Advanced SearchSearch Tips
Voltage Stability Prediction on Power System Network via Enhanced Hybrid Particle Swarm Artificial Neural Network
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Voltage Stability Prediction on Power System Network via Enhanced Hybrid Particle Swarm Artificial Neural Network
Lim, Zi-Jie; Mustafa, Mohd Wazir; Jamian, Jasrul Jamani;
  PDF(new window)
Rapid development of cities with constant increasing load and deregulation in electricity market had forced the transmission lines to operate near their threshold capacity and can easily lead to voltage instability and caused system breakdown. To prevent such catastrophe from happening, accurate readings of voltage stability condition is required so that preventive equipment and operators can execute security procedures to restore system condition to normal. This paper introduced Enhanced Hybrid Particle Swarm Optimization algorithm to estimate the voltage stability condition which utilized Fast Voltage Stability Index (FVSI) to indicate how far or close is the power system network to the collapse point when the reactive load in the system increases because reactive load gives the highest impact to the stability of the system as it varies. Particle Swarm Optimization (PSO) had been combined with the ANN to form the Enhanced Hybrid PSO-ANN (EHPSO-ANN) algorithm that worked accurately as a prediction algorithm. The proposed algorithm reduced serious local minima convergence of ANN but also maintaining the fast convergence speed of PSO. The results show that the hybrid algorithm has greater prediction accuracy than those comparing algorithms. High generalization ability was found in the proposed algorithm.
Voltage stability;Fast Voltage Stability Index;artificial neural network;particle swarm optimization;back propagation artificial neural network;prediction;gradient descend;
 Cited by
P. Kundur, Power system stability and control. Tata McGraw- Hill Education, 1994.

M. Randhawa, B. Sapkota, V. Vittal, S. Kolluri, and S. Mandal, “Voltage stability assessment of a large power system,” in Power and Energy Society General Meeting- Conversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE, pp. 1-7, IEEE, 2008.

C. W. Taylor, “Improving grid behaviour,” Spectrum, IEEE, vol. 36, no. 6, pp. 40-45, 1999.

M. Klaric, I. Kuzle, and S. Tesnjak, “Undervoltage load shedding using global voltage collapse index,” in Power Systems Conference and Exposition, 2004. IEEE PES, pp. 453-459, IEEE, 2004.

P. Kundur, J. Paserba, V. Ajjarapu, G. Andersson, A. Bose, C. Canizares, N. Hatziargyriou, D. Hill, A. Stankovic, C. Taylor, et al., “Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions,” Power Systems, IEEE Transactions on, vol. 19, no. 3, pp. 1387-1401, 2004. crossref(new window)

V. Venikov, V. Stroev, V. Idelchick, and V. Tarasov, “Estimation of electrical power system steady-state stability in load low calculations,” Power Apparatus and Systems, IEEE Transactions on, vol. 94, no. 3, pp. 1034-1041, 1975. crossref(new window)

P.-A. Lof, T. Smed, G. Andersson, and D. Hill, “Fast calculation of a voltage stability index,” Power Systems, IEEE Transactions on, vol. 7, no. 1, pp. 54-64, 1992.

B. Gao, G. Morison, and P. Kundur, “Voltage stability evaluation using modal analysis,” Power Systems, IEEE Transactions on, vol. 7, no. 4, pp. 1529-1542, 1992. crossref(new window)

N. Flatabo, R. Ognedal, and T. Carlsen, “Voltage stability condition in a power transmission system calculated by sensitivity methods,” Power Systems, IEEE Transactions on, vol. 5, no. 4, pp. 1286-1293, 1990. crossref(new window)

A. Semlyen, B. Gao, and W. Janischewskyj, “Calculation of the extreme loading condition of a power system for the assessment of voltage stability,” Power Systems, IEEE Transactions on, vol. 6, no. 1, pp. 307-315, 1991. crossref(new window)

V. Balamourougan, T. Sidhu, and M. Sachdev, “A technique for real time detection of voltage collapse in power systems,” in Developments in Power System Protection, 2004. Eighth IEE International Conference on, vol. 2, pp. 639-642, IET, 2004.

V. Ajjarapu and C. Christy, “The continuation power flow: a tool for steady state voltage stability analysis,” Power Systems, IEEE Transactions on, vol. 7, no. 1, pp. 416-423, 1992.

C. Muriithi, L. Ngoo, G. Nyakoe, and S. Njoroge, “Voltage stability analysis using a modified continuation load flow and optimal capacitor bank placement,” Journal of Agriculture, Science and Technology, vol. 13, no. 2, 2012.

I. Musirin and T. Abdul Rahman, “Novel fast voltage stability index (fvsi) for voltage stability analysis in power transmission system,” in Research and Development, 2002. SCOReD 2002. Student Conference on, pp. 265-268, IEEE, 2002.

