Optimal Phasor Measurement Unit Placement in Power Systems

Optimal placement of Phasor Measurement Units (PMUs) has emerged as a cornerstone of modern power-system monitoring and control. By providing time-synchronised measurements of voltage and current phasors, PMUs enable real-time situational awareness, rapid detection of disturbances and enhanced state estimation accuracy. Since full instrumentation of every bus is economically prohibitive, the challenge lies in identifying a minimal set of PMU locations that guarantees complete or near-complete network observability under normal and contingency conditions. Recent advances have incorporated multi-objective frameworks balancing installation cost, measurement redundancy and robustness to device failures. Integer linear programming and meta-heuristic algorithms—such as genetic algorithms and nondominated sorting genetic algorithm II—are routinely deployed to explore complex combinatorial search spaces. Emerging indices, including fault-location observability and depth-limited observability propagation, enrich traditional topological formulations. Practical applications span transmission grids, distribution networks with high renewable penetration and smart microgrids. By optimising PMU deployment, operators can secure grid resilience, reduce blackout risk and unlock advanced wide-area monitoring and control functions vital to the global transition towards decarbonised and decentralised energy systems.

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Recent work has introduced a multi-objective model that integrates fault-location observability as a second objective alongside cost. By defining a new index for directly observable fault-prone lines, the formulation maximises line-failure detectability while minimising PMU count. A mixed-integer linear programming approach yields Pareto-optimal solutions, demonstrating improved performance on both standard IEEE test systems and an actual national grid.

A tri-objective strategy has been proposed for distribution systems, simultaneously minimising the number of PMU channels, the worst-case state-estimation uncertainty and sensitivity to line-parameter tolerances. The formulation accounts for single-line and PMU outages, zero-injection buses and channel constraints. A customised implementation of NSGA-II reveals that beyond certain instrumentation thresholds, additional PMUs yield diminishing returns in estimation accuracy, guiding cost-effective deployment for smart feeders.

A critical review of state-of-the-art PMU placement techniques synthesises two decades of research, categorising methods into optimisation-based and heuristic-based approaches. It highlights gaps in accommodating renewable variability, communication delays and cyber-resilience. The survey calls for unified benchmarks and stresses the need for scalable algorithms capable of handling ultra-large networks, thus charting a roadmap for next-generation observability frameworks.

Optimal Phasor Measurement Unit Placement in Power Systems publication trend

The graph below shows the total number of articles in optimal phasor measurement unit placement in power systems across all publications each year (not limited to Nature Index journals).

Technical terms

Phasor Measurement Unit (PMU): A device that records time-synchronised voltage and current phasors across the grid, enabling precise monitoring and control.

Observability: The ability to infer the complete system state from available measurements, crucial for accurate state estimation.

State Estimation: A computational process that fuses measurements to estimate bus voltages and line flows, forming the basis for control decisions.

Multi-objective Optimisation: An approach that seeks simultaneous improvement of conflicting criteria—such as cost versus redundancy—yielding a set of compromise solutions.

Pareto Front: The set of non-dominated solutions in multi-objective problems, where no single objective can be improved without degrading another.

References

1. A multi-objective optimal PMU placement considering fault-location topological observability of lengthy lines: A case study in OMAN grid. Energy Reports (2023).
2. Tri-Objective Optimal PMU Placement Including Accurate State Estimation: The Case of Distribution Systems. IEEE Access (2021).
3. A Critical Review of State-of-the-Art Optimal PMU Placement Techniques. Energies (2022).
4. Enhanced Optimal PMU Placements With Limited Observability Propagations. IEEE Access (2020).