Power Quality Identification Based on S-transform and RBF Neural Network
This paper presents a new power quality (PQ) disturbances identification method based on S-transform time-frequency analysis and RBF network. The proposed technique consists of time-frequency analysis, feature extraction, and pattern classification. Though there are several time-frequency analysis methods existing in the literature, this paper uses S-transform to obtain the time-frequency characteristics of PQ events because of its superior performance under noise. Using the time-frequency characteristics, a set of features is extracted for identification of power quality disturbances. Finally, a RBF network is developed for classification of the power quality disturbances. The proposed method is simple and reached 97.5% identification correct ratio under high signal to noise ratio for those most important disturbances in power system.
KeywordsSine Wave Power Quality Correct Ratio Good Generalization Ability Power Quality Disturbance
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