University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 8 2 2017 08 23 Power Transformer Protection Using Fast Discrete S-Transform and Optimized Support Vector Machine Classifier with Bee Algorithm Power Transformer Protection Using Fast Discrete S-Transform and Optimized Support Vector Machine Classifier with Bee Algorithm 41 54 21743 10.22108/isee.2017.21743 FA Amangaldi Koochaki Department of Electrical Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad katoul, Iran Ali Akbar Abdoos Department of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran Ghasem Mirbabaee Rokni Department of Electrical Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad katoul, Iran Journal Article 2015 10 22 This study presents a Fast Discrete S-Transform based method to discriminate internal fault currents of power transformer from other disturbances. A criterion function is proposed based on some extracted features from the obtained S-Matrix and frequency contours. First, the Support Vector Machine (SVM) is extended for feature classification. Then, the Bee optimization algorithm is implemented to select optimal parameters of SVM classifier. To do this, several conditions of external and internal faults, inrush current and different levels of current transformer saturation are simulated using PSCAD/EMTDC software. In addition, differential currents are contaminated by noise for modeling real conditions. To evaluate the performance of proposed scheme, the obtained results are compared with results of other methods. Comparing the results shows that the proposed method remains stable with high accuracy during transformer excitation and external faults. Also, the proposed approach is effective, fast and not affected by noise during classification of different events.   This study presents a Fast Discrete S-Transform based method to discriminate internal fault currents of power transformer from other disturbances. A criterion function is proposed based on some extracted features from the obtained S-Matrix and frequency contours. First, the Support Vector Machine (SVM) is extended for feature classification. Then, the Bee optimization algorithm is implemented to select optimal parameters of SVM classifier. To do this, several conditions of external and internal faults, inrush current and different levels of current transformer saturation are simulated using PSCAD/EMTDC software. In addition, differential currents are contaminated by noise for modeling real conditions. To evaluate the performance of proposed scheme, the obtained results are compared with results of other methods. Comparing the results shows that the proposed method remains stable with high accuracy during transformer excitation and external faults. Also, the proposed approach is effective, fast and not affected by noise during classification of different events.   https://isee.ui.ac.ir/article_21743_0eacb8ce6c7b0fba582ca23b6f5f3337.pdf