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Energies
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Wind Power Generation Fault Diagnosis and Detection
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Wind Power Generation Fault Diagnosis and Detection

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 (30 October 2021) | Viewed by 7984

Special Issue Editor

Dr. Jordi Cusido

E-Mail Website
Guest Editor
Project and Construction Engineering UPC, Universitat Politécnica de Catalunya, 08034 Barcelona, Spain
Interests: wind power; machine learning; deep learning; diagnosis; performance analytics; prognosis and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your original research or overview papers to this Special Issue on “Wind Power Generation Fault Diagnosis and Detection” in Energies.

Wind energy is one of the most sustainable and important sources of energy, accounting for 21% (591 GW) of total renewable electricity. Due to technological advances made in the last decade, wind power has become more competitive with traditional power prices. However, wind energy needs to solve several challenges that do not allow it to be equal to or more competitive than traditional energy sources or renewable energies such as hydropower and solar PV, which has been the most installed renewable energy in the last three years. A major challenge exists in the strict O&M for turbines to make wind farms profitable beyond their original useful lifetime, without the need for any incentives.

This Special Issue is looking for contributions on novel methodologies for fault diagnosis and early detection on wind power fleets. The contributions encourage real operation validations considering actual available data. Contributions should consider SCADA data, working order data integration, condition monitoring techniques, novel IoT solutions, high-frequency data, aggregated data, cloud computing, quantum computing, machine learning, and deep learning. Hybrid technologies and fault-tolerant control proposals are also welcome as a novel and disruptive proposals. Applications should consider wind power fleet and multisite validation.

Dr. Jordi Cusido
Guest Editor

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

  • Machine learning
  • Deep learning
  • Condition monitoring
  • Text mining
  • Performance analytics and control

Published Papers (3 papers)

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Research

14 pages, 4974 KiB  
Article
Coordinated LVRT Support for a PMSG-Based Wind Energy Conversion System Integrated into a Weak AC-Grid
by Akrama Khan, Hasnain Ahmad, Syed Muhammad Ahsan, Muhammad Majid Gulzar and Sadia Murawwat
Energies 2021, 14(20), 6588; https://doi.org/10.3390/en14206588 - 13 Oct 2021
Cited by 16 | Viewed by 1657
Abstract
In a grid, the choice of the point of common coupling (PCC) does not solely rely on the voltage level alone but also conjointly depends on the grid strength for many explicit purposes. Nowadays, the affinity of low SCR grid connections has become [...] Read more.
In a grid, the choice of the point of common coupling (PCC) does not solely rely on the voltage level alone but also conjointly depends on the grid strength for many explicit purposes. Nowadays, the affinity of low SCR grid connections has become a crucial thought once it involves the integration of wind generation plants (WPPs). Since the quality of wind resources is a critical issue, these plants are usually placed in remote areas with a sophisticated potential of wind flow. These remote areas are typically less inhabited, where the grid does not perpetually always have to be sturdy. Moreover, the exceeded power demand loading and higher wind penetration affect the generation, transmission, and distribution utilities by permitting the flow of unbalanced voltages and currents in the power system. Therefore, the quality of transmitted power is becoming a crucial facet of distributed energy generation units. In this paper, a permanent-magnet synchronous generator (PMSG) based wind energy conversion system (WECS) is presented. It discusses a solution, which provides the low voltage ride through (LVRT) provision by the suppression of DC link overvoltage and active power limitation during an asymmetrical grid fault. With improved back-to-back converter control, the machine side converter (MSC) is employed to control the DC-link voltage. Furthermore, the grid side converter (GSC) is used to implement the active/reactive current injection according to the outlined limits. The need for external hardware is eventually avoided, which is typically required to dissipate the additional energy generated during a grid fault. Hence, it is proven to be an affordable solution. Full article
(This article belongs to the Special Issue Wind Power Generation Fault Diagnosis and Detection)
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20 pages, 7115 KiB  
Article
Fault-Tolerant Control of a Wind Turbine Generator Based on Fuzzy Logic and Using Ensemble Learning
by Jordi Cusidó, Arnau López and Mattia Beretta
Energies 2021, 14(16), 5167; https://doi.org/10.3390/en14165167 - 20 Aug 2021
Cited by 3 | Viewed by 2184
Abstract
Wind energy is a form of renewable energy with the highest installed capacity. However, it is necessary to reduce the operation and maintenance costs and extend the lifetime of wind turbines to make wind energy more competitive. This paper presents a power-derating-based Fault-Tolerant [...] Read more.
Wind energy is a form of renewable energy with the highest installed capacity. However, it is necessary to reduce the operation and maintenance costs and extend the lifetime of wind turbines to make wind energy more competitive. This paper presents a power-derating-based Fault-Tolerant Control (FTC) model in 2 MW three-bladed wind turbines implemented using the National Renewable Energy Laboratory’s (NREL) Fatigue, Aerodynamics, Structures, and Turbulence (FAST) wind turbine simulator. This control strategy is potentially supported by the health status of the gearbox, which was predicted by means of algorithms and quantified in an indicator denominated as a merge developed by SMARTIVE, a pioneering of in this idea. Fuzzy logic was employed in order to decide whether to down-regulate the output power or not, and to which level to adjust to the needs of the turbines. Simulation results demonstrated that a reduction in the power output resulted in a safer operation, since the stresses withstood by the blades and tower significantly decreased. Moreover, the results supported empirically that a diminution in the generator torque and speed was acheived, resulting in a drop in the gearbox bearing and oil temperatures. By implementing this power-derating FTC, the downtime due to failure stops could be controlled, and thus the power production noticeably grew. It has been estimated that more than 325,000 tons of CO2 could be avoided yearly if implemented globally. Full article
(This article belongs to the Special Issue Wind Power Generation Fault Diagnosis and Detection)
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26 pages, 1528 KiB  
Article
Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data
by Cristian Velandia-Cardenas, Yolanda Vidal and Francesc Pozo
Energies 2021, 14(6), 1728; https://doi.org/10.3390/en14061728 - 20 Mar 2021
Cited by 25 | Viewed by 3497
Abstract
Wind power is cleaner and less expensive compared to other alternative sources, and it has therefore become one of the most important energy sources worldwide. However, challenges related to the operation and maintenance of wind farms significantly contribute to the increase in their [...] Read more.
Wind power is cleaner and less expensive compared to other alternative sources, and it has therefore become one of the most important energy sources worldwide. However, challenges related to the operation and maintenance of wind farms significantly contribute to the increase in their overall costs, and, therefore, it is necessary to monitor the condition of each wind turbine on the farm and identify the different states of alarm. Common alarms are raised based on data acquired by a supervisory control and data acquisition (SCADA) system; however, this system generates a large number of false positive alerts, which must be handled to minimize inspection costs and perform preventive maintenance before actual critical or catastrophic failures occur. To this end, a fault detection methodology is proposed in this paper; in the proposed method, different data analysis and data processing techniques are applied to real SCADA data (imbalanced data) for improving the detection of alarms related to the temperature of the main gearbox of a wind turbine. An imbalanced dataset is a classification data set that contains skewed class proportions (more observations from one class than the other) which can cause a potential bias if it is not handled with caution. Furthermore, the dataset is time dependent introducing an additional variable to deal with when processing and splitting the data. These methods are aimed to reduce false positives and false negatives, and to demonstrate the effectiveness of well-applied preprocessing techniques for improving the performance of different machine learning algorithms. Full article
(This article belongs to the Special Issue Wind Power Generation Fault Diagnosis and Detection)
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