Transformer fault diagnosis and positioning system based on digital twinning

Abstract

The invention discloses a transformer fault diagnosis and positioning system based on digital twinning, which belongs to the field of fault diagnosis of power equipment, and comprises a communicationsensing module used for transmitting bottom monitoring data acquired from an equipment entity to a system support module; a system supporting module used for receiving and preprocessing bottom-layer monitoring data and storing various data, models and expert systems; a dynamic twinning module used for analyzing the multi-dimensional probability condition of the equipment fault, constructing dynamic degradation modeling of different health states, and realizing model correction through human-computer interaction, real-time measurement and dynamic updating subsequently; a decision diagnosis module used for constructing a digital twinborn body of to-be-diagnosed data and realizing diagnosis and positioning of a health state; and a user interface module used for displaying the diagnosis resultand the related information through a UI interface and providing further decision and man-machine interaction. Accurate cognition and closed-loop optimization of fault diagnosis and positioning can be realized, and the economy and reliability of transformer equipment are improved.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of electric equipment, and more specifically relates to a digital twin-based transformer fault diagnosis and positioning system. Background technique [0002] With the proposal of the power Internet of Things, the power system gradually presents an open, flat, decentralized, and boundary-fuzzy development. Accurate perception and extraction of equipment status information and deduction of system trends are important for realizing intelligent optimal dispatch of power systems. premise. However, there are several problems in the current cognition of physical equipment diagnosis in the power system: (1) There are barriers between historical documents and various types of monitoring information, there is a lag in updating system information, and the operating status of the equipment itself is uncertain. Evolving all the time; (2) Equipment information may be inaccurate, such as changes in the equipment...

AI Technical Summary

Problems solved by technology

However, there are several problems in the current cognition of physical equipment diagnosis in the power system: (1) There are barriers between historical documents and various types of monitoring information, there is a lag in updating system information, and the operating status of the equipment itself is uncertain. Evolving all the time; (2) Equipment information may be inaccurate, such as changes in the equipment environment and missing and abnormal measurement data; (3) Operation and maintenance management is less intelligent, with a single data source, and it is difficult to achieve intelligence with a small amount of data Training of Diagnostic Algorithms

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