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

Haifeng Li, Qing Chen, Chang Fu, Zhe Yu, Di Shi and Zhiwei Wang
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Haifeng Li: State Grid Jiangsu Electric Power Company Ltd., Nanjing 210008, Jiangsu, China
Qing Chen: State Grid Jiangsu Electric Power Company Ltd., Nanjing 210008, Jiangsu, China
Chang Fu: GEIRI North America, 250 W Tasman Dr. STE 100, San Jose, CA 95134, USA
Zhe Yu: GEIRI North America, 250 W Tasman Dr. STE 100, San Jose, CA 95134, USA
Di Shi: GEIRI North America, 250 W Tasman Dr. STE 100, San Jose, CA 95134, USA
Zhiwei Wang: GEIRI North America, 250 W Tasman Dr. STE 100, San Jose, CA 95134, USA

Energies, 2019, vol. 12, issue 3, 1-16

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.

Keywords: Bayesian estimation; dynamic model; Gibbs sampling; parameter estimation; static model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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