Transformer operation safety monitoring platform

Abstract

The invention discloses a transformer operation safety monitoring platform, which comprises a transformer monitoring platform, the transformer monitoring platform comprises a data acquisition unit, and the data acquisition unit is in butt joint with an edge calculation unit, and relates to the technical field of transformers. The transformer operation safety monitoring platform performs fusion analysis on various types of monitoring data such as electrical parameters, temperature, vibration, acoustics and gas through a multi-modal data fusion technology, can comprehensively and accurately reflect the operation state of the transformer, improves the accuracy and reliability of fault diagnosis, and improves the safety of the transformer operation. By processing and analyzing the data at the edge computing unit, the data transmission quantity and delay are reduced, and real-time monitoring and rapid decision making are realized. Meanwhile, the edge computing nodes have local decision-making and early warning capabilities and can send out early warning signals in time under the condition of network failure or communication interruption, so that the operation safety of the transformer is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of transformers, in particular to a transformer operation safety monitoring platform. BACKGROUND

[0002] As a key device in the power system, the operation safety of the transformer is crucial for ensuring the stability and reliability of power supply.

[0003] At present, although there are various transformer operation monitoring technologies and methods, such as partial discharge monitoring, gas analysis in oil, temperature monitoring, etc., these methods mostly have limitations. For example, a single monitoring method may not fully reflect the operation state of the transformer, and there is a lack of effective fusion and analysis between different monitoring data, resulting in insufficient timely and accurate early warning of potential faults. In addition, the existing monitoring system usually relies on centralized data processing and analysis, and the delay of data transmission and processing is large, which is difficult to meet the needs of real-time monitoring and rapid decision-making. Therefore, a transformer operation safety monitoring platform is proposed to solve the existing problems. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a transformer operation safety monitoring platform, which solves the problem that the monitoring system usually relies on centralized data processing and analysis, and the delay of data transmission and processing is large, which is difficult to meet the needs of real-time monitoring and rapid decision-making.

[0005] To achieve the above purpose, the present application is realized by the following technical scheme: a transformer operation safety monitoring platform, comprising a transformer monitoring platform, the transformer monitoring platform comprising a data acquisition unit, the data acquisition unit being connected with an edge computing unit, the edge computing unit being connected with a network transmission, the network transmission being connected with a cloud analysis and decision unit, the cloud analysis and decision unit being connected with a remote interaction unit, the remote interaction unit being connected with a frequency regulation unit, the frequency regulation unit being connected with the data acquisition unit, and the edge computing unit being connected with the frequency regulation unit. The data acquisition unit adopts sensors to comprehensively monitor different types of operation states of the transformer, and converts analog signals collected by the sensors into digital signals, and performs preliminary filtering and amplification processing on the data.

[0006] The present application further provides that: the edge computing unit comprises an edge computing node module, a multi-modal fusion module and an abnormal early warning module, the edge computing node module being connected with the multi-modal fusion module, and the multi-modal fusion module being connected with the abnormal early warning module. The edge computing node module is used for receiving, computing and storing collected data, and processing and analyzing data transmitted by the connected data acquisition unit. The multi-modal fusion module is used for fusing data collected by different types of sensors. The abnormality early warning module is used for real-time analysis, judgment and early warning of the fused data.

[0007] The application further provides that the network transmission is used for transmitting the data processed by the edge computing node and the early warning information to the cloud analysis decision unit, and the transmitted data is encrypted.

[0008] The application further provides that the cloud analysis decision unit comprises a cloud server, a data mining module and a fault diagnosis module, the cloud server is connected with the data mining module, and the data mining module is connected with the fault diagnosis module.

[0009] The application further provides that the cloud server is used for receiving data and early warning information transmitted from each edge computing node, and storing and managing the data and the early warning information. The data mining module uses big data analysis to deeply mine and analyze historical data and real-time data stored in the cloud server, and predicts the operation trend and potential fault of the transformer. The fault diagnosis module is used for establishing a fault diagnosis model and early warning rules, and making a fault diagnosis report and processing suggestion for the potential fault of the transformer.

[0010] The application further provides that the remote interaction unit is used for remotely displaying the operation state, monitoring data and fault early warning information of the transformer to the user in an intuitive manner, and realizing the interaction between the user and the system.

[0011] The application further provides that the frequency adjustment unit comprises a classification storage module, a category comparison module and a frequency adjustment module, the classification storage module is connected with the category comparison module, and the category comparison module is connected with the frequency adjustment module.

[0012] The application further provides that the classification storage module is used for receiving and storing different types of data collected by the sensor, and forming a database. The category comparison module is used for receiving early warning data of the edge computing unit and fault judgment data of the cloud analysis decision unit, comparing the early warning data and the fault judgment data with the database in type, and finding out abnormal data types and time periods to which the abnormal data types belong. The frequency adjustment module is used for controlling the collection frequency of the abnormal data points.

