CN116720607A - Substation secondary equipment fault prediction method and device based on multi-source data - Google Patents
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Abstract
The application discloses a transformer substation secondary equipment fault prediction method and device based on multi-source data, wherein the method comprises the following steps: acquiring a multi-source data set of secondary equipment of a transformer substation; carrying out three-dimensional modeling processing according to the multi-source data set to obtain a digital twin model; performing state evaluation processing on secondary equipment of the transformer substation according to the digital twin model to obtain equipment state data; performing data comparison processing on the equipment state data according to a preset reference value to obtain abnormal data; acquiring a working ticket of the secondary equipment of the transformer substation through a power grid management platform, and comparing the working ticket with an image recognition result acquired by a video monitoring system to acquire comparison data; and carrying out fault prediction according to the comparison data and the abnormal data to obtain a fault prediction result. According to the method and the device for predicting the faults of the secondary equipment of the transformer substation, the faults of the secondary equipment of the transformer substation are predicted through the multi-source data, the cost and the complexity of model construction are reduced, and the method and the device can be widely applied to the technical field of fault prediction.
Description
Technical Field
The application relates to the technical field of fault prediction, in particular to a method and a device for predicting faults of secondary equipment of a transformer substation based on multi-source data.
Background
The digital substation system comprises a large number of intelligent electrical devices, and the secondary devices have close relevance, so that the operation and maintenance management work difficulty is relatively high, and the model construction of the multi-source data is required. In the related art, different data models are generally applied according to different data, and potential problem analysis is performed on the different data models, so that state discrimination and fault prediction can be further performed, the prediction cost is high, and certain complexity exists. In view of the foregoing, there is a need for solving the technical problems in the related art.
Disclosure of Invention
In view of the above, the embodiment of the application provides a method and a device for predicting faults of secondary equipment of a transformer substation based on multi-source data, so as to simply and efficiently predict the faults of the equipment.
In one aspect, the application provides a transformer substation secondary equipment fault prediction method based on multi-source data, which comprises the following steps:
acquiring a multi-source data set of secondary equipment of a transformer substation;
carrying out three-dimensional modeling processing according to the multi-source data set to obtain a digital twin model;
performing state evaluation processing on the secondary equipment of the transformer substation according to the digital twin model to obtain equipment state data;
performing data comparison processing on the equipment state data according to a preset reference value to obtain abnormal data;
acquiring a working ticket of the substation secondary equipment through a power grid management platform, and comparing the working ticket with an image recognition result acquired by a video monitoring system to acquire comparison data;
and carrying out fault prediction according to the comparison data and the abnormal data to obtain a fault prediction result.
Optionally, the acquiring the multi-source data set of the secondary device of the substation includes:
the method comprises the steps of obtaining multi-source data of each secondary device in a transformer substation, wherein the multi-source data comprise standing account information, monitoring information, defect information and device structure characteristic information of the secondary devices of the transformer substation;
and integrating the multi-source data to obtain a multi-source data set of the secondary equipment of the transformer substation.
Optionally, the performing three-dimensional modeling processing according to the multi-source dataset to obtain a digital twin model includes:
performing point cloud data conversion processing on the multi-source data set to obtain a multi-source point cloud data set;
modeling the secondary equipment of the transformer substation according to the multi-source point cloud data set to obtain a data twin model.
Optionally, the performing state evaluation processing on the secondary device of the substation according to the digital twin model to obtain device state data includes:
acquiring equipment data through patrol images or on-line communication according to the digital twin model;
and analyzing and judging the state of the equipment data to obtain the equipment state data.
Optionally, the performing data comparison processing on the device state data according to a preset reference value to obtain abnormal data includes:
acquiring a preset reference value;
and carrying out mutation comparison and heterologous comparison treatment on the equipment state data according to the preset reference value to obtain abnormal data.
