Abstract
This paper presents a performance comparison between three optimization techniques, namely, quantum-inspired binary particle swarm optimization, binary particle swarm optimization and genetic algorithm in application to optimal power quality monitor (PQM) placement method for voltage sag assessment. The optimization handles the observability constraints based on the topological monitor reach area concept and solves a multi-objective function in obtaining the optimal number and placement of PQMs in power systems. The objective function consists of two functions which are based on monitor overlapping index and sag severity index. All the optimization algorithms have been implemented and tested on the IEEE 34-node, the 69-bus and the IEEE 118-bus test systems to evaluate the effectiveness of the aforementioned techniques. The results show that QBPSO provide a better optimal solution than the standard binary particle swarm optimization and the existing genetic algorithm by 56% and 31%, respectively. The validation test illustrated that the optimal PQM placements can detect and record the voltage sag events due to any fault occurrence in the systems.
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