Hybrid neuro-swarm optimization approach for design of distributed generation power systems

Ganesan, T. and Vasant, P. and Elamvazuthi, I. (2013) Hybrid neuro-swarm optimization approach for design of distributed generation power systems. Neural Computing and Applications, 23 (1). pp. 105-117. ISSN 09410643

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Abstract

The global energy sector faces major challenges in providing sufficient energy to the worlds ever-increasing energy demand. Options to produce greener, cost effective, and reliable source of alternative energy need to be explored and exploited. One of the major advances in the development of this sort of power source was done by integrating (or hybridizing) multiple different alternative energy sources (e.g., wind turbine generators, photovoltaic cell panels, and fuel-fired generators, equipped with storage batteries) to form a distributed generation (DG) power system. However, even with DG power systems, cost effectiveness, reliability, and pollutant emissions are still major issues that need to be resolved. The model development and optimization of the DG power system were carried out successfully in the previous work using particle swarm optimization (PSO). The goal was to minimize cost, maximize reliability, and minimize emissions (multi-objective function) subject to the requirements of the power balance and design constraints. In this work, the optimization was performed further using Hopfield neural networks (HNN), PSO, and HNN-PSO techniques. Comparative studies and analysis were then carried out on the optimized results. © 2012 Springer-Verlag London Limited.

Item Type: Article
Additional Information: cited By 34
Uncontrolled Keywords: Alternative energy; Hopfield neural networks (HNN); Hybrid algorithms; Multi objective; Optimization strategy, Cost effectiveness; Distributed power generation; Electric power supplies to apparatus; Hopfield neural networks; Multiobjective optimization; Photovoltaic cells, Particle swarm optimization (PSO)
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 15:51
Last Modified: 09 Nov 2023 15:51
URI: https://khub.utp.edu.my/scholars/id/eprint/3567

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