A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Localization of the complex spectrum: the S transform
IEEE Transactions on Signal Processing
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Non-stationary power signal classification using local linear radial basis function neural networks
International Journal of Knowledge-based and Intelligent Engineering Systems
Power quality time series data mining using S-transform and fuzzy expert system
Applied Soft Computing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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This paper presents an advanced signal processing technique known as S-transform (ST) to detect and quantify various power quality (PQ) disturbances. ST is also utilized to extract some useful features of the disturbance signal. The excellent time-frequency resolution characteristic of the ST makes it an attractive candidate for analysis of power system disturbance signals. The number of features required in the proposed approach is less than that of the wavelet transform (WT) for identification of PQ disturbances. The features extracted by using ST are used to train a support vector machine (SVM) classifier for automatic classification of the PQ disturbances. Since the proposed methodology can reduce the features of disturbance signal to a great extent without losing its original property, it efficiently utilizes the memory space and computation time of the processor. Eleven types of PQ disturbances are considered for the classification purpose. The simulation results show that the combination of ST and SVM can effectively detect and classify different PQ disturbances.