Open AccessResearch on mechanical fault diagnosis method of power transformer winding
Author(s) -
Bin Zhang,
Dan Zhao,
Feiming Wang,
Kejian Shi,
Zhenyang Zhao
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8712
Subject(s) - reactance , autotransformer , transformer , electromagnetic coil , delta wye transformer , leakage inductance , vibration , engineering , linear variable differential transformer , computer science , voltage , distribution transformer , electrical engineering , acoustics , physics
Today, the accuracy of the fault mechanical diagnosis of transformer winding is low and the fault types cannot be judged, this study proposes a machine condition diagnosis method of transformer winding based on the combination of short‐circuit reactance and mechanical vibration. During the process of diagnosis, first of all, from a power transformer's one and two side current and voltage, it can calculate the internal short‐circuit reactance of the winding to judge the winding state. Then, it uses the wavelet transform to analyse the vibration signals of the transformer windings under different conditions and it uses the signal spectrum entropy as the input feature vector. Finally, using multi class support vector machine to train and test the feature vector, it realises the classification diagnosis of transformer winding in different states. By setting the actual different transformer winding faults of type S11‐M‐500/35, it gathers the corresponding parameter data and it tests the diagnosis method for the fault diagnosis of transformer winding verification. The diagnosis results are consistent with the actual fault, which verifies the validity and accuracy of the proposed method that is applied to the transformer winding fault diagnosis.