Robust design of multimachine power system stabilizers (PSSs) using simulated annealing (SA) optimization technique is presented in this paper. The proposed approach employs SA to search for optimal parameter settings of a widely used conventional fixed-structure lead-lag PSS (CPSS). The parameters of the proposed simulated annealing based power system stabilizer (SAPSS) are optimized in order to shift the system electromechanical modes at different loading conditions and system configurations simultaneously to the left in the s-plane. Incorporation of SA as a derivative-free optimization technique in PSS design significantly reduces the computational burden. One of the main advantages of the proposed approach is its robustness to the initial parameter settings. In addition, the quality of the optimal solution does not rely on the initial guess. The performance of the proposed SAPSS under different disturbances and loading conditions is investigated for two multimachine power systems. The eigenvalue analysis and the nonlinear simulation results show the effectiveness of the proposed SAPSS's to damp out the local as well as the interarea modes and enhance greatly the system stability over a wide range of loading conditions and system configurations

Robust design of multimachine power system stabilizers usingsimulated annealing

Bacterial Particle Swarm Optimization

Optimization in multicore processor environment is significant in real world dynamic applications, as it is crucial to find and track the change effectively ov er time, which requires an optimization algorithm. In massively parallel processing multicore processor a rchitectures, like other population based metaheuri stics Constraint based Bacterial Foraging Particle Swarm Optimization (CBFPSO) scheduling can be effectively implemented. In this study we discuss possible appr oaches to parallelize CBFPSO in multicore system, which uses different constraints; to exploit parall elism are explored and evaluated. Due to the abilit y of keeping good balance between convergence and maintenance, for real world applications, among the various algorithms for parallel architecture optimi zation CBFPSOs are attracting more and more attentions in recent years. To tackle the challenges of parall el architecture optimization, several strategies ha ve been proposed, to enhance the performance of Particle Swarm Optimization (PSO) and have obtained success on various multicore parallel architecture optimizatio n problems. But there still exist some issues in mu lticore architectures which require to be analyzed carefull y. In this study, a new Constraint based Bacterial Foraging Particle Swarm Optimization (CBFPSO) scheduling for multicore architecture is proposed, which updates the velocity and position by two bacterial behaviours, i.e., reproduction and elimination disp ersal. The performance of CBFPSO is compared with the simulation results of GA and the result shows that the proposed algorithm has pretty good performance on almost all types of cores compared to GA with respec t to completion time and energy consumption.

Low complexity constraints for energy and performance management of heterogeneous multicore processors using dynamic optimization

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Low complexity constraints for energy and performance management of heterogeneous multicore processors using dynamic optimization