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Optimal PMU Placement to Enhance Observability in Transmission Networks Using ILP and Degree of Centrality
Optimal PMU Placement to Enhance Observability in Transmission Networks Using ILP and Degree of Centrality
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Optimal PMU Placement to Enhance Observability in Transmission Networks Using ILP and Degree of Centrality
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Optimal PMU Placement to Enhance Observability in Transmission Networks Using ILP and Degree of Centrality
Optimal PMU Placement to Enhance Observability in Transmission Networks Using ILP and Degree of Centrality

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Optimal PMU Placement to Enhance Observability in Transmission Networks Using ILP and Degree of Centrality
Optimal PMU Placement to Enhance Observability in Transmission Networks Using ILP and Degree of Centrality
Journal Article

Optimal PMU Placement to Enhance Observability in Transmission Networks Using ILP and Degree of Centrality

2024
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Overview
The optimal PMU placement problem is placing the minimum number of PMUs in the network to ensure complete network observability. It is an NP-complete optimization problem. PMU placement based on cost and critical nodes is solved separately in the literature. This paper proposes a novel approach, a degree of centrality in the objective function, to combine the effect of both strategies to place PMUs in the power network optimally. The contingency analysis and the effect of zero-injection buses are solved to ensure the reliability of network monitoring and attain a minimum number of PMUs. Integer linear programming is used on the IEEE 7-bus, IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus, and IEEE 118-bus systems to solve this problem. The results are evaluated based on two performance measures: the bus observability index (BOI) and the sum of redundancy index (SORI). On comparison, it is found that the proposed methodology has significantly improved results, i.e., a reduced number of PMUs and increased network overall observability (SORI). This methodology is more practical for implementation as it focuses on critical nodes. Along with improvement in the results, the limitations of existing indices are also discussed for future work.