Mayo, M., & Daoud, M. (2016). Informed mutation of wind farm layouts to maximise energy harvest. Renewable Energy, 89, 437–448. http://doi.org/10.1016/j.renene.2015.12.006
Permanent Research Commons link: https://hdl.handle.net/10289/10078
Correct placement of turbines in a wind farm is a critical issue in wind farm design optimisation. While traditional "trial and error"-based approaches suffice for small layouts, automated approaches are required for larger wind farms with turbines numbering in the hundreds. In this paper we propose an evolutionary strategy with a novel mutation operator for identifying wind farm layouts that minimise expected velocity deficit due to wake effects. The mutation operator is based on constructing a predictive model of velocity deficits across a layout so that mutations are inherently biased towards better layouts. This makes the operator informed rather than randomised. We perform a comprehensive evaluation of our approach on five challenging simulated scenarios using a simulation approach acceptable to industry . We then compare our algorithm against two baseline approaches including the Turbine Displacement Algorithm . Our results indicate that our informed mutation approach works effectively, with our approach identifying layouts with the lowest aggregate velocity deficits on all five test scenarios.
This is an authors submitted version of an article published in the journal: Renewable Energy © 2015 Elsevier Ltd.