Heuristic methods for postoutage voltage magnitude calculations

Heuristic methods for postoutage voltage magnitude calculations

Power systems play a significant role in every aspect of our daily lives. Hence, their continuation without any interruption (or with the least duration of interruption due to faults or scheduled maintenances) is one of the key aims of electrical energy providers. As a result, electrical energy providers need to check in great detail the integrity of their power systems by performing regular contingency studies of the equipment involved. Among others, line and transformer outage simulations constitute an integral part of an electrical management system. Both accuracy and calculation speed depend on the branch outage model and/or the solution algorithms applied. In this paper, the local constrained optimization problem of the single-branch outage problem is solved by intelligent methods: particle swarm optimization, differential evolution, and harmony search. Simulations of IEEE 14-, 30-, 118-, and 300-bus systems are computed both by intelligent methods and by AC load flow. The results of the intelligent method-based simulations and AC load flow-based simulations are compared in terms of accuracy and computation speed.

___

  • [1] Wood AJ, Wollenberg BF. Power Generation, Operation and Control. 2nd ed. New York, NY, USA: Wiley, 1996.
  • [2] Lee CY, Chen N. Distribution factors of reactive power flow in transmission line and transformer outage studies. IEEE T Power Syst 1992; 7: 194–200.
  • [3] Ilic-Spong M, Phadke A. Redistribution of reactive power flow in contingency analysis. IEEE T Power Syst 1986; 1: 266–274.
  • [4] Taylor DG, Maahs LJ. A reactive contingency analysis algorithm using MW and MVAR distribution factors. IEEE T Power Syst 1991; 6: 349–355.
  • [5] Jasmon GB, Amin RM, Chuan CY. Performance comparison of two exact outage simulation techniques. IEE Proc-C 1985; 132: 285–293.
  • [6] Iwamoto S, Tamura Y. A fast load flow retaining nonlinearity. IEEE T Power Ap Syst 1978; 97: 1586–1599.
  • [7] Krishnaparandhama T, Elangovan S, Kuppurajulu A, Sankaranarayanan V. Fast power flow solution by the method of reduction and restoration. IEE Proc-C 1980; 127: 90–93.
  • [8] Sachdev MS, Ibrahim SA. A fast approximate technique for outage studies in power system planning and operation, IEEE T Power Ap Syst 1974; 4: 1133–1142.
  • [9] Mamandur KRC, Berg GJ. Efficient simulation of line and transformer outages in power systems. IEEE T Power Syst 1982; 101: 3722–3741.
  • [10] Meng ZJ, Xue Y, Lo KL. A new approximate load flow calculation method for contingency selection. In: IEEE 2006 Power Systems Conference and Exposition; 29 October–9 November 2006; Atlanta, GA, USA. New York, NY,USA: IEEE. pp. 1601–1605.
  • [11] Ruiz AP, Sauer P. Voltage and reactive power estimation for contingency analysis using sensitivities. IEEE T Power Syst 2007; 22: 639–647.
  • [12] Wu ZQ, Hao Z, Yang D. A new MVA sensitivity method for fast accurate contingency evaluation. Int J Elec Power 2012; 38: 1–8.
  • [13] Ozdemir A, Lim YJ, Singh C. Branch outage simulation for MVAR flows: bounded network solution. IEEE T Power Syst 2003; 18: 1523–1528.
  • [14] Ceylan O, Ozdemir A, Dag H. Branch outage solution using particle swarm optimization. In: Proceedings of 2008 Australasian Universities Power Engineering Conference; 14–17 December 2008; Sydney, NSW, Australia. New York, NY, USA: IEEE. pp. 1–5.
  • [15] Ceylan O, Ozdemir A, Dag H. Comparison of post outage bus voltage magnitudes estimated by harmony search and differential evolution methods. In: Proceedings of the 15th International Conference on Intelligent System Applications to Power Systems; 8–12 November 2009; Curitiba, Brazil. pp. 1–6.
  • [16] Kennedy J, Eberhart R. Particle swarm optimization. In: IEEE 1995 International Conference on Neural Networks; 27 November–1 December 1995; Perth, WA, Australia. New York, NY, USA: IEEE. pp. 1942–1948.
  • [17] Pancholi RK, Swarup KS. Particle swarm optimization for security constrained economic dispatch. In: Proceedings of the 2004 International Conference on Intelligent Sensing and Information Processing; 4–7 January 2004; Chennai,India. New York, NY, USA: IEEE. pp. 7–12.
  • [18] Naka S, Genji T, Yura T, Fukuyama Y. A hybrid particle swarm optimization for distribution state estimation. IEEE T Power Syst 2003; 18: 60–68.
  • [19] Abido MA. Optimal power flow using particle swarm optimization. Int J Elec Power 2002; 24: 563–571.
  • [20] Storn R, Price K. Minimizing the real functions of the ICEC’96 contest by differential evolution. In: IEEE 1996 International Conference on Evolutionary Computation; 20–22 May 1996; Nagoya, Japan. New York, NY, USA:IEEE. pp. 842–844.
  • [21] Yang YG, Dong ZY, Wong KP. A modified differential evolution algorithm with fitness sharing for power system planning. IEEE T Power Syst 2008; 23: 514–522.
  • [22] Cai HR, Chung CY, Wong KP. Application of differential evolution algorithm for transient stability constrained optimal power flow. IEEE T Power Syst 2008; 23: 719–728.
  • [23] Kannan S, Slochanal SMR, Baskar S, Murugan P. Application and comparison of metaheuristic techniques to generation expansion planning in partially deregulated environment. IET Gener Transm Dis 2007; 1: 111–118.
  • [24] Patra S, Goswami SK, Goswami B. A binary differential evolution algorithm for transmission and voltage constrained unit commitment. In: POWERCON 2008 Joint International Conference on Power System Technology and IEEE Power India Conference; 12–15 October 2008; New Delhi, India. New York, NY, USA: IEEE. pp. 1–8.
  • [25] Goh SH, Dong ZY, Sana TK. Locating voltage collapse points using evolutionary computation techniques. In: IEEE 2007 Congress on Evolutionary Computation; 25–28 September 2007; Singapore, Singapore. New York, NY, USA:IEEE. pp. 2923–2930.
  • [26] Zhang X, Chen W, Dai C, Guo A. Self-adaptive differential evolution algorithm for reactive power optimization. In: Proceedings of the 4th International Conference on Natural Computation; 18–20 October 2008; Jinan, China.New York, NY, USA: IEEE. pp. 560–564.
  • [27] P´erez-Guerrero RE, Cedeno-Maldonado JR. Economic power dispatch with non-smooth cost functions using differential evolution. In: Proceedings of the 37th Annual North American Power Symposium; 23–25 October 2005;Ames, IA, USA. New York, NY, USA: IEEE. pp. 183–190.
  • [28] Manoharan PS, Kannan PS, Baskar S, Iruthayarajan MW. Penalty parameter-less constraint handling scheme based evolutionary algorithm solutions to economic dispatch. IET Gener Transm Dis 2008; 2: 478–490.
  • [29] Geem ZW, Kim JH, Loganathan GV. A new heuristic optimization algorithm: harmony search. Simulation 2001; 76: 60–68.
  • [30] Lee KS, Geem ZW. A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Method Appl M 2005; 194: 3902–3933.
  • [31] Vasebi A, Fesanghary M, Bathaee SMT. Combined heat and power economic dispatch by harmony search algorithm. Int J Elec Power 2007; 29: 713–719.
  • [32] Kazemi A, Parizad A, Baghaee HR. On the use of harmony search algorithm in optimal placement of facts devices to improve power systems security. In: EUROCON 2009; 18–23 May 2009; Saint Petersburg, Russia. New York,NY, USA: IEEE. pp. 570–576.
  • [33] Kazali AH, Kalantar M. Optimal reactive power dispatch based on harmony search algorithm. Int J Elec Power 2011; 33: 684–692.
  • [34] Zimmerman RD, Murillo-S´anchez CE, Thomas RJ. MATPOWER’s extensible optimal power flow architecture. In: IEEE 2009 Power and Energy Society General Meeting; 26–30 July 2009; Calgary, AB, Canada. New York, NY,USA: IEEE. pp. 1–7.
  • [35] Eberhart RC, Shi C. Computational Intelligence: Concepts to Implementations. 1st ed. Burlington, MA, USA:Elsevier, 2007.
Turkish Journal of Electrical Engineering and Computer Sciences-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK
Sayıdaki Diğer Makaleler

