Design and Simulation of Optimized Load Frequency Control in Multi-Area Electrical Interconnection Systems

Ihsan Jabbar Hasan, Saif Ahmed Abed, Nahla Abdul Jalil Salih, Nadhir Ibrahim Abdulkhaleq

Submitted : 2025-06-15, Published : 2025-08-27.

Abstract

Maintaining frequency stability in modern interconnected power systems is critical for operational reliability, especially under varying load demands. Load Frequency Control (LFC) plays a pivotal role in balancing power exchanges and preserving nominal frequency across multi-area grids. This paper presents the design, modeling, and optimization of a two-area Load Frequency Control (LFC) system in interconnected power networks using MATLAB/Simulink. Each area comprises a governor, turbine, generator-load system, and a PID controller to regulate frequency deviations and maintain system stability following load disturbances. The study investigates the effects of key system parameters—including governor and turbine time constants, generator inertia, and tie-line coupling—on dynamic performance. To address mismatched responses between areas, Particle Swarm Optimization (PSO) is employed to tune system parameters and improve coordination. The optimization aims to minimize frequency deviations and tie-line power fluctuations while enhancing system response. Simulation results show that the proposed optimization approach significantly improves dynamic performance. Specifically, frequency deviations in both areas are reduced by over 55%, tie-line power fluctuation is minimized by 62.5%, and settling times for frequency responses are shortened by over 44%. These improvements demonstrate the effectiveness of the optimization strategy in enhancing inter-area coordination and system resilience. The framework also serves as a practical simulation-based educational tool for power engineering students and researchers to exploreLFC design and control strategies in multi-area systems.

Keywords

Load Frequency Control (LFC); multi-area power system; simulink; PID controller; Particle Swarm Optimization (PSO).

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