Data-Driven Security Assessment of the Electric Power System

TL;DR

This paper presents a novel, efficient machine learning-based decomposition method for assessing short-term system security in power grids, effectively distinguishing stability status with high accuracy amidst increasing renewable integration.

Contribution

It introduces an extendable, computationally efficient approach for using supervised learning to evaluate transient stability in power systems, addressing challenges from renewable energy growth.

Findings

Achieved 0.57% error in stability classification

Predicted instability timing with 6.8% error

Demonstrated high accuracy on unseen test data

Abstract

The transition to a new low emission energy future results in a changing mix of generation and load types due to significant growth in renewable energy penetration and reduction in system inertia due to the exit of ageing fossil fuel power plants. This increases technical challenges for electrical grid planning and operation. This study introduces a new decomposition approach to account for the system security for short term planning using conventional machine learning tools. The immediate value of this work is that it provides extendable and computationally efficient guidelines for using supervised learning tools to assess first swing transient stability status. To provide an unbiased evaluation of the final model fit on the training dataset, the proposed approach was examined on a previously unseen test set. It distinguished stable and unstable cases in the test set accurately, with only 0.57% error, and showed a high precision in predicting the time of instability, with 6.8% error and mean absolute error as small as 0.0145.