A power system transient stability assessment method based on active learning

Abstract

Due to the wide deployment of phasor measurement unit, the real-time assessment of transient stability based on machine learning shows great potential for development. In order to solve the problem of time-consuming data generation of offline training in such methods and the difficulty of quickly updating the model after the grid changes, the paper proposes a method for transient stability assessment (TSA) of power systems based on active learning. Firstly, different operation conditions and different faults are considered to perform short-time simulation (simulation to the instant of fault clearance) to generate unlabelled samples. After the careful selection of critical TSA features, a part of samples are randomly selected for long-term simulation to label the transient status of these samples, and random forest is further trained to construct TSA model. Finally some data is selected in the remaining unlabelled samples with higher information entropy to label and retrain the model until the model accuracy no longer changes. The simulation on the test power system shows that the method proposed in this paper can effectively reduce the time of offline simulation, and greatly improve the efficiency of model, and is also robust to wide-area noise.