Transfer learning-based updating method of transient stability assessment model

Abstract

An updating method for data-driven based transient stability assessment model of power systems is proposed in this paper. To cope with potential faults in the real-time operation of power systems, the parameters of models can be modified online through the proposed method. Firstly, according to the long-short term memory (LSTM) network, transient stability assessment models are initially trained offline by expected faults. Then, the feature distribution differences between expected faults and potential faults are evaluated by the time-sequence maximum mean discrepancy (TMMD). Compared with the traditional maximum mean discrepancy (MMD), the proposed TMMD approach takes the temporal properties of transient stability into account, which can reflect the relationship between fault samples and time series better. Finally, the parameters of models are adjusted by transfer learning to reduce the feature distribution differences, by which the updated models can be more suitable for the different but related potential faults. Therefore, through the proposed model updating method, the extensibility of evaluation models is greatly enhanced, which is more in line with the practical conditions of power systems. In this paper, the effectiveness of the proposed method has been demonstrated by the evaluation results in the IEEE 39-bus system and a realistic power system.