Partial Discharge Pattern Recognition for Underground Cable Joints Using Convolutional Neural Network

Chen, Chang; Wu, Ruay‐Nan; Lai, Chung-Ching; Chang, Hsuan-Hao; Boyanapalli, Bharath Kumar

doi:10.1109/icpai51961.2020.00050

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…A study analysis has been done on CNNbased PD pattern recognition for the high voltage joints evaluated in terms of the number of network layers, the convolutional kernel size and the activation function [21]. Based on the foregoing analysis mentioned in [21], the final adopted architecture off CNN configuration has been established. Therefore, the optimal CNN configuration was ascertained based on the highest defect-type recognition accuracy [19,21].…”

Section: Defect Recognition Using Cnns At Different Pd Measurement Cy...mentioning

confidence: 99%

“…Based on the foregoing analysis mentioned in [21], the final adopted architecture off CNN configuration has been established. Therefore, the optimal CNN configuration was ascertained based on the highest defect-type recognition accuracy [19,21]. Prior investigations have presented optimal parameters for modeling CNNs to recognize PRPD patterns and pulse sequence analysis [19].…”

Section: Defect Recognition Using Cnns At Different Pd Measurement Cy...mentioning

confidence: 99%

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Study of partial discharges measurement cycles effect on defect recognition for underground cable joints

Chang,

Boyanapalli

2024

Meas. Sci. Technol.

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This study investigates the influence of various partial discharge (PD) measurement durations on defect recognition in high voltage power cable joints, aiming to address a crucial challenge in the field of insulation assessment. The main objective is to determine the optimal measurement cycle duration for accurate defect recognition, thereby enhancing the reliability of PD-based diagnostic techniques. Totally 14 cable joints, each containing three different types of prefabricated artificial defects, were analyzed across five different measurement cycle durations: 40, 80, 120, 200, and 1200 cycles. Subsequently, Convolutional Neural Networks (CNNs) were employed for defect recognition analysis. The results reveal a significant impact of measurement cycle duration on defect recognition accuracy. Particularly, a CNN based on 200 measurement cycles demonstrates superior performance compared to models with fewer cycles, achieving a total defect recognition accuracy of 100%. This finding underscores the importance of sufficient measurement cycles for obtaining comprehensive PRPD patterns and accurate defect classification. Furthermore, the study highlights the significance of setting a threshold value to mitigate false conditions in defect type recognition, offering valuable insights for practical applications in power system maintenance and diagnostics. Overall, this research contributes to advancing the understanding of PD-based insulation assessment techniques and provides practical recommendations for optimizing measurement cycle duration to enhance defect recognition accuracy in high voltage power cable joints.

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Section: Defect Recognition Using Cnns At Different Pd Measurement Cy...mentioning

confidence: 99%

Section: Defect Recognition Using Cnns At Different Pd Measurement Cy...mentioning

confidence: 99%

Study of partial discharges measurement cycles effect on defect recognition for underground cable joints

Chang,

Boyanapalli

2024

Meas. Sci. Technol.

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“…networks, which can accurately identify various types of defects in cable ter ditionally, reference [19] successfully identifies local discharge patterns in cable joints using convolutional neural networks.…”

Section: Cable Structurementioning

confidence: 99%

“…In another study, reference [18] presents a method for partial discharge pattern in cable termination based on convolutional neural networks, which can accurately identify various types of defects in cable termination. Additionally, reference [19] successfully identifies local discharge patterns in underground cable joints using convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

High-Voltage Cable Buffer Layer Ablation Fault Identification Based on Artificial Intelligence and Frequency Domain Impedance Spectroscopy

Liu,

Ma,

Liu

et al. 2024

Sensors

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In recent years, the occurrence of high-voltage cable buffer layer ablation faults has become frequent, posing a serious threat to the safe and stable operation of cables. Failure to promptly detect and address such faults may lead to cable breakdowns, impacting the normal operation of the power system. To overcome the limitations of existing methods for identifying buffer layer ablation faults in high-voltage cables, a method for identifying buffer layer ablation faults based on frequency domain impedance spectroscopy and artificial intelligence is proposed. Firstly, based on the cable distributed parameter model and frequency domain impedance spectroscopy, a mathematical model of the input impedance of a cable containing buffer layer ablation faults is derived. Through a simulation, the input impedance spectroscopy at the first end of the cables under normal conditions, buffer layer ablation, local aging, and inductive faults is performed, enabling the identification of inductive and capacitive faults through a comparative analysis. Secondly, the frequency domain amplitude spectroscopy of the buffer layer ablation and local aging faults are used as datasets and are input into a neural network model for training and validation to identify buffer layer ablation and local aging faults. Finally, using multiple evaluation metrics to assess the neural network model validates the superiority of the MLP neural network in cable fault identification models and experimentally confirms the effectiveness of the proposed method.

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Application of Convolutional Neural Networks on Insulation Status Prediction Using Partial discharges for Underground Cable Joints

Chang,

Boyanapalli

2022

2022 International Conference on Computing, Communication and Power Technology (IC3P)

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Partial Discharge Pattern Recognition for Underground Cable Joints Using Convolutional Neural Network

Cited by 5 publications

References 17 publications

Study of partial discharges measurement cycles effect on defect recognition for underground cable joints

Study of partial discharges measurement cycles effect on defect recognition for underground cable joints

High-Voltage Cable Buffer Layer Ablation Fault Identification Based on Artificial Intelligence and Frequency Domain Impedance Spectroscopy

Application of Convolutional Neural Networks on Insulation Status Prediction Using Partial discharges for Underground Cable Joints

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