Damage prediction for wind turbines using wireless sensor and actuator networks

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• 作者：Maicon Melo Alves^b ; ^cAuthor Vitae ; Luci Pirmez^aAuthor Vitae ; Silvana Rossetto^aAuthor Vitae ; Flavia C. Delicato^a ; ^c ; ^{fdelicato@gmail.com}Author Vitae ; Claudio M. de Farias^aAuthor Vitae ; Paulo F. Pires^a ; ^cAuthor Vitae ; Igor L. dos Santos^aAuthor Vitae ; Albert Y. Zomaya^cAuthor Vitae
• 关键词：Wireless sensor and actuator networks ; Structural health monitoring ; Damage prediction ; ARIMA model ; Adaptive Neuro Fuzzy Inference System (ANFIS)
• 刊名：Journal of Network and Computer Applications
• 出版年：2017
• 出版时间：15 February 2017
• 年：2017
• 卷：80
• 期：Complete
• 页码：123-140
• 全文大小：1607 K
• 卷排序：80

文摘

The depletion of oil and gas reserves is bringing up economic, political and social issues which encourage the adoption of renewable, green energy sources. Wind energy is a major source of renewable energy because of the maturity and competitive costs of technological solutions to exploit this type of green energy. This kind of power generation is achieved through the use of wind turbines, which convert translational kinetic energy into rotational kinetic energy. The benefits already proven of this type of renewable energy source have motivated nations worldwide to adopt policies to improve the use of wind energy in order to minimize their dependence on oil and natural gas. However, the adoption of wind turbines poses several challenges. A key challenge is properly and timely identifying structural damages which affect the structural health of the wind turbine. In this context, we propose a damage prediction system for wind turbines based on wireless sensor and actuator network. The proposed system, called Delphos, is a decentralized system where all decision-making process is performed within the network, in a collaborative way by the nodes. The purpose of Delphos is to accurately predict when the turbine will reach a damage state, thus allowing timely actions on the turbine operation to prevent accidents, reducing maintenance costs and delays in the power generation. Delphos relies on a time series forecasting model, called ARIMA, and a fuzzy system to eliminate the influence of temperature in the process of damage prediction.