Intelligent classification of three phase fault and voltage collapse for correct distance relay operation using support vector machine

The conventional techniques used in distance relay operation are not fast enough in distinguishing between a three phase fault and voltage collapse and this may lead to unintended tripping of protection devices. Therefore, there is a need for fast detection of voltage collapse so as to improve the reliability of distance relay operation. This paper presents an intelligent approach to classify a voltage collapse and a three phase fault for distance relay operation by using the under impedance fault detector and support vector machine (SVM). To illustrate the proposed approach, simulations were carried out on the IEEE 39 bus test system using the PSS/E software. Test results shows that the proposed approach can accurately detect and classify fault and voltage collapse events for correct distance relay operation. To demonstrate the effectiveness of the SVM, a comparison is made with the results obtained from the application of the probabilistic neural network.