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Energies
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Condition Monitoring and Fault Detection of Wind Turbines
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Condition Monitoring and Fault Detection of 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 (10 August 2023) | Viewed by 7908

Special Issue Editors

Prof. Dr. Wei Teng

E-Mail Website
Guest Editor
School of Energy, Power and Mechanical Engineering, North China Electric and Power University, Beijing 102206, China
Interests: condition monitoring of energy and power equipment; fault diagnosis and prognostics; control technology of energy equipment
Dr. Dikang Peng

E-Mail Website
Guest Editor
School of Energy, Power and Mechanical Engineering, North China Electric and Power University, Beijing 102206, China
Interests: fault diagnosis and prognostics; condition monitoring; signal processing

Special Issue Information

Dear Colleagues,

The Guest Editor is inviting submissions for a Special Issue of Energies on the subject area of “Condition Monitoring and Fault Detection of Wind Turbines”.

Wind energy has experienced an immense growth with respect to both the turbine size and market share. Although the technology for generating electric power from wind energy systems has matured, the reliability of the wind turbines can still be improved in areas spanning from design, transportation, installation, operation, and maintenance. However, due to the harsh conditions, wind turbines operate under nonstationary conditions (varying speed and load) which affects the operational performance and fault detection. Therefore, successful solutions to these issues are crucial to the growth of the wind power industry. The aim of this Special Issue is to collect the latest advances in the field of condition monitoring and power generation techniques of wind turbines. Topics of interest for publication include but are not limited to:

  • Development of condition monitoring systems
  • Physics-based modeling and data-driven modeling of wind turbines
  • Modeling and condition monitoring of electric machines and drives/wind power generation systems
  • Nonstationary signal processing techniques
  • Condition-based operation and maintenance strategies
  • Physics-based modeling and data-driven modeling
  • Control of wind turbine systems;
  • Wind turbine design and manufacturing;

Prof. Dr. Wei Teng
Dr. Dikang Peng
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 turbine
  • wind turbine drivetrain
  • fault detection
  • condition monitoring
  • reliability

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Published Papers (3 papers)

