A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design

Sohail R. Reddy

Energy, 2021, vol. 220, issue C

Abstract: Wind farm development projects require a detailed survey of the eligible land. The land selected is often segmented into different region, each owned by different landowners with different land pricing. These regions are often complex shaped with irregular boundaries. Therefore, an efficient method for numerically modeling such irregular domains is needed. This work uses support vector data description (SVDD) to generate an analytical, continuous description of the irregular regions. Whereas other methods typically work well for modeling convex domains, the SVDD approach can be used to model irregular regions as a spherical boundary using various kernel mapping. It was demonstrated that the SVDD approach can be used to model any number of complex regions. An error analysis showed that the SVDD approach can construct accurate descriptions using a relatively small data set. The applicability of SVDD method in wind farm layout optimization is also demonstrated. The wind farm optimization study considered that the terrain is divided into several regions each owned by a different owner offering the land at a different price. Two different methods for considering the cost of the land are presented. The differences in optimal farm layouts using the two land cost models were also presented. In each case, the optimized wind farm layouts resulted in lower cost-of-energy relative to the reference wind farm. It was shown that the SVDD approach can also be used to restrict the placement of wind turbines in infeasible/restricted regions. The library for support vector data description was also made available to the public.

Keywords: Wind farm layout optimization; Wind energy; Complex terrain; Support vector machine; Machine learning (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:220:y:2021:i:c:s0360544220327985

DOI: 10.1016/j.energy.2020.119691

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