Identification of Acoustic Signals of Internal Electric Discharges on Glass Insulator under Variable Applied Voltage

Author(s):  
Nasir A. Al-geelani ◽  
M. Afendi M. Piah ◽  
Ibrahim Saeh ◽  
Nordiana Azlin Othman ◽  
Fatin Liyana Muhamedin ◽  
...  

<p>A Partial Discharge (PD) is an unwanted phenomenon in electrical equipment. Therefore it is of great importance to identify different types of PD and assess their severity. This paper investigates the acoustic emissions associated with Internal Discharge (ID) from different types of sources in the time-domain. An experimental setup was arranged in the high voltage laboratory, a chamber with an electrode configuration attached to it was connected to a high voltage transformer for generating various types of PD. A laboratory experiment was done by making the models of these discharges. The test equipment including antennas as a means of detection and digital processing techniques for signal analysis were used. Wavelet signal processing was used to recover the internal discharge acoustic signal by eliminating the noises of many natures.</p>

Author(s):  
Nasir A. Al-geelani ◽  
M. Afendi M. Piah ◽  
Ibrahim Saeh ◽  
Nordiana Azlin Othman ◽  
Fatin Liyana Muhamedin ◽  
...  

<p>A Partial Discharge (PD) is an unwanted phenomenon in electrical equipment. Therefore it is of great importance to identify different types of PD and assess their severity. This paper investigates the acoustic emissions associated with Internal Discharge (ID) from different types of sources in the time-domain. An experimental setup was arranged in the high voltage laboratory, a chamber with an electrode configuration attached to it was connected to a high voltage transformer for generating various types of PD. A laboratory experiment was done by making the models of these discharges. The test equipment including antennas as a means of detection and digital processing techniques for signal analysis were used. Wavelet signal processing was used to recover the internal discharge acoustic signal by eliminating the noises of many natures.</p>


Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7426
Author(s):  
Imene Mitiche ◽  
Tony McGrail ◽  
Philip Boreham ◽  
Alan Nesbitt ◽  
Gordon Morison

The reliability and health of bushings in high-voltage (HV) power transformers is essential in the power supply industry, as any unexpected failure can cause power outage leading to heavy financial losses. The challenge is to identify the point at which insulation deterioration puts the bushing at an unacceptable risk of failure. By monitoring relevant measurements we can trace any change that occurs and may indicate an anomaly in the equipment’s condition. In this work we propose a machine-learning-based method for real-time anomaly detection in current magnitude and phase angle from three bushing taps. The proposed method is fast, self-supervised and flexible. It consists of a Long Short-Term Memory Auto-Encoder (LSTMAE) network which learns the normal current and phase measurements of the bushing and detects any point when these measurements change based on the Mean Absolute Error (MAE) metric evaluation. This approach was successfully evaluated using real-world data measured from HV transformer bushings where anomalous events have been identified.


2013 ◽  
Vol 183 (1) ◽  
pp. 32-38
Author(s):  
Jun Izutsu ◽  
Satoru Nagata ◽  
Tadayuki Wada ◽  
Masahito Shimizu ◽  
Kenji Ohta

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