CN116861503A - Method for constructing digital twin model of power transformer based on big data - Google Patents
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Abstract
The invention discloses a method for constructing a digital twin model of a power transformer based on big data, which relates to the technical field of power transformer monitoring and comprises the following steps: collecting data information of the power transformer under normal working conditions and fault working conditions; collecting a time stamp of an update state of the power transformer, uploading the time stamp to a big data platform, and dynamically mapping a virtual object of the power transformer; after the uploaded collected data is subjected to an ETL process, the program updates the attribute state through a hash mapping tag; the beneficial effects of the invention are as follows: the three-dimensional visualization of the power transformer can be realized by constructing a digital twin standard model, and the running state of the power transformer can be reflected in real time; data information of the power transformer under normal working conditions and fault working conditions is collected and uploaded to a large data platform, so that various data sources and types can be monitored, and the range and accuracy of the built digital twin model can be monitored are higher.
Description
Technical Field
The invention relates to a method for constructing a digital twin model, in particular to a method for constructing a digital twin model of a power transformer based on big data, and belongs to the technical field of power transformer monitoring.
Background
The power equipment is an important component of the power system, the running reliability of the power equipment can be obviously improved by comprehensively detecting and diagnosing the running state of the power equipment, so that the reliability of the whole power system is improved, and the power transformer is one of the power equipment; at present, periodic maintenance and state maintenance become important means for judging the running state of the power equipment, values of various state quantities in the running process of the power equipment are obtained based on a periodic test and an on-line monitoring technology, and the judgment of the running state, fault condition and service life condition of the power equipment can be realized based on the values of the state quantities. However, the current periodic overhaul and state overhaul generally only aim at single type equipment, namely, a corresponding test method and state evaluation method are formulated for each type of equipment, and overall consideration of association relations among different power equipment is lacking;
the "a method for constructing a digital twin model of an electrical device" disclosed in application number CN202210076240.4 is also an increasingly mature technology, and includes: a power equipment entity; acquiring a physical association equipment entity physically associated with the power equipment entity; acquiring an electrical association equipment entity electrically associated with the electrical equipment entity; constructing an attribute twin sub-model based on the attribute data of the equipment entity; constructing a state twin sub-model based on the state data of the equipment entity; and merging the attribute twin sub-model and the state twin sub-model of the electric power equipment entity, the physical association equipment entity and the electric association equipment entity to obtain the electric power equipment digital twin model. The method solves the technical problem that the digital twin model of the power equipment cannot comprehensively and accurately reflect the attribute information and the operation information of the power equipment due to the fact that only the power equipment is considered, and the influence of the physical association equipment and the electrical association equipment on the construction of the digital twin model of the power equipment is ignored when the digital twin model of the power equipment is constructed in the prior related technology; upon further retrieval, "a method and system for constructing a digital twin body of a power grid device," disclosed in application number "CN202211179871.5," includes: constructing a digital twin standard model of equipment aiming at typical equipment in a target power grid; establishing a mapping relation between a device object and the digital twin standard model; according to the mapping relation, mapping from the equipment object to the digital twin body standard model is realized; constructing a three-dimensional virtual scene of the digital twin standard model by taking a city as a basic geographic background, and displaying the real scene information of a target power grid through the three-dimensional virtual scene; providing visual rendering patterns of the digital twin standard model under different scales through a distribution network topological graph; and realizing three-dimensional visualization of the grid distribution network frame through the digital twin body standard model so as to reflect the running state of the power grid in real time. The method has the advantages that the full-automatic online completion of the construction, networking, updating and displaying of the digital twin bodies of the power grid equipment is realized, and the quality and effect of power grid management are promoted;
however, the two methods have the following defects in actual use: the obtained data has single source and variety, so that the finally constructed model is easy to have defects, and the iterative optimization and verification of the model can not be realized.
Disclosure of Invention
The invention aims to provide a method for constructing a digital twin model of a power transformer based on big data, which aims to solve the problems that the acquired data in the background technology is single in source and category and can not realize iterative optimization and verification of the model.