[0013] Advantages The application provides a transformer operation safety monitoring platform. (1) The transformer operation safety monitoring platform can comprehensively and accurately reflect the operation state of the transformer by fusing and analyzing various types of monitoring data such as electrical parameters, temperature, vibration, acoustics and gas through multi-modal data fusion technology, thereby improving the accuracy and reliability of fault diagnosis.

[0014] (2) The transformer operation safety monitoring platform reduces data transmission volume and delay by processing and analyzing data in the edge computing unit, realizes real-time monitoring and rapid decision-making. At the same time, the edge computing node has local decision-making and early warning capability, which can timely send early warning signals in the case of network failure or communication interruption, and guarantee the operation safety of the transformer.

[0015] (3) The transformer operation safety monitoring platform can predict the operation trend and potential faults of the transformer by deeply mining and analyzing historical data and real-time data of the transformer using big data analysis technology, thereby providing scientific decision support for power operation and maintenance personnel and improving the operation efficiency and management level of the power system.

[0016] (4) The transformer operation safety monitoring platform can automatically adjust the data acquisition frequency of the transformer by setting the frequency adjustment unit 7, thereby enabling the device to automatically coordinate the acquisition frequency and optimize the data acquisition load according to the changes of normal and abnormal data. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is the system principle diagram of the present application; Figure 2 is the system principle diagram of the edge computing unit of the present application; Figure 3 is the system principle diagram of the cloud analysis decision unit of the present application; Figure 4 is the system principle diagram of the frequency adjustment unit of the present application.

[0018] In the figure: 1, transformer monitoring platform; 2, data acquisition unit; 3, edge computing unit; 301, edge computing node module; 302, multi-modal fusion module; 303, abnormality early warning module; 4, network transmission; 5, cloud analysis decision unit; 501, cloud server; 502, data mining module; 503, fault diagnosis module; 6, remote interaction unit; 7, frequency adjustment unit; 701, classification storage module; 702, category comparison module; 703, frequency adjustment module. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0020] Please refer to Figures 1-4The application provides a technical scheme: a transformer operation safety monitoring platform, comprising a transformer monitoring platform 1 composed of a data acquisition unit 2, an edge computing unit 3, a network transmission 4, a cloud analysis and decision unit 5, a remote interaction unit 6 and a frequency adjustment unit 7; The data acquisition unit 2 comprehensively monitors different types of operation states of the transformer by using sensors, and converts analog signals collected by the sensors into digital signals, and performs preliminary filtering and amplification processing on the data. The data acquisition unit 2 includes but is not limited to an electrical parameter sensor, a temperature sensor, a vibration sensor, an acoustic sensor and a gas sensor.

[0021] The remote interaction unit 6 is used for remotely displaying the operation state, monitoring data and fault warning information of the transformer to the user in an intuitive manner, and realizing the interaction between the user and the system. The remote interaction unit 6 displays the operation state, monitoring data and fault warning information of the transformer to the user in the form of intuitive charts, curves and maps by developing a visual display and interaction interface. The user queries, analyzes and manages data through the interaction interface, realizes real-time monitoring and remote control of the operation state of the transformer.

[0022] As a preferred scheme, the data transmission amount and delay are reduced, real-time monitoring and rapid decision-making are realized, the edge computing unit 3 includes an edge computing node module 301, a multi-modal fusion module 302 and an abnormality warning module 303, the edge computing node module 301 is connected to the multi-modal fusion module 302, and the multi-modal fusion module 302 is connected to the abnormality warning module 303. The edge computing node module 301 is used for receiving, computing and storing collected data, and processing and analyzing data transmitted by the connected data acquisition unit; The multi-modal fusion module 302 is used for fusion processing of data collected by different types of sensors. The multi-modal fusion module 302 runs on the edge computing node and processes data by using a neural network algorithm. The abnormality warning module 303 is used for real-time analysis, judgment and warning of the fused data. According to the operation characteristics and historical data of the transformer, a warning threshold is set, including but not limited to a temperature threshold, a vibration signal abnormality threshold and a gas content exceeding threshold.

[0023] The network transmission 4 is used for transmitting data processed by the edge computing node and warning information to the cloud analysis and decision unit 5, and performing encryption processing on the transmitted data. The communication network selects a wired network and a wireless network, and is flexibly configured according to actual application scenarios and requirements.

[0024] As a preferred solution, in order to facilitate the prediction of operation trend and potential failure, the cloud analysis decision unit 5 comprises a cloud server 501, a data mining module 502 and a fault diagnosis module 503, the cloud server 501 is connected with the data mining module 502, and the data mining module 502 is connected with the fault diagnosis module 503; The cloud server 501 is used for receiving data and early warning information transmitted from each edge computing node, and storing and managing the data and early warning information; The data mining module 502 uses big data analysis to deeply mine and analyze historical data and real-time data stored in the cloud server 501, and predict the operation trend and potential failure of the transformer; The fault diagnosis module 503 is used for establishing a fault diagnosis model and early warning rules, and making a fault diagnosis report and processing suggestion for potential failure of the transformer.

