220kV transformer substation state monitoring system based on digital twinning technology

Abstract

The invention relates to the field of intelligent manufacturing equipment, in particular to a 220kV transformer substation state monitoring system based on a digital twin technology. The system comprises a monitoring unit, a digital twin module and an application platform layer, the monitoring unit is used for acquiring operation data of the transformer substation and inputting the operation data of the transformer substation to the digital twin module; the digital twinborn module updates the digital twinborn body of the transformer substation according to the input operation data of the transformer substation; performing substation state evaluation and fault pre-judgment on the updated substation digital twin to obtain a state evaluation result and a fault pre-judgment result, and sending the state evaluation result and the fault pre-judgment result to an application platform layer; the application platform layer is used for carrying out substation distribution network equipment fault early warning according to the received state evaluation result and the fault pre-judgment result; the system is used for solving the problems of many manual intervention links and low overall efficiency of the existing power transformation detection equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent manufacturing equipment, and particularly relates to a 220kV substation state monitoring system based on digital twin technology. BACKGROUND

[0002] The existing substation automation terminal equipment detection method mainly depends on the online qualified rate. When a fault alarm or a long-time non-recovered signal occurs, remote control operation is performed to recover the fault point or maintenance personnel go to the fault point for maintenance. The process has many manual intervention links, the fault detection result cannot be tracked and traced, the overall efficiency is low, and the fault early warning or further research on the fault information cannot be performed. SUMMARY

[0003] To solve the problem of many manual intervention links and low overall efficiency of the existing substation detection equipment, a 220kV substation state monitoring system based on digital twin technology is provided.

[0004] A 220kV substation state monitoring system based on digital twin technology, comprising a monitoring unit, a digital twin module and an application platform layer.

[0005] The monitoring unit is used for collecting substation operation data and inputting the substation operation data into the digital twin module.

[0006] The digital twin module is used for updating a substation digital twin according to the input substation operation data, wherein the substation digital twin is a three-dimensional digital model of the substation corresponding to the substation; the updated substation digital twin performs substation state evaluation and fault prediction according to the substation operation data, obtains a state evaluation result and a fault prediction result, and sends the state evaluation result and the fault prediction result to the application platform layer.

[0007] The application platform layer is used for performing substation distribution network equipment fault early warning according to the received state evaluation result and fault prediction result.

[0008] The beneficial effects of the present application: based on the research and development of the digital twin technology of the 220kV substation state monitoring system, a large amount of maintenance cost can be saved; the service life of the equipment is prolonged; the power supply reliability is improved; the maintenance risk is reduced. The Internet of Things technology is used to improve the performance of condition-based maintenance; the interference signal is eliminated, the high-frequency interference is eliminated, and the measurement data is more stable and reliable; the design of the power transformation equipment can prevent the occurrence of sharp discharge phenomenon in high-voltage environment, and the power transformation equipment is equipped with temperature measurement terminal and adopts wireless connection. The safety and flexibility of the system have been greatly improved. The fault diagnosis method based on deep learning can process the increase of data volume, state quantity and feature extraction capability, has outstanding ability in early fault and micro-fault diagnosis, reduces the dependence on prior knowledge, can diagnose more fault types based on a large amount of data, and has enhanced ability to mine information related to equipment state from general data such as voltage, current and power. BRIEF DESCRIPTION OF DRAWINGS

[0009] Figure 1 A structure block diagram of a 220kV substation state monitoring system based on digital twin technology is provided for the specific embodiment of the present application. DETAILED DESCRIPTION

[0010] In order to make the purpose, technical scheme and advantages of the present application more clear and obvious, the present application is further described in detail below in combination with embodiments and drawings. Herein, the illustrative embodiments of the present application and their descriptions are used to explain the present application, but not as a limitation of the present application.

[0011] A 220kV substation state monitoring system based on digital twin technology, comprising: a monitoring unit, a digital twin module and an application platform layer;

[0012] The monitoring unit is used for collecting substation operation data and inputting the substation operation data into the digital twin module;

[0013] The digital twin module is used for updating the substation digital twin body according to the input substation operation data, wherein the substation digital twin body is a three-dimensional digital model of the substation corresponding to the substation; the updated substation digital twin body performs substation state evaluation and fault prediction according to the substation operation data, obtains state evaluation results and fault prediction results, and sends the state evaluation results and fault prediction results to the application platform layer;

[0014] The application platform layer is used for performing substation distribution network equipment fault warning according to the received state evaluation results and fault prediction results.

