Partial discharge detection and diagnosis in gas insulated switchgear: State of the art

Khan, Qasim; Refaat, Shady S.; Abu‐Rub, Haitham; Toliyat, H.A.

doi:10.1109/mei.2019.8735667

Cited by 117 publications

(54 citation statements)

References 127 publications

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“…Different PD analysis techniques for condition monitoring are performed for PD detection, identification, and diagnosis [27]. Several techniques were developed to detect PD including electrical detection [28]- [32], electromagnetic detection [26], [33]- [37], optical detection [38]- [40], acoustic detection [41]- [45], gas presence detection [16], [46]- [48], and combinational methods [41], [49], [50]. PD sources signals are received through a detector and are further analyzed to identify the locations and severity of insulation defects.…”

Section: Introductionmentioning

confidence: 99%

Overview and Partial Discharge Analysis of Power Transformers: A Literature Review

2021

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Section: Introductionmentioning

confidence: 99%

Overview and Partial Discharge Analysis of Power Transformers: A Literature Review

2021

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“…As such, the literature is extremely vast. PD detection has been studied in many systems such as in transformers [ 4 ], gas-insulated high-voltage switchgear [ 5 ], power plants [ 6 ], and power lines [ 7 ]. The main challenge in PD detection lies in the detection of extremely short and temporally localized events: their wavelength is at the micro-second scale.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Detection of Partial Discharge in Power Lines with Deep Learning

Michau

Hsu

Fink

2021

Sensors

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Partial discharge (PD) is a common indication of faults in power systems, such as generators and cables. These PDs can eventually result in costly repairs and substantial power outages. PD detection traditionally relies on hand-crafted features and domain expertise to identify very specific pulses in the electrical current, and the performance declines in the presence of noise or of superposed pulses. In this paper, we propose a novel end-to-end framework based on convolutional neural networks. The framework has two contributions: First, it does not require any feature extraction and enables robust PD detection. Second, we devise the pulse activation map. It provides interpretability of the results for the domain experts with the identification of the pulses that led to the detection of the PDs. The performance is evaluated on a public dataset for the detection of damaged power lines. An ablation study demonstrates the benefits of each part of the proposed framework.

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“…In terms of the classification methods of GIS PD pattern recognition, there are currently two types of popular methodstraditional machine learning methods supported by feature engineering and deep learning methods with the advantage of automatic feature extraction. Based on the principal component analysis and autoencoder [8,9], feature engineering, which uses Fourier transform, wavelet transform, empirical mode decomposition, and S-transform to construct the feature [10,11], artificially selects the key feature parameters to represent PD and then realises PD pattern recognition by support vector machines, decision trees, random forests, artificial neural networks, and other classifiers [12][13][14]. However, traditional machine learning relies too heavily on expert experience.…”

Section: Introductionmentioning

confidence: 99%

GIS partial discharge pattern recognition via lightweight convolutional neural network in the ubiquitous power internet of things context

Wang

Yan

Yang

et al. 2020

IET sci. meas. technol.

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The construction of the ubiquitous power internet of things (UPIoT) provides a new feasible solution for gas‐insulated switchgear (GIS) online monitoring and fault diagnosis, but it also puts forward greater requirements for time and accuracy. How to find an effective real‐time model that can be applied to the UPIoT mobile terminals has become an urgent problem needing to be solved. To this end, this study proposes a lightweight convolutional neural network (LCNN) for GIS partial discharge (PD) pattern recognition using three lightweight convolutional blocks, and introduces the lowest recognition accuracy of single‐class faults as the primary indicator for selecting the optimal model under the UPIoT. First, three lightweight convolutional blocks are introduced for constructing an LCNN. Then, the optimal model constructed by the lightweight blocks is sought. Next, criteria for determining the best model are introduced, and the best model under the UPIoT is selected. This study provides a reference standard for the construction of GIS PD pattern recognition under the UPIoT. Meanwhile, through the balance of evaluation indicators, this study verifies that the minimum recognition accuracy of the MnasNet model is 98.8%, which is obviously better than other methods and lays a solid foundation for GIS PD pattern recognition.

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Partial discharge detection and diagnosis in gas insulated switchgear: State of the art

Cited by 117 publications

References 127 publications

Overview and Partial Discharge Analysis of Power Transformers: A Literature Review

Overview and Partial Discharge Analysis of Power Transformers: A Literature Review

Interpretable Detection of Partial Discharge in Power Lines with Deep Learning

GIS partial discharge pattern recognition via lightweight convolutional neural network in the ubiquitous power internet of things context

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