A Backend Framework for the Efficient Management of Power System Measurements

TL;DR

This paper presents a comprehensive backend system for managing, analyzing, and visualizing large-scale PMU data in power systems, enabling rapid event detection and fast data retrieval for improved grid monitoring.

Contribution

It introduces a novel PMU data management framework with an event-detection algorithm and an efficient data retrieval method, enhancing real-time grid analysis capabilities.

Findings

Achieved over 30x speedup in high-selectivity data queries.

Developed a system supporting multiple PMU data streams and event visualization.

Enabled rapid correlation and detection of power system events.

Abstract

Increased adoption and deployment of phasor measurement units (PMU) has provided valuable fine-grained data over the grid. Analysis over these data can provide insight into the health of the grid, thereby improving control over operations. Realizing this data-driven control, however, requires validating, processing and storing massive amounts of PMU data. This paper describes a PMU data management system that supports input from multiple PMU data streams, features an event-detection algorithm, and provides an efficient method for retrieving archival data. The event-detection algorithm rapidly correlates multiple PMU data streams, providing details on events occurring within the power system. The event-detection algorithm feeds into a visualization component, allowing operators to recognize events as they occur. The indexing and data retrieval mechanism facilitates fast access to archived PMU data. Using this method, we achieved over 30x speedup for queries with high selectivity. With the development of these two components, we have developed a system that allows efficient analysis of multiple time-aligned PMU data streams.