Transient Stability Assessment of Power System Based on XGBoost and Factorization Machine

As the deployment of wide-area measurement systems (WAMS) expands, data-driven methods are playing an increasingly important role in transient stability assessment (TSA). However, if the measured data is disturbed by noise or the topological structure of the power system changes, the performance of the model based on data-mining would decline so that it could not meet the needs of real-world scenarios. In this paper, we develop a TSA methodology based on extreme gradient boosting (XGBoost) and factorization machine (FM). Utilizing XGBoost to complete automatically the feature construction and transform the power flow into a sparse matrix. The influence of noise can be reduce due to the sparsity of the new feature set. On this basis, FM algorithm, which has advantage in processing the large sparse matrix, is introduced into the model to complete fault classification. Furthermore, the feature crossing function of FM further mines the interactive information of the spatio-temporal features. For changed topology scenarios, we propose a extended-training set scheme. Adding some pivotal data of topology changes to the training set improves the robustness of the model. Compared with existing studies, the proposed assessment model based on XGBoost-FM not only has the better generalization performance in the case of noise interference or changed topology, but also has the least time consumption and complexity.