Load Frequency Control Using the Particle Swarm Optimisation Algorithm and PID Controller for Effective Monitoring of Transmission Line

Vincent N. Ogar, Sajjad Hussain and Kelum A. A. Gamage ()
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Vincent N. Ogar: Department of Electrical and Electronic Engineering, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Sajjad Hussain: Department of Electrical and Electronic Engineering, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Kelum A. A. Gamage: Department of Electrical and Electronic Engineering, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK

Energies, 2023, vol. 16, issue 15, 1-17

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 K i , 0.7741 K p , and 0.1850 K d . 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.

Keywords: load frequency; PID controller; particle swarm optimisation; transmission line (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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