Classification of composite power quality disturbance using support vector machines

This paper presents an effective method based on support vector machines for identification of power system disturbances. Because of its advantages in nonlinear systems applications, phase space reconstruction is used to extract the distinctive features of the voltage signals. Eighteen particular disturbance signals are selected and then analyzed using this tools. Phase space reconstruction and support vector machines are used to identify and classify a variety of power disturbance signals. The results show that the methods proposed in this work are effective.