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Energies 2019, 12(3), 547; https://doi.org/10.3390/en12030547

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

1
State Grid Jiangsu Electric Power Company Ltd., Nanjing 210008, Jiangsu, China
2
GEIRI North America, 250 W Tasman Dr. STE 100, San Jose, CA 95134, USA
*
Author to whom correspondence should be addressed.
Received: 15 January 2019 / Revised: 2 February 2019 / Accepted: 2 February 2019 / Published: 11 February 2019
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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. View Full-Text
Keywords: Bayesian estimation; dynamic model; Gibbs sampling; parameter estimation; static model Bayesian estimation; dynamic model; Gibbs sampling; parameter estimation; static model
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Li, H.; Chen, Q.; Fu, C.; Yu, Z.; Shi, D.; Wang, Z. Bayesian Estimation on Load Model Coefficients of ZIP and Induction Motor Model. Energies 2019, 12, 547.

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