M. Moghavvemi and F. Omar, “Technique for contingency monitoring and voltage collapse prediction,” IEE Proceedings-Generation, Transmission and Distribution, vol. 145, no. 6, pp. 634-640, 1998. crossref(new window)

M. Moghavvemi and M. Faruque, “Technique for assessment of voltage stability in ill-conditioned radial distribution network,” Power Engineering Review, IEEE, vol. 21, no. 1, pp. 58-60, 2001.

A. Yazdanpanah-Goharrizi and R. Asghari, “A novel line stability index (NLSI) for voltage stability assessment of power systems,” in Proceedings of 7th International Conference on Power Systems (WSEAS), Beijing, China, pp. 164-167, 2007.

P. Kessel and H. Glavitsch, “Estimating the voltage stability of a power system,” Power Delivery, IEEE Transactions on, vol. 1, no. 3, pp. 346-354, 1986. crossref(new window)

I. Musirin and T. A. Rahman, “On-line voltage stability based contingency ranking using fast voltage stability index (FVSI),” in Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. IEEE/PES, vol. 2, pp. 1118-1123, IEEE, 2002.

A. P. Engelbrecht, Computational intelligence: An introduction second edition. John Wiley & Sons Ltd, 2007.

R. C. Eberhart and Y. Shi, Computational intelligence: Concepts to implementations. Morgan Kaufmann Publishers, 2007.

J. Momoh, L. Dias, and R. Adapa, “Voltage stability assessment and enhancement using artificial neural networks and reactive compensation,” in Intelligent Systems Applications to Power Systems, 1996. Proceedings, ISAP’96., International Conference on, pp. 410-415, IEEE, 1996.

N. Izzri, O. H. Mehdi, A. N. Abdalla, A. S. Jaber, N. A. Shalash, and Y. N. Lafta, “Fast prediction of power transfer stability index based on radial basis function neural network,” International Journal of the Physical Sciences, Vol. 6(35), pp. 7978-7984, 2011.

K. Chakraborty, A. De, and A. Chakrabarti, “Voltage stability assessment in power network using self organizing feature map and radial basis function,” Computers & Electrical Engineering, vol. 38, no. 4, pp. 819-826, 2012. crossref(new window)

S. Kamalasadan, D. Thukaram, and A. Srivastava, “A new intelligent algorithm for online voltage stability assessment and monitoring,” International Journal of Electrical Power & Energy Systems, vol. 31, no. 2, pp. 100-110, 2009. crossref(new window)

K. Verma and K. Niazi, “Supervised learning approach to online contingency screening and ranking in power systems,” International Journal of Electrical Power & Energy Systems, vol. 38, no. 1, pp. 97-104, 2012. crossref(new window)

E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm intelligence: From natural to artificial systems, vol. 4. Oxford University Press New York, 1999.

J. F. Kennedy, J. Kennedy, and R. C. Eberhart, Swarm intelligence. Morgan Kaufmann, 2001.

A. Banks, J. Vincent, and C. Anyakoha, “A review of particle swarm optimization. Part I: Background and development,” Natural Computing, vol. 6, no. 4, pp. 467-484, 2007. crossref(new window)

A. Banks, J. Vincent, and C. Anyakoha, “A review of particle swarm optimization. Part II: Hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications,” Natural Computing, vol. 7, no. 1, pp. 109-124, 2008. crossref(new window)

A. Demiroren and M. Guleryuz, “PSO algorithmbased optimal tuning of statcom for voltage control in a wind farm integrated system,” in Electrical and Electronics Engineering (ELECO), 2011 7th International Conference on, pp. 1-367, IEEE, 2011.

M. Assadian, M. M. Farsangi, and H. Nezamabadipour, “GCPSO in cooperation with graph theory to distribution network reconfiguration for energy saving,” Energy Conversion and Management, vol. 51, no. 3, pp. 418-427, 2010. crossref(new window)

W. Nakawiro and I. Erlich, “A combined GA-ANN strategy for solving optimal power flow with voltage security constraint,” in Power and Energy Engineering Conference, 2009. APPEEC 2009. Asia-Pacific, pp. 1-4, IEEE, 2009.

H. Sayyad, A. K. Manshad, and H. Rostami, “Application of hybrid neural particle swarm optimization algorithm for prediction of MMP,” Fuel, vol. 116, pp. 625-633, 2014 crossref(new window)

M. Geethanjali, S. M. R. Slochanal, R. Bhavani, “PSO trained ANN-based differential protection scheme for power transformer,” Neurocomputing, vol. 71, pp. 904-918, 2008. crossref(new window)

M. Khajeh, M. Kaykhaii, and A. Sharafi, “Application of PSO-artificial neural network and response surface methodology for removal of methylene blue using silver nanoparticles from water samples,” Journal of Industrial and Engineering Chemistry, 2013.

H.-Y. Chen and J.-J. Leou, “Saliency-directed color image interpolation using artificial neural network and particle swarm optimization,” Journal of Visual Communication and Image Representation, vol. 23, no. 2, pp. 343-358, 2012. crossref(new window)

M. Rashidi, M. Ali, N. Freidoonimehr, and F. Nazari, “Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm,” Energy, 2013.