A Computational Framework for Life-Cycle Management of Wind...

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A Computational Framework for Life-Cycle Management of Wind Turbines incorporating Structural Health Monitoring Kay Smarsly 1* , Dietrich Hartmann 2 and Kincho H. Law 1 1 Stanford University, Department of Civil and Environmental Engineering, 473 Via Ortega, Stanford, CA 94305, USA 2 Ruhr University Bochum, Department of Civil and Environmental, Universitätsstr. 150, 44801 Bochum, Germany Abstract The integration of structural health monitoring into life-cycle management strategies can help facilitating a reliable operation of wind turbines and reducing the life-cycle costs significantly. This paper presents a life-cycle management (LCM) framework for online monitoring and performance assessment of wind turbines, enabling optimum maintenance and inspection planning at minimum associated life-cycle costs. Incorporating continuously updated monitoring data (i.e. structural, environmental and operational data), the framework allows capturing and understanding the actual wind turbine condition and, hence, reduces uncertainty in structural responses as well as load effects acting on the structure. As will be shown in this paper, the framework integrates a variety of heterogeneous hardware and software components, including sensors and data acquisition units, server systems, Internet-enabled user interfaces as well as finite element models for system identification and a multi-agent system for self-detecting sensor malfunctions. To validate its capabilities and to demonstrate 1
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its practicability, the framework is deployed for continuous monitoring and life-cycle management of a 500 kW wind turbine. Remote life-cycle analyses of the monitored wind turbine are conducted and case studies are presented investigating both the structural performance and the operational efficiency of the wind turbine. Keywords: Life-cycle management, structural health monitoring, wind turbines, long-term monitoring, wind turbine operational efficiency, wind turbine structural performance, statistical analysis 1 Introduction In 2012, the globally installed wind energy capacity has reached 282 GW, as the Global Wind Energy Council (GWEC) reports [1]. According to GWEC, the worldwide clean energy investments, having more than doubled in the past five years, have reached a new record with US$ 260 billion last year [2]. However, according to the International Energy Agency (IEA) US$ 380 trillion are needed to meet the projected worldwide energy demand until 2035 [3]; a significant portion is due to maintenance and operation of wind energy systems. Cost-efficient maintenance and reliable operation of wind turbines are among the major concerns of owners and operators. Therefore, research on life-cycle management (LCM) of wind turbines has considerably been fostered in the past several years, enabling operators and owners to efficiently organize, analyze, and manage information and life-cycle activities.
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  • Spring '18
  • casarona
  • Wind turbine

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