Abstract: There are many methods available for Load frequency control in an interconnected power system. This paper deals with tuning of PID controller for Load frequency control. A two area thermal power system is considered for study. Both the areas are equipped with PID controller. Parameters of these PID controllers are obtained using Particle Swarm Optimization. A comparative study on tuned values has been presented to verify effectiveness. Ziegler-Nichols method is used for tuning of parameters for comparative study. The simulation results demonstrate the effectiveness of the designed system in terms of reduced settling time and oscillations. MATLAB/SIMULINK was used as simulation tool. [Copyright &y& Elsevier]
Abstract: This paper presents a novel improved fuzzy particle swarm optimization (IFPSO) algorithm to the intelligent identification and control of a dynamic system. The proposed algorithm estimates optimally the parameters of system and controller by minimizing the mean of squared errors. The particle swarm optimization is enhanced intelligently by using a fuzzy inertia weight to rationally balance the global and local exploitation abilities. In the proposed IFPSO, every particle dynamically adjusts inertia weight according to particles best memories using a nonlinear fuzzy model. As a result, the IFPSO algorithm has a faster convergence speed and a higher accuracy. The performance of IFPSO algorithm is compared with advanced algorithms such as Real-Coded Genetic Algorithm (RCGA), Linearly Decreasing Inertia Weight PSO (LDWPSO) and Fuzzy PSO (FPSO) in terms of parameter accuracy and convergence speed. Simulation results demonstrate the effectiveness of the proposed algorithm. [Copyright &y& Elsevier]
Abstract: This paper discusses a novel technique to extract small signal equivalent circuit model parameters of GaAs MESFET device based on particle swarm optimization (PSO) technique. Three different variants of PSO namely basic PSO, Delta well quantum PSO (DQPSO) and Harmonic well quantum PSO (HQPSO) are implemented and compared. We find that these techniques extract the 16-element small signal model parameters of MESFET accurately. The simulations show that these algorithms are robust and are able to extract physically meaningful values for all circuit elements. The efficiency of this approach is demonstrated by the results that provide a good fit between measured and modeled S-parameter data over a frequency range of 0.5–25GHz. Comparative results indicate that both DQPSO and HQPSO give good quality of solutions. We also find that basic PSO algorithm is better than DQPSO and HQPSO for all the performance evaluation parameters, i.e. mean, standard deviation, amplitude and phase relative error and computational time. [Copyright &y& Elsevier]