Skip to main content
Log in

Power grid stability analysis using pipeline machine

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The problems associated with the stability analysis of power system are very important and has a wide scope of improvement. Severity of the transient disturbances arising in power system are usually studied through critical contingencies simulation. There proper study and assessment is extremely important for a reliable, uninterrupted operation, along with ensuring that no generating unit get desynchronized. The main objective of this research is to develop a fast and robust online transient stability assessment tool to classify the system operating states and to identify system critical generators in case of instability. This research proposes a pipeline machine learning multi-feature hybrid network framework that captures the phasor measurement unit (PMU) measurements and monitor the system transient stability in real-time. The test results verified that our proposed framework is fast and accurate, thereby a viable approach for system stability monitoring applications.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Algorithm 2
Fig. 6
Fig. 7
Algorithm 1
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data Availability

Authors confirm that the data supporting the findings of this study are available within the article.

References

  1. Abbas Q, Celebi ME (2019) Dermodeep-a classification of melanoma-nevus skin lesions using multi-feature fusion of visual features and deep neural network. Multimed Tools Appl 78(16):23559–23580

    Article  Google Scholar 

  2. Abdelgayed TS, Morsi WG, Sidhu TS (2017) Fault detection and classification based on co-training of semisupervised machine learning. IEEE Trans Ind Electron 65(2):1595–1605

    Article  Google Scholar 

  3. Alimi OA, Ouahada K, Abu-Mahfouz AM (2020) A review of machine learning approaches to power system security and stability. IEEE Access 8:113512–113531

    Article  Google Scholar 

  4. Arora S, Bala A (2020) Pap: power aware prediction based framework to reduce disk energy consumption. Clust Comput 23(4):3157–3174

    Article  Google Scholar 

  5. Castillo C, El-Haddad M, Pfeffer J, Stempeck M (2014) Characterizing the life cycle of online news stories using social media reactions. In: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing. ACM, pp 211–223

  6. Chen C-R, Hsu Y-Y (1991) Synchronous machine steady-state stability analysis using an artificial neural network. IEEE Trans Energy Convers 6(1):12–20

    Article  Google Scholar 

  7. Deo R, Samui P, Roy SS (2020) Predictive modelling for energy management and power systems engineering. Elsevier

  8. Dharmapala KD, Rajapakse A, Narendra K, Zhang Y (2020) Machine learning based real-time monitoring of long-term voltage stability using voltage stability indices. IEEE Access 8:222544–222555

    Article  Google Scholar 

  9. Gao DW, Wang Q, Zhang F, Yang X, Huang Z, Ma S, Li Q, Gong X, Wang F-Y (2019) Application of ai techniques in monitoring and operation of power systems. Frontiers in Energy 13(1):71–85

    Article  Google Scholar 

  10. Geeganage J, Annakkage U, Weekes T, Archer BA (2014) Application of energy-based power system features for dynamic security assessment. IEEE Trans Power Syst 30(4):1957–1965

    Article  Google Scholar 

  11. Geetha R, Ramyadevi K, Balasubramanian M (2021) Prediction of domestic power peak demand and consumption using supervised machine learning with smart meter dataset. Multimed Tools Appl 80(13):19675–19693

    Article  Google Scholar 

  12. Geurts P, Wehenkel L (1998) Early prediction of electric power system blackouts by temporal machine learning. In: Proceedings of the ICML98/AAAI98 workshop on predicting the future: AI approaches to time series analysis, pp 24–26

  13. Gupta A, Gurrala G, Sastry P (2018) An online power system stability monitoring system using convolutional neural networks. IEEE Trans Power Syst 34(2):864–872

    Article  Google Scholar 

  14. Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Transactions on Pattern Analysis & Machine Intelligence 10:993–1001

    Article  Google Scholar 

  15. Hossain E, Khan I, Un-Noor F, Sikander SS, Sunny MSH (2019) Application of big data and machine learning in smart grid, and associated security concerns: a review. Ieee Access 7:13960–13988

    Article  Google Scholar 

  16. Khan A, Li JP, Ahmad N, Sethi S, Haq AU, Patel SH, Rahim S (2020) Predicting emerging trends on social media by modeling it as temporal bipartite networks. IEEE Access 8:39635–39646

