Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/108038
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Type: Conference paper
Title: Constrained evolutionary wind turbine placement with penalty functions
Author: Luckehe, D.
Wagner, M.
Kramer, O.
Citation: Proceedings of the 2016 IEEE Congress on Evolutionary Computation, 2016, pp.4903-4910
Publisher: IEEE
Issue Date: 2016
Series/Report no.: IEEE Congress on Evolutionary Computation
ISBN: 9781509006229
Conference Name: IEEE Congress on Evolutionary Computation,(CEC) (24 Jul 2016 - 29 Jul 2016 : Vancouver, Canada)
Statement of
Responsibility: 
Daniel Lückehe, Markus Wagner, Oliver Kramer
Abstract: Geographical constraints are essential when planning the locations for wind turbines. In real-world scenarios, especially in densely populated countries, the designated area where turbines can be placed is not an empty map on which the turbines can be placed arbitrarily. Even in rural areas, streets, buildings, and rivers have to be considered. In this paper, we model two constrained turbine placement scenarios and use evolutionary algorithms to find optimized turbine locations. To evaluate the locations, we combine a proven wind model with real-world data of a wind prediction model from a meteorological service. Geographical data from a free map service is used to define constrained areas in the scenarios based on administrative rules. For the evolutionary optimization process, we consider five ways to handle penalties. Starting with a simple specification that can only achieve two different values, we end up in a definition that considers distances relative of the required minimum distances to all geographical objects for each turbine. We combine the penalty definitions with three types of penalty functions. In the experimental section, we compare the various configurations and show a detailed analysis of the results.
Rights: © 2016 by IEEE.
DOI: 10.1109/CEC.2016.7744419
Published version: http://dx.doi.org/10.1109/cec.2016.7744419
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Computer Science publications

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