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Subject: search.filters.subject.Particle Swarm Optimization

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Now showing 1 - 3 of 3
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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.
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    PublicationOpen Access
    A Hybrid Parti cle Swarm Opti mizati on – Travelling Salesman Problem for Effi cient Multi Depot Vehicle Routi ng
    (Faculty of Humanities and Sciences, SLIIT, 2024-12-04) Senevirathne, S.S.M.A.C.; Samarasinghe, C. J
    The Fast-Moving Consumer Goods (FMCG) industry faces increasing demand to opti mize distributi on networks to reduce costs. The company seeks to establish a proper redistributi on route network, opti mize truck allocati on, and minimize warehouse operati ons, administrati on, and transportati on costs while adhering to capacity and volume constraints. To achieve this, the study formulates the problem as a Multi -Depot Vehicle Routi ng Problem (MDVRP) with 3 depots. The proposed model with the additi on of a parti cle swarm algorithm yields a substanti al cost reducti on of 21.41% compared to the existi ng system, demonstrati ng the potenti al of hybrid metaheuristi c algorithms for addressing complex logisti cs challenges in the FMCG industry.
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    PublicationOpen Access
    A Fuzzy-Neural Network Based Human-Machine Interface for Voice Controlled Robots Trained by a Particle Swarm Optimization
    (Korean Institute of Intelligent Systems, 2003-09-25) Watanabe, K; Chatterjee, A; Pulasinghe, K; Izumi, K; Kiguchi, K
    Particle swarm optimization (PSO) is employed to train fuzzy-neural networks (FNN), which can be employed as an important building block in real life robot systems, controlled by voice-based commands. The FNN is also trained to capture the user spoken directive in the context of the present performance of the robot system. The system has been successfully employed in a real life situation for navigation of a mobile robot.