In order to achieve the above purpose, the present invention provides the following technical solutions: a construction method of a digital twin model of a power transformer based on big data comprises the following steps:
s1: collecting data information of the power transformer under normal working conditions and fault working conditions;
s2: collecting a timestamp of the update state of the power transformer, uploading the timestamp to a big data platform, and using a Flink frame to process all collected data with state time marks in real time and in batches to dynamically map virtual objects of the power transformer;
s3: after the uploaded collected data is subjected to an ETL process, the program updates the attribute state through a hash mapping tag;
s4: after comparing the big data platform with the acquired data, the state of a plurality of data sources is updated, the real-time scale of the power equipment is compared with the acquired time scale, if the real-time scale is larger than the acquired time scale, the state data is discarded, and if the real-time scale is smaller than the acquired time scale, the state is updated, and if the real-time scale is smaller than the acquired time scale, the real-time scale is updated;
s5: constructing a digital twin standard model of the power transformer aiming at a typical power transformer in a target;
s6: establishing a mapping relation between the power transformer real object and the digital twin standard model, realizing the mapping from the power transformer real object to the digital twin standard model according to the mapping relation, and carrying out data mining on perceived data, state data and historical data and deep learning based on a convolutional neural network;
s7: constructing a three-dimensional virtual scene of a digital twin standard model for displaying the real scene information of a target power transformer;
s8: and constructing a multi-time space scale digital twin decision model, and completing iterative optimization and verification of the model based on measured data under the normal working condition of the power transformer and twin data under the fault working condition.
As a preferable technical scheme of the invention, the data types adopted by the data acquisition comprise historical data, current real-time data, design data and detection data of the running state of equipment.
As a preferred technical scheme of the invention, sources of data acquisition include sensors, remote sensing monitoring, video monitoring and image shooting.
As a preferable embodiment of the present invention, the data information includes attribute data information and status data information of the power transformer.
As a preferable technical scheme of the invention, the attribute information of the power transformer comprises manufacturers, product models, rated capacities, rated voltages, operation time, cooling modes and historical fault conditions.
As a preferable technical scheme of the invention, the state data of the power transformer comprises test data of each state quantity obtained in inspection and routine tests, real-time data of each state quantity monitored on line and operation environment data of the power transformer.
As a preferable technical scheme of the invention, the large data platform synchronously counts the time and the frequency of the power transformer for subsequent analysis.
As a preferable technical scheme of the invention, the twin data is obtained nondestructively under the fault working condition of the power transformer, so that the space-time characteristic analysis of the twin data under the fault working condition of the power transformer is completed, and the generation of the twin data under the fault working condition is realized.
As a preferable technical scheme of the invention, the invention further comprises a visualization module for visually displaying the digital twin model.
As a preferred technical scheme of the invention, after the digital twin model is constructed, the data association is analyzed and sorted, and the association, correlation or causal structure existing between item sets or object sets is searched, namely, rules describing the relationship existing between different data items in the database are sorted and constructed into a canonical model.
Compared with the related art, the method for constructing the digital twin model of the power transformer based on big data has the following beneficial effects:
1. the three-dimensional visualization of the power transformer can be realized by constructing a digital twin standard model, and the running state of the power transformer can be reflected in real time;
2. data information of the power transformer under normal working conditions and fault working conditions is collected and uploaded to a large data platform, so that various data sources and types can be monitored, and the range and accuracy of the built digital twin model can be higher;
3. by constructing the multi-time space scale digital twin decision model, iteration optimization and verification of the model are completed based on measured data under the normal working condition of the power transformer and twin data under the fault working condition, so that the model can be updated in real time.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples:
referring to fig. 1, the invention provides a method for constructing a digital twin model of a power transformer based on big data, which comprises the following steps:
s1: collecting data information of the power transformer under normal working conditions and fault working conditions;
s2: collecting a timestamp of the update state of the power transformer, uploading the timestamp to a big data platform, and using a Flink frame to process all collected data with state time marks in real time and in batches to dynamically map virtual objects of the power transformer;
s3: after the uploaded collected data is subjected to an ETL process, the program updates the attribute state through a hash mapping tag;
s4: after comparing the big data platform with the acquired data, the state of a plurality of data sources is updated, the real-time scale of the power equipment is compared with the acquired time scale, if the real-time scale is larger than the acquired time scale, the state data is discarded, and if the real-time scale is smaller than the acquired time scale, the state is updated, and if the real-time scale is smaller than the acquired time scale, the real-time scale is updated;
s5: constructing a digital twin standard model of the power transformer aiming at a typical power transformer in a target;
s6: establishing a mapping relation between the power transformer real object and the digital twin standard model, realizing the mapping from the power transformer real object to the digital twin standard model according to the mapping relation, and carrying out data mining on perceived data, state data and historical data and deep learning based on a convolutional neural network;
s7: constructing a three-dimensional virtual scene of a digital twin standard model for displaying the real scene information of a target power transformer;
s8: and constructing a multi-time space scale digital twin decision model, and completing iterative optimization and verification of the model based on measured data under the normal working condition of the power transformer and twin data under the fault working condition.
