Machine learning approach to solving the transient stability assessment problem

In this paper, transient stability assessment is performed on a power system using a classification approach and data mining algorithms. As a first step, offline training data was collected by conducting load flow studies under normal operating conditions and faulty operating conditions at buses, at three different locations at lines and at different load levels. Twenty-three features were chosen to represent the training data for each load flow simulation. A support vector machine model was built and trained using the training data as well as a Naïve Bayes model and Decision Tree model. Then an online testing model was developed and real-time data was used to test the validity of the model developed. The results indicate a higher accuracy and less time consumed by the core vector machine model compared to previous models available in literature. The IEEE 14 bus system was used for training data and for verifying the speed and accuracy of the proposed data mining algorithm.