SYNERGY OF SIMULATED ANNEALING AND PARTICLE SWARM ALGORITHMS FOR OPTIMIZING STATCOM DAMPING CONTROLLER | ||||
| JES. Journal of Engineering Sciences | ||||
| Article 4, Volume 43, No 6, November and December 2015, Page 857-881 PDF (958.18 K) | ||||
| Document Type: Research Paper | ||||
| DOI: 10.21608/jesaun.2015.115298 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Ahmed A. A. Hafez | ||||
| Electrical Engineering Department, Faculty of Engineering, Assiut University, Assiut, Egypt | ||||
| Abstract | ||||
| Synergy of Simulated Annealing (SA) and Particle Swarm Optimization (PSO) for optimal design of Static Synchronous Compensator (STATCOM) controller is advised in this article. The advised SA-PSO algorithm remedies the premature convergence and parameter dependency of PSO via SA probabilistic jumping property, metropolis process. The STATCOM controller design is formulated as nonlinear constrained optimization problem. The objective function considers the key operating states, while satisfying a predesigned stability margin and achieving performance objectives. The advised SA-PSO efficiently damps the power system oscillations following severe disturbance/fault conditions, while fulfilling the STATCOM basic function in regulating voltage profiles and confining with the operational limits. The dynamic performance of the Single Machine Infinite Bus (SMIB) and Multi Machine Power (MMP) systems equipped with STATCOM tuned via PSO, SA and SA-PSO is investigated under different operating conditions. The functionality of STATCOM stabilizing controller to restore SMIB system stability under different disturbances and loading conditions is verified. Furthermore, STATCOM damping controller capability of maintaining stability under fault scenarios is corroborated for MMP system. The comprehensive simulation results from SMIB and MMP systems demonstrate robustness, effectiveness, and visibility of the advised SA-PSO in tuning STATCOM compared with PSO and SA. | ||||
| Keywords | ||||
| Static Synchronous Compensator; Particle Swarm Optimization; Simulated Annealing; Hybrid; Single-Machine Infinite Bus System; Multi-Machine Power System; Disturbances; Faults | ||||
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