Transient stability assessment via decision trees and multivariate adaptive regression splines

- A reliable method is proposed for real-time transient stability assessment.
- CART and MARS are used as the classification and regression methods, respectively.
- The method is tested on the model of BC Hydro power system with limited number of PMUs.
- The sensitivity of the models to data fed earlier or later sampling times is uncovered.
- The sensitivity of the models to noisy data is shown and discussed.

This paper focuses on the practical implementation of online transient stability assessment (TSA) tools that employ, in conjunction with high-speed synchronized phasor measurements obtained from phasor measurement units (PMUs), classification and regression trees (CART) and multivariate adaptive regression splines (MARS) models. To build CART and MARS models that are amenable to real-time applications, pertinent transient stability-related system characteristics are identified; these include voltage and current phasors, deviations from the centre-of-inertia angle and speed, and potential- and kinetic-energy related quantities. These characteristic quantities are evaluated using PMU measurements and then leveraged to train CART and MARS models for the full Western Electricity Coordinating Council (WECC) system. The resultant models are tested and validated with the full WECC system using credible contingency scenarios in the BC Hydro subsystem. High prediction accuracy rates are observed for both CART and MARS methods, making them attractive options for real-time TSA.