Sequential Bayesian Parameter Estimation of Stochastic Dynamic Load Models
Optimization and Control
2020-04-30 v1 Signal Processing
Abstract
In this paper we focus on the parameter estimation of dynamic load models with stochastic terms, in particular, load models where protection settings are uncertain, such as in aggregated air conditioning units. We show how the uncertainty in the aggregated protection characteristics can be formulated as a stochastic differential equation with process noise. We cast the parameter inversion within a Bayesian parameter estimation framework, and we present methods to include process noise. We demonstrate the benefits of considering stochasticity in the parameter estimation and the risks of ignoring it.
Cite
@article{arxiv.2004.13964,
title = {Sequential Bayesian Parameter Estimation of Stochastic Dynamic Load Models},
author = {Daniel Adrian Maldonado and Vishwas Rao and Mihai Anitescu and Vivak Patel},
journal= {arXiv preprint arXiv:2004.13964},
year = {2020}
}
Comments
7 pages, 9 figures, to be published in Electric Power Systems Research PSCC 2020