Journal of Advances in Engineering and Technology [JAET]

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The Journal of Advances in Engineering and Technology (JAET) is an international, open access, double blind peer-reviewed journal. It is published by the Faculty of Engineering of Sri Lanka Institute of Information Technology (SLIIT). The JAET aims at fostering research and development work in Engineering and Technology and bringing researchers on to a common platform. Furthermore, JAET will also accept review articles on appropriate subject areas including concept papers of academic opinions, book reviews, etc. for publication therein.

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Subject: search.filters.subject.Interconnected Power Systems

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    PublicationOpen Access
    Enhancing Load Frequency Control in Interconnected Power Systems with Zone-Specific Fuzzy Controllers: Principles and Methods
    (SLIIT Faculty of Engineering, 2025-02) Jahangiri, S; Jones, K.O
    This work focuses on load frequency control in interconnected power systems, a critical aspect of modern power grid operations. However, sudden load disturbances and generator outages can lead to transient oscillations between control areas, posing challenges to frequency control. The aim of the work was to investigate and enhance load frequency control behaviour, considering dynamic load changes and uncertainties. Fuzzy Logic Controllers optimized with Particle Swarm Optimization were applied to improve control robustness. The Particle Swarm Optimisation algorithm was used to tune the scaling factors and parameters of the fuzzy controllers to optimize their performance. The methods were tested on a standard four-area interconnected power system model equipped with load frequency control blocks, reheaters, governors, rate constraints, and thermal components. Different disturbance scenarios including parameter fluctuations and load changes were evaluated. The Fuzzy Logic Controllers demonstrate resilient response across scenarios without needing extensive tuning. Particle Swarm Optimization improves robustness through systematic exploration for constraint-based nonlinear optimization. Tuning fuzzy controllers with bio-inspired algorithms enhances efficiency in addressing complex grid conditions. The results provide insights into designing more secure and resilient grid controls, contributing to power system stability research.