Time-Series Data Engineering for Smart Grid Event Streams and Fault Classification

Abstract

Smart grid monitoring depends on continuous time-series streams from PMUs, smart meters, relays, feeder sensors, substations, and SCADA-connected devices. This article presents a scalable data engineering framework for preparing smart grid event streams for near real-time fault classification. The framework aligns timestamps, cleans noisy measurements, handles missing samples, extracts voltage, current, frequency, harmonic, and phase-angle features, and serves fault classification models through a scalable streamprocessing pipeline. The analysis shows that medium-length event windows provide a stronger balance between classification accuracy, event recall, and inference stability than very short or excessively long windows. The scalability evaluation shows that distributed and auto-scaled stream processing improves throughput capacity, latency control, and fault alert completion. These findings indicate that smart grid fault classification requires both high-quality timeseries feature preparation and a stream pipeline capable of handling event bursts without delaying operational alerts.