A Novel PD Detection Technique for Use in GIS Based on a Combination of UHF and Optical Sensors

Han, Xutao; Li, Junhao; Zhang, Liang; Pang, Peichuan; Shen, Shuhang

doi:10.1109/tim.2018.2867892

Cited by 59 publications

(20 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fluorescent fibre is led to the exterior of the GIS through a sealing fibre joint. The safety and measurement performance of the integrated sensor has been optimised by simulation work in the previous study [24].…”

Section: Sensor Integrated With Uhf Antenna and Fluorescent Optical Fmentioning

confidence: 99%

“…The combination of these two methods can effectively improve the field PD detection accuracy and sensitivity in GIS. To overcome the problem of mounting optical sensor into GIS, we have developed a novel PD detection sensor integrating UHF and optical fibre sensors in our previous researches [23,24]. The integrated sensor can measure the optical and UHF signals induced by PD simultaneously.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Self Cite

View full text Add to dashboard Cite

Section: Sensor Integrated With Uhf Antenna and Fluorescent Optical Fmentioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

“…Electrical, mechanical, and chemical methods have been used in order to detect PDs in GISs [ 4 , 5 , 6 , 7 ]. Dissolved-gas analysis was employed in the chemical methods and ultra-high-frequency (UHF) sensors were used in the electromagnetic methods to detect PDs for on-site PD monitoring [ 8 , 9 ]. UHF sensors have the advantages of external interference immunity and high-sensitivity detection [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

One-Shot Learning for Partial Discharge Diagnosis Using Ultra-High-Frequency Sensor in Gas-Insulated Switchgear

Tuyet-Doan

Tran-Thi

et al. 2020

Sensors

View full text Add to dashboard Cite

In recent years, deep learning has been successfully used in order to classify partial discharges (PDs) for assessing the condition of insulation systems in different electrical equipment. However, fault diagnosis using deep learning is still challenging, as it requires a large amount of training data, which is difficult and expensive to obtain in the real world. This paper proposes a novel one-shot learning method for fault diagnosis using a small dataset of phase-resolved PDs (PRPDs) in a gas-insulated switchgear (GIS). The proposed method is based on a Siamese network framework, which employs a distance metric function for predicting sample pairs from the same PRPD class or different PRPD classes. Experimental results over the small PRPD dataset that was obtained from an ultra-high-frequency sensor in the GIS show that the proposed method achieves outstanding performance for PRPD fault diagnosis as compared with the previous methods.

show abstract

“…According to the statistics, insulation faults are the main cause of accidents in GIS [6], and most insulation faults are manifested in the form of partial discharges (PDs), which will further accelerate equipment aging. Currently, the detection of insulation faults is mainly done by means of measuring sound, light-scale, heat, and electromagnetic waves and the decomposition of chemical products induced by PD [7]. Detection methods include pulse current, ultra-high-frequency (UHF), ultrasonic, optical detection, and gas decomposition product detection methods [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network

et al. 2019

View full text Add to dashboard Cite

Partial discharge (PD) is one of the major form expressions of gas-insulated switchgear (GIS) insulation defects. Because PD will accelerate equipment aging, online monitoring and fault diagnosis plays a significant role in ensuring safe and reliable operation of the power system. Owing to feature engineering or vanishing gradients, however, existing pattern recognition methods for GIS PD are complex and inefficient. To improve recognition accuracy, a novel GIS PD pattern recognition method based on a light-scale convolutional neural network (LCNN) without artificial feature engineering is proposed. Firstly, GIS PD data are obtained through experiments and finite-difference time-domain simulations. Secondly, data enhancement is reinforced by a conditional variation auto-encoder. Thirdly, the LCNN structure is applied for GIS PD pattern recognition while the deconvolution neural network is used for model visualization. The recognition accuracy of the LCNN was 98.13%. Compared with traditional machine learning and other deep convolutional neural networks, the proposed method can effectively improve recognition accuracy and shorten calculation time, thus making it much more suitable for the ubiquitous-power Internet of Things and big data.

show abstract

A Novel PD Detection Technique for Use in GIS Based on a Combination of UHF and Optical Sensors

Cited by 59 publications

References 19 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

One-Shot Learning for Partial Discharge Diagnosis Using Ultra-High-Frequency Sensor in Gas-Insulated Switchgear

Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network

Contact Info

Product

Resources

About