A Hybrid Intelligent System for Estimating a Load Margin to Saddle Node Bifurcation Point of Voltage Stability

This paper proposes a hybrid intelligent system for estimating a load margin to the saddle node bifurcation point of voltage stability. The proposed method is based on the integration of Regression Tree (RT) and Artificial Neural Network (ANN). Voltage stability analysis is one of the main concerns in power system operating and planning. Voltage stability analysis aims at evaluating the saddle node bifurcation point on PV or QV carves. So, it is necessary to estimate a load margin to the saddle node bifurcation point of voltage stability efficiently. In this paper, a new method is proposed to estimate the load margin with the hybrid method of RT and ANN. RT is used to classify data into terminal nodes and extract rules from each terminal node. ANN is constructed to estimate the load margin to the bifurcation points at each terminal node. Also, a new method for generating power system conditions is presented to consider the correlation of the nodal specified values. The proposed method is successfully applied to the IEEE 30-bus system in terms of computational accuracy and computational time.