Scalable Cloud-Native Architectures for Intelligent PMU Data Processing

TL;DR

This paper introduces a scalable cloud-native architecture integrating AI, edge, and cloud computing for efficient, real-time processing of high-volume PMU data in power grids, improving latency, scalability, and reliability.

Contribution

It presents a novel cloud-native framework combining distributed stream processing, microservices, and machine learning for intelligent PMU data analytics at scale.

Findings

Achieves sub-second response times for large PMU deployments

Demonstrates high scalability and reliability in real-time analytics

Enhances grid observability with advanced AI models

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.