity library.
Step two: data entity identification
The description with reference to fig. 4 includes:
step 2.1: structured and semi-structured data entity identification
The power grid operation section data reflects the power grid operation state through the power grid equipment operation parameters, and simultaneously comprises equipment names and topological relations among the equipment. And (3) matching connected topological nodes in the power grid operation section data by using topological attributes of 7 types of equipment such as a transformer, a bus, a switch, a disconnecting link, a capacitor and the like, and indirectly supplementing missing relations by combining equipment names and belonged intervals, thereby realizing automatic construction of an equipment topological relation summary table. The verification of a primary wiring diagram of the power grid equipment proves that the topological relation of the equipment obtained by the method is accurate. And obtaining a device topology information data summary table.
The fault handling plan has large information quantity, and comprises standardized contents such as transformer substation and line watching personnel information, scheduling personnel information, switching operation and power transmission recovery operation, and differentiated contents such as transformer substation basic information and typical fault handling information. Firstly, the normalized information is locked according to the name of a corresponding transformer substation or a fault case, and is extracted into a pre-arranged plan data table. And identifying key fields before and after the target information, locking the target information, removing invalid characters and matching the key information by using a regularization expression method, and extracting the invalid characters and the matched key information into a predetermined plan data table. And the automatic extraction of the failure disposal plan information is realized by locking the target information. And obtaining a failure plan information summary table.
Step 2.2: unstructured data entity identification
And carrying out entity identification on the scheduling procedure data by adopting a BERT-BilSMT-CRF model. Firstly, extracting text features by using a BERT language model with Google as an open source to obtain a word granularity vector matrix, using BilSTM for extracting context information, simultaneously extracting a global optimal sequence by combining a CRF model (conditional random field model), and finally obtaining a scheduling named entity and a scheduling information summary table.
The long-term and short-term memory neural network (LSTM) is a popular recurrent neural network at present, is sensitive to short-term input and can store a long-term state, and the input and the output of a unit are controlled by 3 switches in the LSTM.
(1) A forgetting gate, namely a decision that the unit state ct-1 is reserved to the current moment ct, wherein the calculation formula is as shown in formula (1):
ft＝σ(wfh·ht-1+wfx·xt+bf) (1)
in the formula, wfhCorresponding to the input item ht-1；wfxCorresponding to entry xt；wfhAnd wfxWeight matrix w forming a forgetting gatef，bfTo bias the top, σ is the activation function.
(2) Input Gate Current input xtSave to ctThe calculation formula is as follows (2):
it＝σ(wi·[ht-1,xt]+bi) (2)
in the formula, wiAs a weight matrix, biIs the offset top.
By using
The cell state representing the current input, determined by the last output and the current input, is given by equation (3):
current time cell state ctAs in formula (4):
in the formula, ct-1Indicating the state of the previous cell, ftTo forget the door. Symbol
Meaning multiplication by element.
(3) Calculating an output gate as shown in formula (5):
the input gate and cell states determine the output of the long-and-short memory neural network, as in equation (6):
the neural network can automatically extract features according to the distributed expression of words in the text, a BilSTM-CRF model of the word vector, and after a prediction is output by a BilSTM (bidirectional long and short memory neural network), a globally optimal labeling sequence is found by using labels predicted by the context by a CRF layer.
Step three: knowledge graph construction
For the structured data, an ER model (Entity-relationship model) can be adopted for carrying out ontology model design. And for unstructured data and semi-structured data, a top-down construction mode is adopted, and a Prot g ontology construction tool is used for constructing an ontology model. And finally, combining the three types of ontology models to complete the construction of the fault handling ontology model. The ontology model is shown in figure 5.
Based on a power grid fault entity library consisting of an equipment topology information data general table, a fault plan information general table and a scheduling regulation information general table, a Py2Neo (python drive of a Neo4j database) is connected with a Neo4j (a high-performance NOSQL graphic database) database, and a repetitive node and relation are automatically removed through a Merge (function in SQL statement for combination) method, so that a power grid fault disposal knowledge graph is constructed.
