Special Issue "Maintenance Management of Wind Turbines"

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

Deadline for manuscript submissions: 22 December 2019.

Special Issue Editor

Guest Editor
Prof. Fausto Pedro García Márquez Website E-Mail
Ingenium Research Group, Universidad Castilla-La Mancha, 13071 Ciudad Real, Spain
Interests: operations research; management science; maintenance; renewable energy

Special Issue Information

Dear Colleagues,

Maintenance Management of Wind Turbines” considers the main concepts, the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness, being the most important, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. This issue will consider original research works that show content complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines.

The issue will also show real case studies. They will consider topics, such as failures detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.). It is essential to link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs, downtimes, etc., in a wind turbine.

Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in maintenance management.

It will also consider computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements.

Prof. Fausto Pedro García Márquez
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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

  • Maintenance
  • Wind Turbines
  • Diagnosis
  • Prognosis
  • Predictive Maintenance
  • Preventive Maintenance
  • Corrective Maintenance
  • Downtime
  • Strategy on Maintenance
  • Maintenance Planning
  • Resources Management
  • Organization Management
  • Finance
  • Cost
  • Profit
  • Efficiency
  • Reliability
  • Availability
  • Safety
  • Maintainability
  • Durability
  • Alarms
  • Condition Monitoring
  • Maintenance Software

Published Papers (13 papers)