[0015] Specifically, the 220kV substation state monitoring system based on digital twin technology of the application takes digital twin technology as the basis for information interaction between the substation automation terminal device and the substation master station, fault prediction, and auxiliary distribution network operation and maintenance. By statistically analyzing historical data and missing information collected by substation automation terminals in a certain area, the state evaluation comment set, evaluation weight, and fault set are constructed and updated and corrected in real time in the digital twin system. At the same time, an intelligent state evaluation and fault prediction simulation model is built, which provides reliable reference data for real-time monitoring of the state of substation automation terminal devices and distribution network operation and maintenance.

[0016] This work framework is realized through efficient interaction among physical entities, virtual models, and substation master stations. It includes bidirectional data flow, a closed driving environment, and real-time operation. The entity layer transmits data to the virtual model, which is simulated through state evaluation and fault prediction models, and the results are fed back to the application platform for maintenance measures.

[0017] The system saves a large amount of maintenance costs, and the annual operation and maintenance cost of a single station is expected to be 1 million yuan. At the same time, the sensor is suitable for environments with a temperature lower than -45℃.

[0018] Further, the detection unit includes a physical device layer, a data sensing layer, and a data transmission layer.

[0019] The physical device layer is used to obtain real-time data of substation equipment and event record report information, and send the real-time data of substation equipment and the event record report information to the data sensing layer.

[0020] The data sensing layer is used to obtain accurate data of the substation, including voltage value, current value, and power value of the substation, and send the obtained accurate data of the substation and the received real-time data of the substation equipment and the event record report information to the data transmission layer.

[0021] The data transmission layer is used to store the received accurate data of the substation, real-time data of the substation equipment, and event record report information, and send the accurate data of the substation, real-time data of the substation equipment, and event record report information to the digital twin module.

[0022] Specifically, the digital twin is a state evaluation and fault prediction model based on intelligent algorithms; the physical equipment layer is the physical entity of the digital twin of the fault prediction system, providing the data perception layer with real-time equipment data and event log reports. It can also accept feedback commands from the digital twin and the application platform layer; the data perception layer is responsible for the dynamic acquisition of accurate data and sending the measurement information obtained from the physical equipment layer to the intelligent substation; the data transmission layer is an efficient network transmission and data storage system, realizing the rapid transmission of real-time operating data and information of substation equipment, with data and information transmitted from the physical entity to the digital twin; the digital twin performs state evaluation and fault prediction and transmits it to the application platform layer, assisting in substation operation and maintenance management, issuing early fault warnings and conducting repairs, and ensuring the reliable operation of the substation.

[0023] Furthermore, the digital twin is a digital twin based on the matter-element extension algorithm and the FCM clustering algorithm; the digital twin is trained using historical operating data of the substation;

[0024] The historical operating data of the substation includes historical precise data of the substation, historical data of the substation equipment, and historical event record report information; the historical precise data of the substation includes historical voltage values, historical current values, and historical power values ​​of the substation.

[0025] Specifically, a digital twin can accurately reflect the characteristics of a physical entity, make status assessments of substation automation equipment and predict potential faults, thus providing guidance for distribution network operation and maintenance. The status assessments and fault predictions made by the digital twin are transmitted to the application platform layer to assist in distribution network operation and maintenance management, provide early warnings of faults in distribution network equipment, and conduct timely maintenance to ensure the reliable operation of the distribution network.

[0026] Construct a real-time, accurate status assessment and fault prediction framework. Ensure an effective closed loop between the digital twin and the corresponding physical entity, enabling early fault prediction and maintenance guidance for substation automation terminal equipment, while avoiding unnecessary personnel consumption and reducing operation and maintenance costs.

[0027] Furthermore, the matter-element extension algorithm is trained by taking historical substation operation data as input and the substation state corresponding to the historical substation operation data as output, and training the matter-element extension-based state evaluation algorithm to obtain the trained matter-element extension-based state evaluation algorithm.

[0028] Furthermore, the FCM clustering algorithm is trained by taking historical substation operation data as input and the substation fault type corresponding to the historical substation operation data as output, and training the fault prediction algorithm based on FCM clustering to obtain the trained fault prediction algorithm based on FCM clustering.

[0029] Specifically, the model algorithm mainly includes state evaluation based on matter element extension and fault judgment based on FCM clustering. The triangular fuzzy number analytic hierarchy process is used to determine the weight while citing the matter element extension method, which reduces the subjectivity of weight distribution.

[0030] Further, the 220kV substation state monitoring system based on digital twinning technology further comprises a device management unit;

[0031] The monitoring unit digitizes the collected substation operation data on site to obtain a visual substation main wiring diagram and a visual substation table, and sends the visual substation main wiring diagram and the visual substation table to the device management unit.