In this embodiment, preferably, the data types used for data acquisition include history data, current real-time data, design data, and detection data of the operation state of the apparatus;
in this embodiment, preferably, the sources of data acquisition include sensors, remote sensing monitoring, video monitoring, and image capturing;
in the present embodiment, preferably, the data information includes attribute data information and status data information of the power transformer; the attribute information of the power transformer comprises manufacturers, product models, rated capacity, rated voltage, operation time, cooling modes and historical fault conditions; the state data of the power transformer comprises test data of each state quantity obtained in inspection and routine tests, real-time data of each state quantity monitored on line and operation environment data of the power transformer;
in this embodiment, preferably, the large data platform performs synchronous statistics on time and frequency of the power transformer for subsequent analysis;
in this embodiment, preferably, the twin data is obtained nondestructively under the fault condition of the power transformer, so as to complete the space-time characteristic analysis of the twin data under the fault condition of the power transformer and realize the generation of the twin data under the fault condition;
in this embodiment, preferably, the system further includes a visualization module, configured to visually display the digital twin model;
in this embodiment, it is preferable that after the digital twin model is constructed, the data association is analyzed and sorted, and the association, correlation or causal structure existing between the item set or the object set, that is, the rule describing the relationship existing between different data items in the database is searched, and the sorted and built model is normalized.
Working principle: firstly, collecting data information of a power transformer under normal working conditions and fault working conditions, wherein the data types adopted by data acquisition comprise historical data, current real-time data, design data and detection data of equipment operation states, the data information comprises attribute data information and state data information of the power transformer, the attribute information of the power transformer comprises manufacturers, product models, rated capacity, rated voltage, operation time, cooling modes and historical fault conditions, and the state data of the power transformer comprises test data of all state quantities obtained in routing inspection and routine tests, real-time data of all state quantities monitored on line and operation environment data of the power transformer; secondly, collecting a timestamp of the update state of the power transformer and uploading the timestamp to a big data platform, using a Flink frame to process all collected data with state time marks in real time and in batches, dynamically mapping virtual objects of the power transformer, and updating attribute states by a program through a hash mapping tag after the uploaded collected data is subjected to an ETL process; comparing the state update of the plurality of data sources after the large data platform is compared with the acquired data, comparing the real-time scale of the power equipment with the acquired time scale, discarding the state data if the real-time scale is larger than the acquired time scale, and updating the state if the real-time scale is smaller than the acquired time scale; then, aiming at a typical power transformer in a target, constructing a digital twin standard model of the power transformer, establishing a mapping relation between a power transformer object and the digital twin standard model, realizing mapping from the power transformer object to the digital twin standard model according to the mapping relation, performing data mining on perceived data, state data and historical data and performing deep learning based on a convolutional neural network, and then constructing a three-dimensional virtual scene of the digital twin standard model for displaying live-action information of the target power transformer; finally, constructing a multi-time space scale digital twin decision model, and completing iterative optimization and verification of the model based on measured data of the power transformer under normal working conditions and twin data of the power transformer under fault working conditions; after the digital twin model is constructed, the data association is analyzed and organized, and association, correlation or causal structures among item sets or object sets are searched, namely rules describing the relations among different data items in the database are organized and constructed into a canonical model.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The method for constructing the digital twin model of the power transformer based on big data is characterized by comprising the following steps of:
s1: collecting data information of the power transformer under normal working conditions and fault working conditions;
s2: collecting a timestamp of the update state of the power transformer, uploading the timestamp to a big data platform, and using a Flink frame to process all collected data with state time marks in real time and in batches to dynamically map virtual objects of the power transformer;
s3: after the uploaded collected data is subjected to an ETL process, the program updates the attribute state through a hash mapping tag;
s4: after comparing the big data platform with the acquired data, the state of a plurality of data sources is updated, the real-time scale of the power equipment is compared with the acquired time scale, if the real-time scale is larger than the acquired time scale, the state data is discarded, and if the real-time scale is smaller than the acquired time scale, the state is updated, and if the real-time scale is smaller than the acquired time scale, the real-time scale is updated;
s5: constructing a digital twin standard model of the power transformer aiming at a typical power transformer in a target;
s6: establishing a mapping relation between the power transformer real object and the digital twin standard model, realizing the mapping from the power transformer real object to the digital twin standard model according to the mapping relation, and carrying out data mining on perceived data, state data and historical data and deep learning based on a convolutional neural network;
s7: constructing a three-dimensional virtual scene of a digital twin standard model for displaying the real scene information of a target power transformer;
s8: and constructing a multi-time space scale digital twin decision model, and completing iterative optimization and verification of the model based on measured data under the normal working condition of the power transformer and twin data under the fault working condition.
