Structural health monitoring of offshore wind turbines : a review through the statistical pattern recognition paradigm

Martinez-Luengo, Maria and Kolios, Athanasios and Wang, Lin (2016) Structural health monitoring of offshore wind turbines : a review through the statistical pattern recognition paradigm. Renewable and Sustainable Energy Reviews, 64. pp. 91-105. ISSN 1879-0690 (https://doi.org/10.1016/j.rser.2016.05.085)

[thumbnail of Martinez-Luengo-etal-RSER2016-Structural-health-monitoring-offshore-wind-turbines-review-through-statistical]
Preview
 Text. Filename: Martinez_Luengo_etal_RSER2016_Structural_health_monitoring_offshore_wind_turbines_review_through_statistical.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (453kB)| Preview

Abstract

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs' inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines' overloading, therefore, maximizing the investments' return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK's 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE).

CORE (COnnecting REpositories)