Synergy of Bacterial Foraging Algorithm and Particle Swarm optimization for Secure Power System Operation

Abstract

Bacterial Foraging (BF) and Particle Swarm optimizer (PSO) are two fertile and robust optimization methods. The synergy of these two optimizers improves their search capability while overcoming their handicaps as premature convergence and parameter dependency. A Multi-Objective hybrid Bacterial Foraging Particle Swarm (MOBFPS) optimizer is applied to maintain the stability and the security of the electrical power supply post disturbances/faults via minimizing the amount of the dropped load while increasing the lowest swing frequency. In this research, the Under-Frequency Load Shedding (UFLS) scheme based on MOBFPS was manipulated as bounded optimization problem with limits depicting the boundaries of the power system state variables. The viability of MOBFPS is validated against traditional approach, BF and PSO UFLS methods. Two different test cases, IEEE 9-bus and 39-bus systems, are considered for investigating the performance of the proposed UFLS schemes. The test systems are subjected to variety of the operating scenarios as: lose of single/multiple plants and substantial abrupt load increase. The IEEE 9- and 39-bus systems are simulated in the DigSilent power factor software, whereas MATLAB code was used for traditional, BF, PSO and MOBFPS. MOBFPS provided the highest swing frequency and the lowest dropped load amount. Moreover, the computation requirements of MOBFPS are comparable with PSO.