relation: https://khub.utp.edu.my/scholars/2574/
title: Design optimization of a fuzzy distributed generation (DG) system with multiple renewable energy sources
creator: Ganesan, T.
creator: Elamvazuthi, I.
creator: Shaari, K.Z.K.
creator: Vasant, P.
description: The global rise in energy demands brings major obstacles to many energy organizations in providing adequate energy supply. Hence, many techniques to generate cost effective, reliable and environmentally friendly alternative energy source are being explored. One such method is the integration of photovoltaic cells, wind turbine generators and fuel-based generators, included with storage batteries. This sort of power systems are known as distributed generation (DG) power system. However, the application of DG power systems raise certain issues such as cost effectiveness, environmental impact and reliability. The modelling as well as the optimization of this DG power system was successfully performed in the previous work using Particle Swarm Optimization (PSO). The central idea of that work was to minimize cost, minimize emissions and maximize reliability (multi-objective (MO) setting) with respect to the power balance and design requirements. In this work, we introduce a fuzzy model that takes into account the uncertain nature of certain variables in the DG system which are dependent on the weather conditions (such as; the insolation and wind speed profiles). The MO optimization in a fuzzy environment was performed by applying the Hopfield Recurrent Neural Network (HNN). Analysis on the optimized results was then carried out. © 2012 American Institute of Physics.
date: 2012
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Ganesan, T. and Elamvazuthi, I. and Shaari, K.Z.K. and Vasant, P.  (2012) Design optimization of a fuzzy distributed generation (DG) system with multiple renewable energy sources.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874148941&doi=10.1063%2f1.4757429&partnerID=40&md5=a017ea2877249d2ac8b05ae05da16a94
relation: 10.1063/1.4757429
identifier: 10.1063/1.4757429