Abstract
Very often, voltage instability causes millions of money to cater with the negative effects it gave to the people; therefore, it is very important that prior to this worst situation, some manual or automatic recovery system had to be turn on to minimize or totally avoid the entire situation. But these recovery systems will not be turn on if there is no good indication or alarm that controls or informs. In this paper, evolved intelligent clustered artificial bee colony (EICBC) is introduced to predict the voltage stability condition of the IEEE 30-bus test system. Fast Voltage Stability Index is utilized as an indicator to measure the distance of the power system network to the voltage collapse point when the reactive load is varied slowly as reactive load gives the most impact on the stability of the system. EICBC is able to converge faster to its best solution while maintaining the stability of the prediction system by avoiding local minima convergence. The results also show that the proposed algorithm is superior in the prediction accuracy and can be used to categorize the conditions of the network for the ease of identification.
Similar content being viewed by others
References
Abu-Mouti FS, El-Hawary M (2011) Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm. Power Delivery, IEEE Transactions on 26(4):2090–2101
Ajjarapu V, Christy C (1992) The continuation power flow: a tool for steady state voltage stability analysis. Power Systems, IEEE Transactions on 7(1):416–423
Assadian M, Farsangi MM, Nezamabadi-pour H (2010) Gcpso in cooperation with graph theory to distribution network reconfiguration for energy saving. Energy Conversion and Management 51(3):418–427
Balamourougan V, Sidhu T, Sachdev M (2004) A technique for real time detection of voltage collapse in power systems. In: Developments in Power System Protection, 2004. Eighth IEE International Conference on, IET, vol 2, pp 639–642
Banks A, Vincent J, Anyakoha C (2007) A review of particle swarm optimization. part i: background and development. Natural Computing 6(4):467–484
Banks A, Vincent J, Anyakoha C (2008) A review of particle swarm optimization. part ii: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications. Natural Computing 7(1):109–124
Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems, vol 4. Oxford University Press, New York
Chakraborty K, De A, Chakrabarti A (2012) Voltage stability assessment in power network using self organizing feature map and radial basis function. Computers & Electrical Engineering 38(4):819–826
Chen HY, Leou JJ (2012) Saliency-directed color image interpolation using artificial neural network and particle swarm optimization. Journal of Visual Communication and Image Representation 23(2):343–358
Chen MY, Chen DR, Fan MH, Huang TY (2013a) International transmission of stock market movements: an adaptive neuro-fuzzy inference system for analysis of taiex forecasting. Neural Computing and Applications 23(1):369–378
Chen MY, Fan MH, Chen YL, Wei HM (2013b) Design of experiments on neural network’s parameters optimization for time series forecasting in stock markets. Neural Network World 4(13):369–393
Demiroren A, Guleryuz M (2011) Pso algorithm-based optimal tuning of statcom for voltage control in a wind farm integrated system. In: Electrical and Electronics Engineering (ELECO), 2011 7th International Conference on, IEEE, pp I-367
Eberhart RC, Shi Y (2011) Computational intelligence: concepts to implementations. Access Online via Elsevier
Engelbrecht AP (2007) Computational intelligence: an introduction. Wiley. com
Flatabo N, Ognedal R, Carlsen T (1990) Voltage stability condition in a power transmission system calculated by sensitivity methods. Power Systems, IEEE Transactions on 5(4):1286–1293
Gao B, Morison G, Kundur P (1992) Voltage stability evaluation using modal analysis. Power Systems, IEEE Transactions on 7(4):1529–1542
Hemamalini S, Simon SP (2010) Artificial bee colony algorithm for economic load dispatch problem with non-smooth cost functions. Electric Power Components and Systems 38(7):786–803
Izzri N, Mehdi OH, Abdalla AN, Jaber AS, Shalash NA, Lafta YN (2011) Fast prediction of power transfer stability index based on radial basis function neural network. International Journal of Physical Sciences 6(35):7978–7984
Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10(3):215–236
Kamalasadan S, Thukaram D, Srivastava A (2009) A new intelligent algorithm for online voltage stability assessment and monitoring. International Journal of Electrical Power & Energy Systems 31(2):100–110