Optionally, the step of obtaining the working ticket of the substation secondary device through the power grid management platform, and comparing with the image recognition result obtained by the video monitoring system to obtain comparison data includes:
acquiring a working ticket of the secondary equipment of the transformer substation through a power grid management platform, and analyzing the working ticket to obtain working data of the secondary equipment;
acquiring an image recognition result of the secondary equipment through a video monitoring system;
and comparing the working data of the secondary equipment with the image recognition result to obtain comparison data.
Optionally, the performing fault prediction according to the comparison data and the abnormal data to obtain a fault prediction result includes:
performing fault recognition on the secondary equipment according to the comparison data and the abnormal data to obtain a recognition result;
and carrying out fault prediction on the secondary equipment according to the identification result to obtain a fault prediction result.
On the other hand, the embodiment of the application also provides a transformer substation secondary equipment fault prediction device based on multi-source data, which comprises the following steps:
the first module is used for acquiring a multi-source data set of the secondary equipment of the transformer substation;
the second module is used for carrying out three-dimensional modeling processing according to the multi-source data set to obtain a digital twin model;
the third module is used for carrying out state evaluation processing on the secondary equipment of the transformer substation according to the digital twin model to obtain equipment state data;
a fourth module, configured to perform data comparison processing on the device state data according to a preset reference value, so as to obtain abnormal data;
the fifth module is used for acquiring a working ticket of the secondary equipment of the transformer substation through the power grid management platform, and comparing the working ticket with an image recognition result acquired by the video monitoring system to acquire comparison data;
and a sixth module, configured to perform fault prediction according to the comparison data and the abnormal data, so as to obtain a fault prediction result.
On the other hand, the embodiment of the application also discloses electronic equipment, which comprises a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
In another aspect, embodiments of the present application also disclose a computer readable storage medium storing a program for execution by a processor to implement a method as described above.
In another aspect, embodiments of the present application also disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read from a computer-readable storage medium by a processor of a computer device, and executed by the processor, to cause the computer device to perform the foregoing method.
Compared with the prior art, the technical scheme provided by the application has the following technical effects: according to the embodiment of the application, the multi-source data set of the secondary equipment of the transformer substation is obtained, and the multi-source data set is subjected to three-dimensional modeling processing to obtain the digital twin model, so that the data association of the equipment can be enhanced; the state of the secondary equipment of the transformer substation can be evaluated through the digital twin model; finally, the abnormal data is obtained through data comparison of the equipment state data, a prediction result is obtained, the fault prediction can be carried out through the association relation of the multi-source data, the complexity of the prediction is reduced, and the accuracy of the prediction of the power transformation equipment is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for predicting faults of secondary equipment of a transformer substation based on multi-source data, which is provided by an embodiment of the application;
fig. 2 is a schematic structural diagram of a fault prediction device for secondary equipment of a transformer substation based on multi-source data according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a multi-source data modeling device for secondary equipment of a transformer substation, which is provided by an embodiment of the application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
First, several nouns involved in the present application are parsed:
substation secondary equipment: the device mainly comprises a relay protection device, an automatic device, a measurement and control device, a metering device, an automatic system and direct current equipment for providing power for secondary equipment.
In the related art, a digital substation system comprises a large number of intelligent electrical devices, and the secondary devices have close relevance, so that the operation and maintenance management work is relatively difficult. Generally, different data models are applied according to different data, potential problem analysis is performed on the different data models, state discrimination and fault prediction can be further performed, prediction cost is high, and certain complexity exists. In view of the above, the embodiment of the application provides a method and a device for predicting faults of secondary equipment of a transformer substation based on multi-source data, so as to improve the accuracy of fault prediction.
Referring to fig. 1, an embodiment of the present application provides a method for predicting a fault of a secondary device of a substation based on multi-source data, including:
s110, acquiring a multi-source data set of secondary equipment of a transformer substation;
s120, carrying out three-dimensional modeling processing according to the multi-source data set to obtain a digital twin model;
s130, performing state evaluation processing on the secondary equipment of the transformer substation according to the digital twin model to obtain equipment state data;
s140, carrying out data comparison processing on the equipment state data according to a preset reference value to obtain abnormal data;
s150, acquiring a work ticket of the substation secondary equipment through a power grid management platform, and comparing the work ticket with an image recognition result acquired by a video monitoring system to acquire comparison data;
s160, performing fault prediction according to the comparison data and the abnormal data to obtain a fault prediction result.