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Research

21 pages, 5429 KiB  
Article
Early Detection and Diagnosis of Wind Turbine Abnormal Conditions Using an Interpretable Supervised Variational Autoencoder Model
by Adaiton Oliveira-Filho, Ryad Zemouri, Philippe Cambron and Antoine Tahan
Energies 2023, 16(12), 4544; https://doi.org/10.3390/en16124544 - 6 Jun 2023
Cited by 17 | Viewed by 2568
Abstract
The operation and maintenance of wind turbines benefit from reliable information on the wind turbine condition. Data-driven models use data from the supervisory data acquisition system. In particular, great performance is reported for artificial intelligence models. However, the lack of interpretability limits their [...] Read more.
The operation and maintenance of wind turbines benefit from reliable information on the wind turbine condition. Data-driven models use data from the supervisory data acquisition system. In particular, great performance is reported for artificial intelligence models. However, the lack of interpretability limits their effective industrial implementation. The present work introduces a new condition-monitoring approach for wind turbines featuring a built-in visualization tool that confers interpretability upon the model outcomes. The proposed approach is based on a supervised implementation of the variational autoencoder model, which allows the projection of the wind turbine system onto a low-dimensional representation space. Three outcomes follow from such representation: a health indicator for the early detection of abnormal conditions, a classifier providing the diagnosis status, and a visualization tool depicting the wind turbine condition as a trajectory in a 2D plot. The approach is implemented with a vast database. Two case studies demonstrate the potential of the proposed approach. The proposed health indicator detects the main bearing overtemperature 11 days before the control system alarm, one week earlier than a competing approach. Study cases illustrate that the built-in visualization tool enhances the interpretability and trust in the model outcomes, thus supporting wind turbine operation and maintenance. Full article
(This article belongs to the Special Issue Condition Monitoring and Fault Detection of Wind Turbines)
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14 pages, 8500 KiB  
Article
Wind Turbine Load Optimization Control Strategy Based on LIDAR Feed-Forward Control for Primary Frequency Modulation Process with Pitch Angle Reservation
by Deyi Fu, Lingxing Kong, Lice Gong, Anqing Wang, Haikun Jia and Na Zhao
Energies 2023, 16(1), 510; https://doi.org/10.3390/en16010510 - 2 Jan 2023
Cited by 3 | Viewed by 2403
Abstract
Because wind power is connected to the grid on a large scale, frequency fluctuation in the power grid, which is defined as a system safety risk to the power grid, occurs from time to time. According to the grid code rules of China, [...] Read more.
Because wind power is connected to the grid on a large scale, frequency fluctuation in the power grid, which is defined as a system safety risk to the power grid, occurs from time to time. According to the grid code rules of China, wind turbines are required to be equipped with primary frequency modulation or inertia response control capability, which are used to support the safe and stable operation of the power grid. During the traditional frequency modulation process of the wind turbine, power limiting operation or pitch angle reservation is generally adopted to ensure that the reserved energy can be released at any time to support the frequency change in the power grid. However, the frequency support method leads to a large loss of power generation, and does not consider the coordination between mechanical load characteristics control and primary frequency modulation. In this paper, a mechanical load optimization control strategy for a wind turbine during the primary frequency modulation process, based on LIDAR (light detection and ranging) feed forward control technology, is proposed and verified. Through LIDAR feed forward control, the characteristics of incoming wind speed can be sensed in advance, with the consequence that the wind turbine can participate in, and actively control, the primary frequency modulation procedure. According to the characteristics of incoming wind, for instance the amplitude and turbulence, simultaneously, the size of the reserved pitch angle can be adjusted in real time. This kind of method, coordinating with the mechanical load of the wind turbine, can be used to reduce both the ultimate load and fatigue damage as much as possible. Finally, the mechanical load characteristics of the wind turbine with and without the control strategy are compared and studied through simulation. The research results show that the load optimization control strategy based on LIDAR feed-forward control technology can effectively reduce the fatigue and ultimate loads of the wind turbine while supporting the frequency change in the power grid; especially for the fatigue load of tower base tilt and roll bending moments, the reducing proportion will be about 4.3% and 6.3%, respectively. Full article
(This article belongs to the Special Issue Condition Monitoring and Fault Detection of Wind Turbines)
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24 pages, 9016 KiB  
Article
Research on Vibration Data-Driven Fault Diagnosis for Iron Core Looseness of Saturable Reactor in UHVDC Thyristor Valve Based on CVAE-GAN and Multimodal Feature Integrated CNN
by Xiaolong Zhang, Xiaoguang Wei, Lin Zheng, Chenghao Wang and Huafeng Wang
Energies 2022, 15(24), 9508; https://doi.org/10.3390/en15249508 - 15 Dec 2022
Cited by 7 | Viewed by 2344
Abstract
The imbalance of data samples and fluctuating operating conditions are the two main challenges faced by vibration data-driven fault diagnosis for the iron core looseness of saturable reactors in UHVDC thyristor valves. This paper proposes a vibration data-driven saturable reactor iron core looseness [...] Read more.
The imbalance of data samples and fluctuating operating conditions are the two main challenges faced by vibration data-driven fault diagnosis for the iron core looseness of saturable reactors in UHVDC thyristor valves. This paper proposes a vibration data-driven saturable reactor iron core looseness fault diagnosis strategy named CVG-MFICNN based on CVAE-GAN and MFICNN to overcome the two challenges. This strategy uses a novel 1-D CVAE-GAN model to produce generated samples and expand the training set based on imbalanced training samples. An MFICNN model structure is designed to allow the simultaneous processing of multimodal features such as the SST time-frequency spectrum, time-domain vibration sequence, frequency-domain power spectrum sequence, and time-domain statistics. Using these multimodal features and the MFICNN model, the hidden fault information in vibration data can be effectively mined. An experiment is conducted to collect vibration data of saturable reactors with different faults. Models based on the proposed strategy and other methods are trained and tested using the collected data. The comparison results show that the performance of the proposed CVG-MFICNN approach is significantly superior to that of single-feature CNNs, traditional machine learning methods, and classical image classification CNNs in the application of UHVDC thyristor valve saturable reactor iron core looseness fault diagnosis. Full article
(This article belongs to the Special Issue Condition Monitoring and Fault Detection of Wind Turbines)
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