Of course, the present application is not limited to the above specific models and algorithms, but only to a preferred implementation, and other models and algorithms may be used to achieve the above objectives.
Step four: real-time aid decision-making method
The real-time fault handling assistance is a fault assistance handling method which searches and infers fault handling knowledge based on a power grid fault handling knowledge map and combines real-time power flow data of power grid equipment. When a fault occurs, the fault information is searched in the knowledge graph, a fault point and a fault type are deduced, and after manual confirmation, a corresponding fault handling process and a corresponding attention item are pushed for the fault. And judging whether the equipment has the trial sending condition or not based on the real-time power flow data of the equipment and the relevant knowledge of the trial sending condition in the knowledge graph. After the condition is judged, the push trial delivery operation is confirmed manually, and whether the trial delivery is successful is calculated by combining the load flow data. And if the trial delivery is successful, finishing the fault handling. And if the trial delivery fails, judging whether switching operation is performed or not by combining the knowledge graph. And manually intervening to confirm the switching operation flow deduced by the knowledge graph, judging whether the equipment resumes power transmission after switching is finished, and finishing fault handling after the equipment resumes power transmission and manually confirms the power transmission operation. The real-time aid decision business process is shown in figure 6.
Examples
And the real-time aid decision-making functional module is realized based on the real-time aid decision-making business process. The real-time assistant decision-making function based on the knowledge graph is taken as a core, and provincial and local cooperative fault handling application can be further provided. And the application selects django (python language framework) of a web application framework as a front-end page development framework, integrates the trained entity recognition model and the power grid fault handling knowledge map, and realizes the storage and visualization of various kinds of knowledge. Two functions of intelligent diagnosis and assistant decision making are mainly realized through a friendly human-computer interaction interface.
And judging whether the fault reaches the major event degree or not according to the fault phenomenon fed back on site and major event types and bases obtained by knowledge reasoning, and if the fault needs to be reported to the upper level, reasoning the attribute of the dispatching unit of the substation where the fault equipment is located and information of dispatching operators, substations and line watchers of the substation through a knowledge graph.
In the fault handling process, the application can provide real-time operation parameters of equipment such as bus voltage, transformer load rate and the like for scheduling personnel. According to the power grid fault handling knowledge graph, the application can be used for making a next auxiliary fault handling decision for a scheduling worker according to the equipment state and the handling progress, and when trial transmission, switching, power transmission recovery and other operations are needed in the fault handling process, the application can automatically acquire response data to calculate whether the response data meet the operation conditions. If the operation conditions are met, the application informs a dispatcher, and the treatment operation can be carried out after manual confirmation. Besides the faulty equipment, the application will also take care of and show the operation of the adjacent equipment in order to schedule personnel to do load shedding work. The fault handling steps are displayed in a node mode, and the backtracking handling process in the dispatching work is facilitated. Taking a conventional line fault treatment as an example, the conventional treatment is often 30 minutes, and the treatment time can be shortened to 12 minutes by adopting the application, so that the efficiency of treating the power grid fault is improved. An example of an aid decision is shown in fig. 5.
As shown in fig. 7, another objective of the present invention is to provide a grid fault handling real-time aid decision system, which includes:
the acquisition module is used for acquiring fault information;
and the auxiliary decision module is used for searching and reasoning fault information based on the power grid fault handling knowledge graph and performing real-time auxiliary decision of the real-time power flow data by combining the real-time power flow data of the power grid equipment.
