Bayesian Estimation on Load Model Coefficients of ZIP and Induction Motor Model

Haifeng Li,Di Shi,Chang Fu,Qing Chen,Zhe Yu,Zhiwei Wang

doi:10.3390/en12030547

Haifeng Li, Di Shi + Show 4 more

Open Access

https://doi.org/10.3390/en12030547

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Abstract

Parameter identification in load models is a critical factor for power system computation, simulation, and prediction, as well as 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 statistics (Bayesian Estimation) based distribution estimation approach is proposed for both static and dynamic load models. When dealing with multiple parameters, Gibbs sampling method is employed. The proposed method samples all parameters in each iteration and updates one parameter while others remain fixed. The proposed method provides a distribution estimation for load model coefficients and is robust for measuring errors. The proposed parameter identification approach is generic and can be applied to both transmission and distribution networks. Simulations using a 33-feeder system illustrated the efficiency and robustness of the proposal.

Highlights

IntroductionLoad modeling is a traditional topic in power systems and has been widely studied over the years [1,2,3,4,5,6,7,8]
Load models can be categorized into static models and dynamic models
The research focus to date has been on composite load modeling, which consists of static and dynamic models [1]

Summary

Introduction

Load modeling is a traditional topic in power systems and has been widely studied over the years [1,2,3,4,5,6,7,8]. Static models include the ZIP (constant impedance (Z), constant current (I), and constant power (P)) model, the exponential model, and the frequency dependent model. Examples of widely used dynamic models include the induction motor (IM) and the exponential recovery load model (ERL) [1]. The research focus to date has been on composite load modeling, which consists of static and dynamic models [1]. In [1], it is shown that the ZIP+IM model can be used under various conditions and in different locations and compositions.

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Discussion

Conclusion