  The system comprises of three interconnected power system networks based on
thermal, wind and hydro power generation. The load variation in any one of the
network results in frequency deviation in all the connected systems.The PI
controllers have been connected separately with each system for the frequency
control and the gains (Kp and Ki) of all the controllers have been optimized
along with frequency bias (Bi) and speed regulation parameter (Ri). The
computationally intelligent techniques like bacterial foraging optimization
(BFO) and particle swarm optimization (PSO) have been applied for the tuning of
controller gains along with variable parameters Bi and Ri. The gradient descent
(GD) based conventional method has also been applied for optimizing the
parameters Kp, Ki,Bi and Ri.The frequency responses are obtained with all the
methods. The performance index chosen is the integral square error (ISE). The
settling time, peak overshoot and peak undershoot of all the frequency
responses on applying three optimization techniques have been compared. It has
been observed that the peak overshoot and peak undershoot significantly reduce
with BFO technique followed by the PSO and GD techniques. While obtaining such
optimum response the settling time is increased marginally with bacterial
foraging technique due to large number of mathematical equations used for the
computation in BFO. The comparison of frequency response using three techniques
show the superiority of BFO over the PSO and GD techniques. The designing of
the system and tuning of the parameters with three techniques has been done in
MATLAB/SIMULINK environment.
