Oppositional cuckoo optimization algorithm for optimal tuning of power system stabilizers

In this novel approach, oppositional based learning is implemented on cuckoo optimization algorithm to tune the power system stabilizer parameters which are added to the excitation system to damp low frequency oscillation that pertain in large power system. Eigen value based objective function incorporating damping factor and damping ratio of lightly damped electromechanical modes is considered in the PSSs design problem. The effectiveness of the proposed algorithm is demonstrated on a single machine infinite bus from the Heffron-Phillips model. The most important advantage of applying oppositional based cuckoo optimization algorithm is that it quickly reaches to the optimal solution. From the computational results, it is revealed that the proposed algorithm gains upper hand in damping the low frequency oscillations. Results are presented over a wide range of loading condition to show the effectiveness of the proposed algorithm. Simulated results of the proposed OCOA method are compared with those obtained by COA, OGSA, GSA and DE to demonstrate the feasibility of the proposed algorithm.