Preventive control for power system transient security based on XGBoost and DCOPF with consideration of model interpretability

This paper proposes a new approach for online power system transient security assessment (TSA) and preventive control based on XGBoost and DC optimal power flow (DCOPF). The novelty of this proposal is that it applies the XGBoost and data selection method based on the 1-norm distance in local feature importance evaluation which can provide a certain model interpretability. The method of SMOTE+ENN is adopted for data rebalancing. The contingency-oriented XGBoost model is trained with databases generated by time domain simulations to represent the transient security constraint in the DCOPF model, which has a relatively fast speed of calculation. The transient security constrained generation rescheduling is implemented with the differential evolution algorithm, which is utilized to optimize the rescheduled generation in the preventive control. Feasibility and effectiveness of the proposed approach are demonstrated on an IEEE 39-bus test system and a 500-bus operational model for South Carolina, USA.