Several incidents that occurred around the world involving power failure caused by unscheduled line outages were identified as one of the main contributors to power failure and cascading blackout in electric power environment. With the advancement of computer technologies, artificial intelligence (AI) has been widely accepted as one method that can be applied to predict the occurrence of unscheduled disturbance. This paper presents the development of automatic contingency analysis and ranking algorithm for the application in the Artificial Neural Network (ANN). The ANN is developed in order to predict the post-outage severity index from a set of pre-outage data set. Data were generated using the newly developed automatic contingency analysis and ranking (ACAR) algorithm. Tests were conducted on the 24-bus IEEE Reliability Test Systems. Results showed that the developed technique is feasible to be implemented practically and an agreement was achieved in the results obtained from the tests. The developed ACAR can be utilised for further testing and implementation in other IEEE RTS test systems particularly in the system, which required fast computation time. On the other hand, the developed ANN can be used for predicting the post-outage severity index and hence system stability can be evaluated.
Control action based on steady-state security assessment using an Artificial Neural Network
