Analysis of wind turbine Gearbox's environmental impact considering its reliability

Jiang, Li; Xiang, Dong; Tan, Yong; Nie, Yunlong; Cao, Haiying; Wei, Yifan; Zeng, Deqiang; Shen, Yun; Shen, Gang

doi:10.1016/j.jclepro.2018.01.078

Cited by 28 publications

(16 citation statements)

References 12 publications

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“…Many FDD approaches have been proposed for WEC systems in the literature [12][13][14]. Using the gearbox vibration signal, the authors in [15] have proposed a deep learning technique while a multiscale convolutional neural network was proposed in [4] to extract the faulty WT features under different operating modes. In [16], the authors propose a fault detection and identification approaches, which can identify faults, determine the corresponding time and location where the fault occurs, and estimate its severity.…”

Section: Introductionmentioning

confidence: 99%

Reduced Gaussian process regression based random forest approach for fault diagnosis of wind energy conversion systems

Mansouri

Fezai

Trabelsi

et al. 2021

IET Renewable Power Gen

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This paper proposes a novel Reduced Gaussian Process Regression (RGPR)-based Random Forest (RF) technique (RGPR-RF) for fault detection and diagnosis (FDD) of wind energy conversion (WEC) systems. First, two RGPR models are proposed to deal with WEC features extraction and selection. The proposed RGPR models extract the most relevant information from the WEC system data while reducing the computation burden compared to the classical GPR model. The complexity reduction is ensured by the selection of the most effective samples through the dimensionality reduction (DR) metrics including Hierarchical K-means (HKmeans) clustering and Euclidean distance (ED). Next, in order to classify the WEC faults and improve the diagnosis abilities, RF classifier is developed. The proposed RGPR HKmeans -RF and RGPR ED -RF techniques boost the classification speed and accuracy using a reduced number of features where only the most relevant and sensitive characteristics are kept in case of redundancy. The open-circuit, wear-out, and short-circuit are the three transistor faults considered in order to illustrate the effectiveness and robustness of the developed techniques. The obtained results show that the proposed RGPR-RF technique is characterized by a low computation time and high diagnosis accuracy (an average accuracy of 99.9%) compared to the conventional RF classifiers.

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Section: Introductionmentioning

confidence: 99%

Reduced Gaussian process regression based random forest approach for fault diagnosis of wind energy conversion systems

Mansouri

Fezai

Trabelsi

et al. 2021

IET Renewable Power Gen

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“…In [1], [3], the authors presented a brief description of different kinds of faults, their generated signatures, and diagnosis solutions. Using the gearbox vibration signal, the au-VOLUME 0, 2020 thors in [14] have proposed a deep learning technique while a multiscale convolutional neural network was proposed in [15] to extract the faulty wind turbine features under different operating modes. In [16], the authors proposed fault detection and identification approaches which can identify faults, determine the occurring time and location, and estimate its severity.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems

et al. 2020

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Fault detection and diagnosis (FDD) of wind energy conversion (WEC) systems play an important role in reducing the maintenance and operational costs and increase system reliability. Thus, this paper proposes a novel Interval Gaussian Process Regression (IGPR)-based Random Forest (RF) technique (IGPR-RF) for diagnosing uncertain WEC systems. In the proposed IGPR-RF technique, the effective interval-valued nonlinear statistical features are extracted and selected using the IGPR model and then fed to the RF algorithm for fault classification purposes. The proposed technique is characterized by a better handling of WEC system uncertainties such as wind variability, noise, measurement errors, which leads to an improved fault classification accuracy. The obtained results show that the proposed IGPR-RF technique is characterized by a high diagnosis accuracy (an average accuracy of 99.99%) compared to the conventional classifiers.

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“…However, their studies were only focused on the requirements of rare earth elements (REE) in the generator and do not consider other materials that can be found in the entire turbine. Other papers only analyze the material demand of particular components, most notably rotor blades (Mishnaevsky et al 2017) and generators (Jiang et al 2018;Lacal-Arántegui 2015). This paper contributes to the existing literature by combining the focuses of the aforementioned publications to present a comprehensive analysis of the metallic raw material requirements for the wind energy deployment by considering the latest technological trends of 8 different wind turbine drive train systems.…”

Section: Introductionmentioning

confidence: 99%

Raw metal needs and supply risks for the development of wind energy in Germany until 2050

Shammugam

Gervais

Schlegl

et al. 2019

Journal of Cleaner Production

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Facilitated by the environmental goals set by the government, wind turbines will be one of the main pillars of the future electricity production in Germany. In this paper, a comprehensive assessment of the future metallic raw material requirements for the development of the German wind energy sector was conducted, which is closely based on the current and future market conditions. Copper and dysprosium are identified as the most critical materials since they face the possibility of supply bottlenecks while being fundamental to the functionality of wind turbines. While the cumulative demand for copper may require 0.2% of the current known reserves, the demand for dysprosium may reach up to 0.6% of the reserve levels. Both metals clearly exceed the allocations for renewable energy technologies in Germany and would face strong competition from other sectors in securing raw materials. Although recycling is able to reduce the bottleneck risks, it does not completely mitigate them. More efforts are therefore required to improve material efficiency by means of alternative turbine designs, efficient production techniques, highly reliable components and material substitution.

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Analysis of wind turbine Gearbox's environmental impact considering its reliability

Cited by 28 publications

References 12 publications

Reduced Gaussian process regression based random forest approach for fault diagnosis of wind energy conversion systems

Reduced Gaussian process regression based random forest approach for fault diagnosis of wind energy conversion systems

A Novel Fault Diagnosis of Uncertain Systems Based on Interval Gaussian Process Regression: Application to Wind Energy Conversion Systems

Raw metal needs and supply risks for the development of wind energy in Germany until 2050

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