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Sequential Bayesian parameter estimation of stochastic dynamic load models
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DOI：10.1016/j.epsr.2020.106606.png)
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Abstract
En 中文
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.
Keywords:
Power system identification
Power system dynamics
Load modeling
Bayesian statistics
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