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

Citation:

PDF (1250 KB)

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

More Information

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

Graphical Abstract

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

FullText(HTML)

References (20)

References

[1]	杨新法, 苏剑, 吕志鹏, 等. 微电网技术综述[J]. 中国电机工程学报, 2014, 34(1): 57-70. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201401008.htm YANG X F, SU J, LLÜ Z P, et al. Overview on micro-grid technology[J]. Proceedings of the Chinese Society for Electrical Engineering, 2014, 34(1): 57-70. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201401008.htm
[2]	黄建明, 瞿合祚, 李晓明. 基于短时傅里叶变换及其谱峭度的电能质量混合扰动分类[J]. 电网技术, 2016, 40(10): 3184-3191. https://www.cnki.com.cn/Article/CJFDTOTAL-DWJS201610036.htm HUANG J M, QU H Z, LI X M. Classification for hybrid power quality disturbance based on STFT and its spectral kurtosis[J]. Power System Technology, 2016, 40(10): 3184-3191. https://www.cnki.com.cn/Article/CJFDTOTAL-DWJS201610036.htm
[3]	王仁明, 汪宏阳, 张赟宁, 等. 基于分段改进S变换和随机森林的复合电能质量扰动识别方法[J]. 电力系统保护与控制, 2020, 48(7): 19-28. https://www.cnki.com.cn/Article/CJFDTOTAL-JDQW202007003.htm WANG R M, WANG H Y, ZHANG Y N, et al. Composite power quality disturbance recognition based on segmented modified S-transform and random forest[J]. Power System Protection and Control, 2020, 48(7): 19-28. https://www.cnki.com.cn/Article/CJFDTOTAL-JDQW202007003.htm
[4]	郑戍华, 张宁宁, 王向周. 基于提升小波和Hilbert变换的暂态电能质量检测[J]. 北京理工大学学报, 2019, 39(2): 162-168. https://www.cnki.com.cn/Article/CJFDTOTAL-BJLG201902009.htm ZHENG S H, ZHANG N N, WANG X Z. A lifting wavelet and Hilbert transform fusion method for transient power quality detection[J]. Transactions of Beijing Institute of Technology, 2019, 39(2): 162-168. https://www.cnki.com.cn/Article/CJFDTOTAL-BJLG201902009.htm
[5]	徐艳春, 高永康, 李振兴, 等. 基于VMD初始化S变换的混合动力系统电能质量扰动检测与分类[J]. 中国电机工程学报, 2019, 39(16): 4786-4798. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201916014.htm XU Y C, GAO Y K, LI Z X, et al. Power quality disturbance detection and classification of hybrid power system based on VMD initialization S-transform[J]. Proceedings of the Chinese Society for Electrical Engineering, 2019, 39(16): 4786-4798. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201916014.htm
[6]	唐小煜, 黄进波, 冯洁文, 等. 基于U-net和YOLOv4的绝缘子图像分割与缺陷检测[J]. 华南师范大学学报(自然科学版), 2020, 52(6): 15-21. doi: 10.6054/j.jscnun.2020088 TANG X Y, HUANG J B, FENG J W, et al. Image segmentation and defect detection of insulators based on U－net and YOLOv4[J]. Journal of South China Normal University(Natural Science Edition), 2020, 52(6): 15-21. doi: 10.6054/j.jscnun.2020088
[7]	王维博, 张斌, 曾文入, 等. 基于特征融合一维卷积神经网络的电能质量扰动分类[J]. 电力系统保护与控制, 2020, 48(6): 53-60. https://www.cnki.com.cn/Article/CJFDTOTAL-JDQW202006007.htm WANG W B, ZHANG B, ZENG W R, et al. Power quality disturbance classification of one-dimensional convolutional neural networks based on feature fusion[J]. Power System Protection and Control, 2020, 48(6): 53-60. https://www.cnki.com.cn/Article/CJFDTOTAL-JDQW202006007.htm
[8]	陈伟, 何家欢, 裴喜平. 基于相空间重构和卷积神经网络的电能质量扰动分类[J]. 电力系统保护与控制, 2018, 46(14): 87-93. doi: 10.7667/PSPC171080 CHEN W, HE J H, PEI X P. Classification for power quality disturbance based on phase-space reconstruction and convolution neural network[J]. Power System Protection and Control, 2018, 46(14): 87-93. doi: 10.7667/PSPC171080
[9]	曹梦舟, 张艳. 基于卷积-长短期记忆网络的电能质量扰动分类[J]. 电力系统保护与控制, 2020, 48(2): 86-92. https://www.cnki.com.cn/Article/CJFDTOTAL-JDQW202002011.htm CAO M Z, ZHANG Y. Classification for power quality disturbances based on CNN-LSTM network[J]. Power System Protection and Control, 2020, 48(2): 86-92. https://www.cnki.com.cn/Article/CJFDTOTAL-JDQW202002011.htm
[10]	许立武, 李开成, 罗奕, 等. 基于不完全S变换与梯度提升树的电能质量复合扰动识别[J]. 电力系统保护与控制, 2019, 47(6): 24-31. https://www.cnki.com.cn/Article/CJFDTOTAL-JDQW201906004.htm XU L W, LI K C, LUO Y, et al. Classification of complex power quality disturbances based on incomplete S-transform and gradient boosting decision tree[J]. Power System Protection and Control, 2019, 47(6): 24-31. https://www.cnki.com.cn/Article/CJFDTOTAL-JDQW201906004.htm
[11]	陈晓静, 李开成, 肖剑, 等. 一种实时电能质量扰动分类方法[J]. 电工技术学报, 2017, 32(3): 45-55. https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201703006.htm CHEN X J, LI K C, XIAO J, et al. A method of real-time power quality disturbance classification[J]. Transactionsof China Electrotechnical Society, 2017, 32(3): 45-55. https://www.cnki.com.cn/Article/CJFDTOTAL-DGJS201703006.htm
[12]	张巧革, 刘志刚, 朱玲, 等. 基于多标签Rank-WSVM的复合电能质量扰动分类[J]. 中国电机工程学报, 2013, 33(28): 114-120. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201328016.htm ZHANG Q G, LIU Z G, ZHU L, et al. Recognition of multiple power quality disturbances using multi-label wavelet support vector machine[J]. Proceedings of the Chinese Society for Electrical Engineering, 2013, 33(28): 114-120. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201328016.htm
[13]	周雒维, 管春, 卢伟国. 多标签分类法在电能质量复合扰动分类中的应用[J]. 中国电机工程学报, 2011, 31(4): 45-50. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201104009.htm ZHOU L W, GUAN C, LU W G. Application of multi-label classification method to categorization of multiple power quality disturbances[J]. Proceedings of the Chinese Society for Electrical Engineering, 2011, 31(4): 45-50. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGDC201104009.htm
[14]	HUANG G B, ZHU Q Y, SIEW C K. Extreme learning machine: theory and applications[J]. Neuro-computing, 2006, 70: 489-501.
[15]	KASUN L L C, ZHOU H, HUANG G B, et al. Representational learning with extreme learning machine for big data[J]. IEEE Intelligent Systems, 2013, 28(6): 31-34.
[16]	YADAV R K, ABHISHEK V, et al. Regularization on a rapidly varying manifold[J]. International Journal of Machine Learning and Cybernetics, 2020, 11(3): 1571-1590. doi: 10.1007/s13042-019-01059-5
[17]	ZHAO C, LI K C, LI Y Z, et al. Novel method based on variational mode decomposition and a random discriminative projection extreme learning machine for multiple power quality disturbance recognition[J]. IEEE Transactions on Industrial Informatics, 2019, 15(5): 2915-2926. doi: 10.1109/TII.2018.2871253
[18]	HUANG G, SONG S J, GUPTA J, et al. Semi-supervised and unsupervised extreme learning machines[J]. IEEE Transactions on Cybernetics, 2014, 44(12): 2405-2417. doi: 10.1109/TCYB.2014.2307349
[19]	GU Y, CHEN Y, LIU J, et al. Semi-supervised deep extreme learning machine for WiFi based localization[J]. Neurocomputing, 2015, 166(20): 282-293. http://www.sciencedirect.com/science/article/pii/S092523121500418X
[20]	SUN K, ZHANG J, ZHANG C, et al. Generalized extreme learning machine autoencoder and a new deep neural network[J]. Neurocomputing, 2016, 230(22): 374-381. http://www.sciencedirect.com/science/article/pii/S092523121631503X