[0032] The device management unit displays the received visual substation main wiring diagram and visual substation table.

[0033] Further, the device management unit and the monitoring unit interact through information transmission supporting IEC61850 protocol.

[0034] Specifically, the monitoring unit in the system digitizes the collected various monitoring data on site, the system visually displays the monitoring parameters through the main wiring diagram or table form, the system completes information interaction with the dispatching system by supporting IEC61850 protocol, and the system is used for information interaction with the device management unit through a reserved interface.

[0035] Further, the digital twin is carried on the ARM9 chip.

[0036] Specifically, a low-power microcontroller minimum system is designed by using an embedded real-time operating system kernel and an ARM9 structure chip. The system can digitize and network the collected data, support IEC61850 protocol, and complete interaction with the device management unit.

[0037] The ARM9 core chip intelligent module has advanced performance advantages for making early warning judgments on the faults of the power system, uses Internet of Things technology to improve the condition-based maintenance performance, applies digital coding and decoding technology in software design to eliminate interference signals, and uses software filtering technology, and uses metal shielding in hardware to strengthen filtering at all levels to eliminate high-frequency interference.

[0038] While the application has been described with reference to particular embodiments thereof, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present application. It will be apparent to those skilled in the art that numerous modifications can be made within the scope of the present application as defined by the appended claims. It is intended that all such modification fall within the spirit and scope of the present application. It will be understood that the features described in connection with one embodiment can be used in connection with another embodiment.

Claims

1. A 220 kV substation condition monitoring system based on digital twin technology, characterized in that, Comprise: a monitoring unit, a digital twin module and an application platform layer; the monitoring unit is used for collecting substation operation data and inputting the substation operation data into the digital twin module; the digital twin module is used for updating the substation digital twin according to the input substation operation data, the substation digital twin being a three-dimensional digital model of the substation corresponding to the substation; the updated substation digital twin performs substation state evaluation and fault prediction according to the substation operation data, obtains state evaluation results and fault prediction results, and sends the state evaluation results and the fault prediction results to the application platform layer; the application platform layer is used for performing substation distribution network equipment fault early warning according to the received state evaluation results and fault prediction results.

2. The 220kV substation state monitoring system based on the digital twin technology according to claim 1, wherein: the detection unit comprises a physical device layer, a data sensing layer and a data transmission layer; the physical device layer is used for acquiring substation device real-time data and event record report information, and sending the substation device real-time data and the event record report information to the data sensing layer; the data sensing layer is used for acquiring substation accurate data, the substation accurate data comprising substation voltage value, substation current value and substation power value, and sending the acquired substation accurate data and the received substation device real-time data and event record report information to the data transmission layer; the data transmission layer is used for storing the received substation accurate data, substation device real-time data and event record report information, and sending the substation accurate data, substation device real-time data and event record report information to the digital twin module.

3. The 220kV substation state monitoring system based on the digital twin technology according to claim 2, wherein: the digital twin is a digital twin based on the matter-element extension algorithm and the FCM clustering algorithm; the digital twin is trained using substation historical operation data; the substation historical operation data comprises substation historical accurate data, substation device historical data and historical event record report information; the substation historical accurate data comprises substation historical voltage value, substation historical current value and substation historical power value.

4. The 220kV substation state monitoring system based on the digital twin technology according to claim 3, wherein: training the matter-element extension algorithm comprises: taking the substation historical operation data as input, taking the substation state corresponding to the substation historical operation data as output, training the state evaluation algorithm based on the matter-element extension, and obtaining the trained state evaluation algorithm based on the matter-element extension.

5. The 220kV substation state monitoring system based on the digital twin technology according to claim 4, wherein: training the FCM clustering algorithm comprises: taking the substation historical operation data as input, taking the substation fault type corresponding to the substation historical operation data as output, training the fault prediction algorithm based on the FCM clustering, and obtaining the trained fault prediction algorithm based on the FCM clustering.

6. The 220 kV substation condition monitoring system based on digital twin technology according to claim 1, characterized in that: Further comprising a device management unit; The monitoring unit digitizes the collected substation operation data on site, obtains a visual substation main wiring diagram and a visual substation table, and sends the visual substation main wiring diagram and the visual substation table to the equipment management unit; The equipment management unit displays the received visual substation main wiring diagram and the visual substation table.

7. The 220kV substation state monitoring system based on the digital twin technology according to claim 6, characterized in that: The equipment management unit and the monitoring unit interact through information transmission supporting the IEC61850 protocol.

8. The 220kV substation state monitoring system based on the digital twin technology according to claim 6, characterized in that: The digital twin is carried on an ARM9 chip.

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