An Application of Artificial Bee Colony and Cohort Intelligence in the Automatic Generation Control of Thermal Power Systems

The interconnected power system encompasses multiple regions, and various parameters play a crucial role in achieving stability and efficiency in power systems. Recent research has proposed a numerical optimization approach, specifically utilizing the Artificial Bee Colony (ABC) and Cohort Intelligence Optimization (CIO) methods, to fine-tune the parameters of a Proportional-Integral-Derivative (PID) controller for automatic generation control in a mutually dependent two-area power system with reheat thermal power generation. Both ABC and CIO are employed to determine the optimal gain values for the controller, involving the evaluation of multiple cost functions. To evaluate the effectiveness of these techniques in the context of the interconnected reheat thermal power system, a comparison is made between the performance of ABC-optimized controllers and CIO-optimized PID controllers. The findings of the study indicate that PID controllers optimized using the CIO method outperform those optimized using the ABC method in the context of the mutually dependent reheat thermal power system.