An improved spotted hyena optimizer for PID parameters in an AVR system

Area of Science:

• Engineering
• Computer Science
• Control Systems

Background:

• Automatic Voltage Regulators (AVRs) are crucial for stable power systems.
• Proportional Integral Derivative (PID) controllers are widely used in AVRs but require precise tuning.
• Optimization algorithms are essential for effective PID parameter tuning in AVR systems.

Purpose of the Study:

• To propose an Improved Spotted Hyena Optimizer (ISHO) for PID parameter optimization in AVRs.
• To enhance the exploration and exploitation capabilities of the Spotted Hyena Optimizer (SHO).
• To improve solution precision and convergence rate for PID controller tuning.

Main Methods:

• Implementing an opposition-based learning strategy for initial population diversity.
• Developing a novel nonlinear update equation for the convergence factor.
• Applying the ISHO algorithm to optimize PID parameters for an AVR system.

Main Results:

• The ISHO algorithm demonstrated superior performance over other tested algorithms.
• ISHO achieved higher solution precision in PID parameter optimization.
• ISHO exhibited a faster convergence rate in the optimization process.

Conclusions:

• The proposed ISHO is an effective method for PID parameter optimization in AVRs.
• ISHO enhances the global search and local exploitation abilities of the SHO algorithm.
• The ISHO algorithm offers a promising approach for improving AVR system stability and performance.