الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
The electromechanical oscillations that observed in the electrical power system, due to the increasing of the power system in demand and structure. This increase in the power system structure occurred since the economic and environmental constraints prevent build new power lines. Also, the increase in power demand leads to meet a great extent by the boost loading of available lines. So, this oscillation leads to decrease the damping power among generators and the overall system stability. The aim of the power system stabilizer (PSS) is to damp the oscillation and enhance the dynamic system stability through the generators excitation control system. This thesis proposes to produce an intelligent power system stabilizer (IPSS). The proposal for producing IPSS comprise two steps, optimize conventional PSS parameters depending on a nonlinear manner offline by an artificial intelligence tool and real-time implementation of the designed IPSS of hardware-in-the-loop configuration. The optimization process of the IPSS implemented by optimizes the well-known CPSS IEEE type-PSS1A (Lead-Lag) parameters by two AI algorithms bacterial foraging (BG), and modified particle swarm optimization (MPSO). Two familiar fitness functions have used in this study, maximizing the smallest damping ratio, and minimizing of the biggest real part of the Eigenvalues. Moreover, the maximum overshoot, the settling time, and the steady-state error are used as performance indices to analyze the test results.iii The benchmark simulation problem contains a 4-machine 11- buse 2-area power system is used for stability studies in a MATLAB / SIMULINK. The proposed BG and MPSO-based lead-lag PSS compared with multi-band MB-PSS with simplified settings: IEEE® type PSS4B according to IEEE Std. 421.5, lead-lag PSS with (∆𝜔) as input signal, and lead-lag PSS with (∆𝑃𝑎) as input signal to demonstrate its robustness. Two test cases used for the assessment method of the proposed IPSS in comparison with the other three mentioned PSSs, small signal, and large signal tests. The second part of this thesis has covered the intelligent power system stabilizer (PSS) performance assessed in HIL real-time test using dSPACE real-time controller. Intelligent PSS build on the dSPACE DS1104 real-time controller board. A workbench multi-machine electrical power system stability problem used to connect with the PSS and to apply the dynamic tests from MATLAB / SIMULINK program during real-time and simulation tests. Four test cases utilized in real-time to evaluate the PSS performance. All the real-time tests applied to the system during light, nominal, and heavy loading condition. An ITAE and ISE performance indices used to analyze and compare between PSS performance during real-time and simulation.