2019
DOI: 10.3390/app9163230
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Repair Process Analysis for Wind Turbines Equipped with Hydraulic Pitch Mechanism on the U.S. Market in Focus of Cost Optimization

Abstract: In recent years both the demand and supply for upgrade solutions and repair services are growing. The majority of the American turbine owners are motivated to be able to operate their various fleets of wind turbines on their own and gain sufficient knowledge to do so in a professional manner. With this goal in mind, the learning curve includes optimizing operation cost, fine-tuning practices, and building a network with suppliers. This work focused on hydraulic pitch system designed for a modern wind turbine, … Show more

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Cited by 14 publications
(5 citation statements)
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“…In the course of research and tuning of optimal machine learning models, it was determined that the most qualitative model for solving this problem is a combination of a "riskier" [15] random forest algorithm and a more "careful" logistic regression. At the same time, the approach to building machine learning models using the ensemble technique (the weighted voting technique was applied) was as follows: each basic algorithm b N (x) participating in the construction of the composition was assigned a weight coefficient β, and then, for each element of the sample, by voting these basic algorithms, the answer a(x) (14) was chosen that optimizes the quality metrics of the machine learning model.…”
Section: Approbation Of the Results And Discussion Of Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…In the course of research and tuning of optimal machine learning models, it was determined that the most qualitative model for solving this problem is a combination of a "riskier" [15] random forest algorithm and a more "careful" logistic regression. At the same time, the approach to building machine learning models using the ensemble technique (the weighted voting technique was applied) was as follows: each basic algorithm b N (x) participating in the construction of the composition was assigned a weight coefficient β, and then, for each element of the sample, by voting these basic algorithms, the answer a(x) (14) was chosen that optimizes the quality metrics of the machine learning model.…”
Section: Approbation Of the Results And Discussion Of Resultsmentioning
confidence: 99%
“…The presence of this large task facing energy enterprises has led to a broad discussion within the scientific community focused on various methods and approaches that could be applied to the construction of systems for predicting defects and failures of power equipment. The most promising from the point of view of the implementation of the mathematical apparatus of forecasting are the methods and algorithms of machine learning used to solve problems of classification, regression and clustering [11][12][13][14][15].…”
Section: Introductionmentioning
confidence: 99%
“…It is generally accepted that to avoid the most severe impacts of climate change, a reduction in carbon emissions is necessary. Wind energy is an increasingly low‐cost solution to reduce carbon emissions by replacing retiring thermal generators and avoiding additional thermal units where there is a need to meet rising demand 45 . “In 2019, wind energy generation avoided an estimated 198 million metric tons of carbon dioxide (CO 2 )—equivalent to 11% of annual U.S. electric sector emissions” 16 .…”
Section: Discussionmentioning
confidence: 99%
“…Although the GCC region is typically thought of as a collection of countries that almost exclusively export crude oil or natural gas, most countries in the region possess welldeveloped petrochemical industries in addition to mineral resources that have enabled the production of steel, aluminum and copper. As such, there may be interest in localizing some elements of wind turbine component manufacturing, given that many of the metals and plastics needed for their production can be locally sourced (Kocsis and Xydis, 2019). While this industry would take some time to develop, there is already a long-term demand for wind turbine components in the wider Middle East and North Africa region, since Egypt, Jordan and Morocco are much further along in the development of their wind energy sectors.…”
Section: Discussionmentioning
confidence: 99%