[0025] As a preferred solution, in order to optimize the data acquisition load, the frequency adjustment unit 7 comprises a classified storage module 701, a category comparison module 702 and a frequency adjustment module 703, the classified storage module 701 is connected with the category comparison module 702, and the category comparison module 702 is connected with the frequency adjustment module 703; The classified storage module 701 is used for receiving and storing different types of data collected by the sensors, forming a database; The category comparison module 702 is used for receiving early warning data of the edge computing unit 3 and fault judgment data of the cloud analysis decision unit 5, and comparing the early warning data and the fault judgment data with the database in type to find out abnormal data types and time periods to which the abnormal data types belong; The frequency adjustment module 703 is used for controlling the acquisition frequency of the abnormal data points.

[0026] In use, different operation data of the transformer are collected by various types of sensors, and then local data processing and analysis are performed by the corresponding edge computing nodes. A multi-modal data fusion algorithm is run on the edge computing nodes to fuse the data collected by the different types of sensors. Based on data fusion and the results of big data analysis of the operation characteristics and historical data of the transformer, each early warning threshold is set for fault and early warning reference. Then, historical and real-time data are deeply mined by using big data technology to establish a consistent data model and algorithm, and to predict operation trend and potential failure. Meanwhile, based on the results of multi-modal data fusion and big data analysis, a fault diagnosis model and early warning rules are established to provide a reference for diagnosis report and processing suggestion. The frequency adjustment unit 7 finds out corresponding data acquisition points according to the data early warning categories of the edge computing unit 3 and the abnormal data categories of the operation trend in the cloud analysis decision unit 5, and increases the acquisition frequency of the data acquisition points in the same time.

Claims

1. A transformer operational safety monitoring platform comprising a transformer monitoring platform (1), characterized in that: The transformer monitoring platform (1) comprises a data acquisition unit (2), the data acquisition unit (2) is connected with an edge computing unit (3), the edge computing unit (3) is connected with a network transmission (4), the network transmission (4) is connected with a cloud analysis decision unit (5), the cloud analysis decision unit (5) is connected with a remote interaction unit (6), the remote interaction unit (6) is connected with a frequency adjustment unit (7), the frequency adjustment unit (7) is connected with the data acquisition unit (2), and the edge computing unit (3) is connected with the frequency adjustment unit (7). The data acquisition unit (2) comprehensively monitors different types of operating states of the transformer through sensors, converts analog signals collected by the sensors into digital signals, and performs preliminary filtering and amplification processing on the data.

2. The transformer operational safety monitoring platform of claim 1, wherein: The edge computing unit (3) comprises an edge computing node module (301), a multi-modal fusion module (302) and an abnormal early warning module (303), the edge computing node module (301) is connected with the multi-modal fusion module (302), and the multi-modal fusion module (302) is connected with the abnormal early warning module (303). The edge computing node module (301) is used for receiving, calculating and storing collected data, and processing and analyzing data transmitted by the connected data acquisition unit. The multi-modal fusion module (302) is used for fusion processing of data collected by different types of sensors. The abnormal early warning module (303) is used for real-time analysis, judgment and early warning of the fused data.

3. The transformer operational safety monitoring platform of claim 1, wherein: The network transmission (4) is used for transmitting data and early warning information processed by the edge computing node to the cloud analysis decision unit (5), and performing encryption processing on the transmitted data.

4. The transformer operational safety monitoring platform of claim 1, wherein: The cloud analysis decision unit (5) comprises a cloud server (501), a data mining module (502) and a fault diagnosis module (503), the cloud server (501) is connected with the data mining module (502), and the data mining module (502) is connected with the fault diagnosis module (503).

5. The transformer operational safety monitoring platform of claim 4, wherein: The cloud server (501) is used for receiving data and early warning information transmitted from each edge computing node, and storing and managing the data. The data mining module (502) uses big data analysis to deeply mine and analyze historical data and real-time data stored in the cloud server (501), and predicts the running trend and potential fault of the transformer. The fault diagnosis module (503) is used for establishing a fault diagnosis model and early warning rules, and performing fault diagnosis report and processing suggestion on potential faults of the transformer.

6. The transformer operational safety monitoring platform of claim 1, wherein: The remote interaction unit (6) is used for remotely displaying the running state, monitoring data and fault early warning information of the transformer to the user in an intuitive manner, and realizing the interaction between the user and the system.

7. The transformer operational safety monitoring platform of claim 1, wherein: The frequency adjustment unit (7) comprises a classification storage module (701), a category comparison module (702) and a frequency adjustment module (703), the classification storage module (701) is connected with the category comparison module (702), and the category comparison module (702) is connected with the frequency adjustment module (703).

8. The transformer operational safety monitoring platform of claim 7, wherein: The classification storage module (701) is used for receiving and storing different types of data collected by sensors to form a database; The category comparison module (702) is used for receiving early warning data of the edge computing unit (3) and fault judgment data of the cloud analysis decision unit (5), and comparing the early warning data and the fault judgment data with the database to find out the type of abnormal data and the time period to which the abnormal data belongs; The frequency adjustment module (703) is used for controlling the collection frequency of the abnormal data points.

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