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  1. Optimal placement of PMUs to maintain network observability using a modified BPSO algorithm
Optimal placement of PMUs to maintain network observability using a modified BPSO algorithm
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Optimal placement of PMUs to maintain network observability using a modified BPSO algorithm

Hajian, M

Optimal placement of PMUs to maintain network observability using a modified BPSO algorithm

Hajian, M ; Sharif University of Technology | 2011

995 Viewed
  1. Type of Document: Article
  2. DOI: 10.1016/j.ijepes.2010.08.007
  3. Publisher: 2011
  4. Abstract:
  5. This paper presents a novel approach to optimal placement of Phasor Measurement Units (PMUs) for state estimation. At first, an optimal measurement set is determined to achieve full network observability during normal conditions, i.e. no PMU failure or transmission line outage. Then, in order to consider contingency conditions, the derived scheme in normal conditions is modified to maintain network observability after any PMU loss or a single transmission line outage. Observability analysis is carried out using topological observability rules. A new rule is added that can decrease the number of required PMUs for complete system observability. A modified Binary Particle Swarm Optimization (BPSO) algorithm is used as an optimization tool to obtain the minimal number of PMUs and their corresponding locations while satisfying associated constraint. Numerical results on different IEEE test systems are presented to demonstrate the effectiveness of the proposed approach
  6. Keywords:
  7. Power system observability ; Binary particle swarm optimization ; Complete system ; IEEE test systems ; Network observability ; Normal condition ; Numerical results ; Observability analysis ; Optimal measurements ; Optimal placements ; Optimization tools ; Phasor Measurement Unit ; Power systems ; Single transmission ; Topological observability ; Transmission line ; Electric lines ; Observability ; Outages ; Phase measurement ; Topology ; Transmission line theory ; Units of measurement ; Particle swarm optimization (PSO)
  8. Source: International Journal of Electrical Power and Energy Systems ; Volume 33, Issue 1 , January , 2011 , Pages 28-34 ; 01420615 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S0142061510001432