Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm. (October 2016)

• Record Type: Journal Article
• Authors: Martinez-Luengo, Maria; Kolios, Athanasios; Wang, Lin
• Is Part Of: Renewable & sustainable energy reviews. Volume 64(2016:Nov.)
• Page Start: 91
• Page End: 105
• Publication Date: 2016-10
• Subjects: AE Acoustic Emission -- OM Operational Management -- CB Carbon Fiber -- OMA Operational Modal Analysis -- CM Condition Monitoring -- OWF Offshore Wind Farm -- EOC Environmental and Operational Conditions -- OWT Offshore Wind Turbine -- EU European Union -- O&M Operations and Maintenance -- FBG Fiber Bragg Grating -- RSA Response Surface Analysis -- FEA Finite Element Analysis -- SHMS Structural Health Monitoring Systems -- FMECA Failure Mode, Effects and Criticality Analysis -- SVM Support Vector Machines -- LCOE Levelised Cost of Energy -- WF Wind Farm -- MEMS micro-electromechanical system -- WSN Wireless Sensor Network -- NN Neural Networks -- WT Wind Turbine; Offshore wind turbines -- Structural health monitoring -- Statistical Pattern Recognition Paradigm -- Sensors -- Statistical model development; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2016.05.085
• Languages: English
• ISSNs: 1364-0321

Abstract

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).