In the embodiment of the application, the data of the secondary equipment of the transformer substation is comprehensively acquired, a multi-source data set is modeled by using three-dimensional modeling technologies such as laser point cloud data and the like, and the digital twin model is obtained by combining the corresponding data of the equipment. And carrying out state evaluation on the secondary equipment of the transformer substation through a digital twin model, visually presenting the state of the secondary equipment according to model visualization, and obtaining equipment state data through evaluation. And combining a preset reference value or threshold value, wherein the reference value can be configured according to actual conditions, and the data with abnormal change is found. Comparing the work ticket acquired by the power grid management platform with an image recognition result acquired by the video monitoring system to acquire comparison data; and finally, carrying out fault prediction according to the comparison data and the abnormal data to obtain a fault prediction result.
Further as a preferred embodiment, in the step S101, the acquiring a multi-source dataset of the secondary device of the substation includes:
the method comprises the steps of obtaining multi-source data of each secondary device in a transformer substation, wherein the multi-source data comprise standing account information, monitoring information, defect information and device structure characteristic information;
and integrating the multi-source data to obtain a multi-source data set of the secondary equipment of the transformer substation.
In the embodiment of the application, the multi-source data modeling of the secondary equipment of the transformer substation needs to complete the design of the conceptual model, namely, the data is described from the perspective of a user. The substation secondary equipment conceptual model is mainly used for carrying out unified association modeling on standing account information, monitoring information, defect information and equipment structure characteristic information of secondary equipment in a substation, wherein the standing account information, the monitoring information, the defect information and the equipment structure characteristic information comprise space coordinates, external shapes, internal structures and the like of the secondary equipment. Therefore, the embodiment of the application obtains the multi-source data of each secondary device in the transformer substation, integrates the multi-source data, and combines the multi-source data to obtain the multi-source data set of the secondary device of the transformer substation.
Further as a preferred embodiment, in step S102, the performing three-dimensional modeling processing according to the multi-source dataset to obtain a digital twin model includes:
performing point cloud data conversion processing on the multi-source data set to obtain a multi-source point cloud data set;
modeling the secondary equipment of the transformer substation according to the multi-source point cloud data set to obtain a data twin model.
In the embodiment of the application, the multi-source point cloud data set is obtained by carrying out point cloud data conversion on the multi-source data set according to the laser point cloud technology. Modeling the secondary equipment of the transformer substation through a modeling method such as a three-dimensional visualization platform according to the converted multi-source point cloud data set to obtain a data twin model.
Further as a preferred embodiment, in step S103, the performing, according to the digital twin model, a state evaluation process on the secondary device of the substation to obtain device state data includes:
acquiring equipment data through patrol images or on-line communication according to the digital twin model;
and analyzing and judging the state of the equipment data to obtain the equipment state data.
In the embodiment of the application, the digital twin model can acquire equipment data through a patrol image or on-line communication, wherein the patrol image is monitoring equipment of the digital twin model, the monitoring equipment acquires image information of the power transformation secondary equipment, and equipment data such as the running state of the power transformation secondary equipment is judged according to the image information; and the online communication is to connect the power transformation secondary equipment and the digital twin model through a network, and upload the data recorded by the power transformation secondary equipment to the digital twin model to obtain equipment data. Finally, analyzing and judging the state of the equipment data to obtain the equipment state data, wherein the equipment state data can be in a normal working state or a fault state. It is conceivable that, because of the variety of secondary devices, the operation state of this embodiment may further include an acquisition state, a monitoring state, and a control state, where data is acquired by the secondary device, the acquired data is monitored, or the primary device is controlled to be turned on or off.
