Ambient Signal based Load Modelling: Identification & Clustering

Abstract

Modelling the dynamic behaviours of the load is a difficult topic due to the complex, stochastic and time-varying properties of the load in power systems. In this talk, a new ambient signal based load model identification and clustering framework is introduced. Compared with traditional post fault response based load modelling approach, the ambient signal based approach can be conducted without the existence of fault, through which the time varying dynamics of load can be better tracked. In this talk, the ANN and optimization based model identification method is introduced first to show how to get the load models from the measurement data. Then, the clustering method to pick up representative load models from the identification results' set is introduced in order to provide a limited number of load models for power system simulation.