A Method of Separating Fundamental Frequency Signals of Transformer Vibration Based on Power Factor Angle Correction

Abstract

The invention discloses a transformer vibration fundamental frequency signal separation method based on power factor angle correction. 3) Let the steady-state short-circuit vibration signal of the transformer be the no-load vibration signal of the transformer, which is caused by the current difference between the two working conditions before and after, due to the electromagnetic force and current of the transformer winding The square is directly proportional, thus, the steady-state short-circuit vibration signal of the transformer under the above two working conditions and 4) The power factor angles of the vibration signals collected on the surface of the transformer oil tank under the above two working conditions are respectively θ 1 ,θ 2 , the angle between and is and the angle between is and is the no-load vibration signal. This method can separate the no-load vibration signal and the steady-state short-circuit vibration signal of the transformer, and has high reliability and accuracy.

Description

technical field [0001] The invention belongs to the technical field of transformer vibration signal separation, and relates to a transformer vibration fundamental frequency signal separation method based on power factor angle correction. Background technique [0002] Transformer is one of the most important equipment in the power system, and its safe operation has been closely related to the national economic development. If the transformer fails, it will cause a large-scale power outage, which will not only affect the production of the factory, but also affect the lives of the people. Therefore, in order to discover potential accidents of transformers in time, avoid sudden accidents, and improve the reliability of transformer operation, it is of great significance to carry out research on transformer fault diagnosis methods. [0003] Based on the current research status of transformer vibration signal separation at home and abroad, the blind source separation algorithm rep...

AI Technical Summary

Problems solved by technology

[0003] Based on the current research status of transformer vibration signal separation at home and abroad, the blind source separation algorithm represented by the independent component analysis algorithm (ICA) has high requirements on the independence of the source signal. Vibration signals have a high similarity in amplitude and frequency spectrum, and the reliability of the mixing matrix is ​​doubtful. Therefore, the signals separated by blind source separation algorithms such as mainstream blind source separation cannot accurately retain the vibration characteristics of windings and cores.

Images