Design and Implementation of Brain Emotional Controller for Load Frequency Control in Multi-Area AGC
Keywords:
Load frequency control (LFC), Brain emotional learning based intelligent controller (BELBIC), Automatic generation control (AGC), Genetic Algorithm (GA), Particle Swarm Optimization (PSO).Abstract
An artificial intelligence controller called the Brain Emotional Learning Based Intelligent Controller (BELBIC) was inspired by the limbic systems of mammals. It has been used successfully in the field of control systems. This abstract presents simulation findings obtained using MATLAB/Simulink to analyze the performance of a power system exposed to shocks and parameter changes while accounting for the system's intrinsic nonlinearity. An automatic generation control (AGC) system was created by this work. The parameters of PI controllers are optimized using a range of readily available optimization techniques, such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The controller parameter (ITSE) is evaluated by each of these algorithms using a different cost function, such as the square error multiplied by integral time. By employing the computationally intelligent BELBIC technique, the performance of the AGC system was enhanced. Using readily available optimization approaches, the effectiveness of the GA-PI and PSO-PI controllers was compared with that of the BELBIC. The majority of the work currently in publication on AGC uses traditional methods for selecting the best controller parameters, such as Integral Time Squared Error (ITSE). This problem is addressed by the proposed BELBIC. The simulation results demonstrate the BELBIC's dependability and sensitivity to changes in parameters and load. It outperforms the GA-PI and PSO-PI controllers under a range of operational load scenarios, particularly when it comes to managing the load frequency tuning problem in multi-area power plants. The results demonstrate the BELBIC's efficacy as a power system control solution.
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