Further as a preferred embodiment, in step S104, the performing data comparison processing on the device state data according to a preset reference value to obtain abnormal data includes:
acquiring a preset reference value;
and carrying out mutation comparison and heterologous comparison treatment on the equipment state data according to the preset reference value to obtain abnormal data.
In the embodiment of the application, a reference value, namely a state threshold value of each power transformation device, such as a temperature threshold value, a sensing data threshold value and the like, is preset for each power transformation device in the digital twin model. And carrying out mutation comparison and heterogeneous comparison on the equipment state data through a preset reference value, marking the data exceeding or falling below the reference value, recording the part exceeding or falling below the reference value, and marking the secondary equipment with the abnormality to obtain the abnormal data.
Further as a preferred embodiment, in step S105, the step of obtaining, by the power grid management platform, a work ticket of the substation secondary device, and comparing the work ticket with an image recognition result obtained by the video monitoring system to obtain comparison data includes:
acquiring a working ticket of the secondary equipment of the transformer substation through a power grid management platform, and analyzing the working ticket to obtain working data of the secondary equipment;
acquiring an image recognition result of the secondary equipment through a video monitoring system;
and comparing the working data of the secondary equipment with the image recognition result to obtain comparison data.
In the embodiment of the application, the working ticket of the secondary equipment of the transformer substation is acquired through the power grid management platform, the working ticket records the operation data and the monitoring data of the secondary equipment of the transformer substation, and the working ticket is analyzed to obtain the working data of the secondary equipment. And acquiring an image recognition result of the secondary equipment by the video monitoring system, wherein the image recognition result comprises the running state and the running environment data of the secondary equipment. And comparing the working data of the secondary equipment with the image recognition result to prevent erroneous judgment or data abnormality caused by environmental reasons and obtain comparison data, wherein the comparison data is obtained by comparing the working state of the secondary equipment with the data of the working environment.
Further, in a preferred embodiment, in step S106, the performing fault prediction according to the comparison data and the abnormal data to obtain a fault prediction result includes:
performing fault recognition on the secondary equipment according to the comparison data and the abnormal data to obtain a recognition result;
and carrying out fault prediction on the secondary equipment according to the identification result to obtain a fault prediction result.
In the embodiment of the application, the secondary equipment is subjected to fault identification again by combining the comparison data with the abnormal data, and the reasons of the abnormal data are analyzed by combining the abnormal data with the comparison data, so that the accuracy of fault identification is improved. And after the data abnormality is determined, predicting the running state of the secondary equipment according to the identification result to obtain a fault prediction result.
In the embodiment of the application, a novel power transformation intelligent gateway is also adopted, the novel power transformation intelligent gateway is provided with a main board with an industrial expansion bus for collecting network access measurement and control, protection, security control and fault recording data, the main board is provided with a panel for providing basic running state indication lamps, 8 RS-232/422/485 interfaces, 6 10M/100M self-adaptive RJ-45 Ethernet interfaces and PC monitoring and debugging software. The novel power transformation intelligent gateway adopts an AC/DC 110-220V power supply, accords with general standards of products and other main current technical standards, and comprises technical standards of relay, protection and automatic device general technical requirements Q/XJ 20.50-2009, communication network in a transformer substation, system IEC61850 and the like.
The novel transformer intelligent gateway runs an installation program under a WINDOWS system, a window for selecting and installing a catalog appears, a window for selecting and installing a telecontrol or relay protection substation system appears after the window for installing the catalog is selected, a shortcut of a communication service unit debugging tool and a communication service unit configuration tool is arranged on a desktop after the installation, and menu items are added in a start menu. The communication service unit debugging computer and the communication service unit provided with the system are connected to a local area network through the Ethernet, and the currently set debugging network port of the communication service unit is network port 1, namely only network port 1 transmits the broadcasted local network card and software information. And finally, automatically identifying the programs adapted by different operating systems through automatic identification, upgrading programs and system upgrading of the operating systems, and completing the installation of the novel intelligent substation gateway.
