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This paper presents a methodology of cross-wavelet transform aided Fischer linear discriminant analysis (FLDA)-based feature selection and classification for sensing simultaneous occurrence of multiple power quality disturbances. A linear support vector machine is used for classification of the extracted features as it suits well with FLDA. This scheme is implemented in a general purpose microcontroller...
This paper presents a new methodology of Cross-Hilbert Huang transform based feature selection for sensing simultaneous occurrence of multiple power quality disturbances. Kernel PCA is used for feature selection because this method is well suited for non-linear and non-stationary multiple power quality disturbances. A linear support vector machine is used for classification of the extracted features...
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