Transformer self-diagnosis evaluation device and method based on edge calculation

Abstract

The invention discloses a transformer self-diagnosis evaluation device based on edge calculation, which comprises a data acquisition module, a data preprocessing module, a data fusion module and a rule reasoning module. The temperature, pressure, vibration and partial discharge signals in a transformer are comprehensively acquired by means of optical fiber sensing, deep learning, edge calculation, Internet of Things and the like; signals such as external visible light images, infrared images, sleeve dielectric loss and partial discharge are analyzed and judged on site on the transformer, self-sensing, self-diagnosis and self-adjustment of the transformer are achieved, the intelligent level of the transformer is improved, monitoring precision is improved, and real-time sensing of the running state of the transformer is achieved.

Description

Technical field [0001] The present invention relates to the field of intelligent technology of electrical equipment, and more particularly to a self-diagnosis assessment apparatus and method based on an edge-calculated transformer. Background technique [0002] The power transformer is one of the core energy transmission and conversion in the grid. It is one of the most widely used hub devices. It runs closely with the safe and reliable operation of the power system, so it has always been electric power. The level of intelligence is always power. An important topic of industry research. [0003] Improve the reliability of transformer operations in implementing transformer status self-perception, self-diagnosis, and evaluation, and adaptive automation adjustment. The previous online monitoring device, the charging detecting device, the robot inspection, etc. After the transformer is installed, it is added, and it is not considered from the beginning of the transformer design, and ...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the traditional transformer fault analysis cannot analyze the internal temperature, pressure, vibration and partial discharge detection data of the transformer, and provide a transformer self-diagnosis and evaluation device and method based on edge computing. Sensing, deep learning, edge computing, Internet of Things and other means, comprehensively collect the internal temperature, pressure, vibration, partial discharge signals of the transformer, external visible light images, infrared images, bushing dielectric loss, partial discharge and other signals, and perform on-site monitoring on the transformer. State analysis and judgment, realizing transformer self-perception, self-diagnosis, and self-regulation, improving the intelligence level of transformers, improving monitoring accuracy and realizing real-time perception of transformer operating status

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