Noniterative Method for Combined Acoustic-Electrical Partial Discharge Source Localization

Antony, Deepthi; Punekar, Gururaj S.

doi:10.1109/tpwrd.2017.2769159

Cited by 31 publications

(11 citation statements)

References 30 publications

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“…Therefore, synchronous measurement of multiple signals can improve the accuracy and effectiveness of PD detection, diagnosis and localisation. Nowadays, more and more technologies combining multiple PD detection methods have been developed and applied [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Study on the combined characteristics of UHF and optical signals induced by partial discharge at spacer surface in GIS

Han

Guo

Shen

et al. 2020

IET Generation, Transmission & Distribution

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Section: Introductionmentioning

confidence: 99%

Study on the combined characteristics of UHF and optical signals induced by partial discharge at spacer surface in GIS

Han

Guo

Shen

et al. 2020

IET Generation, Transmission & Distribution

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“…However, differentiating defect types through PD pattern recognition is one of the most difficult challenges and has restricted the large-scale industrial application of PD based condition monitoring. PD signals induced by insulation defects which show a high similarity in the PD pattern structure are especially difficult to distinguish [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network-Based Deep Learning Methodology for Recognition of Partial Discharge Patterns from High-Voltage Cables

Peng

Fan

Wang

et al. 2019

IEEE Trans. Power Delivery

154

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It is a great challenge to differentiate Partial Discharge (PD) induced by different types of insulation defects in high voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to be differentiate, even for the most experienced specialists. T o overcome the challenge, a Convolutional Neural Network (C NN) based deep learning methodology for PD pattern recognition is presented in this paper. Firstly, PD testing for five types of artificial defects in E thylene-Propylene-R ubber (E PR) cables was carried out in the High V oltage (HV) laboratory to generate signals containing PD data. Secondly, 3500 sets of PD transient pulses were extracted and then 33 kinds of PD features were established. T he third stage applies a C NN to the data: typical C NN architecture and the key factors which affect the C NN based pattern recognition accuracy, are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. T he paper presents a flowchart of the C NN based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the C NN based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e. Support V ector Machine (SV M) and Back Propagation Neural Network (BPNN). T he results show that the proposed C NN method has higher pattern recognition accuracy than SV M and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications. Index Terms-C onvolutional neural network, deep learning, high voltage cables, partial discharge, pattern recognition.

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“…The short-circuit current method is not suitable for GIL when it is running under the condition of small load current. Ultrasonic method has been widely studied and applied in partial discharge (PD) localization for power transformers [17,18]. It can locate the fault according to the amplitude of ultrasound, which is less affected by background noise, convenient to operate and easy to realize on-line monitoring.…”

Section: Introductionmentioning

confidence: 99%

The Attenuation and Propagation Law of Ultrasonic Wave in UHV Gas Insulated Line

Fei

Liu

Chen³

et al. 2020

IEEE Access

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Gas insulated transmission line (GIL) is widely used for power transmission in special geographical environment such as river crossing, mountain crossing and city tunnel crossing scenarios. Because of its totally enclosed structure, it is difficult to locate the fault position when the internal breakdown discharge occurs. Fault location by using the ultrasonic wave aroused by discharge is an efficient method for GIL maintenance. This paper reports the experimental and theoretical results of attenuation and propagation law of ultrasonic signal in GIL. An ultra-high voltage (UHV) GIL model was set up to simulate the geometry and material conditions of real GIL. The common lead breaking signal was adopted to generate ultrasonic source, and the propagation velocity and attenuation coefficient of ultrasonic wave were obtained. The experimental results show that the attenuation coefficient of ultrasonic wave on GIL linear unit is about 0.234 dB/m, and its propagation velocity is about 3.3 km/s. But the attenuation level of ultrasonic wave is much higher when it passes through the epoxy insulators or expansion joints. The validity of the method is verified by the creeping discharge test on the GIL model. In order to analyze the propagation path of ultrasonic wave, the relationship between the path and time is calculated and the shortest propagation time is obtained. The results of this research provide support for the fault location of GIL by ultrasonic wave.

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Noniterative Method for Combined Acoustic-Electrical Partial Discharge Source Localization

Cited by 31 publications

References 30 publications

Study on the combined characteristics of UHF and optical signals induced by partial discharge at spacer surface in GIS

Study on the combined characteristics of UHF and optical signals induced by partial discharge at spacer surface in GIS

A Convolutional Neural Network-Based Deep Learning Methodology for Recognition of Partial Discharge Patterns from High-Voltage Cables

The Attenuation and Propagation Law of Ultrasonic Wave in UHV Gas Insulated Line

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