The Bees Algorithm Tuned Sliding Mode Control for Load Frequency Control in Two-Area Power System

Shouran, Mokhtar ORCID: https://orcid.org/0000-0002-9904-434X, Anayi, Fatih ORCID: https://orcid.org/0000-0001-8408-7673 and Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 2021. The Bees Algorithm Tuned Sliding Mode Control for Load Frequency Control in Two-Area Power System. Energies 14 (18) , 5701. 10.3390/en14185701

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Abstract

This paper proposes a design of Sliding Mode Control (SMC) for Load Frequency Control (LFC) in a two-area electrical power system. The mathematical model design of the SMC is derived based on the parameters of the investigated system. In order to achieve the optimal use of the proposed controller, an optimisation tool called the Bees Algorithm (BA) is suggested in this work to tune the parameters of the SMC. The dynamic performance of the power system with SMC employed for LFC is studied by applying a load disturbance of 0.2 pu in area one. To validate the supremacy of the proposed controller, the results are compared with those of recently published works based on Fuzzy Logic Control (FLC) tuned by Teaching–Learning-Based Optimisation (TLBO) algorithm and the traditional PID optimised by Lozi map-based Chaotic Optimisation Algorithm (LCOA). Furthermore, the robustness of SMC-based BA is examined against parametric uncertainties of the electrical power system by simultaneous changes in certain parameters of the testbed system with 40% of their nominal values. Simulation results prove the superiority and the robustness of the proposed SMC as an LFC system for the investigated power system.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Additional Information:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Publisher:	MDPI
ISSN:	1996-1073
Date of First Compliant Deposit:	16 September 2021
Date of Acceptance:	8 September 2021
Last Modified:	10 Feb 2024 02:09
URI:	https://orca.cardiff.ac.uk/id/eprint/144103

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