Predicting Fault-Ride-Through Probability of Inverter-Dominated Power Grids using Machine Learning

TL;DR

This paper demonstrates that machine learning models can accurately predict the fault-ride-through probability in inverter-dominated power grids, aiding risk assessment and stability analysis with reduced computational costs.

Contribution

The study introduces a new dataset and shows that ML models can generalize from synthetic to real power grid systems for stability prediction.

Findings

ML models accurately predict fault-ride-through probability.

ML models generalize to IEEE-96 Test System.

Synthetic data enables effective stability analysis.

Abstract

Due to the increasing share of renewables, the analysis of the dynamical behavior of power grids gains importance. Effective risk assessments necessitate the analysis of large number of fault scenarios. The computational costs inherent in dynamic simulations impose constraints on the number of configurations that can be analyzed. Machine Learning (ML) has proven to efficiently predict complex power grid properties. Hence, we analyze the potential of ML for predicting dynamic stability of future power grids with large shares of inverters. For this purpose, we generate a new dataset consisting of synthetic power grid models and perform dynamical simulations. As targets for the ML training, we calculate the fault-ride-through probability, which we define as the probability of staying within a ride-through curve after a fault at a bus has been cleared. Importantly, we demonstrate that ML models accurately predict the fault-ride-through probability of synthetic power grids. Finally, we also show that the ML models generalize to an IEEE-96 Test System, which emphasizes the potential of deploying ML methods to study probabilistic stability of power grids.