Transient Stability Improvement based on Optimal Power Flow using Particle Swarm Optimization

Optimal Power Flow (OPF) is one of most important aspect in power system operation and control. It is a non linear optimization problem based on minimization an objective function such as the active power losses, fuel cost, voltage deviation, voltage stability, reliability evaluation,...etc. Transient stability analysis is an important concept to determine whether the system is stable or not when a heavy disturbance such as the fault or loss of generation or a sudden large increased in the load,...etc occur in the system. In this article the transient stability according to the fault occurrence are used as a constraint in the optimal power flow, where minimization a three objective function of the active power losses, the fuel cost of thermal generation units and the voltage deviation at the load buses separately for each one can improve the transient stability and keep all the generators in the synchronization system. Particle Swarm Optimization PSO of an artificial intelligence optimization techniques has been used for this purpose. Minimization the objective function can be satisfied by choosing an optimal control variables from their constraints keeping the state variables in their limits. The control variables in this article are the generator voltage magnitude, the transformer tap changer and the generator active power except the slack generator while the state variables are the stability system based on increasing the clearing time of the circuit breaker, the slack generator active power, the generator reactive power and the magnitude of the load voltage. Increasing the clearing time of the circuit breaker leads to increase the maximum value of the generator rotor angle and go towards the instability system. The maximum clearing time that keep the system stable is called the Critical Clearing Time TCC. This article used the Optimal Power Flow with Transient Stability as a constraint to increase the Critical Clearing Time TCC with stable system. The proposed algorithm has been tested on the two systems of IEEE 9 bus and IEEE 30 bus and compare the result with other reference. The implementation of this work are programming by the author using matlab software.