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Publisher DOI: 10.3390/infrastructures6040050
Title: Optimal temperature-based condition monitoring system for wind turbines
Language: English
Authors: Teimourzadeh Baboli, Payam 
Babazadeh, Davood  
Raeiszadeh, Amin 
Horodyvskyy, Susanne 
Koprek, Isabel 
Keywords: artificial neural network;condition-based maintenance;health monitoring;wind turbine
Issue Date: 26-Mar-2021
Publisher: Multidisciplinary Digital Publishing Institute
Source: Infrastructures 6 (4): 50 (2021)
Journal or Series Name: Infrastructures 
Abstract (english): 
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.
DOI: 10.15480/882.3413
ISSN: 2412-3811
Other Identifiers: doi: 10.3390/infrastructures6040050
Institute: Elektrische Energietechnik E-6 
Document Type: Article
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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