Total Transfer Capability Evaluation of Power Systems Based on Stacking Ensemble Learning

摘要

The total transfer capability (TTC) of flowgate is an important concern for operator during the power system operation. To provide fast and accurate TTC evaluation, this paper presents a TTC evaluation methods using the stacking ensemble learning method. Firstly, the repeated power flow is applied to calculate the TTC value under different scenarios. Then, the steady variables, including the active and reactive power, voltage and angle of generators, the active and reactive power of loads and the active power flow on transmission lines, are used as the input features. Finally, the stacking ensemble learning based on the XGBoost, RF, GBDT, MLP and SVM is used to obtain the final TTC evaluation model. The simulation results illustrate the effectiveness of the proposed methods.