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Research

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Open AccessArticle
A Context-Aware Oil Debris-Based Health Indicator for Wind Turbine Gearbox Condition Monitoring
Energies 2019, 12(17), 3373; https://doi.org/10.3390/en12173373 - 02 Sep 2019
Abstract
One of the greatest challenges of optimising the correct operation of wind turbines is detecting the health status of their core components, such as gearboxes in particular. Gearbox monitoring is a widely studied topic in the literature, nevertheless, studies showing data of in-service [...] Read more.
One of the greatest challenges of optimising the correct operation of wind turbines is detecting the health status of their core components, such as gearboxes in particular. Gearbox monitoring is a widely studied topic in the literature, nevertheless, studies showing data of in-service wind turbines are less frequent and tend to present difficulties that are otherwise overlooked in test rig based works. This work presents the data of three wind turbines that have gearboxes in different damage stages. Besides including the data of the SCADA (Supervisory Control And Signal Acquisition) system, additional measurements of online optical oil debris sensors are also included. In addition to an analysis of the behaviour of particle generation in the turbines, a methodology to identify regimes of operation with lower variation is presented. These regimes are later utilised to develop a health index that considers operation states and provides valuable information regarding the state of the gearboxes. The proposed health index allows distinguishing damage severity between wind turbines as well as tracking the evolution of the damage over time. Full article
(This article belongs to the Special Issue Maintenance Management of Wind Turbines)
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Open AccessArticle
Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks
Energies 2019, 12(17), 3256; https://doi.org/10.3390/en12173256 - 23 Aug 2019
Abstract
Pitch system failures occur primarily because wind turbines typically work in dynamic and variable environments. Conventional monitoring strategies show limitations of continuously identifying faults in most cases, especially when rapidly changing winds occur. A novel selective-ensemble monitoring strategy is presented to diagnose the [...] Read more.
Pitch system failures occur primarily because wind turbines typically work in dynamic and variable environments. Conventional monitoring strategies show limitations of continuously identifying faults in most cases, especially when rapidly changing winds occur. A novel selective-ensemble monitoring strategy is presented to diagnose the most pitch failures using Supervisory Control and Data Acquisition (SCADA) data. The proposed strategy consists of five steps. During the first step, the SCADA data are partitioned according to the turbine’s four working states. Correlation Information Entropy (CIE) and 10 indicators are used to select correlation signals and extract features of the partition data, respectively. During the second step, multiple Small-World Neural Networks (SWNNs) are established as the ensemble members. Regarding the third step, all the features are randomly sampled to train the SWNN members. The fourth step involves using an improved global correlation method to select appropriate ensemble members while in the fifth step, the selected members are fused to obtain the final classification result based on the weighted integration approach. Compared with the conventional methods, the proposed ensemble strategy shows an effective accuracy rate of over 93.8% within a short delay time. Full article
(This article belongs to the Special Issue Maintenance Management of Wind Turbines)
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Open AccessArticle
Hierarchical Fault-Tolerant Control using Model Predictive Control for Wind Turbine Pitch Actuator Faults
Energies 2019, 12(16), 3097; https://doi.org/10.3390/en12163097 - 12 Aug 2019
Abstract
Wind energy is one of the fastest growing energy sources in the world. It is expected that by the end of 2022 the installed capacity will exceed 250 GW thanks to the supply of large scale wind turbines in Europe. However, there are [...] Read more.
Wind energy is one of the fastest growing energy sources in the world. It is expected that by the end of 2022 the installed capacity will exceed 250 GW thanks to the supply of large scale wind turbines in Europe. However, there are still challenging problems with wind turbines. In particular, off-shore and large-scale wind turbines are required to tackle the issue of maintainability and availability because they are installed in harsh off-shore environments, which may also prevent engineers from accessing the site for immediate repair works. Fault-tolerant control techniques have been widely exploited to overcome this issue. This paper proposes a novel fault-tolerant control strategy for wind turbines. The proposed strategy has a hierarchical structure, consisting of a pitch controller and a wind turbine controller, with parameter estimations using the adaptive fading Kalman filter technique. The pitch controller compensates any fault with a pitching actuator, while the wind turbine controller computes the optimal reference command for pitching behavior so that the effect of the fault with a pitch actuator can be minimized. The performance of the proposed approach is demonstrated through a set of simulations with a wind turbine benchmark model. Full article
(This article belongs to the Special Issue Maintenance Management of Wind Turbines)
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Open AccessArticle
Use of Markov Decision Processes in the Evaluation of Corrective Maintenance Scheduling Policies for Offshore Wind Farms
Energies 2019, 12(15), 2993; https://doi.org/10.3390/en12152993 - 03 Aug 2019
Abstract
Optimization of the maintenance policies for offshore wind parks is an important step in lowering the costs of energy production from wind. The yield from wind energy production is expected to fall, which will increase the need to be cost efficient. In this [...] Read more.
