Probabilistic Time-Varying Parameter Identification for Load Modeling: A Deep Generative Approach

Abstract

The uncertainty of power resources introduces significant challenges for classic load modeling approaches. Moreover, load parameter identification techniques are affected by various load components with highly nonlinear time-varying behaviors and dependencies. This article presents a new deep generative architecture (DGA) based on the long short-term memory (LSTM) network for probabilistic time-varying parameter identification (PTVPI). In contrast to previous methods that merely compute point estimations of load parameters, our objective is to learn the continuous probability density function (PDF) of load parameters for composite load modeling (CLM) with ZIP load and induction motor. The proposed DGA learns complex temporal patterns from the time-varying parameters/measurements to estimate load parameters in a probabilistic fashion. Leveraging the LSTM network, our DGA computes deep temporal states and state transitions of load parameters. An encoding neural network extracts useful latent variables from the captured temporal states that are further mapped by a decoding neural network into the observed load parameters; hence, learning the underlying PDF of these parameters. Numerical results on the 68-bus New England and New York Interconnect System with four CLMs show accurate results for PTVPI in terms of various probabilistic estimation metrics, including reliability, sharpness, and continuous ranked probability score.