Comparison and Optimal Design of SSSC Controller Based on ICA and PSO for Power System Dynamic Stability Improvement
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Abstract
A new imperialist competitive algorithm (ICA)-based approach is proposed for optimal selection of the static synchronous series compensator (SSSC) damping controller parameters in order to shift the closed loop eigenvalues toward the desired stability. The optimal selection of the parameters for the SSSC controllers is converted to an optimization problem which is solved by recently developed evolutionary ICA method. This optimization algorithm has a strong ability to find the most optimistic results for dynamic stability improvement. Single machine infinite bus (SMIB) system has been considered to examine the operation of proposed controllers. The input power variation of generator is considered as a disturbance. The effectiveness of the proposed controller for damping low frequency oscillations is tested and results compared with particle swarm optimization (PSO). Also, the performance of proposed method is tested in dierent loading conditions. In addition, the potential and superiority of the proposed ICA method over the PSO is demonstrated. Also, performance of ICA in 10 times run is the same as in 1 time run. The simulation results analysis show that the designed ICA based SSSC damping controller has an excellent capability in damping low frequency oscillations and enhance rapidly and greatly the dynamic stability of the power systems, in compare to PSO technique.
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