Using a support vector machine (SVM) to improve generalization ability of load model parameters

Ma, Jian, Dong, Zhao Yang and Zhang, Pei (2009). Using a support vector machine (SVM) to improve generalization ability of load model parameters. In: Power Systems Conference and Exposition, 2009, PSCE '09, IEEE/PES. 2009 IEEE/PES Power Systems Conference and Exposition, PSCE 2009, Seattle, WA United States, (). 15 - 18 March 2009. doi:10.1109/PSCE.2009.4839969


Author Ma, Jian
Dong, Zhao Yang
Zhang, Pei
Title of paper Using a support vector machine (SVM) to improve generalization ability of load model parameters
Conference name 2009 IEEE/PES Power Systems Conference and Exposition, PSCE 2009
Conference location Seattle, WA United States
Conference dates 15 - 18 March 2009
Proceedings title Power Systems Conference and Exposition, 2009, PSCE '09, IEEE/PES
Journal name IEEE Applied Power Electronics Conference and Exposition. Conference Proceedings
Place of Publication Piscataway, NJ United States
Publisher I E E E
Publication Year 2009
Year available 2009
Sub-type Fully published paper
DOI 10.1109/PSCE.2009.4839969
Open Access Status
ISBN 9781424438112
9781424438105
ISSN 1048-2334
Total pages 8
Collection year 2009
Language eng
Abstract/Summary Load modeling plays an important role in power system stability analysis and planning studies. The parameters of load models may experience variations in different application situations. Choosing appropriate parameters is critical for dynamic simulation and stability studies in power system. This paper presents a method to select the parameters with good generalization ability based on a given large number of available parameters that have been identified from dynamic simulation data in different scenarios. Principal component analysis is used to extract the major features of the given parameter sets. Reduced feature vectors are obtained by mapping the given parameter sets into principal component space. Then support vectors are found by implementing a classification problem. Load model parameters based on the obtained support vectors are built to reflect the dynamic property of the load. All of the given parameter sets were identified from simulation data based on the New England 10-machine 39-bus system, by taking into account different situations, such as load types, fault locations, fault types, and fault clearing time. The parameters obtained by support vector machine have good generalization capability, and can represent the load more accurately in most situations.
Subjects 2102 Curatorial and Related Studies
2207 Control and Systems Engineering
2208 Electrical and Electronic Engineering
Keyword Generalization
Load modeling
Parameter identification
Principal components analysis
Support vector machine
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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