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Tech. rep., Technical report-tr06, Erciyes university, engineering faculty, computer engineering department
Karaboga D, Akay B (2009) A survey: algorithms simulating bee swarm intelligence. Artificial Intelligence Review 31(1–4):61–85
Karaboga D, Akay B (2011) A modified artificial bee colony (abc) algorithm for constrained optimization problems. Applied Soft Computing 11(3):3021–3031
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. Journal of global optimization 39(3):459–471
Kennedy JF, Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufmann
Kessel P, Glavitsch H (1986) Estimating the voltage stability of a power system. Power Delivery, IEEE Transactions on 1(3):346–354
Khajeh M, Kaykhaii M, Sharafi A (2013) 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
Klaric M, Kuzle I, Tesnjak S (2004) Undervoltage load shedding using global voltage collapse index. Power Systems Conference and Exposition, 2004. IEEE PES, IEEE, pp 453–459
Kundur P, Paserba J, Ajjarapu V, Andersson G, Bose A, Canizares C, Hatziargyriou N, Hill D, Stankovic A, Taylor C et al (2004) Definition and classification of power system stability ieee/cigre joint task force on stability terms and definitions. Power Systems, IEEE Transactions on 19(3):1387–1401
Lof PA, Smed T, Andersson G, Hill D (1992) Fast calculation of a voltage stability index. Power Systems, IEEE Transactions on 7(1):54–64
Moghavvemi M, Faruque M (2001) Technique for assessment of voltage stability in ill-conditioned radial distribution network. Power Engineering Review, IEEE 21(1):58–60
Moghavvemi M, Omar F (1998) Technique for contingency monitoring and voltage collapse prediction. IEE Proceedings-Generation, Transmission and Distribution 145(6):634–640
Momoh J, Dias L, Adapa R (1996) 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, IEEE, pp 410–415
Muriithi C, Ngoo L, Nyakoe G, Njoroge S (2012) Voltage stability analysis using a modified continuation load flow and optimal capacitor bank placement. Journal of Agriculture. Science and Technology 13(2):
Musirin I, Abdul Rahman T (2002) Novel fast voltage stability index (fvsi) for voltage stability analysis in power transmission system. In: Research and Development, 2002. SCOReD 2002. Student Conference on, IEEE, pp 265–268
Musirin I, Rahman TA (2002) 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, IEEE, vol 2, pp 1118–1123
Randhawa M, Sapkota B, Vittal V, Kolluri S, Mandal S (2008) 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, IEEE, pp 1–7
Rashidi M, Ali M, Freidoonimehr N, Nazari F (2013) 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
Reichl J, Schmidthaler M, Schneider F (2013) The value of supply security: The costs of power outages to austrian households, firms and the public sector. Energy Economics 36:256–261
Rosehart WD, Cañizares CA (1999) Bifurcation analysis of various power system models. International Journal of Electrical Power & Energy Systems 21(3):171–182
Sauer P, Pai M (1990) Power system steady-state stability and the load-flow jacobian. Power Systems, IEEE Transactions on 5(4):1374–1383
Saxena A, Verma N, Tripathi K (2013) A review study of weather forecasting using artificial neural network approach. In: International Journal of Engineering Research and Technology, ESRSA Publications, vol 2
Sode-Yome A, Mithulananthan N, Lee KY (2006) A maximum loading margin method for static voltage stability in power systems. Power Systems, IEEE Transactions on 21(2):799–808
Taylor CW (1999) Improving grid behaviour. Spectrum, IEEE 36(6):40–45
Verma K, Niazi K (2012) Supervised learning approach to online contingency screening and ranking in power systems. International Journal of Electrical Power & Energy Systems 38(1):97–104
Wiszniewski A (2007) New criteria of voltage stability margin for the purpose of load shedding. Power Delivery, IEEE Transactions on 22(3):1367–1371
Yazdanpanah A, Asghari R (2007) A novel line stability index (nlsi) for voltage stability assessment of power systems. Proceedings of 7th International Conference on Power Syatems (WSEAS). Beijing, China, pp 164–167
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by V. Loia.
Appendix
Appendix
The IEEE 30-bus test system (Fig. 11).
Rights and permissions
About this article
Cite this article
Lim, Z.J., Mustafa, M.W. Evolved intelligent clustered bee colony for voltage stability prediction on power transmission system. Soft Comput 20, 3215–3230 (2016). https://doi.org/10.1007/s00500-015-1697-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-015-1697-2