Optimization of the maintenance policies for offshore wind parks is an important step in lowering the costs of energy production from wind. The yield from wind energy production is expected to fall, which will increase the need to be cost efficient. In this article, the Markov decision process is presented and how it can be applied to evaluate different policies for corrective maintenance planning. In the case study, we show an alternative to the current state-of-the-art policy for corrective maintenance that will achieve a cost-reduction when energy production prices drop below the current levels. The presented method can be extended and applied to evaluate additional policies, with some examples provided. Full article
(This article belongs to the Special Issue Maintenance Management of Wind Turbines)
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Open AccessArticle
An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach
Energies 2019, 12(14), 2764; https://doi.org/10.3390/en12142764 - 18 Jul 2019
Cited by 1
Abstract
Wind power penetration has increased rapidly in recent years. In winter, the wind turbine blade imbalance fault caused by ice accretion increase the maintenance costs of wind farms. It is necessary to detect the fault before blade breakage occurs. Preliminary analysis of time [...] Read more.
Wind power penetration has increased rapidly in recent years. In winter, the wind turbine blade imbalance fault caused by ice accretion increase the maintenance costs of wind farms. It is necessary to detect the fault before blade breakage occurs. Preliminary analysis of time series simulation data shows that it is difficult to detect the imbalance faults by traditional mathematical methods, as there is little difference between normal and fault conditions. A deep learning method for wind turbine blade imbalance fault detection and classification is proposed in this paper. A long short-term memory (LSTM) neural network model is built to extract the characteristics of the fault signal. The attention mechanism is built into the LSTM to increase its performance. The simulation results show that the proposed approach can detect the imbalance fault with an accuracy of over 98%, which proves the effectiveness of the proposed approach on wind turbine blade imbalance fault detection. Full article
(This article belongs to the Special Issue Maintenance Management of Wind Turbines)
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Open AccessArticle
Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance
Energies 2019, 12(11), 2036; https://doi.org/10.3390/en12112036 - 28 May 2019
Abstract
The growth in the wind energy sector is demanding projects in which profitability must be ensured. To fulfil such aim, the levelized cost of energy should be reduced, and this can be done by enhancing the Operational Expenditure through excellence in Operations & [...] Read more.
The growth in the wind energy sector is demanding projects in which profitability must be ensured. To fulfil such aim, the levelized cost of energy should be reduced, and this can be done by enhancing the Operational Expenditure through excellence in Operations & Maintenance. There is a considerable amount of work in the literature that deals with several aspects regarding the maintenance of wind farms. Among the related works, several focus on describing the reliability of wind turbines and many set the spotlight on defining the optimal maintenance strategy. It is in this context where the presented work intends to contribute. In the paper a technical framework is proposed that considers the data and information requisites, integrated in a novel approach a clustering-based reliability model with a dynamic opportunistic maintenance policy. The technical framework is validated through a case study in which simulation mechanisms allow the implementation of a multi-objective optimization of the maintenance strategy for the lifecycle of a wind farm. The proposed approach is presented under a comprehensive perspective which enables the discovery an optimal trade-off among competing objectives in the Operations & Maintenance of wind energy projects. Full article
(This article belongs to the Special Issue Maintenance Management of Wind Turbines)
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Open AccessArticle
A Decision-making Model for Corrective Maintenance of Offshore Wind Turbines Considering Uncertainties
Energies 2019, 12(8), 1408; https://doi.org/10.3390/en12081408 - 12 Apr 2019
Cited by 1
Abstract
Maintenance optimization has received special attention among the wind energy research community over the past two decades. This is mainly because of the high degree of uncertainties involved in the execution of operation and maintenance (O&M) activities throughout the lifecycle of wind farms. [...] Read more.
Maintenance optimization has received special attention among the wind energy research community over the past two decades. This is mainly because of the high degree of uncertainties involved in the execution of operation and maintenance (O&M) activities throughout the lifecycle of wind farms. The increasing complexity in offshore maintenance execution demands applied research and brings forth a need to develop problem-specific maintenance decision-making models. In this paper, a mathematical model is proposed to assist wind farm stakeholders in making critical resource- related decisions for corrective maintenance at offshore wind farms (OWFs), considering uncertainties in turbine failure information. Full article
(This article belongs to the Special Issue Maintenance Management of Wind Turbines)
Open AccessArticle
Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis
Energies 2019, 12(4), 676; https://doi.org/10.3390/en12040676 - 20 Feb 2019
Cited by 2
Abstract
Timely detection of surface damages on wind turbine blades is imperative for minimizing downtime and avoiding possible catastrophic structural failures. With recent advances in drone technology, a large number of high-resolution images of wind turbines are routinely acquired and subsequently analyzed by experts [...] Read more.
