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Energies
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Condition Monitoring, Fault Diagnosis and Fault-Tolerant Control for Wind Turbines
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Condition Monitoring, Fault Diagnosis and Fault-Tolerant Control for Wind Turbines

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: closed (20 June 2024) | Viewed by 4135

Special Issue Editors

Dr. Mahdi Ghane

E-Mail Website
Guest Editor
Department of Computer Science, Electrical Engineering, and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway
Interests: wind energy; fault diagnosis and fault-tolerant control; reliability and optimization
Dr. Surya Teja Kandukuri

E-Mail Website
Guest Editor
NORCE Norwegian Research Centre AS, University of Agder, Kristiansand, Norway
Interests: fault diagnosis; condition monitoring; filtering and system identification; asset health management
Dr. Omid Rahmani Seryasat

E-Mail Website
Guest Editor
Department of Electrical Engineering, Adiban Higher Education Institute, Garmsar, Iran
Interests: fault diagnosis; biomedical engineering; image processing; artificial intelligence
Dr. Afshin Abbasi

E-Mail Website
Guest Editor
Lead Data Scientist@Skyfri Intelligence, Oslo, Norway
Interests: digital twin and application of ml/dl to leverage big data in renewable energy

Special Issue Information

Dear Colleagues,

Wind energy, both onshore and offshore, is one of the fastest growing sources of clean energy, being of significant interest for tackling global energy needs and the climate crisis. It provides reliable power supplies and fuel diversification, which enhance energy security and lower the risk of conventional power plants. To ensure safe and efficient operation of wind turbines, the whole cycle of wind energy, especially operation and maintenance, must be optimized. This has pushed forward research and innovations in condition monitoring, fault diagnosis, and fault-tolerant control (FTC) with a vast range of technologies and algorithms being used to form remote sensing, data mining and fusion, digital twins, and advanced control techniques.

Wind turbines are dynamical systems with a high degree of nonlinearity and stochastic inputs, thus indicating many challenges from the modeling point of view. The stochastic nature of wind turbine inputs complicates fault diagnosis of wind turbines. Moreover, fault-tolerant control methods offer sustainable operation over a wider range of conditions than would otherwise be expected.

This Special Issue aims to explore advances and challenges in condition monitoring, fault diagnosis, and fault-tolerant control for wind turbines and other subsystems found on a wind farm. This includes techniques for detecting isolation and estimation faults, remaining useful life estimation, methods for implementing fault-tolerant control, and case studies of real-world wind energy applications. This Special Issue aims to bring together researchers and industry professionals to share their expertise and showcase the latest developments in this important area of renewable energy.

Topics of interest for publication include, but are not limited to:

  • Condition monitoring;
  • Fault-tolerant control;
  • Fault detection, estimation, and isolation;
  • Fault accommodation;
  • Observer design;
  • Robust control;
  • Wind turbines;
  • Sensor, actuator, and grid faults;
  • Event triggered fault-tolerant control;
  • Active and passive fault-tolerant control;
  • Digital twins in fault diagnosis and condition monitoring of wind turbines.

Dr. Mahdi Ghane
Dr. Surya Teja Kandukuri
Dr. Omid Rahmani Seryasat
Dr. Afshin Abbasi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wind energy
  • wind turbine
  • condition monitoring
  • fault diagnosis
  • fault-tolerant control
  • sensor faults
  • actuator faults
  • observer design
  • grid faults
  • remote sensing
  • data driven fault diagnosis
  • model-based fault diagnosis
  • digital twins

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Published Papers (3 papers)

