الفهرس 
IntroductionOnly 14 pages are availabe for public view
 |  | from | 132 |  |  |
| | | from | 132 | | |
Abstract
The performance of a power system is affected when a disturbance occurs. This will result in insufficient or loss of power. Controllers are used to bring the system back to its stable operating conditions. Nowadays, the conventional lead-lag power system stabilizer (PSS) controller is widely used by the power system utility.
The PID controller is a well-established type of controller. It is traditionally tuned using standard techniques such as Ziegler-Nichols methods. The results of these techniques are not satisfied with the nonlinear systems. Today, artificial intelligent techniques are used for tuning the PID controller such as Genetic Algorithm (GA) and Simulated annealing (SA).
In this thesis, the power system under study is considered as a synchronous machine connected to an infinite bus. When it is exposed to a disturbance, it takes more time to bring back to its equilibrium state. So, the PID controller is used and its parameters are tuned by the classical method (Ziegler and Nichols method). The results show that the power system is stable and the time-domain specifications are slightly improved but this improvement is not satisfied.
To improve stability and performance of the power system under study, an artificial intelligent technique is used for tuning the PID controller which is called Bacterial Foraging Algorithm. The results show that the power system performance and stability are improved, and the time-domain specifications are enhanced.
To more enhancement and marked improvement in the power system performance and stability, the Hybrid Particle Swarm-Bacterial Foraging Optimization (HPS-BFO) technique is used for tuning the PID controller. from the simulation results, the proposed method is capable of guaranteeing the stability and performance of the power system better than the PID-PSS based classical PID or BFA. It has proved its effectiveness, which gives good promotion for its use in optimal control of electric power system stability.