GA Tuned PI controller for Two Area System Using MATLAB SIMULINK
Introduction
Greetings, viewers! In today's exploration, we'll be delving into the application of a Genetic Algorithm (GA) for tuning the Proportional-Integral (PI) controller parameters in a two-area power system using MATLAB. This innovative approach allows us to optimize the controller gains for improved system performance.
Two-Area Power System Overview
The two-area power system consists of two interconnected areas connected by a tie line. Each area has its own set of generators, loads, and a tie line representing power exchange. The goal is to optimize the system's performance, considering factors like frequency and tie line power flow, by tuning the PI controller parameters.
Controller Implementation
In the absence of a controller, we observe the system's response. Subsequently, an Integral Controller is introduced with a fixed gain of 0.3 for both areas. This initial implementation provides a baseline for comparison.
Genetic Algorithm Optimization
The real magic happens when we employ a Genetic Algorithm to dynamically optimize the PI controller gains. The algorithm's objective function is designed to minimize the frequency deviation in both areas. Through iterations, the GA refines the gains of the PI controller to enhance system performance.
Simulation and Results
The MATLAB code for the GA optimization is executed, and the algorithm goes through multiple iterations, progressively refining the controller gains. Fitness function values are tracked through each iteration, indicating the improvement in performance.
After convergence, the optimized gains (kp1, ki1, kp2, ki2) obtained from the GA are implemented in the PI controller. The system response shows a notable reduction in undershoot, improved settling time, and enhanced performance in terms of frequency and tie line power flow.
Conclusion
The application of Genetic Algorithms for tuning PI controller parameters in a two-area power system demonstrates the effectiveness of evolutionary algorithms in optimizing complex systems. This approach allows for dynamic adjustments based on system responses, leading to improved stability and performance.
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