Semnan University Press Journal of Modeling in Engineering 2008-4854 13 41 2015 08 23 Simulation and classification of power quality disturbances Using Neural Network Simulation and classification of power quality disturbances Using Neural Network 137 146 1732 10.22075/jme.2017.1732 FA Journal Article 2017 01 28 Nowadays, increasing use of electronic instruments and nonlinear loads in Power systems, make the power quality problem as one of the most important issues. In this article, the produced data from mathematical equations and PSCAD software simultaneously have been used to simulate power quality disturbances. Because of super performance of neural networks in pattern recognition and classification, the MLP neural network for classification of power quality disturbances is used in this paper. The neural networks have been developed by simulation of nonlinear terms, and they indicated their priority for pattern recognition and classification. STFT and DWT transform to extract signal's features have been used. After classification of disturbances using MLP, the neural network robustness has been examined in different levels in presence of the noise. With presence of noise, neural network classifies all the events with 98.22 percent of accuracy. Finally, results of this article are compared with other researcher's works. Nowadays, increasing use of electronic instruments and nonlinear loads in Power systems, make the power quality problem as one of the most important issues. In this article, the produced data from mathematical equations and PSCAD software simultaneously have been used to simulate power quality disturbances. Because of super performance of neural networks in pattern recognition and classification, the MLP neural network for classification of power quality disturbances is used in this paper. The neural networks have been developed by simulation of nonlinear terms, and they indicated their priority for pattern recognition and classification. STFT and DWT transform to extract signal's features have been used. After classification of disturbances using MLP, the neural network robustness has been examined in different levels in presence of the noise. With presence of noise, neural network classifies all the events with 98.22 percent of accuracy. Finally, results of this article are compared with other researcher's works. https://modelling.semnan.ac.ir/article_1732_f4c999c3afdd093ba5e3fc338064dba1.pdf