A new algorithm of parameter estimation of a photovoltaic solar panel

Mustapha BELARBI, Amine STAMBOULI BOUDGHENE, El-Habib BELARBI, Kamel HADDOUCHE

Analysis of active power control algorithms of variable speed wind generators for power system frequency stabilization

Elvisa BECIROVIC, Mirza KUSLJUGIC, Jakub OSMIC, Daniel TOAL

A robust estimator-based optimal algebraic approach to steam generator feedwater control system

Günyaz ABLAY

Modeling based on 3D finite element analysis and experimental study of a 24-slot 8-pole axial-flux permanent-magnet synchronous motor for no cogging torque and sinusoidal back-EMF

Mehmet GÜLEÇ, Ersin YOLAÇAN, Oğuzhan OCAK, Metin AYDIN, Yücel DEMİR

Highly efficient three-phase three-level multilevel inverter employing different commutation strategies

Saad MEKHILEF, Ammar MASAOUD, Hew Wooi PING2, Hamza BELKAMEL

Heuristic sample reduction method for support vector data description

Feng GAO, Wenzhu SUN, Jianling QU, Yazhou DI, Yang CHEN

PAPR reduction using genetic algorithm in lifting-based wavelet packet modulation systems

Necmi TAŞPINAR, Yüksel BOZKURT TOKUR

Optimal power flow with SVC devices by using the artificial bee colony algorithm

Ali AKDAĞLI, Kadir ABACI, Volkan YAMAÇLI

Towards a semantic-based information extraction system for matching résumés to job openings

Duygu ÇELİK

The classification of EEG signals using discretization-based entropy and the adaptive neuro-fuzzy inference system

Mahmut HEKİM