Static Voltage Stability Analysis by Using SVM and Neural Network

Voltage stability is an important problem in power system networks. In this paper، in terms of static voltage stability، and application of Neural Networks (NN) and Supported Vector Machine (SVM) for estimating of voltage stability margin (VSM) and predicting of voltage collapse has been investigated. This paper considers voltage stability in power system in two parts. The first part calculates static voltage stability margin by Radial Basis Function Neural Network (RBFNN). The advantage of the used method is high accuracy in online detecting the VSM. Whereas the second one، voltage collapse analysis of power system is performed by Probabilistic Neural Network (PNN) and SVM. The obtained results in this paper indicate، that time and number of training samples of SVM، are less than NN. In this paper، a new model of training samples for detection system، using the normal distribution load curve at each load feeder، has been used. Voltage stability analysis is estimated by well-know L and VSM indexes. To demonstrate the validity of the proposed methods، IEEE 14 bus grid and the actual network of Yazd Province are used.