SFR Modeling for Hybrid Power Systems Based on Deep Transfer Learning

Abstract

A deep transfer learning method is presented for establishing the aggregated system frequency response (SFR) model of wind-thermal hybrid power systems (HPSs). In order to deal with nonlinearities and non-Gaussian disturbances, the quadratic survival information potential (QSIP) of the squared identification error is employed to construct the performance index when training recurrent neural networks (RNNs). A pre-trained SFR model is then obtained by the improved RNNs using the source domain data collected from the HPS in historical scenarios. Subsequently, the maximum mean difference is utilized to test the similarity of the HPS in historical and current scenarios. After that, the pre-trained SFR model is fine-tuned by adding some nodes to the recurrent layer and a functional link to the input layer. The SFR model of the HPS operating in current scenario can then be built based on the transferred source domain pre-trained SFR model. Simulation results illustrate that the proposed data driven modelling method can obtain accurate, effective and timely SFR model for a wind-thermal HPS with different wind speeds and load disturbances.