WECC Composite Load Model Parameter Identification Using Deep Learning Approach

Abstract

The increased integration of distributed energy resources (DERs) has significantly changed the load composition. As compared to traditional static or induction motor loads, these inverter-based resources (IBRs) exhibit different behavior. Then, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to address the current load characteristic transitions. Western Electricity Coordinating Council (WECC) has developed the composite load model with distributed generation (CMPLDWG) to accurately represent static load, induction motors, and IBRs. In this paper, we propose a novel deep learning-based method using a conditional variational autoencoder (CVAE) to estimate the CMPLDWG model's posterior distributions. The effectiveness of the proposed method is validated using an IEEE 39-bus test system. The results show that the proposed approach can accurately and efficiently estimate the parameters' posterior distributions.