CN113569904A - Bus connection type identification method and system, storage medium and computing equipment - Google Patents
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Abstract
The invention discloses a bus connection type identification method, which comprises the steps of analyzing a CIM file to be identified, and acquiring a bus group in the CIM file to be identified; and inputting the characteristics of the bus group into a pre-trained bus connection type classification model based on a random forest algorithm, and identifying the connection type of the bus in the bus group. Corresponding systems, storage media, and computing devices are also disclosed. The bus connection type identification method based on the random forest algorithm adopts the bus connection type classification model based on the random forest algorithm to identify the bus connection type, and compared with the traditional method, the accuracy and efficiency of identification are improved.
Description
Technical Field
The invention relates to a bus connection type identification method, a bus connection type identification system, a storage medium and computing equipment, and belongs to the field.
Background
In recent years, with the continuous development and progress of power systems, grid safety monitoring is receiving wide attention. The power grid operation personnel mainly adopt the graphical interface mode to monitor and manage, however, the electric power wiring diagram is mainly drawn by manual means at present, and extra work burden can be brought to electric power construction and operation maintenance by the drawing mode, so that the work efficiency of the electric power operation and maintenance personnel is influenced, and therefore, the automatic drawing of the electric power wiring diagram is realized by utilizing the automatic model identification technology to form a key research direction in the field of electric power technology.
In the automatic drawing process of the power wiring diagram, a common Information model (common Information model) file can be analyzed according to an IEC61970 protocol to obtain model Information and topology connection conditions of power equipment in the diagram. Due to the complexity of the topology structure of the power network, the connection mode of the power equipment needs to be identified before the power wiring diagram is automatically drawn, wherein the bus serves as one of the main equipment in the power wiring diagram, and different wiring types exist in different electrical wiring diagrams.
The existing bus connection type identification method is characterized in that identification is carried out through a manually set strategy, internal rules among different bus connection modes are difficult to fully dig, model information of equipment is not fully utilized in the identification process, and the identification result is poor in accuracy.
Disclosure of Invention
The invention provides a bus connection type identification method, a bus connection type identification system, a storage medium and computing equipment, and solves the problem that the identification result of the existing identification method is poor in accuracy.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
the bus connection type identification method comprises the steps of,
analyzing the CIM file to be identified to obtain a bus group in the CIM file to be identified;
and inputting the characteristics of the bus group into a pre-trained bus connection type classification model based on a random forest algorithm, and identifying the connection type of the bus in the bus group.
The bus connection type classification model based on the random forest algorithm is trained, and the specific process is as follows,
collecting and analyzing CIM files of historical stations, and constructing a training sample set;
and training a bus connection type classification model based on a random forest algorithm by adopting a training sample set.
Collecting and analyzing CIM files of historical stations, and constructing a training sample set,
collecting and analyzing a CIM file of a historical plant station, and acquiring a topological relation among all devices in the CIM file;
according to the topological relation, buses connected with the same load are classified into the same bus group;
acquiring characteristics of a bus group;
taking the characteristics of the bus group and the connection type of the bus in the bus group as samples to construct a training sample set; wherein all bus connections in the same bus group are of the same type.
The characteristics of the bus group are obtained by the specific process,
obtaining paths from each bus to other buses of the bus group; wherein, non-knife switch nodes are stored in the path;
and classifying the paths according to the nodes in the paths, acquiring the number of various paths in the bus bar group, and taking the number of various paths in the bus bar group as the characteristic of the bus bar group.
The types of the paths comprise a multi-device path, a mixed path, a single-device path and a virtual path;
if the path comprises a plurality of equipment nodes, the path is a multi-equipment path;
if one equipment node and one connection point exist in the path, the path is a mixed path;
if only one equipment node exists in the path, the path is a single equipment path;
and if the equipment node does not exist in the path, the path is a virtual path.
And cleaning the topological relation, and classifying the buses connected with the same load into the same bus group according to the cleaned topological relation.
The method also comprises the steps of carrying out reverse check and verification on the identified bus connection type according to the model information of the equipment; wherein the equipment is equipment in an incoming and outgoing line interval associated with the bus group in the plant.
A bus bar connection type identification system comprises,
bus group obtains module: analyzing the CIM file to be identified to obtain a bus group in the CIM file to be identified;
an identification module: and inputting the characteristics of the bus group into a pre-trained bus connection type classification model based on a random forest algorithm, and identifying the connection type of the bus in the bus group.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a bus bar connection type recognition method.
A computing device comprising one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs including instructions for performing a bus wiring type recognition method.
The invention achieves the following beneficial effects: 1. the bus connection type identification method adopts the bus connection type classification model based on the random forest algorithm to identify the bus connection type, and compared with the traditional method, the identification accuracy and efficiency are improved; 2. the method adopts the model information of the equipment to carry out reverse checking and verification on the identified bus connection type, thereby further improving the identification accuracy; 3. the invention takes the number of various paths in the bus bar group as the characteristics of the bus bar group, can fully reflect the characteristics of the bus bar group in different wiring modes, and increases the credibility of the identification process.
