Salp swarm algorithm and phasor measurement unit based hybrid robust neural network model for online monitoring of voltage stability

  • A. Nageswara Rao
  • P. VijayapriyaEmail author


Incessant assessment of voltage stability is lively aspect to safeguard the electrical power system operation. The traditional methods for online assessment in terms of voltage stability analysis are extremely time consuming and also difficult for supervising it in online. In connection to this a novel model based on Salp swarm algorithm and artificial neural network (SSA–ANN) is considered for online monitoring of voltage stability in this manuscript. As we know that ANN is an influential and promising predictive tool to attain the efficacy and accuracy in terms of training and testing time. The input for model is the PMU data and the output for the model is voltage stability margin index which is used for voltage stability monitoring. SSA is used for tuning the Meta parameters such as the activation functions and number of nodes along with the learning rate. The solution method opted for this stability monitoring utilises the magnitude of voltage and its corresponding phase angle which are attained from the PMU as the inputs to the neural network model. The efficiency of the proposed model is verified by means of various test cases and compared with the same data set to attest it’s pre-eminence.


Artificial neural network (ANN) Salp swarm algorithm (SSA) Phasor measurement units (PMU) Voltage stability monitoring index (VSMI) 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Vladimir, T., et al. (2011). Wide-area monitoring, protection, and control of future electric power networks. Proceedings of the IEEE, 99(1), 80–93.CrossRefGoogle Scholar
  2. 2.
    Anandakumar, H., & Umamaheswari, K. (2017). An efficient optimized handover in cognitive radio networks using cooperative spectrum sensing. Intelligent Automation & Soft Computing. CrossRefGoogle Scholar
  3. 3.
    IEEE, CIGRE Joint Task Force on Stability Terms and Definitions. (2004). Definition and classification of power system stability. IEEE Transactions on Power Systems, 19(2), 1387–1401.Google Scholar
  4. 4.
    Van Cutsem, T., & Mailhot, R. (1997). Validation of a fast voltage stability analysis method on the Hydro-Qubec System. IEEE Transactions on Power Systems, 12, 282–292.CrossRefGoogle Scholar
  5. 5.
    Ainsworth, J. D., Gavrilovic, A., & Thanawala, H. L. (1980). Static and synchronous compensators for HVDC transmission convertors connected to weak systems. In 28th session CIGRE (pp. 31–01).Google Scholar
  6. 6.
    Atputharajah, A., & Saha, T. K. (2009). Power system blackouts—Literature review. In: Fourth international conference on industrial and information systems (pp. 460–465).Google Scholar
  7. 7.
    Ilic, M., et al. (2005). Special issue on power technology and policy: Fourty years after the 1965 Blackout. In: Proceedings of the IEEE.Google Scholar
  8. 8.
    U.S.—Canada Power System Outage Task Force. (2004). Final Report on the August 14, 2003 Blackout in the United States and Canada: Causes and Recomendations.Google Scholar
  9. 9.
    Galiana, F. D. (1984). Load flow feasibility and the voltage collapse problem. In IEEE proceedings of 23rd conference on control and design (pp. 485–487).Google Scholar
  10. 10.
    Kundur, P. (1994). Power system stability and control. Surrey, BC: McGraw-Hill Inc.Google Scholar
  11. 11.
    Gao, B., Morison, G. K., & Kundur, P. (1992). Voltage stability evaluation using modal analysis. Transactions on Power Systems, 7(4), 1529–1542.CrossRefGoogle Scholar
  12. 12.
    Tamura, Y., Mori, H., & Iwamoto, S. (1983). Relationship between voltage instability and multiple load flow solutions in electrtic power system. IEEE Transactions on PAS, 5, 1115–1125.CrossRefGoogle Scholar
  13. 13.
    Yorion, N., et al. (1992). An investigation of voltage instability problem. Transactions on Power Systems, 7(2), 600–611.CrossRefGoogle Scholar
  14. 14.
    Löf, P.-A., Andersson, G., & Hill, D. J. (1993). Voltage stability indices for stressed power systems. IEEE Transactions on Power Systems, 8, 326–335.CrossRefGoogle Scholar
  15. 15.
    Li-Jun, C., & Istavan, E. (2007). Power system static voltage stability analysis considering all active and reactive power controls- singular value approach. In POWERTECH.Google Scholar
