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- W4322760484 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> Renewable energy is a clean and inexhaustible source of energy, so every year interest in the study and the search for improvements in production increases. Wind energy is one of the most used and therefore the need for predictive maintenance management to guarantee the reliableness and operability of each of the wind turbines has become a great study opportunity. In this work, a fault detection system is developed by applying an anomaly detector based on principal component analysis (PCA), in order to state early warnings of possible faults in the main bearing. For the development of the model, SCADA (supervisory control and data acquisition) data from a wind park in operation are utilized. The results obtained allow detection of failures even months before the fatal breakdown occurs. This model requires (to be constructed) only the use of healthy SCADA data, without the need to obtain the fault history or install additional equipment or sensors that require greater investment. In conclusion, this proposed strategy provides a tool for the planning and execution of predictive maintenance within wind parks." @default.
- W4322760484 created "2023-03-03" @default.
- W4322760484 creator A5022079712 @default.
- W4322760484 date "2023-03-02" @default.
- W4322760484 modified "2023-10-01" @default.
- W4322760484 title "Reply on RC3" @default.
- W4322760484 doi "https://doi.org/10.5194/wes-2022-111-ac3" @default.
- W4322760484 hasPublicationYear "2023" @default.
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