Support Vector Regression Model for the prediction of Loadability Margin of a Power System

SVR methodology will estimate the loadability margin not only under normal conditions and different loading directions.It improves the accuracy of mean square error and reduces the prediction time, by choosing various kernel type and SVR parameter.The results of SVRs (nu-SVR and epsilon-SVR) are compared with RBF networks corresponding to various loading scenarios of a power system to estimate the voltage stability margin.The estimation of loading margin is achieved with least absolute error, minimum training and testing computational time. Loadability limits are critical points of particular interest in voltage stability assessment, indicating how much a system can be stressed from a given state before reaching instability. Thus estimating the loadability margin of a power system is essential in the real time voltage stability assessment. A new methodology is developed based on Support Vector Regression (SVR) which is the most common application form of Support Vector Machines (SVM). The proposed SVR methodology can successfully estimate the loadability margin under normal operating conditions and different loading directions. SVR has the feature of minimizing the generalization error in achieving the generalized network over the other mapping methods. In this paper, the SVR input vector is in the form of real and reactive power load, while the target vector is lambda (loading margin). To reduce both mean square error and prediction time in SVR, the kernel type and SVR parameters are chosen determined by using grid search based on 10-fold cross-validation method for the best SVR network. The results of SVRs (nu-SVR and epsilon-SVR) are compared with RBF neural networks and validated in the IEEE 30 bus system and IEEE 118 bus system at different operating scenarios. The results demonstrate the effectiveness of the proposed method for on-line prediction of loadability margins of a power system.