A novel method for classification of power quality disturbances based on a new one dimensional local binary pattern approach

Providing stable and robust power signals for electrical consumers and apparatuses is the most important responsibility of all electric power providers. Whenever the electric power signals suffer from disturbances which affect their quality and consequently peril the safety and right operation of electrical appliances, it is the main task of suppliers to detect and solve such obstacles. For defect prevention and faulty situation treatment caused by power quality disturbances, it is necessary to detect and classifythem in a reliable and guaranteed manner. In this paper, an innovative approach toward confident classification of four distinct types of power quality disturbances is proposed. The proposed method comprises of two main stages. In the first stage, noise resistive and steady features based on a new one dimensional local binary pattern approach are extracted and the desired feature vectors are formed. The second stage devotes to the reliable classification of the feature vectors belonging to the studied power quality disturbances based on conventional neural networks. The evaluation results are implemented in the form of Precision, Recall and F-measure. The F-measure about 91% demonstrates the higher efficiencyand performance of proposed method in comparison to the previously proposed strategies based on discrete wavelet and some statistical features with the same neural network classification.