Adaptive S transform for feature extraction in voltage sags

Abstract

This paper proposes an adaptive S transform (AST) to extract the feature vectors of voltage sags. With the effective window width matches the Fourier spectrum of sag signals, the standard deviation σ of Gaussian window may be determined as well. The narrowest and the widest window width of AST are obtained without additional parameters and iterative computing. Then, the optimal frequency resolution and time resolution are got respectively. Compared with ST, AST provides better time–frequency resolution to extract more precise feature vectors of eight types of voltage sags. Based on the time–frequencyrepresentation of AST, five disturbance features are extracted to construct the feature vector in this paper. In addition, four machine learning classifiers and two fuzzy clustering classifiers are used to analyze the validity and redundancy of these features. Through analyzing the classification accuracies and time costs of these classifiers with different training sets and different level of noise, it can be concluded that the machine learning classifiers perform better in classification accuracy and stability than fuzzy clustering classifiers.