Application of support vector machines for fault diagnosis in power transmission system

Post-fault studies of recent major power failures around the world reveal that mal-operation and/or improper co-ordination of protection system were responsible to some extent. When a major power disturbance occurs, protection and control action are required to stop the power system degradation, restore the system to a normal state and minimise the impact of the disturbance. However, this has indicated the need for improving protection co-ordination by additional post-fault and corrective studies using intelligent/knowledge-based systems. A process to obtain knowledge-base using support vector machines (SVMs) is presented for ready post-fault diagnosis purpose. SVMs are used as Intelligence tool to identify the faulted line that is emanating and finding the distance from the substation. Also, SVMs are compared with radial basis function neural networks in datasets corresponding to different fault on transmission system. Classification and regression accuracies are is reported for both strategies. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighbouring line connected to the same substation. This may help to improve the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. To validate the proposed approach, results on IEEE 39-Bus New England system are presented for illustration purpose.