Abstract
Voltage stability has become an important issue in planning and operation of many power systems. Contingencies such as unexpected line outages in a stressed system may often result in voltage instability, which may lead to voltage collapse. This paper presents evolutionary algorithm techniques like Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm II (NSGA II) approach for solving the Voltage Stability Constrained Optimal Power Flow (VSC-OPF). Base-case generator power output, voltage magnitude of generator buses are taken as the control variables. Maximum L-index of load buses is used to specify the voltage stability level of the system. An improved GA which permits the control variables to be represented in their natural form is proposed to solve the Optimal Power Flow (OPF) problem and NSGA II is proposed to solve the VSC-OPF optimization problem. For effective genetic operation, crossover and mutation operators which can directly operate on floating point numbers and integers are used. The proposed approach has been evaluated on the IEEE 30-bus test system. Simulation results show the effectiveness of the proposed NSGA II approach than Multi-Objective Genetic Algorithm (MOGA) for improving the voltage security of the system.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Alsac, O., Scott, B.: Optimal load flow with steady state security. IEEE Transactions on Power Systems PAS-93(3), 745–751 (1974)
Adel, A., Ela, A.E.L., Spea, S.R.: Optimal Correction Actions for Power Systems using Multi-Objective Genetic Algorithm. Electric Power Systems Research 79, 722–733 (2009)
Wood, A.J., Wollenberg, B.F.: Power Generation Operation and Control. John Wiley & Sons, Inc., NewYork (1996)
Baghaee, H.R., Jannati, M., Vahidi, B., Hosseinian, S.H., Rastegar, H.: Improment of Voltage Stability and Reduce Power System Losses by Optimal GA-Based Allocation of Multi–type FACTS Devices. In: IEEE Conference Publications, pp. 209–214 (2008)
Canizares, C., et al.: Comparison of voltage security constrained optimal power flow techniques. In: Proc. 2001, IEEE-PES Summer Meeting, Vancouver, BC (2001)
Devaraj, D., Yegnanarayana, B.: A combined genetic algorithm approach for optimal power flow. In: National Power Systems Conference, NPSC 2000, Bangalore, India, pp. 1866–1876 (2000)
Devaraj, D., Roselyn, J.P.: Improved genetic algorithm for voltage security constrained optimal power flow problem. Int. J. Energy Technology and Policy 5(4), 475–488 (2007)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Publishing Company, Inc., USA (1989)
Dommel, H.W., Tinney, W.F.: Optimal power flow solutions. IEEE Transactions on Power Apparatus and Systems PAS-87(10), 1866–1876 (1968)
Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation 3(4) (November 1999)
Sadat, H.: Power systems analysis. McGraw Hill Publication, New Delhi (1997)
Stott, B., Hobson, E.: Power system security control calculations using linear Programming. IEEE Transactions on Power Apparatus and Systems, PAS 97, 1713–1931 (1978)
Beyer, H.-G., Deb, K.: On Self-Adaptive Features in Real-Parameter Evolutionary Algorithm. IEEE Transactions on Evolutionary Computation 5(3), 250–270 (2001)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast Elitist Multi-objective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Deb, K., Agarwal, R.B.: Simulated Binary Crossover for Continuous Search Space. Complex Systems 9, 115–148 (1995)
Kessel, P., Glavitsch, H.: Estimating the voltage stability of power systems. IEEE Transactions on Power Delivery 1(3), 346–354 (1986)
Lee, K.Y., Park, Y.M., Ortiz, J.L.: A United Approach to Optimal Real and Reactive Power Dispatch. IEEE Transactions on Power Systems 3, 104 (1986)
Raghuwanshi, M.M., Kakde, O.G.: Survey on multiobjective evolutionary and real coded genetic algorithms. In: Proceedings of the 8th Asia Pacific Symposium on Intelligent and Evolutionary Systems, pp. 150–161 (2004)
Srinivas, N., Deb, K.: Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation 2(3), 221–248 (1994)
Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Technical report, Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, India (1993)
Paranjothi, S.R., Anburaja, K.: Optimal power flow using refined genetic algorithm. Electric Power Components and Systems 30, 1055–1063 (2002)
Fletcher, R.: Practical Methods of Optimisation. John Willey & Sons (1986)
Somasundaram, P., Kuppusamy, K., Kumudini Devi, K.P.: Evolutionary programming based security constrained optimal power flow. Electric Power Systems Research 72, 137–145 (2004)
Bouktir, T., Belkacemi, M., Zehar, K.: Optimal power flow using modified gradient method. In: Proceeding ICEL 2000, U.S.T.Oran, Algeria, November 13-15, vol. 2, pp. 436–442 (2000)
Yuryevich, J., Wang, K.P.: Evolutionary programming based optimal power flow algorithms. IEEE Transactions on Power Systems 14(4), 1245–1250 (1999)
Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P.N., Zhang, Q.: Multiobjective Evolutionary Algorithms: A Survey of the State-of-the-art. Swarm and Evolutionary Computation 1(1), 32–49 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nithya, C., Roselyn, J.P., Devaraj, D., Dash, S.S. (2012). Voltage Stability Constrained Optimal Power Flow Using Non-dominated Sorting Genetic Algorithm-II (NSGA II). In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_93
Download citation
DOI: https://doi.org/10.1007/978-3-642-35380-2_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35379-6
Online ISBN: 978-3-642-35380-2
eBook Packages: Computer ScienceComputer Science (R0)