The novel power transformation intelligent gateway is a 2U high-standard chassis, is embedded on a screen (cabinet), and comprises a menu bar, a tool bar, a database configuration module, a service configuration module, a protocol function module, a network proxy module, a transfer-out protocol configuration module and an acquisition module. In the debugging process, the novel power transformation intelligent gateway needs to be provided with a data service, an application service, a protocol device, a transmission module and a historic base module, wherein the transmission module comprises a unidirectional transmission module, remote signaling and remote measuring data of a station control layer in a transformer substation are forwarded to an analysis end, and a wave recording file, a constant value area file, a constant value file, a fault report file and a spectrogram file can be generated. And then carrying out database configuration and forwarding configuration on the novel intelligent transformer gateway, wherein a protection device, a wave recording device and an online monitoring device are required to be added into the module configuration in the forwarding configuration. And finally, the intelligent gateway machine is accessed to an application program for configuration, and the configuration can be directly realized by modifying a configuration file. The multi-source data set of the secondary equipment of the transformer substation can be acquired by using the novel intelligent gateway, and the multi-source data can be acquired for storage, transmission and other processing.
On the other hand, referring to fig. 2, the embodiment of the present application further provides a device for predicting a fault of a secondary device of a substation based on multi-source data, including:
a first module 101, configured to acquire a multi-source data set of a secondary device of a substation;
the second module 102 is configured to perform three-dimensional modeling processing according to the multi-source dataset to obtain a digital twin model;
a third module 103, configured to perform a state evaluation process on the secondary device of the substation according to the digital twin model, to obtain device state data;
a fourth module 104, configured to perform data comparison processing on the device state data according to a preset reference value, so as to obtain abnormal data;
a fifth module 105, configured to obtain a working ticket of the substation secondary device through a power grid management platform, and compare the working ticket with an image recognition result obtained by a video monitoring system to obtain comparison data;
and a sixth module 106, configured to perform fault prediction according to the comparison data and the abnormal data, so as to obtain a fault prediction result.
Referring to fig. 3, corresponding to the method of fig. 1, an embodiment of the present application further provides an electronic device, including a processor 201 and a memory 202; the memory is used for storing programs; the processor executes the program to implement the method as described above.
Corresponding to the method of fig. 1, an embodiment of the present application also provides a computer-readable storage medium storing a program to be executed by a processor to implement the method as described above.
Embodiments of the present application also disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read from a computer-readable storage medium by a processor of a computer device, and executed by the processor, to cause the computer device to perform the method shown in fig. 1.
In summary, the embodiment of the application has the following advantages: according to the embodiment of the application, the multi-source data set of the secondary equipment of the transformer substation is obtained, and the multi-source data set is subjected to three-dimensional modeling processing to obtain the digital twin model, so that the data association of the equipment can be enhanced; the state of the secondary equipment of the transformer substation can be evaluated through the digital twin model; finally, the abnormal data is obtained through data comparison of the equipment state data, a prediction result is obtained, the fault prediction can be carried out through the association relation of the multi-source data, the complexity of the prediction is reduced, and the accuracy of the prediction of the power transformation equipment is improved.
In some alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present application are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed, and in which sub-operations described as part of a larger operation are performed independently.
Furthermore, while the application is described in the context of functional modules, it should be appreciated that, unless otherwise indicated, one or more of the described functions and/or features may be integrated in a single physical device and/or software module or one or more functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present application. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be apparent to those skilled in the art from consideration of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement the application as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and are not intended to be limiting upon the scope of the application, which is to be defined in the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the application, the scope of which is defined by the claims and their equivalents.
While the preferred embodiment of the present application has been described in detail, the present application is not limited to the embodiments described above, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present application, and these equivalent modifications or substitutions are included in the scope of the present application as defined in the appended claims.
