DocumentCode :
3214185
Title :
Using a support vector machine (SVM) to improve generalization ability of load model parameters
Author :
Ma, Jian ; Dong, Zhao Yang ; Zhang, Pei
Author_Institution :
Pacific Northwest Nat. Lab. (PNNL), Richland, WA
fYear :
2009
fDate :
15-18 March 2009
Firstpage :
1
Lastpage :
8
Abstract :
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.
Keywords :
load forecasting; power engineering computing; power system stability; principal component analysis; support vector machines; generalization ability; load model parameters; power system planning; power system stability analysis; principal component analysis; support vector machine; Data mining; Load modeling; Power system analysis computing; Power system dynamics; Power system modeling; Power system planning; Power system simulation; Power system stability; Principal component analysis; Support vector machines; Load modeling; clearing time; fault location; fault type; generalization; load type; parameter identification; principal components analysis; support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Systems Conference and Exposition, 2009. PSCE '09. IEEE/PES
Conference_Location :
Seattle, WA
Print_ISBN :
978-1-4244-3810-5
Electronic_ISBN :
978-1-4244-3811-2
Type :
conf
DOI :
10.1109/PSCE.2009.4839969
Filename :
4839969
Link To Document :
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