Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/6934
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dc.contributor.authorShetty, R.P.
dc.contributor.authorSathyabhama, A.
dc.contributor.authorSrinivasa, Pai, P.
dc.date.accessioned2020-03-30T09:46:26Z-
dc.date.available2020-03-30T09:46:26Z-
dc.date.issued2018
dc.identifier.citationIOP Conference Series: Materials Science and Engineering, 2018, Vol.376, 1, pp.-en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/6934-
dc.description.abstractThe wind being a most promising renewable energy, has become a strong contender for fossil fuels. Optimizing the blade pitch angle of a wind turbine is important to obtain the maximum power output, as the other variables are considered to be uncontrollable. In this paper an effort has been made to compare performances of three different optimization algorithms namely Particle swarm optimization (PSO), Artificial bee colony (ABC) and cuckoo search (CS) for optimizing the blade pitch angle and hence optimize the power output of a 1.5 MW capacity, pitch regulated, three-bladed horizontal axis wind turbine operating at a large wind farm in central dry zone of Karnataka. The objective function development is done using Artificial Neural Network. The CS algorithm is found to be faster and more efficient as compared to ABC and PSO for the problem under consideration. � Published under licence by IOP Publishing Ltd.en_US
dc.titleWind Power Optimization: A Comparison of Meta-Heuristic Algorithmsen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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