Optimized PID controller and model order reduction of reheated turbine for load frequency control using teaching...

Area of Science:

• Electrical Engineering
• Control Systems Engineering
• Computational Intelligence

Background:

• Load frequency control (LFC) systems are crucial for power grid stability but face computational challenges.
• Existing methods often struggle to balance performance with computational complexity.

Purpose of the Study:

• To develop an optimized PID controller tuning method for single-area LFC systems.
• To enhance system stability and reduce computational load using model order reduction and TLBO.

Main Methods:

• Implemented three model order reduction techniques (Routh Approximation, Balanced Truncation, Hankel Norm Approximation) to reduce system order from 4th to 2nd.
• Utilized Teaching Learning-Based Optimization (TLBO) for tuning PID controllers.
• Compared the proposed method with conventional tuning techniques (Ziegler-Nichols, AMIGO, S-IMC, CHR).

Main Results:

• Achieved a 47.3% reduction in computational time through model order reduction.
• Demonstrated superior performance with a 38.2% decrease in settling time and 42.7% reduction in peak overshoot compared to conventional methods.
• Routh Approximation yielded optimal results with minimum settling time (2.8s) and peak overshoot (8.4%).
• Reduced Integral Square Error by 56.8%.

Conclusions:

• The proposed optimized LFC approach effectively enhances power system stability and performance.
• Model order reduction combined with TLBO-based PID tuning offers a robust framework for modern power grids.
• The Routh Approximation method is particularly effective for achieving fast and stable LFC response.