Rapid and Accurate Changepoint Detection of Power System Forced Oscillations

TL;DR

This paper introduces a faster, more accurate changepoint detection method for identifying forced oscillations in power systems, reducing computation time significantly while maintaining high accuracy.

Contribution

It presents a manual penalty parameter tuning approach for the PELT algorithm, reducing computation time and input complexity in power system oscillation detection.

Findings

98% reduction in computation time

High estimation accuracy maintained

Fewer input parameters required

Abstract

This paper describes a new approach for using changepoint detection (CPD) to estimate the starting and stopping times of a forced oscillation (FO) in measured power system data. As with a previous application of CPD to this problem, the pruned exact linear time (PELT) algorithm is used. However, instead of allowing PELT to automatically tune its penalty parameter, a method of manually providing it is presented that dramatically reduces computation time without sacrificing accuracy. Additionally, the new algorithm requires fewer input parameters and provides a formal, data-driven approach to setting the minimum FO segment length to consider as troublesome for an electromechanical mode meter. A low-order ARMAX representation of the minniWECC model is used to test the approach, where a 98\% reduction in computation time is enjoyed with high estimation accuracy.