A Novel Power Quality Monitor Placement Method Using Adaptive Quantum-Inspired Binary Particle Swarm Optimization

This paper presents a novel method for solving optimal power quality monitor placement problem in monitoring voltage sags in power systems using the adaptive quantum-inspired particle swarm optimization (PSO). The optimization considers multi objective functions and handles observability constraint determined by the concept of the topological monitor reach area. The overall objective function consists of two functions which are based on monitor overlapping index and sag severity index. In this algorithm, the standard quantum-inspired binary PSO is modified by applying the concept of artificial immune system as an adaptive element to make it more flexible towards better quality of solution and computational speed. The proposed algorithm is applied on the IEEE 30-bus transmission system and the IEEE 34-node distribution system and compared to the conventional PSO.

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

[2]  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.

[3]  R. Marler,et al.  The weighted sum method for multi-objective optimization: new insights , 2010 .

[4]  M.M.A. Salama,et al.  Optimum number and location of power quality monitors , 2004, 2004 11th International Conference on Harmonics and Quality of Power (IEEE Cat. No.04EX951).

[5]  P.F. Ribeiro,et al.  Transmission systems power quality monitors allocation , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[6]  Graham Kendall,et al.  Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques , 2013 .

[7]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[8]  Donald E. Grierson,et al.  Comparison among five evolutionary-based optimization algorithms , 2005, Adv. Eng. Informatics.

[9]  Jong-Bae Park,et al.  A New Quantum-Inspired Binary PSO: Application to Unit Commitment Problems for Power Systems , 2010, IEEE Transactions on Power Systems.

[10]  Tony Hey,et al.  Quantum computing: an introduction , 1999 .