Analysis of Voltage Stability States for Power Transmission Network Using Artificial Neural Network

Abstract

Recent trends reveals that in order to operate the modern transmission and distribution power system networks with maximum reliability and security, a power system operator must have the knowledge of the voltage stability margin. It has become a challenging task to accomplish fast and accurate indications of newlinevoltage stability margin in power systems. Out of the various voltage stability indices sketched out by various researchers, the voltage stability indices proposed in this work is based on two-bus equivalent network of multi-bus network considering the line susceptance because it is simple, fast and estimationally newlineworkable to supervise the voltage stability of a power system. By considering the line susceptance, small variations can be easily identified as compared to other standard indicators. In this work, the effect of Static VAR compensator in the weakest bus has also been considered through contingency analysis and ranking newlineof transmission lines. Furthermore, in this study Artificial Neural Network based supervised learning algorithm has been conferred for the prediction of voltage security in a power system network. Implementation of ANN has been proved efficient in power system networks due to its powerful object recognition and data compression, less computational time required to predict the output during the newlinetesting stage. Probabilistic neural network (PNN) along with Pattern Recognition strategy deals with the classification of patterns into a number of classes. So the work in this study also indulges PNN to provide information regarding the various operating states of a power system network. Finally a distinguishing strategy in Energy Management scenario has been proposed in this work using Artificial Neural Network so that the Energy Control Centre is competent enough to take necessary remedial actions against voltage instability and voltage collapse problems.