Estimating Fatigue in the Main Bearings of Wind Turbines Using Experimental Data

Fava, Giovanni M.; Liberatore, Sauro; Moaveni, Babak

doi:10.1007/978-3-030-12115-0_22

Cited by 3 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Temperature higher than the allowed limit indicates the probability of bearing malfunction, such as: issues on lubrication, electrical leakage through the shaft, aging, and variability of external loads. In case of not perfect bearing operation, the characteristics of the lubricating oil will change, which can result in more fatigue loads [12]. Thus, the bearing is not efficient enough, and leads to loss of energy during the bearing operation and an increase in the bearing oil temperature.…”

Section: Introductionmentioning

confidence: 99%

Optimal Temperature-Based Condition Monitoring System for Wind Turbines

et al. 2021

View full text Add to dashboard Cite

With the increasing demand for the efficiency of wind energy projects due to challenging market conditions, the challenges related to maintenance planning are increasing. In this paper, a condition-based monitoring system for wind turbines (WTs) based on data-driven modeling is proposed. First, the normal condition of the WTs key components is estimated using a tailor-made artificial neural network. Then, the deviation of the real-time measurement data from the estimated values is calculated, indicating abnormal conditions. One of the main contributions of the paper is to propose an optimization problem for calculating the safe band, to maximize the accuracy of abnormal condition identification. During abnormal conditions or hazardous conditions of the WTs, an alarm is triggered and a proposed risk indicator is updated. The effectiveness of the model is demonstrated using real data from an offshore wind farm in Germany. By experimenting with the proposed model on the real-world data, it is shown that the proposed risk indicator is fully consistent with upcoming wind turbine failures.

show abstract

Section: Introductionmentioning

confidence: 99%