2. The method for constructing the digital twin model of the power transformer based on big data as claimed in claim 1, wherein the method comprises the following steps: the data types adopted for data acquisition include historical data, current real-time data, design data and detection data of the running state of equipment.
3. The method for constructing the digital twin model of the power transformer based on big data as claimed in claim 1, wherein the method comprises the following steps: sources of data acquisition include sensors, remote sensing monitoring, video monitoring and image capturing.
4. The method for constructing the digital twin model of the power transformer based on big data as claimed in claim 1, wherein the method comprises the following steps: the data information includes attribute data information and status data information of the power transformer.
5. The method for constructing the digital twin model of the power transformer based on big data as claimed in claim 4, wherein the method comprises the following steps: the attribute information of the power transformer comprises manufacturers, product models, rated capacity, rated voltage, operation time, cooling modes and historical fault conditions.
6. The method for constructing the digital twin model of the power transformer based on big data as claimed in claim 4, wherein the method comprises the following steps: the state data of the power transformer comprises test data of various state quantities obtained in inspection and routine tests, real-time data of various state quantities monitored on line and operation environment data of the power transformer.
7. The method for constructing the digital twin model of the power transformer based on big data as claimed in claim 1, wherein the method comprises the following steps: and carrying out synchronous statistics on the time and frequency of the power transformer in the big data platform for subsequent analysis.
8. The method for constructing the digital twin model of the power transformer based on big data as claimed in claim 1, wherein the method comprises the following steps: and the twin data is obtained nondestructively under the fault working condition of the power transformer, so that the space-time characteristic analysis of the twin data under the fault working condition of the power transformer is completed, and the generation of the twin data under the fault working condition is realized.
9. The method for constructing the digital twin model of the power transformer based on big data as claimed in claim 1, wherein the method comprises the following steps: the system also comprises a visualization module for visually displaying the digital twin model.
10. The method for constructing the digital twin model of the power transformer based on big data as claimed in claim 1, wherein the method comprises the following steps: after the digital twin model is constructed, the data association is analyzed and organized, and association, correlation or causal structures among item sets or object sets are searched, namely rules describing the relations among different data items in the database are organized and constructed into a canonical model.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117543826A (en) * | 2023-11-21 | 2024-02-09 | 利德世普科技有限公司 | Power pipeline monitoring method, system and medium based on digital twin technology |
| CN117933574A (en) * | 2024-03-21 | 2024-04-26 | 国网浙江省电力有限公司宁波供电公司 | Urban cable management and control method, device, equipment and storage medium |
| CN118246772A (en) * | 2024-05-29 | 2024-06-25 | 四川省机场集团有限公司 | Visual operation and maintenance data management method and system for transformer equipment |
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2023
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117543826A (en) * | 2023-11-21 | 2024-02-09 | 利德世普科技有限公司 | Power pipeline monitoring method, system and medium based on digital twin technology |
| CN117933574A (en) * | 2024-03-21 | 2024-04-26 | 国网浙江省电力有限公司宁波供电公司 | Urban cable management and control method, device, equipment and storage medium |
| CN118246772A (en) * | 2024-05-29 | 2024-06-25 | 四川省机场集团有限公司 | Visual operation and maintenance data management method and system for transformer equipment |
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