Contingency analysis in power systems transfer capability computation and enhancement using facts devices in deregulated power system

Abstract

Contingency analysis and risk management are important tasks for the safe operation of electrical energy network. Potential harmful disturbances that occur during the steady state operation of a power system are known as contingencies. Contingency analysis is carried out by using repeated load flow solutions for each of a list of potential component failures. This process has to be executed for all the possible contingencies, and repeated every time when the system load or structure changes significantly. Conventional methods are tedious and time consuming process, which is not desirable for real time applications. Various approximate methods have been proposed already for real time static security analysis of power systems. These methods reduce computational effort but they may not classify system contingencies accurately. In recent years Artificial Neural Networks (ANNs) are becoming popular in power system related applications such as load forecasting, Economic dispatch, Protection, Fault diagnosis and Relay coordination. The aim of this research work is to propose an approach to implement Complex Valued Neural Network (CVNN) for solving some of the problems encountered in power system operation. There is a great importance for load flow studies in planning, designing and future expansion of power systems. It will also help to determine the best operation of the existing system.