  Transient stability assessment is an integral part of dynamic security
assessment of power systems. Traditional methods of transient stability
assessment, such as time domain simulation approach and direct methods, are
appropriate for offline studies and thus, cannot be applied for online
transient stability prediction, which is a major requirement in modern power
systems. This motivated the requirement to apply an artificial
intelligence-based approach. In this regard, supervised machine learning is
beneficial for predicting transient stability status, in the presence of
uncertainties. Therefore, this paper examines the application of a binary
support vector machine-based supervised machine learning, for predicting the
transient stability status of a power system, considering uncertainties of
various factors, such as load, faulted line, fault type, fault location and
fault clearing time. The support vector machine is trained using a Gaussian
Radial Basis function kernel and its hyperparameters are optimized using
Bayesian optimization. Results obtained for the IEEE 14-bus test system
demonstrated that the proposed method offers a fast technique for probabilistic
transient stability status prediction, with an excellent accuracy. DIgSILENT
PowerFactory and MATLAB was utilized for transient stability time-domain
simulations (for obtaining training data for support vector machine) and for
applying support vector machine, respectively.
