Application of Machine Learning for Online Dynamic Security Assessment in Presence of System Variability and Additive Instrumentation Errors

TL;DR

This paper presents a machine learning-based online dynamic security assessment method using PMU data, accounting for system variability, seasonal load changes, renewable penetration, and measurement errors, tested on IEEE-118 bus system.

Contribution

It introduces a novel DSA scheme that incorporates seasonal load profiles, renewable variability, and measurement errors, with a comparative analysis of ML algorithms' accuracy.

Findings

ML algorithms' accuracy varies with system conditions.

Considering measurement errors improves DSA reliability.

Seasonal and renewable variations significantly impact security assessment.

Abstract

Large-scale blackouts that have occurred in the past few decades have necessitated the need to do extensive research in the field of grid security assessment. With the aid of synchrophasor technology, which uses phasor measurement unit (PMU) data, dynamic security assessment (DSA) can be performed online. However, existing applications of DSA are challenged by variability in system conditions and unaccounted for measurement errors. To overcome these challenges, this research develops a DSA scheme to provide security prediction in real-time for load profiles of different seasons in presence of realistic errors in the PMU measurements. The major contributions of this paper are: (1) develop a DSA scheme based on PMU data, (2) consider seasonal load profiles, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA with and without erroneous measurements. The performance of this approach is tested on the IEEE-118 bus system. Comparative analysis of the accuracies of the ML algorithms under different operating scenarios highlights the importance of considering realistic errors and variability in system conditions while creating a DSA scheme.