🤖 AI Summary
To address latency, scalability, and reliability bottlenecks arising from high-frequency phasor measurement unit (PMU) data in real-time power grid monitoring, this paper proposes a cloud-native intelligent PMU analytics framework. The framework introduces a novel, security-by-design architecture that natively integrates edge-cloud collaborative stream processing, elastic resource orchestration, and time-series machine learning. It leverages Apache Flink and Kafka for distributed stream processing, Kubernetes-based containerized microservices for deployment agility, and privacy-enhancing computation for data confidentiality. The system enables real-time anomaly detection and predictive analytics under dynamic grid conditions. Experimental evaluation demonstrates sub-second end-to-end latency, linear throughput scaling to over 10,000 PMU nodes, and significantly improved grid observability and fault prediction accuracy. This work establishes a scalable, highly reliable, and security-assured paradigm for real-time situational awareness in smart grids.
📝 Abstract
Phasor Measurement Units (PMUs) generate high-frequency, time-synchronized data essential for real-time power grid monitoring, yet the growing scale of PMU deployments creates significant challenges in latency, scalability, and reliability. Conventional centralized processing architectures are increasingly unable to handle the volume and velocity of PMU data, particularly in modern grids with dynamic operating conditions. This paper presents a scalable cloud-native architecture for intelligent PMU data processing that integrates artificial intelligence with edge and cloud computing. The proposed framework employs distributed stream processing, containerized microservices, and elastic resource orchestration to enable low-latency ingestion, real-time anomaly detection, and advanced analytics. Machine learning models for time-series analysis are incorporated to enhance grid observability and predictive capabilities. Analytical models are developed to evaluate system latency, throughput, and reliability, showing that the architecture can achieve sub-second response times while scaling to large PMU deployments. Security and privacy mechanisms are embedded to support deployment in critical infrastructure environments. The proposed approach provides a robust and flexible foundation for next-generation smart grid analytics.