Electric transmission and transformation device state abnormity detection method

Abstract

The invention discloses an electric transmission and transformation device state abnormity detection method, comprising steps of installing an on-line comprehensive monitoring device at a transformer station to monitor state quantity, determining a corresponding relation between the state quantity and the device state, performing representation on a time sequence of device state quantity data by utilizing a high dimension random matrix, analyzing spectral distribution and circular ring rate of state data in various time quantum in an operation history in the operation history, converting the device state into a state evaluation value P through quantification, comparing a device state evaluation value P with a device state evaluation value threshold P to determine whether the device state matrix has abnormity and to detect the device state abnormity, and obtaining an abnormal state and an abnormal time according to the normality detection of a residual sequence matrix.

Description

technical field [0001] The invention relates to the technical field of power transmission and transformation equipment detection, in particular to a method for detecting abnormal states of power transmission and transformation equipment. Background technique [0002] With the continuous development of smart grid and energy Internet, the modern power system is gradually evolving into a system that gathers a large amount of data and huge information calculation. Foundation. For the status data of power transmission and transformation equipment, with the diversified development of status monitoring technology and the correlation and interaction of information application systems such as SCADA systems, production management systems, and EMS systems, the amount of data has grown exponentially, gradually showing the scale of big data Large size, many types, and low value density, so higher requirements are put forward for the analysis and processing of the state data of power tra...

AI Technical Summary

Problems solved by technology

[0004] 1) Most of the existing research is based on the fault diagnosis of some equipment information in a single system. In the actual operation and maintenance of equipment, most of them use simple threshold judgment methods to detect abnormalities, equipment information utilization rate and equipment information utilization rate and status evaluation accuracy rate are low

[0005] 2) At present, the big data technologies that have been researched in the abnormal detection of online monitoring data include time series analysis, Markov model, genetic programming algorithm, classification algorithm, etc., but most of these studies are still tentative, and no reasonable and perfect evaluation model has been formed.

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