Partial Discharge Detection Technology for Switchgear Based on Near-Field Detection

Suo, Chunguang; Zhao, Jingjing; Wu, Xiang; Xu, Zhipeng; Zhang, Wenbin; He, Mingxing

doi:10.3390/electronics12020336

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Over time, this will gradually erode the insulation medium and eventually lead to the failure of the insulation system [18]. The causes of discharge are related to the reduction in dielectric strength, such as dust invasion, moisture, and aging of the insulation; a second cause is related to the local electric field strength, such as over-voltage and a metal tip [5].…”

Section: Pdmentioning

confidence: 99%

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Audio General Recognition of Partial Discharge and Mechanical Defects in Switchgear Using a Smartphone

Dai,

Liao,

Sang

et al. 2023

Applied Sciences

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Mechanical defects and partial discharge (PD) defects can appear in the indoor switchgear of substations or distribution stations, making the switchgear a safety hazard. However, traditional acoustic methods detect and identify these two types of defects separately, ignoring the general recognition of audio signals. In addition, the process of using testing equipment is complex and costly, which is not conducive to timely testing and widespread application. To assist technicians in making a quick preliminary diagnosis of defect types for switchgear, improve the efficiency of the subsequent overhaul, and reduce the cost of detection, this paper proposes a general audio recognition method for identifying defects in switchgear using a smartphone. Using this method, we can analyze and identify audio and video files recorded with smartphones and synchronously distinguish background noise, mechanical vibration, and PD audio signals, which have good applicability within a certain range. When testing the feasibility of using smartphones to identify three types of audio signal, through characterizing 12 sets of live audio and video files provided by technicians, it was found that there were similarities and differences in these characteristics, such as the autocorrelation, density, and steepness of the waveforms in the time domain, and the band energy and harmonic components of the frequency spectrum, and new combinations of features were proposed as applicable. To compare the recognition performance for features in the time domain, frequency band energy, Mel-frequency cepstral coefficient (MFCC), and this method, feature vectors were input into a support vector machine (SVM) for a recognition test, and the recognition results showed that the the present method had the highest recognition accuracy. Finally, a set of mechanical defects and PD defects were set up for a switchgear, for practical verification, which proved that this method was general and effective.

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Section: Pdmentioning

confidence: 99%

“…Either type of defect can lead to power system failures and cause serious economic losses. Therefore, the timely detection of possible defects and hidden dangers in the operation of a switchgear can ensure the reliability and safety of power system operation [5].…”

Section: Introductionmentioning

confidence: 99%

Audio General Recognition of Partial Discharge and Mechanical Defects in Switchgear Using a Smartphone

Dai,

Liao,

Sang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…For example, in high-voltage switchgear, its internal structure is complex, and it is in a closed environment. Under working conditions, high voltage and high current cause temperatures to rise, and the continuous rise in temperature may cause fire or even explosion [ 2 , 3 , 4 ]. The use of SAW sensors for temperature monitoring can reduce the accident rate.…”

Section: Introductionmentioning

confidence: 99%

Temperature Performance Study of SAW Sensors Based on AlN and AlScN

et al. 2023

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In this paper, the temperature performance of AlN-SAW resonators and AlScN-SAW resonators is studied. They are simulated by COMSOL Multiphysics, and their modes and the S11 curve are analyzed. The two devices were fabricated using MEMS technology and tested using VNA, and the test results were consistent with the simulation results. Temperature experiments were carried out with temperature control equipment. With the change in temperature, the changes in S11 parameters, TCF coefficient, phase velocity, and quality factor Q were analyzed. The results show that the temperature performance of the AlN-SAW resonator and the AlScN-SAW resonator is very good, and both have good linearity. At the same time, the sensitivity of the AlScN-SAW resonator is greater by 9.5%, the linearity is greater by 15%, and the TCF coefficient is greater by 11.1%. The temperature performance is excellent, and it is more suitable as a temperature sensor.

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