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Research

20 pages, 1540 KiB  
Article
Pitch Actuator Fault-Tolerant Control of Wind Turbines via an L1 Adaptive Sliding Mode Control (SMC) Scheme
by Ali Fayazi, Hossein Ghayoumi Zadeh, Hossein Ahmadian, Mahdi Ghane and Omid Rahmani Seryasat
Energies 2024, 17(16), 3963; https://doi.org/10.3390/en17163963 - 9 Aug 2024
Viewed by 868
Abstract
Effective fault identification and management are critical for efficient wind turbine operation. This research presents a novel L1 adaptive-SMC system designed to enhance fault tolerance in wind turbines, specifically addressing common issues such as pump wear, hydraulic leakage, and excessive air [...] Read more.
Effective fault identification and management are critical for efficient wind turbine operation. This research presents a novel L1 adaptive-SMC system designed to enhance fault tolerance in wind turbines, specifically addressing common issues such as pump wear, hydraulic leakage, and excessive air content in the oil. By combining SMC with L1 adaptive control, the proposed technique effectively controls rotor speed and power, ensuring reliable performance under various conditions. The controller employs an adjustable gain and an integrated sliding surface to maintain robustness. We validate the controller’s performance in the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) simulation environment using a 5-megawatt wind turbine under high wind speeds. Simulation results demonstrate that the proposed L1 adaptive-SMC outperforms traditional adaptive-SMC and adaptive control schemes, particularly in the presence of faults, unknown disturbances, and turbulent wind fields. This research highlights the controller’s potential to significantly improve the reliability and efficiency of wind turbine operations. Full article
(This article belongs to the Special Issue Condition Monitoring, Fault Diagnosis and Fault-Tolerant Control for Wind Turbines)
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14 pages, 823 KiB  
Article
KPI Extraction from Maintenance Work Orders—A Comparison of Expert Labeling, Text Classification and AI-Assisted Tagging for Computing Failure Rates of Wind Turbines
by Marc-Alexander Lutz, Bastian Schäfermeier, Rachael Sexton, Michael Sharp, Alden Dima, Stefan Faulstich and Jagan Mohini Aluri
Energies 2023, 16(24), 7937; https://doi.org/10.3390/en16247937 - 6 Dec 2023
Cited by 2 | Viewed by 1313
Abstract
Maintenance work orders are commonly used to document information about wind turbine operation and maintenance. This includes details about proactive and reactive wind turbine downtimes, such as preventative and corrective maintenance. However, the information contained in maintenance work orders is often unstructured and [...] Read more.
Maintenance work orders are commonly used to document information about wind turbine operation and maintenance. This includes details about proactive and reactive wind turbine downtimes, such as preventative and corrective maintenance. However, the information contained in maintenance work orders is often unstructured and difficult to analyze, presenting challenges for decision-makers wishing to use it for optimizing operation and maintenance. To address this issue, this work compares three different approaches to calculating reliability key performance indicators from maintenance work orders. The first approach involves manual labeling of the maintenance work orders by domain experts, using the schema defined in an industrial guideline to assign the label accordingly. The second approach involves the development of a model that automatically labels the maintenance work orders using text classification methods. Through this method, we are able to achieve macro average and weighted average F1-scores of 0.75 and 0.85 respectively. The third technique uses an AI-assisted tagging tool to tag and structure the raw maintenance information, together with a novel rule-based approach for extracting relevant maintenance work orders for failure rate calculation. In our experiments, the AI-assisted tool leads to an 88% drop in tagging time in comparison to the other two approaches, while expert labeling and text classification are more accurate in KPI extraction. Overall, our findings make extracting maintenance information from maintenance work orders more efficient, enable the assessment of reliability key performance indicators, and therefore support the optimization of wind turbine operation and maintenance. Full article
(This article belongs to the Special Issue Condition Monitoring, Fault Diagnosis and Fault-Tolerant Control for Wind Turbines)
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17 pages, 3485 KiB  
Article
The Use of Coherence Functions of Acoustic Emission Signals as a Method for Diagnosing Wind Turbine Blades
by Artur Bejger, Jan Bohdan Drzewieniecki, Przemysław Bartoszko and Ewelina Frank
Energies 2023, 16(22), 7474; https://doi.org/10.3390/en16227474 - 7 Nov 2023
Cited by 1 | Viewed by 1277
Abstract
Acoustic emission (AE) is one of the methods of non-destructive evaluation (NDE), and functions by means of detecting elastic waves caused by dynamic movements in AE sources, such as cracking in various material structures. In the case of offshore wind turbines, the most [...] Read more.
Acoustic emission (AE) is one of the methods of non-destructive evaluation (NDE), and functions by means of detecting elastic waves caused by dynamic movements in AE sources, such as cracking in various material structures. In the case of offshore wind turbines, the most vulnerable components are their blades. Therefore, the authors proposed a method using AE to diagnose wind turbine blades. In the identification of their condition during monitoring, it was noted that the changes characterising blade damage involve non-linear phenomena; hence, wave phenomena do not occur in the principal components of the amplitudes or their harmonics. When the authors used the inverse transformation in the signal analysis process, which essentially leads to finding a signal measure, it allowed them to distinguish the wave spectrum of an undamaged system from one in which the material structure of the blade was damaged. The characteristic frequencies of individual phenomena interacting with the blade of a working turbine provide the basis for the introduction of filters (or narrowband sensors) that will increase the quality of the diagnosis itself. Considering the above, the use of the coherence function was proposed as an important measure of a diagnostic signal, reflecting a given condition of the blade. Full article
(This article belongs to the Special Issue Condition Monitoring, Fault Diagnosis and Fault-Tolerant Control for Wind Turbines)
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