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• W3168221103 abstract "The historical temperature data logged in the supervisory control and data acquisition (SCADA) system contains a wealth of information that can assist with the performance optimization of wind turbines (WTs). However, mining and using these long-term data is difficult and time-consuming due to their complexity, volume, etc. In this study, we tracked and analyzed the 5-year trends of major SCADA temperature rise variables in relation to the active power of four WTs in a real wind farm. To uncover useful information, an extended version of the bins method, which calculates the standard deviation (SD) as well as the average, is proposed and adopted. The implications of the analysis for engineering practice are discussed from multiple perspectives. The research results demonstrate a change in the patterns of the main temperature rise variables in a real wind farm, completeness of the monitoring of the WT internal temperature state, influence of wind turbine aging on temperature signals, a correlation between different measurement points, and a correlation between signals from different years. The knowledge gained from this research provides a reference for the development of more practical and comprehensive condition monitoring systems and methods, as well as better operation maintenance strategies." @default.
• W3168221103 created "2021-06-22" @default.
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• W3168221103 date "2021-05-01" @default.
• W3168221103 modified "2023-10-16" @default.
• W3168221103 title "Analysis of long-term temperature monitoring of multiple wind turbines" @default.
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• W3168221103 doi "https://doi.org/10.1177/00202940211013061" @default.
• W3168221103 hasPublicationYear "2021" @default.
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