Cited By

Cited by

Periodical cited type(8)

1.	张勇，骆乾坤，刘鑫，李迎春，邓亚平，马雷，钱家忠. ERT法监测含水层重金属污染及原位化学修复过程研究. 高校地质学报. 2025(01): 67-83 .
2.	李婧，韩春媚，郑传鹏，曹晗霖，刘士亮，毛德强. 基于电磁感应法的固废填埋区探测研究. 环境生态学. 2024(07): 143-150 .
3.	李彬，陈佳亮，张路路，叶脉，宋烺，杨泽涛，范彬，王林. 感应电磁法在固废非法填埋环境损害鉴定评估案例中的应用. 环境生态学. 2024(08): 117-124 .
4.	吴阿娜，刘丹青，汤琳. 基于感应电磁法和高密度电阻率法探测的区域土壤物性参数与重金属含量之间的相关性. 中国环境监测. 2024(06): 204-213 .
5.	刘煌睿，闫海虎，常莎，杨元昕，甘峰睿，陈思莉，黄大伟，陈晨，陈禧. 物探技术在滩涂开发地块土壤信息探测中的应用. 昆明理工大学学报(自然科学版). 2024(06): 40-47 .
6.	杨国晶. 基于LIBS技术的土壤重金属元素污染快速检测与治理方法研究. 环境科学与管理. 2023(04): 108-113 .
7.	赵博，张嘉力. 高密度电阻率法在西北地区黄土层条件下的试验应用. 南方农机. 2022(11): 36-38 .
8.	谷艾婷. 重金属污染地块风险评估及土壤修复技术探析. 山西化工. 2022(09): 170-172 .

Other cited types(1)

Get Citation

PDF

XML

Article views (581) PDF downloads (47) Cited by(9)

Turn off MathJax

Article Contents

Abstract

References

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

Abstract

References

Cited by

Periodical cited type(8)

Other cited types(1)

Catalog

Export File

Citation

Format

Content