Real-time Stability Assessment of Power System using ANN without Requiring Expert Experience

  • Hamid Ghadiri Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
Keywords: : Power System Stability, Transient Stability Limit, Steady-State Stability Limit, Security Margin

Abstract

Nowadays, power systems should be operated in the highest level of utilization and near their stability limits because of economic reasons. So stability assessment of the power system to determine the stability limits has been always considered. In SCADA/EMS systems a constant value called security margin and steady-state stability limit are used to determine transient stability limit instead of time-domain simulation. The security margin that is almost constant for power systems is determined experimentally. In this article this constant is computed using a probabilistic neural network and this method is implemented on IEEE 39 bus. As a result, the performance of this neural network is suitable for this application.

References

[1] Savulescu, S.C., “Real-Time Stability Assessment In Modern Power System Control Centers”, ed. M.E. El-Hawary. 2009: Wiley. 425.
[2] Ahmed N. Al-Masri, M.Z.A. Ab Kadir, H. Hizam and N. Mariun, “Simulation of an Adaptive Artificial Neural Network for Power System Security Enhancement Including Control Action”, Applied Soft Computing Journal, Elsevier, 2015: p.1-11.
[3] Atanackovic, D., Clapauch, J.H., Dwernychuk, G., Gurney, J., and Lee,H., “first steps to wide area control”, IEEE, 2008: p. 61-68.
[4] S. Kalyani and K. SHanti Swarup, “Study Of Neural Network Models For Security Assessment In Power Systems”, International Journal of Research and Reviews in Applied Sciences, 2009: p. 104-117.
[5] S.D. Naik, M.K. Khedkar, S.S. Bhat, “Effect of line contingency on static voltage stability and maximum loadability in large multi bus power system”, International Journal of Electrical Power & Energy Systems, Elsevier, 2015:p.448-452.
[6] Savu C. Savulescu., “Fast Computation of Steady State Stability Limits for Real time and Offline Applications”, 7th International Workshop on Electric Power Control Centers. 2003.
[7] M.Aghamohammadi and M. Ali, “Real time Dynamic Stability Assessment of power systems Using Neural Network”, 20th international electricity conference, 2005.
[8] Ayman Hoballah and István Erlich, “Transient Stability Assessment using ANN Considering Power System Topology Changes”, IEEE, 2009.
[9] S. Jadid and S. Jalilzadeh, “Application of Neural Network for Contingency Ranking Based on Combination of Severity Indices”, World Academy Of Science,Engineering And Technology, 2005: p. 173-176.
[10] Mahta Boozari., “State Estimation And Transient Stability Analysis In Power Systems Using Artifitial Neural Networks”, Master Of Applied Science Thesis,Simon Fraser University, 2004.
[11] F.Aboytes and R.Ramirez, “Transient Stability Assessment In Longitudinal Power Systems Using Artificial Neural Networks”, IEEE Transactions on Power Systems, Vol. 11, No.4, 1996, p. 2003–2010.
[12] V.Miranda, J.N.Fidalgo, J.A.Pecas Lopes and L.B.Almeida, “Real Time Preventive Actions for Transient Stability Enhancement with a Hybrid Neural Network Optimization Approach”, IEEE-PES 1994 Summer Meeting, 1994.
[13] Hecht-Nielsen, “Theory of the Backpropagation Neural Network”, IEEE International Conference on Neural Networks, Vol. 1, 1989, p. 593-605.
[14] H. Demuth, M.T Hagan and H.M. Beale, Neural Network Toolbox for Use with Matlab, Mathworks, Natick, Mass., 2010.
[15] Anantha Pai, “Energy Function Analysis for Power System Stability”, Springer, 1989: p.223-225.
[16] Yu, James Jian Qiao, et al. “Intelligent Time-Adaptive Transient Stability Assessment System.” IEEE Transactions on Power Systems (2017).
[17] I. Kamwa, S. Samantaray, and G. Joos, “On the accuracy versus transparency trade-off of data-mining models for fast-response pmu based catastrophe predictors,” IEEE Trans. Smart Grid, Vol. 3, No. 1, pp. 152–161, Mar.2012.
Published
2020-06-01
How to Cite
Ghadiri, H. (2020). Real-time Stability Assessment of Power System using ANN without Requiring Expert Experience. Majlesi Journal of Electrical Engineering, 14(2), 43-49. Retrieved from http://mjee.org/index/index.php/ee/article/view/2057
Section
Articles