A surrogate model for estimating extreme tower loads on wind turbines based on random forest proximities

2019/03/01 by Mikkel Slot Nielsen, Nielsen, Mikkel Slot, Victor Rohde +1
Engineering · Mathematics · #62P30 #65C20 #91B68 #Applications (stat.AP) #FOS: Computer and information sciences #Machine Fault Diagnosis Techniques #Structural Health Monitoring Techniques #Structural Integrity and Reliability Analysis #msc:62P30 #msc:65C20 #msc:91B68 #stat.AP

paper · pdf · doi:10.48550/arxiv.1903.00251

14 pages, 5 figures

openalex publication_date 2019/03/01 · arxiv created 2020/04/30 · arxiv updated 2020/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In the present paper we present a surrogate model, which can be used to estimate extreme tower loads on a wind turbine from a number of signals and a suitable simulation tool. Due to the requirements of the International Electrotechnical Commission (IEC) Standard 61400-1, assessing extreme tower loads on wind turbines constitutes a key component of the design phase. The proposed model imputes tower loads by matching observed signals with simulated quantities using proximities induced by random forests. In this way the algorithm's adaptability to high-dimensional and sparse settings is exploited without using regression-based surrogate loads (which may display misleading probabilistic characteristics). Finally, the model is applied to estimate tower loads on an operating wind turbine from data on its operational statistics.

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