[Paper Review] Implementation of an Innovative Bio Inspired GA and PSO Algorithm for Controller design considering Steam GT Dynamics

R. Shivakumar, R. Lakshmipathi|arXiv (Cornell University)|Feb 5, 2010

Power System Optimization and Stability16 references19 citations

TL;DR

This paper proposes a novel bio-inspired controller design using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to enhance damping of low-frequency oscillations in power systems with steam turbine-governor (GT) dynamics. By formulating an optimization criterion based on system damping ratio, the method computes optimal controller parameters, demonstrating superior stability and robustness through nonlinear time-domain simulations compared to conventional lead-lag controllers.

ABSTRACT

The Application of Bio Inspired Algorithms to complicated Power System Stability Problems has recently attracted the researchers in the field of Artificial Intelligence. Low frequency oscillations after a disturbance in a Power system, if not sufficiently damped, can drive the system unstable. This paper provides a systematic procedure to damp the low frequency oscillations based on Bio Inspired Genetic (GA) and Particle Swarm Optimization (PSO) algorithms. The proposed controller design is based on formulating a System Damping ratio enhancement based Optimization criterion to compute the optimal controller parameters for better stability. The Novel and contrasting feature of this work is the mathematical modeling and simulation of the Synchronous generator model including the Steam Governor Turbine (GT) dynamics. To show the robustness of the proposed controller, Non linear Time domain simulations have been carried out under various system operating conditions. Also, a detailed Comparative study has been done to show the superiority of the Bio inspired algorithm based controllers over the Conventional Lead lag controller.

Motivation & Objective

• To address low-frequency oscillations in power systems after disturbances, which can lead to instability if not adequately damped.
• To develop a systematic optimization-based controller design method using bio-inspired algorithms.
• To incorporate detailed modeling of synchronous generator and steam turbine-governor (GT) dynamics into the controller design process.
• To evaluate the robustness of the proposed controller under various operating conditions.
• To demonstrate the superiority of bio-inspired GA and PSO over conventional lead-lag controllers in damping performance.

Proposed method

• Formulate an optimization criterion based on system damping ratio enhancement to guide controller parameter selection.
• Integrate a comprehensive mathematical model of the synchronous generator including steam turbine-governor (GT) dynamics.
• Apply Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to search for optimal controller parameters that maximize damping ratio.
• Use nonlinear time-domain simulations to validate controller performance under diverse system operating conditions.
• Compare the proposed bio-inspired controllers with conventional lead-lag controllers using simulation-based performance metrics.
• Employ a multi-objective optimization framework to balance stability and dynamic response in the controller design.

Experimental results

Research questions

• RQ1 How can bio-inspired optimization algorithms like GA and PSO be effectively applied to design power system controllers that enhance damping of low-frequency oscillations?
• RQ2 What is the impact of including detailed steam turbine-governor (GT) dynamics on controller performance and stability?
• RQ3 How does the proposed GA and PSO-based controller compare to conventional lead-lag controllers in terms of damping ratio and transient response?
• RQ4 What are the robustness characteristics of the proposed controller under varying system operating conditions?
• RQ5 Can the optimization criterion based on system damping ratio effectively guide the selection of optimal controller parameters?

Key findings

• The proposed GA and PSO-based controllers significantly improve system damping ratio compared to conventional lead-lag controllers.
• Nonlinear time-domain simulations confirm the robustness of the proposed controllers across multiple operating conditions.
• The inclusion of detailed steam turbine-governor (GT) dynamics in the model enhances the accuracy and effectiveness of the controller design.
• The PSO-based approach converges faster than GA in the optimization process, indicating better computational efficiency.
• The comparative study demonstrates that bio-inspired algorithms yield superior damping performance and faster transient response.
• The optimization criterion based on damping ratio effectively guides the computation of optimal controller parameters, leading to enhanced system stability.