Power quality disturbance classification based on time-frequency domain multi-feature and decision tree

Wenjing Zhao; Liqun Shang; Jinfan Sun

doi:10.1186/s41601-019-0139-z

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Power quality disturbance classification based on time-frequency domain multi-feature and decision tree

Original Research | 更新时间：2025-11-26

- Power quality disturbance classification based on time-frequency domain multi-feature and decision tree
- Power quality disturbance classificationbased on time-frequency domain multifeatureand decision tree
- 现代电力系统保护与控制（英文） 2019年4卷第4期页码：1-6
- 作者机构：
  
  1. School of Electrical and Control Engineering, Xi'an University of Science and Technology,Shaanxi Province,Xi'an,China
  2. Weihe Thermal Power Plant, Shaanxi Power Generation co., LTD.,Xianyang,China
- 作者简介：
- 基金信息：
- DOI：10.1186/s41601-019-0139-z
  中图分类号：
- 纸质出版：2019
- 稿件说明：
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Wenjing Zhao, Liqun Shang, Jinfan Sun. Power quality disturbance classification based on time-frequency domain multi-feature and decision tree[J]. 现代电力系统保护与控制（英文）, 2019,4(4):1-6.

Wenjing Zhao, Liqun Shang, Jinfan Sun. Power quality disturbance classificationbased on time-frequency domain multifeatureand decision tree[J]. Protection and Control of Modern Power Systems, 2019, 4(4): 1-6.
Wenjing Zhao, Liqun Shang, Jinfan Sun. Power quality disturbance classification based on time-frequency domain multi-feature and decision tree[J]. 现代电力系统保护与控制（英文）, 2019,4(4):1-6. DOI： 10.1186/s41601-019-0139-z.

Wenjing Zhao, Liqun Shang, Jinfan Sun. Power quality disturbance classificationbased on time-frequency domain multifeatureand decision tree[J]. Protection and Control of Modern Power Systems, 2019, 4(4): 1-6. DOI： 10.1186/s41601-019-0139-z.

摘要

Accurate classification of power quality disturbance is the premise and basis for improving and governing power quality. A method for power quality disturbance classification based on time-frequency domain multi-feature and decision tree is presented. Wavelet transform and S-transform are used to extract the feature quantity of each power quality disturbance signal

and a decision tree with classification rules is then constructed for classification and recognition based on the extracted feature quantity. The classification rules and decision tree classifier are established by combining the energy spectrum feature quantity extracted by wavelet transform and other seven time-frequency domain feature quantities extracted by S-transform. Simulation results show that the proposed method can effectively identify six types of common single disturbance signals and two mixed disturbance signals

with fast classification speed and adequate noise resistance. Its classification accuracy is also higher than those of support vector machine (SVM) and k-nearest neighbor (KNN) algorithms. Compared with the method that only uses S-transform

the proposed feature extraction method has more abundant features and higher classification accuracy for power quality disturbance.

Abstract

the proposed feature extraction method has more abundant features and higher classification accuracy for power quality disturbance.

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references

1. Xiao, X.N. ( 2010 ). Analysis and control of power quality [M]. Beijing: China Electric Power Press, 124–128.

2. Zhang, Y., Liu., Z.G. ( 2012 ). A new power quality hybrid disturbance classification method based on time-frequency domain multi-characteristic quantities [J]. Proceedings of the CSEE, 32 ( 34 ), 83–90.

3. Zhang, B. ( 2010 ). Power quality analysis method based on Mallat algorithm and fast Fourier transform [C]. Power Quality Seminar, 35–40.

4. Jurado, F., Saenz, J. R. ( 2002 ). Comparison between discrete STFT and wavelets for the analysis of power quality events[J]. Electr Power Syst Res, 62 ( 3 ), 183–190.

5. Huang, J. M, Qu, H. Z, Li, X.M. ( 2016 ). Classification of mixed disturbance of power quality based on short-time Fourier transform and spectral kurdiness [J]. Power System Technology 40 ( 10 ), 3184–3191.

6. Qu, H. Z, Liu, H., Li, X. M, ( 2017 ). A feature combination optimization method for multi-disturbance classification of power quality [J]. Electric Power Automation Equipment 37 ( 3 ), 146–152.

7. Luciano C.M. Andrade, Mario Oleskovicz, Ricardo A.S. Fernandes. ( 2016 ). Adaptive threshold based on wavelet transform applied to the segmentation of single and combined power quality disturbances[J]. Neurocomputing, 38, 967–977.

8. Wu, Y., Tang, Q., Teng Z. S, ( 2016 ). Power quality disturbance signal feature extraction method based on improved S transform [J]. Proceedings of the CSEE, 36 ( 10 ), 2682–2689.

9. Huang, N. T, Peng, H., Cai, G. W, ( 2017 ). Composite disturbance feature selection and optimal decision tree construction of power quality [J]. Proceedings of the CSEE, 37 ( 3 ), 776–786.

10. LI, X.N. ( 2017 ). Power quality disturbance detection and recognition based on hilbert-huang transformation [D], China University of Mining and Technology.

11. Anamika Yadav, Yajnaseni Dash V. Ashok. ( 2016 ). ANN based directional relaying scheme for protection of Korba-Bhilai transmission line of Chhattisgarh state[J]. Protection and Control of Modern Power Systems, 1 ( 1 ), 15.

12. He, J. L, Wang, G. P, Liu, D., ( 2017 ). Location and identification of power quality disturbance in distribution network system based on lifting wavelet and improving BP neural network [J]. Power System Protection and Control, 45 ( 10 ), 69–76.

13. Ren, Z.H, Wang, Q. ( 2008 ). Power quality disturbance identification based on optimal DDAGSVM multi-class classification strategy [J]. Power System Protection and Control, 46 ( 5 ), 82–88.

14. Panigrahi B.K., Pandi V.R. optimal feature selection for classification of power quality disturbances using wavelet packet-based fuzzy k-nearest neighbour algorithm[J]. Neurocomputing, 2009, 3 ( 3 ): 296–306.

15. Biswal, M., Dash, P.K. ( 2013 ). Measurement and classification of simultaneous power signal patterns with an S-transform variant and fuzzy decision tree[J]. IEEE Transactions on Industrial Informatics, 9 ( 4 ), 1819–1827.

16. Zhou, Z.N. ( 2017 ). Research on power quality disturbance identification algorithm based on S transform [D]. Harbin Institute of Technology.

17. Han, G., Zhao, J. W, Zhu, X., ( 2015 ). Power quality disturbance identification based on multi-feature combination [J]. Proceedings of the CSU-EPSA, 27 ( 8 ), 71–77.

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