Scalable Cloud-Native Architectures for Intelligent PMU Data Processing

TL;DR

The paper tackles the scalability and reliability challenges of PMU analytics in modern power grids by proposing a cloud-native, edge–fog–cloud architecture that integrates AI with distributed streaming and elastic orchestration. It combines Kafka/Flink for ingestion, tiered storage with Gorilla compression, and Spark/Kubernetes to support real-time analytics and large-scale model training, including LSTM/CNN time-series models, autoencoder and isolation-forest anomaly detectors, and distributed learning via data parallelism and federated learning. The work provides formal performance bounds for latency ($\tau_{total}$), throughput ($\lambda_{max}$), and availability, and introduces security/privacy mechanisms (differential privacy, homomorphic encryption, secure aggregation) to meet critical infrastructure requirements. The proposed framework demonstrates sub-second end-to-end latency, near-linear scalability with PMU counts, and cost-efficient storage, offering a robust foundation for next-generation smart-grid analytics with practical implications for reliability, security, and operational efficiency.

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.

Figures (1)

• Figure 1: Three-tier hierarchical architecture for AI-enhanced PMU systems