Preferably, the grid fault handling knowledge graph in the assistant decision module is constructed by adopting the following method:
analyzing and dividing the data into structured data, semi-structured data and unstructured data according to the field of the power grid;
respectively carrying out data entity identification on the structured data, the semi-structured data and the unstructured data, and extracting to obtain a power grid fault entity library consisting of an equipment topology information data general table, a fault plan information general table and a scheduling procedure information general table;
respectively constructing ontology models for the structured data, the semi-structured data and the unstructured data, and combining the three types of ontology models to complete the construction of the fault handling ontology model; and based on the power grid fault entity library, connecting the data map library, automatically eliminating repeated nodes and relations, and constructing a power grid fault disposal knowledge map.
Preferably, the assistant decision module is specifically configured to: searching the knowledge graph by using the fault information to obtain a fault point and a fault type, and pushing a power grid fault disposal plan of the corresponding fault information after confirming the fault information and a corresponding contact finger strategy;
judging whether the equipment has a trial sending condition or not based on the real-time tide data of the equipment and the trial sending condition in the knowledge graph; after the condition is judged, confirming the push trial delivery operation and calculating whether the trial delivery is successful by combining the load flow data:
if the trial delivery is successful, the fault handling is finished;
and if the trial transmission fails, judging whether switching operation is performed or not by combining the knowledge graph, confirming the switching operation deduced by the knowledge graph, judging whether the equipment resumes power transmission or not after the switching operation is finished, and if the equipment resumes power transmission and confirming that the fault handling is finished after the power transmission operation.
As shown in fig. 8, a third object of the present invention is to provide an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the power grid fault handling real-time assistant decision method when executing the computer program.
The real-time auxiliary decision-making method for power grid fault disposal comprises the following steps:
acquiring fault information;
and searching and reasoning fault information based on the power grid fault handling knowledge graph, and performing real-time auxiliary decision of the real-time power flow data by combining the real-time power flow data of the power grid equipment.
A fourth object of the present invention is to provide a computer readable storage medium, which stores a computer program, which when executed by a processor implements the steps of the grid fault handling real-time aid decision method.
The real-time auxiliary decision-making method for power grid fault disposal comprises the following steps:
acquiring fault information;
and searching and reasoning fault information based on the power grid fault handling knowledge graph, and performing real-time auxiliary decision of the real-time power flow data by combining the real-time power flow data of the power grid equipment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (12)
1. A real-time auxiliary decision-making method for handling a power grid fault is characterized by comprising the following steps:
acquiring fault information;
and searching and reasoning fault information based on a preset power grid fault handling knowledge graph, and performing real-time auxiliary decision of the real-time power flow data by combining with the real-time power flow data of the power grid equipment.
2. The method of claim 1, wherein:
the preset power grid fault handling knowledge graph is constructed by adopting the following method:
analyzing and dividing the data into structured data, semi-structured data and unstructured data according to the field of the power grid;
respectively carrying out data entity identification on the structured data, the semi-structured data and the unstructured data, and extracting to obtain a power grid fault entity library consisting of an equipment topology information data general table, a fault plan information general table and a scheduling procedure information general table;
respectively constructing ontology models for the structured data, the semi-structured data and the unstructured data, and combining the three types of ontology models to complete the construction of the fault handling ontology model; and based on the power grid fault entity library, connecting the data map library, automatically eliminating repeated nodes and relations, and constructing a power grid fault disposal knowledge map.
3. The method of claim 2, wherein:
the structured data comprise power grid operation section data, and the topological structure of the power grid equipment and the operation state of the power grid equipment can be obtained through analyzing the power grid operation section data;
the semi-structured data comprises a grid fault handling plan;
the unstructured data includes grid dispatch control management rules.
4. The method of claim 2, wherein:
performing data entity identification on the structured data, specifically comprising:
matching connected topology nodes in power grid operation section data by using topology attributes among devices, indirectly supplementing missing relations by combining device names and belonged intervals, and automatically constructing a device topology relation summary table; and after primary wiring diagram verification with the power grid equipment, obtaining an equipment topology information data summary table.
