Design of Robust Power System Stabilizers
for Multi-machine Power System via Evolutionary Model Order Reduction Technique using Fast Output Sampling Method
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The accurate analysis of higher order systems are complex, uneconomic and complicated to be used in real problems. Therefore, it is reasonable to sacrifice with model accuracy in order to obtain a simpler model. It is recommended to reduce the order of system model while keeping the dominant behavior of the original system. This low-order model will help to better understanding of the physical system, reduce computational complexity, reduce hardware complexity, economic, simplify the controller design and finally, the simulation becomes computationally cheaper, which saves time and resources. ...
The accurate analysis of higher order systems are complex, uneconomic and complicated to be used in real problems. Therefore, it is reasonable to sacrifice with model accuracy in order to obtain a simpler model. It is recommended to reduce the order of system model while keeping the dominant behavior of the original system. This low-order model will help to better understanding of the physical system, reduce computational complexity, reduce hardware complexity, economic, simplify the controller design and finally, the simulation becomes computationally cheaper, which saves time and resources. This dissertation proposed the designing of robust power system stabilizers (PSSs) using fast output sampling controllers via reduced order model using particle swarm optimization (PSO) method for good damping enhancement for various operating points of multi-machine power systems. Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system to enhance the damping of electrical power system during low frequency oscillations.
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