GA and PSO Techniques Based Optimal Tuning of Power System Stabilizers in a PV Integrated Power Network

Abstract

This paper involves the employment of Genetic Algorithm (GA) and Particle Swam Optimization (PSO) methods for the optimal tuning of gain and time constant parameters of power system stabilizers in a power network with integrated Photovoltaic (PV) energy conversion systems. To examine the critical modes of the PV integrated system and the impact of the PV integration on various modes of oscillations, eigenvalue analysis is carried out under various PV penetration levels. The investigations indicate that the rotor angle oscillations following a disturbance have less damping as the PV penetration level increases. To enhance the small signal stability, the excitation systems are provided with the signal from power system stabilizers (PSS). The gain and time constant parameters of the PSS are tuned utilizing the GA and PSO methods and compared with the conventional proportional-integral (PI) PSS. The preliminary investigations on the PV integrated standard IEEE power system reveal that the PSO and GA-based PSS are significantly better than the PI-PSS, and further, PSO-PSS is marginally more effective than the GA-PSS in damping the rotor angle oscillations.