Data inheritance–based updating method and its application in transient frequency prediction for a power system

Abstract

Transient frequency prediction helps formulate and take emergency control measures in a timely way, which is of great significance to the security and stability of a power system. Conventional frequency prediction methods encounter challenges when taking both speed and accuracy into account. Machine–learning-based prediction methods cannot readily achieve satisfactory prediction accuracy based on historical samples of transient faults; only by updating the prediction model online can these methods accommodate a transient fault, which may occur at any time. Aiming at the online update of the prediction model, the concept of data inheritance is presented and applied to the process of model updating. Based on this update method, a transient frequency prediction method considering model inheritance is proposed. In this prediction method, the historical transient fault samples of the power system are used to train the initial prediction model; in this way, the prediction performance can be quickly improved according to the newly incremented transient fault samples by inheriting the historical prediction model. Validations on the New England 39-bus and NPCC 140-bus systems demonstrate the effectiveness of the proposed method.