Statistical Assessment of New Coordinated Design of PSSs and SVC via Hybrid Algorithm
Ali, E. S.1, Abd-Elazim, S. M2
1E. S. Ali, Electric Power and Machine Department, Zagazig University, Zagazig, Egypt.
2S. M. Abd-Elazim, Electric Power and Machine Department, Zagazig University, Zagazig, Egypt.
Manuscript received on January 21, 2013. | Revised Manuscript received on February 10, 2013. | Manuscript published on February 28, 2013. | PP: 647-654 | Volume-2 Issue-3, February 2013.  | Retrieval Number: C1041022313 /2013©BEIESP

Open Access | Ethics and Policies | Cite
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The assessment of new coordinated design of Power System Stabilizers (PSSs) and Static Var Compensator (SVC) in a multimachine power system via statistical method is proposed in this paper. The coordinated design problem of PSSs and SVC over a wide range of loading conditions is formulated as an optimization problem. The Bacterial Swarming Optimization (BSO), which synergistically couples the Bacterial Foraging (BF) with the Particle Swarm Optimization (PSO), is employed to search for optimal controllers parameters. By minimizing the proposed objective function, in which the speed deviations between generators are involved; stability performance of the system is improved. To compare the capability of PSS and SVC, both are designed independently, and then in a coordinated manner. Simultaneous tuning of the BSO based coordinated controller gives robust damping performance over wide range of operating conditions and large disturbance in compare to optimized PSS controller based on BSO (BSOPSS) and optimized SVC controller based on BSO (BSOSVC). Moreover, a statistical T test is performed to ensure the effectiveness of coordinated controller versus uncoordinated one.
Keywords: Statistical T test, SVC, PSSs, Multimachine Power System, Coordinated design, Bacteria Swarm Optimization.