Application of GA/GA-SA based fuzzy automatic generation control of a multi-area thermal generating system

Optimal integral gains (for integral gain control) and proportional-integral-derivative gains (for PID control) are computed by genetic algorithm (GA) and then hybrid genetic algorithm-simulated annealing (GA-SA) techniques for nominal values of area input parameters and optimal transient responses of area frequency deviations in terms of settling times, undershoots, overshoots and df/dt as output with incremental increase of area load for interconnected three equal generating areas. Though it is well known that the normal PID control is usually superior to integral control because of the advantages of each of the three individual control actions (proportional, integral and derivative), the author's contribution in the paper is optimizing these individual PID gains through GA or GA-SA methods to obtain an optimal PID controller, which would be further better than an optimal integral controller. These optimal PID gains are tested by plotting transient responses analytically by MATLAB based software program and then by "SIMULINK of MATLAB software." Both methods yield same results and prove that optimal PID gains are superior to suboptimal, arbitrary PID gains and optimal integral gains as well with respect to transient responses. The author's next contribution is to show optimal PID gains as determined by hybrid GA-SA technique to be more globally optimal than those determined by GA method. For off-nominal input parameters, transient responses as determined by fast acting Sugeno fuzzy logic technique reflect the same superiority of GA-SA based optimized gains, specially for PID control, the same has also been verified by "MATLAB-SIMULINK" software. (C) 2004 Elsevier B.V. All rights reserved.