Parallel Integrated Model-Driven and Data-Driven Online Transient Stability Assessment Method for Power System

Abstract

More and more uncertain factors in power systems and more and more complex operation modes of power systems put forward higher requirements for online transient stability assessment methods. The traditional model-driven methods have clear physical mechanisms and reliable evaluation results but the calculation process is time-consuming, while the data-driven methods have the strong fitting ability and fast calculation speed but the evaluation results lack interpretation. Therefore, it is a future development trend of transient stability assessment methods to combine these two kinds of methods. In this paper, the rate of change of the kinetic energy method is used to calculate the transient stability in the model-driven stage, and the support vector machine and extreme learning machine with different internal principles are respectively used to predict the transient stability in the data-driven stage. In order to quantify the credibility level of the data-driven methods, the credibility index of the output results is proposed. Then the switching function controlling whether the rate of change of the kinetic energy method is activated or not is established based on this index. Thus, a new parallel integrated model-driven and data-driven online transient stability assessment method is proposed. The accuracy, efficiency, and adaptability of the proposed method are verified by numerical examples.