Load frequency control using the particle swarm optimisation algorithm and PID controller for effective monitoring of transmission line

Ogar, V. N., Hussain, S. and Gamage, K. A. A. (2023) Load frequency control using the particle swarm optimisation algorithm and PID controller for effective monitoring of transmission line. Energies, 16(15), 5748. (doi: 10.3390/en16155748)

Abstract

Load frequency control (LFC) plays a critical role in maintaining the stability and reliability of the power system. With the increasing integration of renewable energy sources and the growth of complex interconnected grids, efficient and robust LFC strategies are in high demand. In recent years, the combination of particle swarm optimisation (PSO) and proportional-integral-derivative (PID) controllers, known as PSP-PID, has been used as a promising approach to enhance the performance of LFC systems. This article focuses on modelling, simulation, optimisation, advanced control techniques, expert knowledge, and iterative refinement of the power system to help achieve suitable PID settings that provide reliable control of the load frequency in the transmission line. The performance indices of the proposed algorithm are measured by the integral time absolute error (ITAE), which is 0.0005757 with 0.9994 Ki, 0.7741 Kp, and 0.1850 Kd. The model system dynamics are tested by varying the load frequency from 300 MW to 350 MW at a load variation of 0.2. The suggested controller algorithm is relatively reliable and accurate in power system management and protection load frequency control compared to conventional methods. This work can be improved by including more generating stations synchronised into a single network.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ogar, VINCENT and Gamage, Professor Kelum and Hussain, Dr Sajjad
• Authors: Ogar, V. N., Hussain, S., and Gamage, K. A. A.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Energies
• Publisher: MDPI
• ISSN: 1996-1073
• ISSN (Online): 1996-1073
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Energies 16(15):5748
• Publisher Policy: Reproduced under a Creative Commons licence