Data-Driven Security Assessment of the Electric Power System
It provides computationally efficient guidelines for short-term planning in electric power systems, addressing technical challenges from the energy transition, though it is incremental in applying existing machine learning tools to this domain.
This study tackled the challenge of assessing power system security amid increasing renewable energy and reduced inertia by introducing a decomposition approach using supervised learning tools, achieving accurate predictions with only 0.57% error for stability status and 6.8% error for instability timing.
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.