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This paper presents a robust load frequency controller using Internal Model Control (IMC) and Genetic Algorithm (GA) for a single-area hydro turbine power system with non-minimum phase dynamics. These dynamics often cause instability and oscillations. The study explores PID, IMC, and GA optimization techniques for Load Frequency Control (LFC) in Hydro Turbine Power Systems (HTPS) with Non-Minimum Phase (NMP) behavior. IMC outperforms conventional PID and modern optimization methods by leveraging system dynamics, maintaining stability and minimizing overshoot, settling time, and steady-state error. MATLAB simulations demonstrate significant improvements in disturbance rejection and system reliability. Comparative analysis highlights the limitations of traditional tuning methods for HTPS with NMP characteristics. IMC offers flexibility in adjusting internal model parameters, optimizing performance and shows potential for soft computing optimization to tailor system responses. While GA iteratively refines solutions, IMC excels in accommodating intricate dynamics and built-in tuning rules, showcasing superior precision and responsiveness, making it highly effective for real-world applications.

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Design of a Robust Load Frequency Controller Using PID, GA and IMC for Hydro Turbine Power Systems with Non-minimum Phase Dynamics

  • 会议论文

  • 发表日期：

    2025年

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  • Supriya Y. Bhuran,
  • Sharad P. Jadhav

摘要

This paper presents a robust load frequency controller using Internal Model Control (IMC) and Genetic Algorithm (GA) for a single-area hydro turbine power system with non-minimum phase dynamics. These dynamics often cause instability and oscillations. The study explores PID, IMC, and GA optimization techniques for Load Frequency Control (LFC) in Hydro Turbine Power Systems (HTPS) with Non-Minimum Phase (NMP) behavior. IMC outperforms conventional PID and modern optimization methods by leveraging system dynamics, maintaining stability and minimizing overshoot, settling time, and steady-state error. MATLAB simulations demonstrate significant improvements in disturbance rejection and system reliability. Comparative analysis highlights the limitations of traditional tuning methods for HTPS with NMP characteristics. IMC offers flexibility in adjusting internal model parameters, optimizing performance and shows potential for soft computing optimization to tailor system responses. While GA iteratively refines solutions, IMC excels in accommodating intricate dynamics and built-in tuning rules, showcasing superior precision and responsiveness, making it highly effective for real-world applications.