Optimizing PMU Placement for Enhanced Observability and Reliability Enhancement in Power Systems
DOI:
https://doi.org/10.22399/ijcesen.5262Keywords:
Optimal PMU Placement, Multi-objective Optimization, System Observability Redundancy Index (SORI), Data-driven Decision Making, Adaptive WeightingAbstract
Phasor Measurement Units (PMUs) play an important role in improving real-time monitoring and situational awareness in modern power systems. However, installing a PMU at every bus is not economically feasible, which makes the Optimal PMU Placement (OPP) problem essential. The goal is to minimize the number of PMUs while still ensuring full system observability and an adequate level of redundancy. In this paper, a two-stage hybrid approach is proposed. First, a Pareto-based multi-objective genetic algorithm (MOGA) is used to generate a set of optimal solutions that reflect the trade-off between reducing the number of PMUs and improving system redundancy, evaluated using the System Observability Redundancy Index (SORI). Unlike many existing approaches that rely on predefined weighting factors, the proposed method uses a data-driven decision-making process to select the most suitable solution from the Pareto set. This reduces subjectivity in the decision process and provides a more consistent way to choose the final solution. The method is tested on IEEE 14-, 24-, 30-, and 118-bus systems. The results show that full observability can be achieved with fewer PMUs compared to traditional methods, with reductions reaching up to 40% in large-scale systems, while maintaining acceptable redundancy levels. In addition, a sensitivity analysis is carried out to examine how different parameters affect the solution, confirming the stability of the proposed approach
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