EMI-based Diagnosis to Grounding Grids by Combining Ensemble Empirical Mode Decomposition and ICA

Song, Hengli; Wu, Qiang; Dong, Haobin

doi:10.1145/3036331.3036361

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…This technique has the ability to discriminate nonlinear, non-stationary, and chaotic signals, although its mode aliasing and anti-aliasing effects are both suboptimal. To overcome this challenge, several researchers have proposed replacing EMD with EEMD and utilizing white noise data analysis to enhance the anti-aliasing effect, leading to significant improvements in signal separation [8,9]. Nonetheless, the reconstruction process of this method lacks a standardized approach, which highlights the necessity of manual judgment.…”

Section: Introductionmentioning

confidence: 99%

A noise reduction method based on PSR-fastICA for spectrum-induced polarization data

Cen,

2023

Second International Conference on Electronic Information Technology (EIT 2023)

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Spectrum-induced polarization (SIP) is a widely used geophysical exploration approach, but it is prone to noise. To address this issue, this paper proposes a noise reduction method that combines phase space reconstruction (PSR) and fast independent component analysis (fastICA). The proposed approach enhances the signal-to-noise ratio of SIP data by effectively removing noise. Experimental results show that the PSR-fastICA method significantly reduces noise in SIP signals.

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Section: Introductionmentioning

confidence: 99%

A noise reduction method based on PSR-fastICA for spectrum-induced polarization data

Cen,

2023

Second International Conference on Electronic Information Technology (EIT 2023)

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“…Conducted and radiated EMI are exploited by EMI experts to gain information on faults type. Consequently, electrical insulation degradation can be identified through EMI diagnosis [ 1 , 2 ]. Popular insulation faults include Partial Discharges (PDs), corona, arcing, sparking, etc.…”

Section: Introductionmentioning

confidence: 99%

Imaging Time Series for the Classification of EMI Discharge Sources

Mitiche

Morison

Nesbitt

et al. 2018

Sensors

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In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome.

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Blind image separation based on attentional generative adversarial network

Sun

Zhao

et al. 2020

J Ambient Intell Human Comput

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EMI-based Diagnosis to Grounding Grids by Combining Ensemble Empirical Mode Decomposition and ICA

Cited by 4 publications

References 6 publications

A noise reduction method based on PSR-fastICA for spectrum-induced polarization data

A noise reduction method based on PSR-fastICA for spectrum-induced polarization data

Imaging Time Series for the Classification of EMI Discharge Sources

Blind image separation based on attentional generative adversarial network

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