Fault classification and localization in microgrids: Leveraging discrete wavelet transform and multi-machine learning techniques considering single point measurements

Basher, Bassem Gehad; Ghanem, Abdelhady; Abulanwar, Sayed; Hassan, Mohammed K.; Rizk, Mohammad E.M.

doi:10.1016/j.epsr.2024.110362

Electric Power Systems Research

2024

DOI: 10.1016/j.epsr.2024.110362

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Fault classification and localization in microgrids: Leveraging discrete wavelet transform and multi-machine learning techniques considering single point measurements

Bassem Gehad Basher,

Abdelhady Ghanem,

Sayed Abulanwar

et al.

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Cited by 3 publications

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Fault classification and localization of multi-machine-based ieee benchmark test case power transmission lines using optimizable weighted extreme learning machine

Hassan,

Biswas,

Chowdhury

et al. 2024

Electric Power Systems Research

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Fault classification and localization of multi-machine-based ieee benchmark test case power transmission lines using optimizable weighted extreme learning machine

Hassan,

Biswas,

Chowdhury

et al. 2024

Electric Power Systems Research

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A novel deep learning framework for rolling bearing fault diagnosis enhancement using VAE-augmented CNN model

Wang,

Li,

et al. 2024

Heliyon

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Advanced Fault Detection in Power Systems Using Wavelet Transform: SIMULINK-Based Implementation and Analysis

Tuhin,

Araf,

Zubayer

et al. 2024

JEET

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Traditional methods struggle to find faults in power transmission lines. This paper presents an approach for short transmission lines, leveraging the power of wavelet transforms. Traditional methods analyze time-domain signals, limiting their ability to differentiate fault transients. Wavelet transforms, offering a combined time-frequency analysis, provide a deeper understanding of these transients. A detailed short transmission line model is built in SIMULINK. Diverse fault scenarios are meticulously simulated, and current signals undergo wavelet transform analysis. Key features extracted from the wavelet coefficients act as fingerprints of potential faults. These features are then utilized to develop a robust fault detection algorithm specifically designed for short transmission lines. The proposed method promises enhanced fault detection capabilities compared to existing techniques in this domain. The results, presented in subsequent sections, will shed light on the effectiveness of wavelet transforms in empowering smarter and more reliable transmission line operations.

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Fault classification and localization in microgrids: Leveraging discrete wavelet transform and multi-machine learning techniques considering single point measurements

Cited by 3 publications

References 40 publications

Fault classification and localization of multi-machine-based ieee benchmark test case power transmission lines using optimizable weighted extreme learning machine

Fault classification and localization of multi-machine-based ieee benchmark test case power transmission lines using optimizable weighted extreme learning machine

A novel deep learning framework for rolling bearing fault diagnosis enhancement using VAE-augmented CNN model

Advanced Fault Detection in Power Systems Using Wavelet Transform: SIMULINK-Based Implementation and Analysis

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