Abstract
Optimal phasor measurement units (PMU) placement was developed to determine the number and locations of PMUs on the premise of full observability of the whole network. In order to enhance reliability under contingencies, redundancy should also be considered beside the number of PMUs in optimal phasor measurement units placement problem. Thus, in this paper, a multi-objective model was established to consider the two conflicting components simultaneously, solved by ε-constraint method and the fuzzy satisfying approach. The redundancy here was formulated as average possibility of observability including random component outages, and full possibility formula was applied to calculate the average possibility of observability in the case of single line outage. Finally, the model was employed to the IEEE-57 bus system, and the results verified that the developed model could provide a placement scheme with higher reliability.
Highlights
The wide-area measurement system (WAMS) was developed to provide real-time data for state estimator with high precision and great efficiency, in order to further guarantee the power system stability [1] and meet the requirement of a rapid increase in energy demand [2,3]
The model consisted of the objective of minimum Phasor measurement units (PMU) number and constraint of full observability, was established, and solved by mixed-integer linear programming (MILP) and nonlinear programming (NLP) in [8], the branch-and-bound algorithm (BB) and binary-bonded genetic algorithm (BCGA) in [9]
A multi-objective model for the optimal PMU placement (OPP) problem was developed with objectives of minimizing the number of PMUs and maximizing redundancy subjecting to complete observability to enhance the reliability of measurement system
Summary
The wide-area measurement system (WAMS) was developed to provide real-time data for state estimator with high precision and great efficiency, in order to further guarantee the power system stability [1] and meet the requirement of a rapid increase in energy demand [2,3]. The model consisted of the objective of minimum PMU number and constraint of full observability, was established, and solved by mixed-integer linear programming (MILP) and nonlinear programming (NLP) in [8], the branch-and-bound algorithm (BB) and binary-bonded genetic algorithm (BCGA) in [9]. A multi-objective model was proposed to simultaneously consider installation cost and measurement redundancy. A multi-objective model was proposed, simultaneously minimizing the PMU cost and unobservable probability under PMU or line failures in [22]. A solution with maximum observable probability considering random component outages was selected as the final optimal scheme. When considering multistage PMU placement, the observable probability was regarded as the objective to optimize at each stage [24]. The developed model was adopted for the IEEE-57 bus system, and results were compared to that in single-objective model
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