Adaptive Updating of Power System Transient Stability Prediction Model Based on Data Inheritance

Transient stability prediction using machine learning algorithms has been highly concerned. Existing researches have made great progress in simplified scenarios, while these simplifications cannot be made in a practical problem. Considering two typical characteristics of power systems: time-varying and data-increasing, two methods of prediction model updating are proposed in this paper. Transfer learning is applied to adding samples to a time-varying system with insufficient training samples. Incremental learning is used to update the prediction model with constantly increasing training samples. Both two methods make the prediction model more robust in a dynamic power system. In IEEE 39-bus system, the proposed methods are tested in angle and frequency stability problem respectively. Results show that transfer learning makes more accurate prediction in a time-varying power system than traditional method and incremental learning enables model updating to be fast enough to be applied online. More importantly, the proposed updating methods can be further investigated for practical application.