Authors: G. Naresh, M. Ramalinga Raju and S. V. L. Narasimham
Power System Stabilizers (PSSs) are used to generate supplementary control signals for the excitation system to damp electromechanical oscillations. This paper presents a new evolutionary learning approach based on a Hybrid of Clonal Selection Algorithm and Particle Swarm Optimization (HCSAPSO) for tuning the parameters of PSSs in a multi-machine power system. The stabilizers are tuned to simultaneously shift the undamped and lightly damped electromechanical modes of all plants to a prescribed zone in the s-plane. A multi-objective problem is formulated to optimize a composite set of objective functions comprising the damping factor and damping ratio of lightly damped electromechanical modes. The performance of the proposed PSSs under different disturbances, loading conditions, and system configurations is investigated on New England 10-machine, 39-bus system. The eigenvalue analysis and nonlinear time domain simulations demonstrate the effectiveness of the proposed HCSAPSO based damping controllers to dampout the local and the inter-area modes of oscillations.
Power System Stabilizer, Multi-objective Optimization, Clonal Selection Algorithm, Particle Swarm Optimization, Multi-machine Power System
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