Authors:
Naveenkumar Kaliyan, C. Poongothai, Bhopendra Singh

Addresses:
Department of Instrumentation and Control Engineering, Sri Manakula Vinayagar Engineering College, Madagadipet, Puducherry, India. Department of Electrical and Electronics Engineering, Muthaiah Polytechnic College, Annamalainagar, Chidambaram, Tamil Nadu, India. Department of Engineering, Amity University Dubai, Dubai, United Arab Emirates.

Abstract:

Phasor Measurement Units (PMUs) have become indispensable in modern power systems, offering high-resolution, real-time data that enhances system monitoring, stability, and control. The strategic placement of PMUs is crucial for maximizing their benefits, ensuring optimal observability, and maintaining system reliability. This review paper provides an extensive analysis of PMU placement strategies over the past decade, capturing significant advancements and trends in the field. The paper systematically examines various optimization techniques, including integer programming, metaheuristic algorithms, and hybrid approaches, highlighting their evolution and effectiveness in improving PMU deployment. It also addresses key challenges, including computational complexity, cost-benefit trade-offs, data integration issues, and cybersecurity concerns. By evaluating recent research, the review identifies gaps and proposes future research directions to address these challenges. Emerging trends, such as the integration of PMUs with smart grid technologies and the utilization of big data analytics, are discussed, highlighting their potential to further enhance grid management and operational efficiency. The paper concludes with a call for the adoption of advanced optimization methods, hybrid monitoring systems, cost-effective solutions, and enhanced cybersecurity measures to advance the deployment and functionality of PMUs in power systems. 

Keywords: Phasor Measurement Unit (PMU); Optimal Placement; Grid Observability; Metaheuristic Algorithms; Data Management; Smart Grid Technologies; Genetic Algorithms; Particle Swarm Optimization.

Received on: 12/07/2024, Revised on: 17/09/2024, Accepted on: 24/11/2024, Published on: 12/11/2025

DOI: 10.69888/FTSES.2025.000471

FMDB Transactions on Sustainable Energy Sequence, 2025 Vol. 3 No. 2, Pages: 84-95

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