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FIG. 1 is a flow chart of the method of the present invention;
fig. 2 shows a specific electrical connection diagram.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, the bus connection type identification method includes the following steps:
step 1, analyzing a CIM file to be identified to obtain a bus group in the CIM file to be identified; wherein, the bus in the bus group is a typical bus;
and 2, inputting the characteristics of the bus group into a pre-trained bus connection type classification model based on a random forest algorithm, and identifying the connection type of the bus in the bus group.
According to the method, the bus connection type is identified by adopting the bus connection type classification model based on the random forest algorithm, and compared with the traditional method, the accuracy and efficiency of identification are improved.
In the step 1, the topological relation among the devices in the CIM file can be obtained by analyzing the CIM file to be identified, then the topological relation is cleaned, atypical connection buses and the devices connected with the atypical connection buses are filtered, and the buses connected with the same load are classified into the same bus group.
The bus connection type classification model structure based on the random forest algorithm is an existing structure, the model needs to be trained in advance, and only the model needs to be called when identification is carried out.
The method comprises the following steps of training a bus connection type classification model based on a random forest algorithm, and specifically comprises the following steps:
21) collecting and analyzing CIM files of historical stations, and constructing a training sample set;
the specific process is as follows:
1) collecting and analyzing 500 parts of CIM files of historical plant stations, and acquiring topological relations among all devices in the CIM files;
2) cleaning the topological relation, namely filtering the atypical wiring bus and the equipment connected with the atypical wiring bus, and classifying the buses connected with the same load into the same bus group according to the cleaned topological relation;
3) acquiring characteristics of a bus group;
here, the number of various paths in the bus bar group is taken as the bus bar group characteristic, and the specific obtaining process is as follows:
A) searching by taking each bus as a starting point, acquiring a path from each bus to other buses of the bus group, and storing non-disconnecting link nodes encountered in the searching process into the path, namely storing the non-disconnecting link nodes in the path;
B) classifying the paths according to the nodes in the paths to obtain the number of various paths in the bus bar group, and taking the number of various paths in the bus bar group as the characteristic of the bus bar group;
the types of the paths comprise a multi-device path, a mixed path, a single-device path and a virtual path; if the path comprises a plurality of equipment nodes, the path is a multi-equipment path; if one equipment node and one connection point exist in the path, the path is a mixed path; if only one equipment node exists in the path, the path is a single equipment path; if the path does not have the equipment node, the path is a virtual path;
4) taking the characteristics of the bus group and the connection type of the bus in the bus group as samples to construct a training sample set; all bus connection types in the same bus group are consistent;
thus the training sample set may be represented as { X }i,YiWhere i ═ 1,2, 3.., 1200, Xi＝{xi 1,xi 2,xi 3,xi 4Represents a row vector consisting of the number of multi-device paths, mixing paths, device paths, and imaginary paths in the ith bus-set, YiThe label of each bus bar group, namely the connection type of the bus bars in the bus bar group, is assigned by the connection type between the bus bars in the bus bar group
22) Training a bus connection type classification model based on a random forest algorithm by adopting a training sample set;
the method comprises the steps of randomly returning a training sample set to sample to generate 10 parts of sub-training sets with the same quantity, classifying bus connection types by using CART classification trees in each sub-training set, and finally identifying bus connection modes by combining classification results obtained by 10 CART classification trees.
The specific process is as follows:
a1) for a sub-training set D of a certain node in the CART tree, if the number of sample features is 0 or the kini coefficient in the training set
If the current node is smaller than the threshold value, the current node stops recursion;
where t represents the number of classes of typical connections of the bus bar, CiRepresents the number of bus bars in the i-th wiring mode, and | D | represents the total amount of samples in the sub-training set.
a2) Calculating the Keyny coefficient of each characteristic value in the existing characteristics under the current node to D
In the formula, D1 and D2 represent two partial subsets into which D is divided according to the eigenvalue a in the eigenvalue a, respectively.
a3) And D is divided into two parts, D1 and D2, by comparing different characteristic values of the characteristics to obtain the Keyny coefficient after D is divided, and selecting the characteristic A with the minimum Keyny coefficient and the characteristic value a as the division standard.
a4) Recursion a1) -a 3) in the segmented subsets finally generate CART classification trees.
a5) And (3) integrating the classification results obtained by the 10 CART trees to finally obtain a bus connection type classification model based on a random forest algorithm.
Classification result of nth CART classification tree
Wherein, C represents the total number of predicted samples in the leaf node where the sample finally enters, and the final classification result of the nth tree is the category with the highest probability of the training sample in the leaf node; classification results from random forests of k CART classification trees
After the bus connection type classification model is trained, the bus connection type classification model can be used, when the bus connection type classification model needs to be identified, the CIM file to be identified is analyzed, a bus group in the CIM file to be identified is obtained, the characteristics of the bus group are input into the bus connection type classification model, the connection type of a bus in the bus group is identified, finally, the identified bus connection type is reversely checked and verified according to model information (such as naming standards of buses and intervals) of equipment, the identification effectiveness and accuracy are ensured, and a positive bus, a negative bus and a side bus in different bus groups are identified according to bus names; wherein the equipment is equipment in an incoming and outgoing line interval associated with the bus group in the plant.
