期刊
ENERGY AND BUILDINGS
卷 253, 期 -, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.enbuild.2021.111488
关键词
Optimal parameter estimation process; Load harmonic modeling; Power system harmonics; Time domain model; Equivalent circuit model
资金
- COLCIENCIAS through Convocatoria Nacional [567]
This paper presents a method for estimating the parameters of an SMPS Equivalent Circuit Model through an optimization process, which improves the accuracy of parameter estimation by using data regression and limiting the parameter region with datasheets, measurements, and manufacturing standards. The method is helpful for various loads based on SMPSs in practical applications related to demand-side management or collective harmonic impact.
The large-scale use of home appliances based on Switch Mode Power Supplies (SMPSs) raises the interest in power quality problems they might cause. Accurate modeling of SMPS-based loads allows predicting the collective impact caused on low voltage networks. However, model parameter values for a specific load are not often available. This paper proposes a nonintrusive Optimal Parameter Estimation (OPE) pro-cess for an SMPS Equivalent Circuit Model (ECM) through an optimization problem formulation using only current measurements. This OPE process is composed of the Narrow Search (NS) and Data Fitting (DF) methods. The NS method advantage is that it limits the parameter region using datasheets, measure-ments, and manufacturing standards to obtain optimal parameters for a specific supply voltage. The DF method leading convenience is that it computes the parameters from equations, as a function of nominal voltage and rated power by using data regression. The parameters estimated by the NS method induce errors that exceed about five times the DF method; in addition, the DF method results improve by about 10% methods available in the state-of-the-art. Thereby, the OPE process is helpful in practical applications related to demand-side management or collective harmonic impact for a wide range of SMPS-based loads. (c) 2021 Elsevier B.V. All rights reserved.
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