Gearbox has proven to be a major contributor toward downtime in wind turbines. The majority of failures in the gearbox originate from the gearbox bearings. An early indication of possible wear and tear in the gearbox bearings may be used for effective predictive maintenance, thereby reducing the overall cost of maintenance. This paper introduces a self-evolving maintenance scheduler framework for maintenance management of wind turbines. Furthermore, an artificial neural network (ANN)-based condition monitoring approach using data from supervisory control and data acquisition system is proposed. The ANN-based condition monitoring approach is applied to gearbox bearings with real data from onshore wind turbines, rated 2 MW, and located in the south of Sweden. The results demonstrate that the proposed ANN-based condition monitoring approach is capable of indicating severe damage in the components being monitored in advance.

An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings

Wind turbine drivetrains consist of components that directly convert kinetic energy from the wind to electricalenergy. Therefore, guaranteeing robust and reliable drivetrain designs is important to prevent turbine downtime. Currentdrivetrain models often lack the ability to model both the impacts of electrical transients as well as wind turbulenceand shear in one package. In this paper, the capability of the FAST wind turbine computer-aided-engineering tool,developed by the National Renewable Energy Laboratory, is enhanced through the integration of a dynamic model of thedrivetrain. The dynamic drivetrain model is built using Simscape in the MATLAB/Simulink environment and incorporatesdetailed electrical generator models. This model can be used to evaluate internal drivetrain loads due to excitationsfrom both the wind and generator.

Gearbox and Drivetrain Models to Study Dynamic Effects of Modern Wind Turbines

This report presents the work done to develop generator and gearbox models in the Matrix Laboratory (MATLAB) environment and couple them to the National Renewable Energy Laboratory's Fatigue, Aerodynamics, Structures, and Turbulence (FAST) program. The goal of this project was to interface the superior aerodynamic and mechanical models of FAST to the excellent electrical generator models found in various Simulink libraries and applications. The scope was limited to Type 1, Type 2, and Type 3 generators and fairly basic gear-train models. Future work will include models of Type 4 generators and more-advanced gear-train models with increased degrees of freedom. As described in this study, implementation of the developed drivetrain model enables the software tool to be used in many ways. Several case studies are presented as examples of the many types of studies that can be performed using this tool.

/pdf/simulation-for-wind-turbine-generators-with-fast-and-matlab-3bgy1ck6ln.pdf

Simulation for Wind Turbine Generators -- With FAST and MATLAB-Simulink Modules

Failure of the drivetrain components is currently listed among the most problematic failures during the operational lifetime of a wind turbine. Guaranteeing robust and reliable drivetrain designs is important to minimize the wind turbine downtime as well as to meet demand in both power quantity and quality. While aeroelastic codes are often used in the design of wind turbine controllers, the drivetrain model in such codes is limited to a few (mostly two) degrees of freedom, resulting in a restricted detail in describing its dynamic behavior and assessing the effectiveness of controllers on attenuating the drivetrain load. In the previous work, the capability of the well-known FAST aeroelastic tool for wind turbine has been enhanced through integration of a dynamic model of a drivetrain. The drivetrain model, built using the Simscape in the MATLAB/Simulink environment, is applied in this paper. The model is used to develop a power-electronics-based controller to prevent excessive drivetrain load. The controller temporarily shifts the closed-loop eigenfrequency of the drivetrain through the addition of virtual inertia, thus avoiding the resonance. Simulation results demonstrating the fidelity of the expanded drivetrain model as well as the effectiveness of the virtual inertia controller are presented.

Modeling and Control to Mitigate Resonant Load in Variable-Speed Wind Turbine Drivetrain

There have been some recent efforts to numerically model and analyse the wind turbine gearbox. To date, much of the focus has been on increasing model refinement and demonstrating its added value. This paper takes a step back and examines in detail the modelling and analysis of an important wind turbine gearbox component, the planet carrier, in a multi-body setting. The planet carrier studied in this work comes from the 750 kW wind turbine gearbox used in the National Renewable Energy Laboratory's Gearbox Reliability Collaborative project. The study is performed in two parts. First, the influence of subcomponents mated to the planet carrier in the gearbox assembly is investigated in detail. These components consist of the planet pins, bearings and the main shaft. In the second part of the study, the flexible body modelling of the planet carrier for use in multi-body simulations is examined through the use of condensed finite element and multi-body simulation models. Both eigenvalue analyses and time domain simulations are performed. Comparisons are made regarding the eigenfrequencies, categorized mode shapes and the maximum and minimum planet carrier rim deflections from the time domain simulations. The mode shapes are categorized into seven distinct deformation patterns. An actual load case from the dynamometer tests, a 100% rated torque loading, is used in the time domain simulations. The results from this comprehensive study provide an insight into the proper modelling of a wind turbine planet carrier in a multi-body setting. Copyright © 2012 John Wiley & Sons, Ltd.

Multi‐body modelling and analysis of a planet carrier in a wind turbine gearbox

This document describes simulated turbine load cases in accordance to the IEC 61400-1 Ed.3 standard, which is representative of the typical wind turbine design process. The information presented herein is intended to provide a broad understanding of the gearbox reliability collaborative 750kW drivetrain and turbine configuration. In addition, fatigue and ultimate strength drivetrain loads resulting from simulations are presented. This information provides the bases for the analytical work of the gearbox reliability collaborative effort.

/pdf/gearbox-reliability-collaborative-grc-description-and-2ameu07my1.pdf

Gearbox Reliability Collaborative (GRC) Description and Loading

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

F. Oyague

Author Tools

Chat about Author

Papers

Gearbox Reliability Collaborative (GRC) Description and Loading