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

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.