Claims (10)
1. A method for predicting faults of secondary equipment of a transformer substation based on multi-source data, which is characterized by comprising the following steps:
acquiring a multi-source data set of secondary equipment of a transformer substation;
carrying out three-dimensional modeling processing according to the multi-source data set to obtain a digital twin model;
performing state evaluation processing on the secondary equipment of the transformer substation according to the digital twin model to obtain equipment state data;
performing data comparison processing on the equipment state data according to a preset reference value to obtain abnormal data;
acquiring a working ticket of the substation secondary equipment through a power grid management platform, and comparing the working ticket with an image recognition result acquired by a video monitoring system to acquire comparison data;
and carrying out fault prediction according to the comparison data and the abnormal data to obtain a fault prediction result.
2. The method of claim 1, wherein the acquiring the multi-source dataset of the substation secondary device comprises:
the method comprises the steps of obtaining multi-source data of each secondary device in a transformer substation, wherein the multi-source data comprise standing account information, monitoring information, defect information and device structure characteristic information of the secondary devices of the transformer substation;
and integrating the multi-source data to obtain a multi-source data set of the secondary equipment of the transformer substation.
3. The method of claim 1, wherein said performing a three-dimensional modeling process from said multi-source dataset results in a digital twin model, comprising:
performing point cloud data conversion processing on the multi-source data set to obtain a multi-source point cloud data set;
modeling the secondary equipment of the transformer substation according to the multi-source point cloud data set to obtain a data twin model.
4. The method according to claim 1, wherein the performing a state evaluation process on the substation secondary device according to the digital twin model to obtain device state data includes:
acquiring equipment data through patrol images or on-line communication according to the digital twin model;
and analyzing and judging the state of the equipment data to obtain the equipment state data.
5. The method according to claim 1, wherein the performing data comparison processing on the device status data according to a preset reference value to obtain abnormal data includes:
acquiring a preset reference value;
and carrying out mutation comparison and heterologous comparison treatment on the equipment state data according to the preset reference value to obtain abnormal data.
6. The method of claim 1, wherein the obtaining, by the power grid management platform, the work ticket of the substation secondary device, and comparing with the image recognition result obtained by the video monitoring system to obtain the comparison data, includes:
acquiring a working ticket of the secondary equipment of the transformer substation through a power grid management platform, and analyzing the working ticket to obtain working data of the secondary equipment;
acquiring an image recognition result of the secondary equipment through a video monitoring system;
and comparing the working data of the secondary equipment with the image recognition result to obtain comparison data.
7. The method according to claim 1, wherein the performing the fault prediction according to the comparison data and the anomaly data to obtain a fault prediction result includes:
performing fault recognition on the secondary equipment according to the comparison data and the abnormal data to obtain a recognition result;
and carrying out fault prediction on the secondary equipment according to the identification result to obtain a fault prediction result.
8. A substation secondary equipment fault prediction device based on multi-source data, the device comprising:
the first module is used for acquiring a multi-source data set of the secondary equipment of the transformer substation;
the second module is used for carrying out three-dimensional modeling processing according to the multi-source data set to obtain a digital twin model;
the third module is used for carrying out state evaluation processing on the secondary equipment of the transformer substation according to the digital twin model to obtain equipment state data;
a fourth module, configured to perform data comparison processing on the device state data according to a preset reference value, so as to obtain abnormal data;
the fifth module is used for acquiring a working ticket of the secondary equipment of the transformer substation through the power grid management platform, and comparing the working ticket with an image recognition result acquired by the video monitoring system to acquire comparison data;
and a sixth module, configured to perform fault prediction according to the comparison data and the abnormal data, so as to obtain a fault prediction result.
9. An electronic device comprising a memory and a processor;
the memory is used for storing programs;
the processor executing the program implements the method of any one of claims 1 to 7.
10. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method of any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118521433A (en) * | 2024-07-24 | 2024-08-20 | 东方电子股份有限公司 | Knowledge-graph-based digital twin early warning decision method and system for transformer substation |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118521433A (en) * | 2024-07-24 | 2024-08-20 | 东方电子股份有限公司 | Knowledge-graph-based digital twin early warning decision method and system for transformer substation |
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