# Bayesian Estimation Based Parameter Estimation for Composite Load

## TL;DR

This paper introduces a Bayesian estimation approach for composite load modeling in power systems, providing robust parameter distribution estimates for static and dynamic loads, improving over traditional point estimation methods.

## Contribution

It proposes a Bayesian estimation framework using Gibbs sampling for static and dynamic load models, offering a more robust and informative parameter estimation method.

## Key findings

- Provides distribution estimates of load model coefficients
- Robust to measurement errors
- Applicable to both static and dynamic load models

## Abstract

Accurate identification of parameters of load models is essential in power system computations, including simulation, prediction, and stability and reliability analysis. Conventional point estimation based composite load modeling approaches suffer from disturbances and noises and provide limited information of the system dynamics. In this work, a statistic (Bayesian Estimation) based distribution estimation approach is proposed for both static (ZIP) and dynamic (Induction Motor) load modeling. When dealing with multiple parameters, Gibbs sampling method is employed. In each iteration, the proposal samples each parameter while keeps others fixed. The proposed method provides a distribution estimation of load models coefficients and is robust to measurement errors.