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Conference programme 

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Poster session

Lead Session Chair:
Stephan Barth, Managing Director, ForWind - Center for Wind Energy Research, Germany
Wenxian Yang Newcastle University, United Kingdom
Wenxian Yang (1) F P Sunny Tian (1) Ziqiang Lang (2) Chong Ng (3) Paul Mckeever (3)
(1) Newcastle University, Newcastle upon Tyne, United Kingdom (2) University of Sheffield, Sheffield, United Kingdom (3) Offshore Renewable Energy CATAPULT Narec, Blyth, United Kingdom

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Presenter's biography

Biographies are supplied directly by presenters at OFFSHORE 2015 and are published here unedited

Dr Wenxian Yang received his PhD degree from Xi’an Jiaotong University in 1999. He then did research in the field of wind and marine renewables and machine condition monitoring in the City University of Hong Kong, Nottingham Trent University, Cranfield University, and the University of Durham. He is currently a Lecturer in Offshore Renewable Energy at Newcastle University. Before he joined Newcastle University in 2013, he has been a technical specialist for 4 years at the Offshore Renewable Energy CATAPULT Narec. His current research is focused on wind turbine condition monitoring and the reliability design of offshore wind platform.


Research on a new technique dedicated for condition monitoring long wind turbine blades


Being expensive in capital cost, difficult to repair and replace, and significant in failure loss, the safe operation of wind turbine (WT) blades by the means of condition monitoring (CM) is critical to lower the energy cost of wind power. However, the continual increase of blade size is challenging the CM and makes it more difficult than ever before. So, a reliable blade CM technique is being thirstily expected today. To meet such a requirement, a new blade CM technique based on the theory of information integration will be developed in this paper dedicatedly for monitoring long WT blades.


The available blade CM techniques prefer to assess blade’s health condition by interpreting individual CM signals. The results obtained by such an approach are usually affected by external loads thus lead to unreliable CM conclusion. To minimize the influences of external loads, a new CM technique is developed in this paper based on investigating the coherence of the CM signals collected from neighbouring blade sections. Owing to the difficulties of accessing data from operating blades, the proposed technique is validated by using the data obtained from both simulation and the lab test of a full-scale WT blade.

Main body of abstract

Following a brief review of existing WT blade CM techniques and their limitations in providing reliable assessment of the structural health condition of WT blade, a new blade CM technique that is sensitive to structural damage while less affected by external load is proposed. The research will be started with investigating the influences of load and structural damage on the dynamic response of the blade, and ended with the establishment of a new load-independent blade CM criterion. For facilitating research, a finite element model of long WT blade will be developed in SolidWorks to simulate the dynamic responses of healthy and unhealthy blades under various loading conditions. In each health and loading condition simulation case, the strains and stresses on blade surface as well as the bending moment at blade root section are calculated. From these data, it is found that, in comparison of the root bending moment, the correlations of the strains and stresses distributed on the surfaces of neighbouring blade sections are less affected by the external load. Therefore, in principle they are able to provide more reliable prediction to the structural health condition of the blade. In order to demonstrate the effectiveness of the proposed CM technique in practical application, the data collected during the lab test of a real WT blade are employed for further validation. It is found that the proposed technique can correctly predict the actual health condition of the blade despite the types of data acquisition transducers and the variation of external loads.


The harsh offshore conditions not only challenge the operation of offshore WTs but make their CM more difficult. Take blade CM as an example, site inspection that is popularly adopted onshore becomes a nearly impossible task in offshore circumstance. With the continual deployment of offshore WTs, operators are under increasing pressure to enhance the remote CM of their turbines to lower the cost. The new blade CM technique developed in this paper very well meets such a requirement. It overcomes the existing problems of available techniques and therefore can provide more reliable prediction to the blade health condition.

Learning objectives
External loads, coupled together with structural defects, have significant influence on the dynamic response of WT blades. Accordingly, blade CM should be based on a load in/less-dependent technique. The existing techniques based on the analysis of individual CM signals don’t meet this requirement thus are not ideal for blade CM. By contrast, the coherence of the CM signals collected from neighbouring blade sections overcomes this issue and therefore is better for the job.