Probabilistic PMU Placement in Electric Power Networks: An MILP-Based Multiobjective Model

This paper presents a multiobjective probabilistic model for the placement of phasor measurement units (PMUs) in electrical power networks. The proposed model simultaneously optimizes two objectives functions: 1) minimizes the number of PMUs; and 2) maximizes the expected value of system's redu...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics Vol. 11; no. 2; pp. 332 - 341
Main Authors: Aghaei, Jamshid, Baharvandi, Amir, Rabiee, Abdorreza, Akbari, Mohammad-Amin
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.04.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Availability
Buses (vehicles)
Fuzzy
Informatics
Linear programming
Mathematical models
Minimum Distance to Utopia Point (MDUP)
Mixed Integer Linear Programming (MILP)
Multi-objective Optimal PMU Placement (MOPP)
Networks
Phasor Measurement Unit (PMU)
Phasor measurement units
Placement
Probabilistic methods
Probability
Probability theory
Production
Redundancy
Minimum distance to Utopia point (MDUP)
phasor measurement unit (PMU)
multiobjective optimal PMU placement (MOPP)
mixed integer linear programming (MILP)
ISSN:1551-3203, 1941-0050
Online Access:Get full text
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Summary:This paper presents a multiobjective probabilistic model for the placement of phasor measurement units (PMUs) in electrical power networks. The proposed model simultaneously optimizes two objectives functions: 1) minimizes the number of PMUs; and 2) maximizes the expected value of system's redundancy (or minimizes the unobservability of the system). Incorporating the impact of zero-injection buses, an efficient formulation is used to evaluate the probability of unobservability of buses resulted from line outages and PMU loss. The extracted formulation is based on the mixed integer linear programming (MILP) framework, which is efficiently solvable by high-performance commercial solvers. This multiobjective optimization problem (MOP) is solved by augmented epsilon-constraint and weighting approach. Accordingly, two new indices are introduced to compare these two multiobjective methods. Finally, the ultimate solution among the Pareto front is recognized using a fuzzy decision-making process. The IEEE 57-bus test system is used to examine the effectiveness of the proposed frameworks.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2015.2389613