    Article  Google Scholar 

  17. Liu R (2018) Power system stability scanning and security assessment using machine learning

  18. Maharjan L, Ditsworth M, Niraula M, Caicedo Narvaez C, Fahimi B (2019) Machine learning based energy management system for grid disaster mitigation. IET Smart Grid 2(2):172–182

    Article  Google Scholar 

  19. Malbasa V, Zheng C, Chen P. -C., Popovic T, Kezunovic M (2017) Voltage stability prediction using active machine learning. IEEE Transactions on Smart Grid 8(6):3117–3124

    Article  Google Scholar 

  20. Miraftabzadeh SM, Foiadelli F, Longo M, Pasetti M (2019) A survey of machine learning applications for power system analytics. In: 2019 IEEE international conference on environment and electrical engineering and 2019 IEEE industrial and commercial power systems europe (EEEIC/I&CPS Europe). IEEE, pp 1–5

  21. Naftel A, Khalid S (2006) Classifying spatiotemporal object trajectories using unsupervised learning in the coefficient feature space. Multimedia Syst 12 (3):227–238

    Article  Google Scholar 

  22. Paital SR, Ray PK, Mohanty A (2018) A review on stability enhancement in smib system using artificial intelligence based techniques. In: 2018 IEEMA engineer infinite conference (eTechNxT). IEEE, pp 1–6

  23. Peiwen L, Yongjing H, Abbas K (2020) Transformers fault prediction: an improved ensembled method. a˜a. 2:1

  24. Sarajcev P, Kunac A, Petrovic G, Despalatovic M (2021) Power system transient stability assessment using stacked autoencoder and voting ensemble. Energies 14(11):3148

    Article  Google Scholar 

  25. Schäfer B., Grabow C, Auer S, Kurths J, Witthaut D, Timme M (2016) Taming instabilities in power grid networks by decentralized control. The European Physical Journal Special Topics 225(3):569–582

    Article  Google Scholar 

  26. Shenoy NK (2015) An approach to assess the resiliency of electric power grids. Ph.D. dissertation, Oklahoma State University

  27. Tan B, Yang J, Pan X, Li J, Xie P, Zeng C (2017) Representational learning approach for power system transient stability assessment based on convolutional neural network. The Journal of Engineering 2017(13):1847–1850

    Article  Google Scholar 

  28. Xu Y, Dai Y, Dong ZY, Zhang R, Meng K (2013) Extreme learning machine-based predictor for real-time frequency stability assessment of electric power systems. Neural Comput Applic 22(3):501–508

    Article  Google Scholar 

  29. Yang Q (2020) Research on power system performance evaluation based on machine learning technology. In: IOP conference series: Materials science and engineering. vol. 782, no. 3. IOP Publishing, p 032011

  30. Yang H, Niu K, Xu D, Xu S (2021) Analysis of power system transient stability characteristics with the application of massive transient stability simulation data. Energy Rep 7:111–117

    Article  Google Scholar 

  31. Zhang Y, Cai W, Wang W, Zhang Y (2017) Stopping criterion for active learning with model stability. ACM Transactions on Intelligent Systems and Technology (TIST) 9(2):1–26

    Google Scholar 

  32. Zhang Y, Li T, Na G, Li G, Li Y (2015) Optimized extreme learning machine for power system transient stability prediction using synchrophasors. Mathematical Problems in Engineering, 2015

  33. Zhou Y, Zhang P (2021) Noise-resilient quantum machine learning for stability assessment of power systems. arXiv:2104.04855

Download references

Acknowledgements

This work supported by ”Key Laboratory of Wavelet Active Media Technology” with the National Natural Science Foundation of China (Grant No. 61370073), the National High Technology Research and Development Program of China (Grant No. 2007AA01Z423), Room No: B1301, School of Computer Science, University of Electronic Science and Technology of China (UESTC), No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan, 611731, P. R. China.

Author information

Authors and Affiliations

Authors

Contributions

In research articles, all authors contributed equally.

Corresponding author

Correspondence to Asif Khan.

Ethics declarations

Conflict of Interests

The authors declaring there is no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

In Table 7 mathematical notions are described.

Table 7 Mathematical notation used in paper

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, A., Li, J.P. & Husain, M.A. Power grid stability analysis using pipeline machine. Multimed Tools Appl 82, 25651–25675 (2023). https://doi.org/10.1007/s11042-023-14384-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-14384-3

Keywords

Navigation