Development of Wireless Partial Discharge Detector based on Ultrasonic Signal

The traditional partial discharge detection method is difficult to be use in the scene charged detection because of its low frequency and vulnerable to test interference, need to power outages. Partial discharge detection technology has been international recognized in the application of the technology and research. This paper develop a system which can test the PD and manage the information. This method provide a precise and effective means of detection for charged equipment. Through the simulation of several typical defects, choose the best center frequency of ultrasound.

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Design of Partial Discharge Test Environment for Oil-Filled Submarine Cable Terminals and Ultrasonic Monitoring

Energies ◽

10.3390/en12244774 ◽

2019 ◽

Vol 12 (24) ◽

pp. 4774

Author(s):

Yulong Wang ◽

Xiaohong Zhang ◽

Lili Li ◽

Jinyang Du ◽

Junguo Gao

Keyword(s):

Partial Discharge ◽

Maximum Amplitude ◽

Signal Amplitude ◽

Ultrasonic Signal ◽

Signal Spectrum ◽

Test Environment ◽

Ultrasonic Signals ◽

Amplitude Coefficient ◽

Marine Cable ◽

Submarine Cables

Based on the principle of operating an oil-filled-cable operation and the explanation of the oil-filling process provided in the cable operation and maintenance manual of submarine cables, this study investigated oil-pressure variation caused by gas generated as a result of cable faults. First, a set of oil-filled cables and their terminal oil-filled simulation system were designed in the laboratory, and a typical oil-filled-cable fault model was established according to the common faults of oil-filled cables observed in practice. Thereafter, ultrasonic signals of partial discharge (PD) under different fault models were obtained via validation experiments, which were performed by using oil-filled-cable simulation equipment. Subsequently, the ultrasonic signal mechanism was analyzed; these signals were generated via electric, thermal, and acoustic expansion and contraction, along with electric, mechanical, and acoustic electrostriction. Finally, upon processing the 400 experimental data groups, four practical parameters—maximum amplitude of the ultrasonic signal spectrum, Dmax, maximum frequency of the ultrasonic signals, fmax, average ultrasonic signal energy, Dav, and the ultrasonic signal amplitude coefficient, M—were designed to characterize the ultrasonic signals. These parameters can be used for subsequent pattern recognition. Thus, in this study, the terminal PD of an oil-filled marine cable was monitored.

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Study on Ultrasonic Characteristics and Sensors in Partial Discharge

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.328-330.1892 ◽

2011 ◽

Vol 328-330 ◽

pp. 1892-1895

Author(s):

Guang Xing Zhao ◽

Qing Yu

Keyword(s):

Sound Pressure ◽

Detection Method ◽

Partial Discharge ◽

Ultrasonic Signal ◽

Signal Spectrum ◽

Discharge Characteristics ◽

Ultrasonic Signals ◽

Piezoelectric Crystals ◽

Performance Requirements ◽

Selection Of

In partial discharge detection of transformer, supersonics detection method is applied widely. In the paper, a model of supersonics is established by MATLAB. Meanwhile, analyses about the relation between amplitude of sound pressure and discharge capacity and ultrasonic signal spectrum are implemented based on the model. By analyzing the partial discharge characteristics of ultrasonic signals, a suitable ultrasonic receiving sensor can be selected that meets the performance requirements. Therefore, detection band of sensor is determined. Meanwhile, the structure of the sensor and the selection of piezoelectric crystals are introduced in details.Finally,the design of ultrasonic receiving sensor is determined.

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Data compression and pattern recognition for partial discharge ultrasonic signal based on fractal theory

Proceedings. International Conference on Power System Technology ◽

10.1109/icpst.2002.1047541 ◽

2003 ◽

Author(s):

Liu Yunpeng ◽

Lu Fangcheng ◽

Chen Zhiye ◽

Li Yanqing

Keyword(s):

Pattern Recognition ◽

Data Compression ◽

Fractal Theory ◽

Partial Discharge ◽

Ultrasonic Signal

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A novel de‐noising method based on coherence average for ultrasonic signal of partial discharge in transformer

IET Science Measurement & Technology ◽

10.1049/smt2.12031 ◽

2021 ◽

Vol 15 (3) ◽

pp. 302-311

Author(s):

Jiangrong Cheng ◽

Yuan Xu ◽

Dengwei Ding ◽

Weidong Liu

Keyword(s):

Partial Discharge ◽

Ultrasonic Signal

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Detection and Localization of Partial Discharge in Connectors of Air Power Lines by Means of Ultrasonic Measurements and Artificial Intelligence Models

Sensors ◽

10.3390/s21010020 ◽

2020 ◽

Vol 21 (1) ◽

pp. 20

Author(s):

Vykintas Samaitis ◽

Liudas Mažeika ◽

Audrius Jankauskas ◽

Regina Rekuvienė

Keyword(s):

Signal Processing ◽

Early Stage ◽

Partial Discharge ◽

Ultrasonic Signal ◽

Support Vector ◽

Air Power ◽

Optical Images ◽

Intelligence Models ◽

Artificial Intelligence Models ◽

Detection And Localization

According to the statistics, 40% of unplanned disruptions in electricity distribution grids are caused by failure of equipment in high voltage (HV) transformer substations. These damages in most cases are caused by partial discharge (PD) phenomenon which progressively leads to false operation of equipment. The detection and localization of PD at early stage can significantly reduce repair and maintenance expenses of HV assets. In this paper, a non-invasive PD detection and localization solution has been proposed, which uses three ultrasonic sensors arranged in an L shape to detect, identify and localize PD source. The solution uses a fusion of ultrasonic signal processing, machine learning (ML) and deep learning (DL) methods to classify and process PD signals. The research revealed that the support vector machines classifier performed best among two other classifiers in terms of sensitivity and specificity while classifying discharge and surrounding noise signals. The proposed ultrasonic signal processing methods based on binaural principles allowed us to achieve an experimental lateral source positioning error of 0.1 m by using 0.2 m spacing between L shaped sensors. Finally, an approach based on DL was suggested, which allowed us to detect a single PD source in optical images and, in such a way, to provide visual representation of PD location.

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