Citation: Zhou, Y.; Wang, B. Acoustic
Multi-Parameter Early Warning
Method for Transformer DC Bias
State. Sensors 2022, 22, 2906.
https://doi.org/10.3390/s22082906
Academic Editors: Hamed Badihi,
Ningyun Lu and Tao Chen
Received: 18 February 2022
Accepted: 8 April 2022
Published: 10 April 2022
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Article
Acoustic Multi-Parameter Early Warning Method for
Transformer DC Bias State
Yuhao Zhou
1
and Bowen Wang
2,
*
1
International Education Institute, North China Electric Power University (Baoding), Baoding 071003, China;
zhouyuhaoncepu@163.com
2
Hebei Provincial Key Laboratory of Power Transmission Equipment Security Defense,
North China Electric Power University (Baoding), Baoding 071003, China
* Correspondence: wangbw1994@163.com; Tel.: +86-166-3123-9723
Abstract:
The acoustic signal in the operation of a power transformer contains a lot of transformer
operation state information, which is of great significance to the detection of DC bias state. In this
paper, three typical parameters used for DC bias state detection are selected by comparing the acoustic
variation of a 500 kV Jingting transformer substation No. 2 transformer with that of the core model
built in the laboratory; then, acoustic samples of the 162 EHV normal state transformers are collected,
and the distribution regularity of three typical parameters in normal state is given. Finally, according
to the distribution regularity, clear warning threshold of typical parameters are given, and the DC
bias cases from the 500 kV Jingting transformer substation are used to verify the effectiveness of
the threshold.
Keywords: transformer; acoustic detection; DC bias; data statistics
1. Introduction
With EHV transmission projects widely used, the number and voltage level of DC
transmission lines are growing, and the DC magnetic bias problem caused by AC/DC
hybrid transmission is becoming increasingly serious [
1
–
4
]. Transformer DC magnetic bias
monitoring is usually carried out by measuring grounding current [
5
–
9
], which requires
electromagnetic coupling and has weak safety. As a mechanical wave detection method,
acoustic signal detection can realize the mechanical state detection of equipment without
electromagnetic coupling. This method has been widely used in saturable reactor mon-
itoring [
10
,
11
], motor monitoring [
12
,
13
], and other fields. During the operation of the
transformer, the core, winding, and other structures will vibrate and produce mechanical
waves. The generated acoustic signal contains a lot of equipment status information, which
can realize transformer DC bias state online monitoring [14–17].
In recent years, the acoustics or vibrations of a transformer have been analyzed from
the perspective of mechanism explanation and experimental simulation [
18
–
23
]. Secic
summarized works related to the acoustic condition assessment of power transformers [
24
].
In terms of transformer acoustic signal parameters, Bartoletti used the total harmonic
distortion (THD) to identify the health condition of the transformer winding [
25
]. Hong
presented frequency complexity (FC), determinism (DET), energy difference ratio (EDR),
and main principal contribution (MPC) to diagnose winding health [
26
]. Belén García
presented a transformer tank vibration estimate model [
27
], and experimental verifica-
tion was carried out [
28
]. Hong Z. proposed a winding vibration model coupled with
electromagnetic force analysis [
29
], and the model was helpful to predict the mechanical
faults of transformer windings. Zhang F. studied the acoustic transmission model of a
transformer and proved that the vibration of the oil tank is mainly affected by the oil-borne
transmission [
29
]. The above theoretical research has carried out a separate analysis of the
possible frequency phenomena in various states of the transformer, which provides a lot of
Sensors 2022, 22, 2906. https://doi.org/10.3390/s22082906 https://www.mdpi.com/journal/sensors
