Power system transient stability assessment method based on XGBoost-EE

Deep learning plays an increasingly important role in transient stability evaluation. However, the increase of power system scale generally results in dimension disasters. In this case, an efficient and tractable computation model is highly desirable. Currently, the construction of transient stability features generally relies on the experience of power system operators, which is more or less subjective. However, the deep learning approach is generally time-consuming and labor-intensive in aspects of design and training. Based on the above two points, a transient stability assessment method of power system based on XGBoost-EE is deve-loped by combining XGBoost(eXtreme Gradient Boosting) algorithm and EE(Entity Embedding) network. Firstly, the path rules of the tree are extracted and the category features are generated by XGBoost algorithm. In this way, the original features are dimensionally reduced. Then, the EE network is used to classify the new features, which provides a fast and accurate assessment. The proposed method, hence, takes full advantage of the fast processing speed of machine learning algorithms and the high accuracy of neural network evaluation. Simulative results based on IEEE New England 10-machine 39-bus system and IEEE 50-machine 145-bus system show that the proposed method exhibits higher prediction accuracy and better anti-noise performance than other approaches. Additionally, the proposed method is not easy to become over-fit during the training process. © 2021, Electric Power Automation Equipment Press. All right reserved.