The Deep Jerk-regularized Extreme Learning Machine for Complex Power Quality Disturbance Classification

ZHAO Chen; LI Kaicheng; LIN Shouying; ZENG Ziying; LIN Weixin

doi:10.6054/j.jscnun.2021052

Journal of South China Normal University (Natural Science Edition) > 2021 > 53(4): 8-16. > DOI: 10.6054/j.jscnun.2021052

ZHAO Chen, LI Kaicheng, LIN Shouying, ZENG Ziying, LIN Weixin. The Deep Jerk-regularized Extreme Learning Machine for Complex Power Quality Disturbance Classification[J]. Journal of South China Normal University (Natural Science Edition), 2021, 53(4): 8-16. DOI: 10.6054/j.jscnun.2021052

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The Deep Jerk-regularized Extreme Learning Machine for Complex Power Quality Disturbance Classification

1.
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2.
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

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Received Date: March 14, 2021
Available Online: September 02, 2021

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Abstract

Abstract

In order to make full use of a large number of unlabeled measured samples in complex power quality disturbance classification, a semi-supervised disturbance classification method based on the deep Jerk-regularized extreme learning machine (DJRELM) is proposed. The method stacks the Jerk-regularized ELM autoencoder (JRELM-AE) to realize the automatic feature extraction while exploring the intrinsic geometric structure of the unlabeled data. The combination of semi-supervised Jerk-regularized ELM and threshold learning ELM in the classification layer extends the multi-layer network to multi-label semi-supervised learning. Experimental results show that the proposed method outperforms several ELM-based supervised and semi-supervised algorithms as well as the state-of-the-art multi-layer ELM algorithms and deep convolutional neural networks under different noise environments. The new method has both academic and practical value.
- power quality,
- disturbance classification,
- extreme learning machine,
- manifold regularization,
- semi-supervised learning

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References

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