Fig. 2 is a detailed electrical wiring diagram including two transformers having a total of three voltage levels. The two I, II buses on the high-voltage side are 3/2 connections, the A/B bus and the C/D bus on the medium-voltage side are both double bus connections, and the III and IV buses on the low-voltage side are both single bus connections.
When identifying the bus connection mode in the diagram, firstly analyzing a CIM file describing the connection diagram, extracting the topological connection relation between buses and equipment in the diagram, and dividing a high-voltage I, II bus, an A-B double-bus connection, a C-T double-bus connection, a III bus and an IV bus into 5 bus groups according to the connection relation;
then, taking each bus in the bus group as a starting point to search paths from the bus to other buses in the bus group, storing non-disconnecting link nodes encountered in the searching process into the paths, dividing the paths into a multi-device path, a mixed path, a single-device path and a virtual path according to node information in the paths, and extracting the number of the four paths in the bus group as features;
and then, inputting the features extracted from each bus group in the graph into a bus connection type classification model, and finally identifying the connection mode of the bus in each bus group: the high-voltage I, II bus is a 3/2 wiring, the A bus and the B bus are double bus wirings, the C bus is double bus wirings, and the III bus and the IV bus are single bus wirings;
and finally, checking and verifying the bus connection mode obtained by identification according to the naming specifications of the buses and the intervals in the power dispatching, and confirming that the first bus and the third bus are respectively a main bus in the double-bus connection, and the second bus and the T bus are respectively a secondary bus in the double-bus connection according to the names of the buses.
After all bus wiring modes are identified, the automatic drawing process of the electrical wiring diagram can be developed on the basis, and the working efficiency of electric power operation and maintenance is improved.
Bus connection type identification system includes:
bus group obtains module: analyzing the CIM file to be identified to obtain a bus group in the CIM file to be identified;
an identification module: and inputting the characteristics of the bus group into a pre-trained bus connection type classification model based on a random forest algorithm, and identifying the connection type of the bus in the bus group.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a bus bar connection type recognition method.
A computing device comprising one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs including instructions for performing a bus wiring type recognition method.
As will be appreciated by one skilled in the art, 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, disk storage, CD-ROM, 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 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.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.
Claims (10)
1. The bus connection type identification method is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
analyzing the CIM file to be identified to obtain a bus group in the CIM file to be identified;
and inputting the characteristics of the bus group into a pre-trained bus connection type classification model based on a random forest algorithm, and identifying the connection type of the bus in the bus group.
2. The bus bar connection type identification method according to claim 1, wherein: the bus connection type classification model based on the random forest algorithm is trained, and the specific process is as follows,
collecting and analyzing CIM files of historical stations, and constructing a training sample set;
and training a bus connection type classification model based on a random forest algorithm by adopting a training sample set.
3. The bus bar connection type identification method according to claim 2, wherein: collecting and analyzing CIM files of historical stations, and constructing a training sample set,
collecting and analyzing a CIM file of a historical plant station, and acquiring a topological relation among all devices in the CIM file;
according to the topological relation, buses connected with the same load are classified into the same bus group;
acquiring characteristics of a bus group;
taking the characteristics of the bus group and the connection type of the bus in the bus group as samples to construct a training sample set; wherein all bus connections in the same bus group are of the same type.
4. The bus bar connection type identification method according to claim 1 or 3, wherein: the characteristics of the bus group are obtained by the specific process,
obtaining paths from each bus to other buses of the bus group; wherein, non-knife switch nodes are stored in the path;
and classifying the paths according to the nodes in the paths, acquiring the number of various paths in the bus bar group, and taking the number of various paths in the bus bar group as the characteristic of the bus bar group.
5. The bus bar connection type identification method according to claim 4, wherein: the types of the paths comprise a multi-device path, a mixed path, a single-device path and a virtual path;
if the path comprises a plurality of equipment nodes, the path is a multi-equipment path;
if one equipment node and one connection point exist in the path, the path is a mixed path;
if only one equipment node exists in the path, the path is a single equipment path;
and if the equipment node does not exist in the path, the path is a virtual path.
6. The bus bar connection type identification method according to claim 3, wherein: and cleaning the topological relation, and classifying the buses connected with the same load into the same bus group according to the cleaned topological relation.
7. The bus bar connection type identification method according to claim 1, wherein: the method also comprises the steps of carrying out reverse check and verification on the identified bus connection type according to the model information of the equipment; wherein the equipment is equipment in an incoming and outgoing line interval associated with the bus group in the plant.
8. Bus connection type identification system, its characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
bus group obtains module: analyzing the CIM file to be identified to obtain a bus group in the CIM file to be identified;
an identification module: and inputting the characteristics of the bus group into a pre-trained bus connection type classification model based on a random forest algorithm, and identifying the connection type of the bus in the bus group.
9. A computer readable storage medium storing one or more programs, characterized in that: the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods of claims 1-7.
10. A computing device, comprising:
one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-7.
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