  16. 16.
    Haque, M. H., & Pothula, U. M. R. (2004). Evaluation of dynamic voltage stability of a power system. In International conference on power system technologyPOWERCON. Google Scholar
  17. 17.
    Chow, J. H., & Gebreselassie, A. (1990). Dynamic voltage stability analysis of a single machine constant power load system. In Proceedings of the 29th conference on decision and control.Google Scholar
  18. 18.
    Hasani, M., & Parniani, M. (2005). Method of combined static and dynamic analysis of voltage collapse in voltage stability assessment. In IEEE/PES transmission and distribution conference & exhibition: Asia and Pacific.Google Scholar
  19. 19.
    Zeng, Y. G., Berizzi, A., & Marannino, P. (1997). Voltage stability analysis considering dynamic load model. In Proceedings of the 4th international conference on advances in power system control, operation and management, APSCOM.Google Scholar
  20. 20.
    Morison, G. K., Gao, B., & Kundar, P. (1993). Voltage stability analysis using static and dynamic approaches. IEEE Transactions on Power Systems, 8(3), 1159–1171.CrossRefGoogle Scholar
  21. 21.
    Nageswara Rao, A., Vijaya, P., & Kowsalya, M. (2018). Voltage stability indices for stability assessment: A review. International Journal of Ambient Energy. Scholar
  22. 22.
    Ajjarapu, V., & Christy, C. (1992). The continuation power flow: A tool for steady state voltage stability analysis. IEEE Transactions on Power Systems, 7(1), 416–423.CrossRefGoogle Scholar
  23. 23.
    Lee, C. Y., Tsai, S. H., & Wu, Y. K. (2010). A new approach to the assessment of steady-state voltage stability margins using the P–Q–V curve. International Journal of Electrical Power & Energy Systems, 32, 1091–1098.CrossRefGoogle Scholar
  24. 24.
    Jain, A. K., Duin, R. P. W., & Mao, J. (2000). Statistical pattern recognition: A review. IEEE Transactions on Pattern Recognition And Machine Intelligence, 22(1), 4–37.CrossRefGoogle Scholar
  25. 25.
    Diao, R., et al. (2009). Decision tree assisted controlled islanding for preventing cascading events. In Power systems conference and exposition, PSCE IEEE/PES (pp. 1–8).Google Scholar
  26. 26.
    Moulin, L. S., Silva, A P Ad, El-Sharkawi, M. A., & Marks, R. J. (2004). Support vector machines for transient stability analysis of large-scale for power systems. IEEE Transactions on Power Systems, 19(2), 818–825.CrossRefGoogle Scholar
  27. 27.
    Sun, K., Likhate, S., Vittal, V., Kolluri, V. S., & Mandal, S. (2007). An online dynamic security assessment scheme using phasor measurements and decision trees. IEEE Transactions on Power Systems, 22(4), 1935–1943.CrossRefGoogle Scholar
  28. 28.
    Anandakumar, H., & Umamaheswari, K. (2017). Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers. Cluster Computing, 20(2), 1505–1515.CrossRefGoogle Scholar
  29. 29.
    Genc, I., Diao, R., & Vittal, V. (2010). Computation of transient security related security regions and generation rescheduling based on decision trees. In Power and energy society general meeting (pp. 1–6).Google Scholar
  30. 30.
    Zhou, D. Q., Annakkage, U. D., & Rajapakse, A. D. (2010). Online monitoring of voltage stability margin using an artificial neural network. IEEE Transactions on Power Systems, 25(3), 1566–1574.CrossRefGoogle Scholar
  31. 31.
    Devaraj, D., & Roselyn, J. P. (2011). On-line voltage stability assessment using radial basis function network model with reduced input features. International Journal of Electrical Power & Energy Systems, 33, 1550–1555.CrossRefGoogle Scholar
  32. 32.
    Hashemi, S., & Aghamohammadi, M. R. (2013). Wavelet based feature extraction of voltage profile for online voltage stability assessment using RBF neural network. International Journal of Electrical Power & Energy Systems, 49, 86–94.CrossRefGoogle Scholar
  33. 33.
    Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 1(114), 163–191.CrossRefGoogle Scholar
  34. 34.
    Anandakumar, H., & Umamaheswari, K. (2018). A bio-inspired swarm intelligence technique for social aware cognitive radio handovers. Computers & Electrical Engineering, 71, 925–937.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.School of Electrical EngineeringVellore Institute of TechnologyVelloreIndia

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