Skip to main content
SpringerLink
Log in

Research on Power Quality Disturbance Signal Classification Based on Random Matrix Theory

  • Conference paper
  • First Online:
Data Science (ICPCSEE 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 728))

  • 1656 Accesses

Abstract

In this paper, a method of power quality disturbance classification based on random matrix theory (RMT) is proposed. The method utilizes the power quality disturbance signal to construct a random matrix. By analyzing the mean spectral radius (MSR) variation of the random matrix, the type and time of occurrence of power quality disturbance are classified. In this paper, the random matrix theory is used to analyze the voltage sag, swell and interrupt perturbation signals to classify the occurrence time, duration of the disturbance signal and the depth of voltage sag or swell. Examples show that the method has strong anti-noise ability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Singh, U., Singh, S.N.: Application of fractional Fourier transform for classification of power quality disturbances. IET Sci. Meas. Technol. 11, 67–76 (2017)

    Article  Google Scholar 

  2. Zhao, F., Yang, R.: Voltage sag disturbance detection based on short time fourier transform. Proc. CSEE 27, 28–34 (2007)

    Google Scholar 

  3. Santoso, S., Powers, E.J., Grady, W.M., Hofmann, P.: Power quality assessment via wavelet transform analysis. IEEE Trans. Power Delivery 11, 924–930 (1996)

    Article  Google Scholar 

  4. Thirumala, K., Umarikar, A.C., Jain, T.: Estimation of single-phase and three-phase power-quality indices using empirical wavelet transform. IEEE Trans. Power Delivery 30, 445–454 (2015)

    Article  Google Scholar 

  5. Li, J., Teng, Z., Tang, Q., Song, J.: Detection and classification of power quality disturbances using double resolution S-Transform and DAG-SVMs. IEEE Trans. Instrum. Meas. 65, 2302–2312 (2016)

    Article  Google Scholar 

  6. Zhao, F., Yang, R.: Power-quality disturbance recognition using S-Transform. IEEE Trans. Power Delivery 22, 944–950 (2007)

    Article  Google Scholar 

  7. Dalai, S., Dey, D., Chatterjee, B., Chakravorti, S., Bhattacharya, K.: Cross-spectrum analysis-based scheme for multiple power quality disturbance sensing device. IEEE Sens. J. 15, 3989–3997 (2015)

    Article  Google Scholar 

  8. Kumar, R., Singh, B., Shahani, D.T.: Symmetrical components-based modified technique for power-quality disturbances detection and classification. IEEE Trans. Ind. Appl. 52, 3443–3450 (2016)

    Article  Google Scholar 

  9. Liu, Z., Cui, Y., Li, W.: A classification method for complex power quality disturbances using EEMD and rank wavelet SVM. IEEE Trans. Smart Grid 6, 1678–1685 (2015)

    Article  Google Scholar 

  10. Manikandan, M.S., Samantaray, S.R., Kamwa, I.: Detection and classification of power quality disturbances using sparse signal decomposition on hybrid dictionaries. IEEE Trans. Instrum. Meas. 64, 27–38 (2015)

    Article  Google Scholar 

  11. He, X., et al.: A big data architecture design for smart grids based on random matrix theory. IEEE Trans. Smart Grid 8, 674–686 (2017)

    Google Scholar 

  12. Xu, X., He, X., Ai, Q., Qiu, R.C.: A correlation analysis method for power systems based on random matrix theory. IEEE Trans. Smart Grid PP, 1–10 (2016)

    Google Scholar 

  13. He, X., et al.: Designing for situation awareness of future power grids: an indicator system based on linear eigenvalue statistics of large random matrices. IEEE Access 4, 3557–3568 (2016)

    Article  Google Scholar 

  14. Han, B., Luo, L., Sheng, G., Li, G., Jiang, X.: Framework of random matrix theory for power system data mining in a non-Gaussian environment. IEEE Access 4, 9969–9977 (2016)

    Article  Google Scholar 

  15. Wu, Q., Zhang, D., et al.: A method for power system steady stability situation assessment based on random matrix theory. Proc. CSEE 36, 5414–5420 (2016)

    Google Scholar 

  16. Liu, W., Zhang, D., et al.: Power system transient stability analysis based on random matrix theory. Proc. CSEE 36, 4854–4863 (2016)

    Google Scholar 

  17. Bai, Z., Silverstein, J.W.: Spectral Analysis of Large Dimensional Random Matrices, 2nd edn. Springer, New York (2010). doi:10.1007/978-1-4419-0661-8

    Book  MATH  Google Scholar 

  18. Guionnet, A.: The single ring theorem. ArXiv e-prints (2009). http://arxiv.org/pdf/0909.2214.pdf

Download references

Acknowledgement

The authors gratefully acknowledge the key technology project of state grid corporation of China (EPRIPDJK[2015]1495).

Author information

Authors and Affiliations

  1. China Electric Power Research Institute, Haidian, Beijing, China

    Keyan Liu, Dongli Jia & Kaiyuan He

  2. College of Information and Electrical Engineering, China Agricultural University, Haidian, Beijing, China

    Tingting Zhao & Fengzhan Zhao

Authors
  1. Keyan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dongli Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kaiyuan He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tingting Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Fengzhan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fengzhan Zhao .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Beiji Zou

  2. Harbin Engineering University, Harbin, China

    Qilong Han

  3. Harbin University of Science and Technology, Harbin, China

    Guanglu Sun

  4. Northeast Forestry University, Harbin, China

    Weipeng Jing

  5. Huaihua University, Huaihua, Hunan, China

    Xiaoning Peng

  6. Sciences of Country Tripod Institute of Data Science, Harbin, China

    Zeguang Lu

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Liu, K., Jia, D., He, K., Zhao, T., Zhao, F. (2017). Research on Power Quality Disturbance Signal Classification Based on Random Matrix Theory. In: Zou, B., Han, Q., Sun, G., Jing, W., Peng, X., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-10-6388-6_30

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6388-6_30

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6387-9

  • Online ISBN: 978-981-10-6388-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation