Multiple Optimal Solutions and Sag Occurrence Index Based Placement of Voltage Sag Monitors

This study presents optimal placement of voltage sag monitors based on new Sag Occurrence Index (SOI) which ensures observability even in case of monitor failure or line outages. Multiple solutions for optimal placement of voltage sag monitors for voltage sag detection have been obtained by genetic algorithm approach such that observability of the whole system is guaranteed. A new Sag Occurrence Index (SOI) is proposed to obtain the severity of voltage sag at all the buses in the system. To obtain the best monitor arrangement in the system, the sum of SOI for each optimal combination is determined. IEEE 24-bus Reliability Test System (RTS) and IEEE 57-bus system were used to demonstrate the effectiveness of the proposed method. The details of implementation and simulation results are also presented.

[1]  H. Wayne Beaty,et al.  Electrical Power Systems Quality , 1995 .

[2]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[3]  Girish Kumar Singh,et al.  Minimization of voltage sag induced financial losses in distribution systems using FACTS devices , 2011 .

[4]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[5]  M.H.J. Bollen,et al.  An optimal monitoring program for obtaining Voltage sag system indexes , 2006, IEEE Transactions on Power Systems.

[6]  Angelo Baggini,et al.  Handbook of Power Quality , 2008 .

[7]  Chao Ma,et al.  Failure Probability Analysis of Sensitive Equipment Due to Voltage Sags Using Fuzzy-Random Assessment Method , 2010, IEEE Transactions on Power Delivery.

[8]  C. F. M. Almeida,et al.  Allocation of Power Quality Monitors by Genetic Algorithms and Fuzzy Sets Theory , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[9]  Christine M. Anderson-Cook Practical Genetic Algorithms (2nd ed.) , 2005 .

[10]  S. Garcia-Martinez,et al.  Analysis of system operation state influence on the optimal location of voltage sag monitors by applying tabu search , 2010, North American Power Symposium 2010.

[11]  Christopher R. Houck,et al.  A Genetic Algorithm for Function Optimization: A Matlab Implementation , 2001 .

[12]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[13]  J.V. Milanovic,et al.  Probabilistic assessment of financial losses due to interruptions and voltage sags-part I: the methodology , 2006, IEEE Transactions on Power Delivery.

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  Math Bollen,et al.  Understanding Power Quality Problems: Voltage Sags and Interruptions , 1999 .

[16]  Mohammad Shahidehpour,et al.  The IEEE Reliability Test System-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee , 1999 .

[17]  Christine M. Anderson-Cook Practical Genetic Algorithms (2nd ed.): Randy L. Haupt and Sue Ellen Haupt , 2005 .

[18]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[19]  Seung-Il Moon,et al.  Optimal number and locations of power quality monitors considering system topology , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[20]  G. Olguin An Optimal Trade-off between Monitoring and Simulation for Voltage Dip Characterization of Transmission Systems , 2005, 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific.

[21]  G. Olguin,et al.  An Approach Based on Analytical Expressions for Optimal Location of Voltage Sags Monitors , 2009, IEEE Transactions on Power Delivery.

[22]  J.V. Milanovic,et al.  Probabilistic assessment of financial losses due to interruptions and voltage sags - part II: practical implementation , 2006, IEEE Transactions on Power Delivery.

[23]  Azah Mohamed,et al.  An effective power quality monitor placement method utilizing quantum-inspired particle swarm optimization , 2011, Proceedings of the 2011 International Conference on Electrical Engineering and Informatics.

[24]  E.E. Juarez,et al.  An analytical approach for stochastic assessment of balanced and unbalanced voltage sags in large systems , 2006, IEEE Transactions on Power Delivery.

[25]  Mark McGranaghan,et al.  Economic Evaluation of Power Quality , 2002, IEEE Power Engineering Review.

[26]  Girish Kumar Singh,et al.  Minimization of Financial Losses due to Voltage Sag in an Indian Distribution System using D-STATCOM , 2009 .

[27]  H. Shareef,et al.  Optimal placement of power quality monitors in distribution systems using the topological monitor reach area , 2011, 2011 IEEE International Electric Machines & Drives Conference (IEMDC).

[28]  M. J. Samotyj,et al.  Voltage sags in industrial systems , 1991, Conference Record. Industrial and Commercial Power Systems Technical Conference 1991.

[29]  Chang Wook Ahn,et al.  On the practical genetic algorithms , 2005, GECCO '05.