5. The method of claim 2, wherein:
performing data entity identification on the semi-structured data, specifically comprising:
locking the normalized information according to the corresponding transformer substation or fault case name, and extracting the normalized information into a predetermined plan data table;
identifying key fields before and after the target information, locking the target information, removing invalid characters and matching key information by using a regularization expression method, and extracting the key information into a predetermined plan data table;
and automatically extracting the failure disposal plan information to obtain a failure plan information summary table in a mode of locking target information.
6. The method of claim 2, wherein:
performing data entity identification on the unstructured data, specifically comprising:
and extracting text features by using a language model to obtain a word granularity vector matrix, extracting context information by using a bidirectional long-time memory neural network, and extracting a global optimal sequence by combining a conditional random field model to obtain a scheduling named entity and a scheduling information summary table.
7. The method of claim 1, wherein:
the method comprises the following steps of searching and reasoning fault information based on the power grid fault handling knowledge graph, and performing real-time auxiliary decision of real-time power flow data by combining the real-time power flow data of power grid equipment, and specifically comprises the following steps:
searching the knowledge graph by using the fault information to obtain a fault point and a fault type, and pushing a power grid fault disposal plan of the corresponding fault information after confirming the fault information and a corresponding contact finger strategy;
judging whether the equipment has a trial sending condition or not based on the real-time tide data of the equipment and the trial sending condition in the knowledge graph; after the condition is judged, confirming the push trial delivery operation and calculating whether the trial delivery is successful by combining the load flow data:
if the trial delivery is successful, the fault handling is finished;
and if the trial transmission fails, judging whether switching operation is performed or not by combining the knowledge graph, confirming the switching operation deduced by the knowledge graph, judging whether the equipment resumes power transmission or not after the switching operation is finished, and if the equipment resumes power transmission and confirming that the fault handling is finished after the power transmission operation.
8. A real-time aid decision-making system for grid fault handling is characterized by comprising:
the acquisition module is used for acquiring fault information;
and the auxiliary decision module is used for searching and reasoning fault information based on the power grid fault handling knowledge graph and performing real-time auxiliary decision of the real-time power flow data by combining the real-time power flow data of the power grid equipment.
9. The grid fault handling real-time assistant decision system according to claim 8,
the power grid fault disposal knowledge graph in the auxiliary decision module is constructed by adopting the following method:
analyzing and dividing the data into structured data, semi-structured data and unstructured data according to the field of the power grid;
respectively carrying out data entity identification on the structured data, the semi-structured data and the unstructured data, and extracting to obtain a power grid fault entity library consisting of an equipment topology information data general table, a fault plan information general table and a scheduling procedure information general table;
respectively constructing ontology models for the structured data, the semi-structured data and the unstructured data, and combining the three types of ontology models to complete the construction of the fault handling ontology model; and based on the power grid fault entity library, connecting the data map library, automatically eliminating repeated nodes and relations, and constructing a power grid fault disposal knowledge map.
10. The grid fault handling real-time assistant decision system according to claim 8,
the aid decision module is specifically configured to: searching the knowledge graph by using the fault information to obtain a fault point and a fault type, and pushing a power grid fault disposal plan of the corresponding fault information after confirming the fault information and a corresponding contact finger strategy;
judging whether the equipment has a trial sending condition or not based on the real-time tide data of the equipment and the trial sending condition in the knowledge graph; after the condition is judged, confirming the push trial delivery operation and calculating whether the trial delivery is successful by combining the load flow data:
if the trial delivery is successful, the fault handling is finished;
and if the trial transmission fails, judging whether switching operation is performed or not by combining the knowledge graph, confirming the switching operation deduced by the knowledge graph, judging whether the equipment resumes power transmission or not after the switching operation is finished, and if the equipment resumes power transmission and confirming that the fault handling is finished after the power transmission operation.
11. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the grid fault handling real-time aid decision method of any one of claims 1-7 when executing the computer program.
12. A computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the grid fault handling real-time aid decision method of any of claims 1-7.
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