Paper ID: SA007
An Optoacoustic Sensor System for Simultaneous Detection of Power
Equipment Voiceprint and Discharge
Ting CHEN, Sen QIAN
State Grid Jiangsu Electric Power Co. Ltd.
State Grid Smart Grid Research Institute Co. Ltd.
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Content
1
Background
2
Methods
3
Results
4
Conclusion
Background
Acoustic signals can carry information about the potential defects of power equipment.
A transformer can hum differently depending on different states. A circuit breaker emits
different sound when its components are defected.
HV Bushing
Conservator
Gauge
Gas Relay
Hoise
LV Bushing
HV
Lead
LV Lead
Cooler
Loosen screw
Winding
Tank
Fatigued spring
Corona discharge
Oil
Core
Contact
Transformer’s sound feature under different states
Pole
Drive
Base
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Operation
Mechanism
Sources:Luo. Research on Typical Defects Recognition of Dry-Type Transformer Based on Voiceprint. North China Electric Power University
Background
Vibroacoustic sensors are widely used, such as microphone recording the sound of
transformers and circuit breakers. Ultrasonic sensors are also used for partial discharge
ultrasonic detection.
Audible voiceprint
Partial discharge
ultrasonic detection
Vibration
PD ultrasonic detection
(airborn)
Because of size and cost, they are mainly deployed for condition monitoring of crucial
power equipment. It is necessary to develop an acoustic sensor system that can be mounted
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on the surveillance robot.
Background
The surveillance robot widely used in substation generally carry an visible camera and an
infrared camera. In this research, we designed a prototype of an optoacoustic sensor system
consisting of wideband micro-electro-mechanical-system (MEMS) microphones and a visible
CMOS image sensor.
The surveillance robot therefore can also sense the acoustic signals of the power equipment to
find the potential faults which are difficult for visible and infrared camera to detect.
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Content
1
Background
2
Methods
3
Results
4
Conclusion
Methods
The optoacoustic sensor system consists of two parts, i.e. the optoacoustic sensing array
and the signal processing unit. The optoacoustic sensing array consisted of 64 off-the-shelf
MEMS microphones. An FPGA drove the visible optical sensor and microphones.
Knowles, SPH0641lU4H
The signal processing unit processed the detected audio and video streams for voiceprint and
ultrasonic discharge detection.
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Methods
The layout of the microphones are important for the design. Generally, a larger number of
microphones could lead to better resolution. However, it also demands more resources for
PDM demodulation.
The -6 dB resolution angles were 11º at 5 kHz, 2.7 º at 20 kHz, and 1.1º at 50 kHz.
Microphone layout
Response to acoustic waves
To balance the resolution and demodulation resources, we adopted 64 MEMS microphones laid in
a multi-spiral arrangement, which offered a balanced response in far-field and near-field cases.
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Methods
A signal processing framework is laid. FIR lowpass filter was used to extract the voiceprint.
FIR hipass filter was used to extract the discharge acoustic emission.
mic1
mic2
mic3
Received signals
Sum of received signals
mic1
mic2
mic3
Received signals (delayed)
Principle of D&S Beamforming
Sum of delayed signals
D&S beamforming was crucial to obtain the denoised waveform and acoustic field distribution.
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Then the acoustic image can be obtained by overlaying the acoustic map on the visible photo.
Methods
A testing platform to verify the proposed design. Wideband audio connected to a
waveform generator emitted ultrasonic pulses to simulate the discharges. Another laptop
played back the recorded sound of the power transformer during switching.
Chessboard calibration was performed in order to get the parameters of the visible sensor, so the
space relationship between the acoustic map and the visible photo can be determined.
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Content
1
Background
2
Methods
3
Results
4
Conclusion
Results
An excerpt of the raw waveform detected by the 1st microphone and its spectrogram
indicates that the wave packet from 0.6 to 0.8 s was the power transformer’s switching sound.
Only 13 ultrasonic pulses can also be seen on the temporal waveform.
Temporal waveform of 1st mic
Spectrogram
The voiceprint of the transformer switching and ultrasonic pulses can be identified with better
clearness by using the spectrogram
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Results
The signal processing framework used the FIR lowpass filtering to reject the ultrasonic
pulses. Then D&S was used to improve the signal-to-noise ratio of the voiceprint of
transformer’s switching.
Original waveform
Denoised voiceprint
The SNR (amplitude of voiceprint to amplitude of noise level) was 3 before denoising and 7 after
denoising.
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Results
The signal processing framework used the FIR hipass filtering to reject the sound of the
transformer’s switching. Then D&S was used to improve the signal-to-noise ratio of the
ultrasonic pulses.
Original waveform
Denoised ultrasonic pulses
Significant SNR improvements can be seen. The 20 ultrasonic pulses can be easily identified on
denoised waveform in contrast to the original waveform.
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Results
D&S algorithm can obtain the acoustic field distribution. Then acoustic images can be
obtained by overlaying the acoustic field distribution on the visible photo.
Visible photo
Acoustic field distribution
Ultrasonic pulse location
Voiceprint location
The resolution was better for higher frequency acoustic signals, which agreed with the previous
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simulation of mic array to acoustic waves of different frequencies.
Content
1
Background
2
Methods
3
Results
4
Conclusion
Conclusions
• An optoacoustic sensor system consisting of wideband MEMS mics and visible
imaging sensor was designed, which can detect both the audible voiceprint of
power equipment operation and ultrasonic emission due to discharge.
• FIR filtering and delay-and-sum beamforming can significantly improve the SNR of
the wanted signal and reject the unwanted interference.
• The location of the acoustic sources can be located by the acoustic imaging
obtained by beamforming.
• Future efforts are needed to test its performance when mounting on surveillance
robot.
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