Real-Time Power System Event Detection: A Novel Instance Selection Approach

TL;DR

This paper introduces a novel similarity-based instance selection method for real-time power system event detection, enhancing data quality and classification performance in high-speed streaming environments.

Contribution

It proposes a new instance selection technique combined with an improved Hoeffding-Tree learner for better classification of disturbances and cyber-attacks in power systems.

Findings

Enhanced classification accuracy in simulated cyber-attack scenarios

Demonstrated real-time deployment feasibility

Improved data quality for energy big data analytics

Abstract

Instance selection is a vital technique for energy big data analytics. It is challenging to process a massive amount of streaming data generated at high speed rates by intelligent monitoring devices. Instance selection aims at removing noisy and bad data that can compromise the performance of data-driven learners. In this context, this paper proposes a novel similarity based instance selection (SIS) method for real-time phasor measurement unit data. In addition, we develop a variant of the Hoeffding-Tree learner enhanced with the SIS for classifying disturbances and cyber-attacks. We validate the merits of the proposed learner by exploring its performance under four scenarios that affect either the system physics or the monitoring architecture. Our experiments are simulated by using the datasets of industrial control system cyber-attacks. Finally, we conduct an implementation analysis which shows the deployment feasibility and high-performance potential of the proposed learner, as a part of real-time monitoring applications.