http://iet.metastore.ingenta.com
1887

Deep-learning based fault diagnosis using computer-visualised power flow

Deep-learning based fault diagnosis using computer-visualised power flow

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Changes in system topology, such as branch breaking and the loss of a generator or load, may profoundly influence the operation security of the power system. This study introduces a novel deep-learning based fault diagnosis method using power flow to diagnose topology changes in the power system. Power flow samples with different system states and topologies are first computed numerically; then, they are transformed into computer-visualised images. Using massive power-flow image samples, a convolutional neural network that aims to identify the system state is trained. A feature-map restriction technique is used to restructure the network. To enhance the robustness of the network, the random noise of branch flow is considered in the sample generation process. The results show that the proposed deep-learning based method may diagnose system faults effectively.

References

    1. 1)
      • 1. Salehi-Dobakhshari, A., Ranjbar, A.M.: ‘Application of synchronised phasor measurements to wide-area fault diagnosis and location’, IET Gener. Transm. Distrib., 2014, 8, (4), pp. 716729.
    2. 2)
      • 2. Sun, J., Qin, S.Y., Song, Y.H.: ‘Fault diagnosis of electric power systems based on fuzzy petri nets’, IEEE Trans. Power Syst., 2004, 19, (4), pp. 20532059.
    3. 3)
      • 3. Xu, L., Kezunovic, M.: ‘Implementing fuzzy reasoning petri-nets for fault section estimation’, IEEE Trans. Power Deliv., 2008, 23, (2), pp. 676685.
    4. 4)
      • 4. Zhu, Y., Huo, L., Lu, J.: ‘Bayesian networks-based approach for power systems fault diagnosis’, IEEE Trans. Power Deliv., 2006, 21, (2), pp. 634639.
    5. 5)
      • 5. Oliveira, A.L., de Araújo, O.C.B., Cardoso, G., et al: ‘A mixed integer programming model for optimal fault section estimation in power systems’, Int. J. Electr. Power Energy Syst., 2016, 77, pp. 372384.
    6. 6)
      • 6. Chen, W.H., Tsai, S.H., Lin, H.I.: ‘Fault section estimation for power networks using logic cause-effect models’, IEEE Trans. Power Deliv., 2011, 26, (2), pp. 963971.
    7. 7)
      • 7. Lee, H.J., Ahn, B.S., Park, Y.M.: ‘A fault diagnosis expert system for distribution substations’, IEEE Trans. Power Deliv., 2000, 15, (1), pp. 9297.
    8. 8)
      • 8. dos Santos Fonseca, W.A., Bezerra, U.H., Nunes, M.V.A., et al: ‘Simultaneous fault section estimation and protective device failure detection using percentage values of the protective devices alarms’, IEEE Trans. Power Syst., 2013, 28, (1), pp. 170180.
    9. 9)
      • 9. Bi, T., Yan, Z., Wen, F., et al: ‘On-line fault section estimation in power systems with radial basis function neural network’, Int. J. Electr. Power Energy Syst., 2002, 24, (4), pp. 321328.
    10. 10)
      • 10. Cardoso, G., Rolim, J.G., Zürn, H.H.: ‘Application of neural-network modules to electric power system fault section estimation’, IEEE Trans. Power Deliv., 2004, 19, (3), pp. 10341041.
    11. 11)
      • 11. LeCun, Y., Bengio, Y., Hinton, G.: ‘Deep learning’, Nature, 2015, 521, pp. 436444.
    12. 12)
      • 12. Hinton, G.E., Salakhutdinov, R.R.: ‘Reducing the dimensionality of data with neural networks’, Science, 2006, 313, (5786), pp. 504507.
    13. 13)
      • 13. Schmidhuber, J.: ‘Deep learning in neural networks: an overview’, Neural Netw., 2015, 61, pp. 85117.
    14. 14)
      • 14. Shi, H., Xu, M., Li, R.: ‘Deep learning for household load forecasting–a novel pooling deep RNN’, IEEE Trans. Smart Grid, 2017, PP, doi: 10.1109/TSG.2017.2686012.
    15. 15)
      • 15. Mocanu, E., Nguyen, P.H., Gibescu, M., et al: ‘Deep learning for estimating building energy consumption’, Sustain. Energy Grids Netw., 2016, 6, pp. 9199.
    16. 16)
      • 16. Varga, E.D., Beretka, S.F., Noce, C., et al: ‘Robust real-time load profile encoding and classification framework for efficient power systems operation’, IEEE Trans. Power Syst., 2015, 30, (4), pp. 18971904.
    17. 17)
      • 17. Nguyen, V.N., Jenssen, R., Roverso, D.: ‘Automatic autonomous vision-based power line inspection: a review of current status and the potential role of deep learning’, Int. J. Electr. Power Energy Syst., 2018, 99, pp. 107120.
    18. 18)
      • 18. Zheng, Z., Yang, Y., Niu, X., et al: ‘Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids’, IEEE Trans. Ind. Inf., 2018, 14, (4), pp. 16061615.
    19. 19)
      • 19. Mocanu, E., Mocanu, D.C., Nguyen, P.H.: ‘On-line building energy optimization using deep reinforcement learning’, IEEE Trans. Smart Grid, 2018, PP, doi: 10.1109/TSG.2018.2834219.
    20. 20)
      • 20. López, K.L., Gagné, C., Gardner, M.A.: ‘Demand-side management using deep learning for smart charging of electric vehicles’, IEEE Trans. Smart Grid, 2018, PP, doi: 10.1109/TSG.2018.2808247.
    21. 21)
      • 21. He, Y., Mendis, G.J., Wei, J.: ‘Real-time detection of false data injection attacks in smart grid: a deep learning-based intelligent mechanism’, IEEE Trans. Smart Grid, 2017, 8, (5), pp. 25052516.
    22. 22)
      • 22. Kong, W., Dong, Z.Y., Hill, D.J., et al: ‘Short-term residential load forecasting based on resident behaviour learning’, IEEE Trans. Power Syst., 2018, 33, (1), pp. 10871088.
    23. 23)
      • 23. Silver, D., Schrittwieser, J., Simonyan, K., et al: ‘Mastering the game of go without human knowledge’, Nature, 2017, 550, pp. 354359.
    24. 24)
      • 24. Mnih, V., Kavukcuoglu, K., Silver, D., et al: ‘Human-level control through deep reinforcement learning’, Nature, 2015, 518, pp. 529533.
    25. 25)
      • 25. Imagenet database’. Available at http://www.image-net.org.
    26. 26)
      • 26. Deep learn toolbox’. Available at https://github.com/rasmusbergpalm/DeepLearnToolbox.
    27. 27)
      • 27. Neural networks and deep learning’. Available at http://neuralnetworksanddeeplearning.com.
    28. 28)
      • 28. Anderson, P.M., Fouad, A.A.: ‘Power system control and stability’ (The Iowa State University Press, Ames, 1977).
    29. 29)
      • 29. scikit-learn’. Available at http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2018.5254
Loading

Related content

content/journals/10.1049/iet-gtd.2018.5254
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address