Composite power system reliability evaluation using support vector machines on a multicore platform

Abstract

Monte Carlo Simulation (MCS) is a very powerful and flexible tool when used for sampling states during the probabilistic reliability assessment of power systems. Despite the advantages of MCS, the method begins to falter when applied to large and more complex systems of higher dimensions. In these cases it is often the process of classifying states that consumes the majority of computational time and resources. This is especially true in power systems reliability evaluation where the main method of classification is typically an Optimal Power Flow (OPF) formulation in the form of a linear program (LP). Previous works have improved the computational time required for classification by using Neural Networks (NN) of varying types in place of the OPF. A method of classification that is lighter weight and often more computationally efficient than NNs is the Support Vector Machine (SVM). This work couples SVM with the MCS algorithm in order to improve the computational time of classification and overall reliability evaluation. The method is further extended through the use of a multi-core architecture in order to further decrease computational time. These formulations are tested using the IEEE Reliability Test Systems (IEEE-RTS79 and IEEE-RTS96). Significant improvements in computational time are demonstrated while a high level of accuracy is maintained.