Improved chaotic Bat algorithm for optimal coordinated tuning of power system stabilizers for multimachine power...

Area of Science:

• Electrical Engineering
• Control Systems
• Computational Intelligence

Background:

• Nonlinearity in power systems leads to dynamic instability and oscillations.
• Traditional methods struggle with optimal tuning of Power System Stabilizers (PSSs) under varying load conditions.
• Electromechanical modes require precise shifting in the s-plane for enhanced dynamic performance.

Purpose of the Study:

• To propose an innovative strategy using the Chaos-based Novel Bat Algorithm (CNBA) for optimal PSS design.
• To enhance the dynamic stability and damping of electromechanical oscillations in multimachine power systems.
• To determine optimal PSS locations and quantities alongside controller gains.

Main Methods:

• Introduction of chaos mapping into the Novel Bat Algorithm (NBA) to create CNBA for improved global search.
• Optimization of PSS parameters targeting the damping ratio of low-damped electromechanical modes.
• Utilizing CNBA and participation factor for PSS location and quantity determination.
• Validation on the interconnected New-England/New-York power grid (16 generators, 68 buses).

Main Results:

• CNBA achieved a minimum damping ratio of 37%, outperforming NBA's 31%.
• Eigenvalue analysis and nonlinear simulations confirmed CNBA's effectiveness.
• CNBA-based PSS demonstrated superior damping of inter-area and local oscillations across diverse operating conditions.

Conclusions:

• The proposed CNBA-based PSS design strategy effectively enhances power system dynamic performance.
• CNBA offers a robust and efficient method for optimizing PSS parameters and placement.
• The approach provides excellent damping capabilities, crucial for grid stability and reliability.