Decentralized automatic generation control of interconnected power systems incorporating asynchronous tie-lines.

Area of Science:

• Electrical Engineering
• Control Systems
• Optimization Algorithms

Background:

• Interconnected power systems require robust automatic generation control (AGC) to maintain stability.
• Conventional controllers like PID may struggle with complex dynamics and disturbances.
• Intelligent optimization techniques offer potential for improved AGC performance.

Purpose of the Study:

• To design and evaluate decentralized automatic generation controllers for interconnected power systems.
• To compare the effectiveness of Proportional-Integral-Derivative (PID), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) controllers.
• To analyze controller performance under different tie-line configurations (AC and asynchronous).

Main Methods:

• Design of decentralized AGC using PID, GA, and PSO.
• Simulation of a two-area interconnected thermal power system.
• Dynamic response analysis following a 1% load perturbation.
• Comparison of controller performance based on system dynamic responses.

Main Results:

• Particle Swarm Optimization (PSO) exhibited superior dynamic response compared to PID and GA controllers.
• The performance benefits of PSO were observed for both AC and asynchronous tie-line interconnections.
• All designed controllers were tested on identical two-area interconnected power systems.

Conclusions:

• PSO is a highly effective method for designing decentralized automatic generation controllers in interconnected power systems.
• Intelligent controllers like PSO offer significant advantages over conventional PID controllers for power system stability.
• The proposed PSO-based controller ensures improved dynamic performance and robustness.