Abstract—The project was done in Nanyang technological university, Singapore at Electric power research Lab during the period from Sep 2006 to Nov 2007. The liberalization of electric market has generated the unbundling of large utilities into separated generation, Transmission and Distribution companies with subsequent changes in the operating condition at electric power systems. Most of the breakdowns in the electric power systems are caused by unfavorable dynamic response of the networks following system disturbances. In addition, environmental and economic consideratioNS are forcing power systems to be operated closer to their limits of stability .Therefore dynamic security assessment of power systems is becoming increasingly important. Voltage collapse is one of the instability which occurs when the system is heavily loaded. In addition, the mechanism of voltage collapse is due to the load reacting to the voltage changed which leads to further voltage changes. In this project, indicators utilizing the generator-load mismatch and active/reactive power margins for on-line assessment of proximity of voltage instability of power system is to be developed. The approach utilizes an artificial neural networks(ANN) function approximator. In the area of Power systems, problems may be expressed in different ways depending upon their nature. The problem formulation may be expressed in terms of complex systems, say nonlinear, large scale, dynamical, discrete, stochastic, ransom like quasi-periodical, time-variant parameter systems etc. Among these factors, the nonlinear and the large scale systems make power system problems more hard to solve. Apart from linear systems, no good analytical system are available for the complicated problems. ANN is a good promising candidate for dealing with them.

Index Terms—SA, GA, SE, , PV curve, TO.

¹²Assistant Professor & senior member of IACSIT, 34Senior Lecturer,
¹Department of Electrical & Electronic Engineering,234Department of computer science and engineering, Bangladesh University
^*Corresponding author’s Email: DIPO0001@ntu.edu.sg,

Cite: Diponkar Paull, Md. Sadiq iqba, Mohammed Ibrahim Hussain and A. H. M. Shahriar parvez., "Voltage Stability Assessment Using Neural Networks in The Deregulated Market Environment,"  International  Journal  of
Computer and Electrical Engineering vol. 2, no. 4, pp. 747-752, 2010.