A deep learning based multiple signals fusion architecture for power system fault diagnosis

Pang, Yanwei; Yang, Dongsheng; Teng, Ran; Zhou, Bowen; Xu, Chi

doi:10.1016/j.segan.2022.100660

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…They preprocessed monitoring parameters to adapt them to the input of a deep convolutional neural network (DCNN) and effectively extracted transferable features between source and target power levels. In reference [21], scholars introduced a novel fault diagnosis method, the multidimensional aggregation decoupling network (MADN). This deep learning structure comprises three sequential stages: the multidimensional image construction (MIB) stage, feature decoupling mapping (FDM) stage, and system fault state classification (SSC) stage.…”

Section: Introductionmentioning

confidence: 99%

A Diagnostic Method for Open-Circuit Faults in DC Charging Stations Based on Improved S-Transform and LightGBM

Chen,

Tang,

Weng

et al. 2024

Energies

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The open-circuit fault in electric vehicle charging stations not only impacts the power quality of the electrical grid but also poses a threat to charging safety. Therefore, it is of great significance to study open-circuit fault diagnosis for ensuring the safe and stable operation of power grids and reducing the maintenance cost of charging stations. This paper addresses the multidimensional characteristics of open-circuit fault signals in charging stations and proposes a fault diagnosis method based on an improved S-transform and LightGBM. The method first utilizes improved incomplete S-transform and principal component analysis (PCA) to extract features of front- and back-stage faults separately. Subsequently, LightGBM is employed to classify the extracted features, ultimately achieving fault diagnosis. Simulation results demonstrate the method’s effectiveness in feature extraction, achieving an average diagnostic accuracy of 97.04% on the test dataset, along with notable noise resistance and real-time performance. Additionally, we designed an experimental platform for diagnosing open-circuit faults in DC charging station and collected experimental fault data. The results further validate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

A Diagnostic Method for Open-Circuit Faults in DC Charging Stations Based on Improved S-Transform and LightGBM

Chen,

Tang,

Weng

et al. 2024

Energies

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show abstract

“…Literature Review: The popularity of deep learning in the field of power systems has been well-documented in recent research [8,9,10], with several studies showing that it can be used to learn the solutions to computationally intensive power system analysis algorithms. In [11], the authors used a combination of recurrent and feed-forward neural networks to solve the power system SE problem using measurement data and the history of network voltages.…”

Section: Introductionmentioning

confidence: 99%

Graph neural networks on factor graphs for robust, fast, and scalable linear state estimation with PMUs

Ognjen¹,

Cosovic

Miskovic³

et al. 2023

Sustainable Energy, Grids and Networks

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A fault diagnosis method for few-shot industrial processes based on semantic segmentation and hybrid domain transfer learning

Tian,

Wang,

Peng

et al. 2023

Appl Intell

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A deep learning based multiple signals fusion architecture for power system fault diagnosis

Cited by 6 publications

References 22 publications

A Diagnostic Method for Open-Circuit Faults in DC Charging Stations Based on Improved S-Transform and LightGBM

A Diagnostic Method for Open-Circuit Faults in DC Charging Stations Based on Improved S-Transform and LightGBM

Graph neural networks on factor graphs for robust, fast, and scalable linear state estimation with PMUs

A fault diagnosis method for few-shot industrial processes based on semantic segmentation and hybrid domain transfer learning

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