Timely detection of surface damages on wind turbine blades is imperative for minimizing downtime and avoiding possible catastrophic structural failures. With recent advances in drone technology, a large number of high-resolution images of wind turbines are routinely acquired and subsequently analyzed by experts to identify imminent damages. Automated analysis of these inspection images with the help of machine learning algorithms can reduce the inspection cost. In this work, we develop a deep learning-based automated damage suggestion system for subsequent analysis of drone inspection images. Experimental results demonstrate that the proposed approach can achieve almost human-level precision in terms of suggested damage location and types on wind turbine blades. We further demonstrate that for relatively small training sets, advanced data augmentation during deep learning training can better generalize the trained model, providing a significant gain in precision. Full article
(This article belongs to the Special Issue Maintenance Management of Wind Turbines)
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Open AccessArticle
Fault Simulation and Online Diagnosis of Blade Damage of Large-Scale Wind Turbines
Energies 2019, 12(3), 522; https://doi.org/10.3390/en12030522 - 07 Feb 2019
Cited by 1
Abstract
Damaged wind turbine (WT) blades have an imbalanced load and abnormal vibration, which affects their safe and stable operation or even results in blade rupture. To solve this problem, this study proposes a new method to detect damage in WT blades using wavelet [...] Read more.
Damaged wind turbine (WT) blades have an imbalanced load and abnormal vibration, which affects their safe and stable operation or even results in blade rupture. To solve this problem, this study proposes a new method to detect damage in WT blades using wavelet packet energy spectrum analysis and operational modal analysis. First, a wavelet packet transform is used to analyze the tip displacement of the blades to obtain the energy spectrum. The damage is detected preliminarily based on the energy change in different frequency bands. Subsequently, an operational modal analysis method is used to obtain the modal parameters of the blade sections and the damage is located based on the modal strain energy change ratio (MSECR). Finally, the professional WT simulation software GH (Garrad Hassan) Bladed is used to simulate the blade damage and the results are verified by developing an online fault diagnosis platform integrated with MATLAB. The results show that the proposed method is able to diagnose and locate the damage accurately and provide a basis for further research of online damage diagnosis for WT blades. Full article
(This article belongs to the Special Issue Maintenance Management of Wind Turbines)
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Open AccessArticle
MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms
Energies 2019, 12(1), 28; https://doi.org/10.3390/en12010028 - 22 Dec 2018
Cited by 6
Abstract
A novel multi-criteria methodology for the identification of defective anemometers is shown in this paper with a benchmarking approach: it is called MIDAS: multi-technique identification of defective anemometers. The identification of wrong wind data as provided by malfunctioning devices is very important, because [...] Read more.
A novel multi-criteria methodology for the identification of defective anemometers is shown in this paper with a benchmarking approach: it is called MIDAS: multi-technique identification of defective anemometers. The identification of wrong wind data as provided by malfunctioning devices is very important, because the actual power curve of a wind turbine is conditioned by the quality of its anemometer measurements. Here, we present a novel method applied for the first time to anemometers’ data based on the kernel probability density function and the recent reanalysis ERA5. This estimation improves classical unidimensional methods such as the Kolmogorov–Smirnov test, and the use of the global ERA5’s wind data as the first benchmarking reference establishes a general method that can be used anywhere. Therefore, adopting ERA5 as the reference, this method is applied bi-dimensionally for the zonal and meridional components of wind, thus checking both components at the same time. This technique allows the identification of defective anemometers, as well as clear identification of the group of anemometers that works properly. After that, other verification techniques were used versus the faultless anemometers (Taylor diagrams, running correlation and R M S E , and principal component analysis), and coherent results were obtained for all statistical techniques with respect to the multidimensional method. The developed methodology combines the use of this set of techniques and was able to identify the defective anemometers in a wind farm with 10 anemometers located in Northern Europe in a terrain with forests and woodlands. Nevertheless, this methodology is general-purpose and not site-dependent, and in the future, its performance will be studied in other types of terrain and wind farms. Full article
(This article belongs to the Special Issue Maintenance Management of Wind Turbines)
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Open AccessArticle
Pitch Angle Misalignment Correction Based on Benchmarking and Laser Scanner Measurement in Wind Farms
Energies 2018, 11(12), 3357; https://doi.org/10.3390/en11123357 - 01 Dec 2018
Cited by 9
Abstract
In addition to human error, manufacturing tolerances for blades and hubs cause pitch angle misalignment in wind turbines. As a consequence, a significant number of turbines used by existing wind farms experience power production loss and a reduced turbine lifetime. Existing techniques, such [...] Read more.
In addition to human error, manufacturing tolerances for blades and hubs cause pitch angle misalignment in wind turbines. As a consequence, a significant number of turbines used by existing wind farms experience power production loss and a reduced turbine lifetime. Existing techniques, such as photometric technology and laser-based methods, have been used in the wind industry for on-field pitch measurements. However, in some cases, regular techniques have difficulty achieving good and accurate measurements of pitch angle settings, resulting in pitch angle errors that require cost-effective correction on wind farms. Here, the authors present a novel patented method based on laser scanner measurements. The authors applied this new method and achieved successful improvements in the Annual Energy Production of various wind farms. This technique is a benchmarking-based approach for pitch angle calibration. Two case studies are introduced to demonstrate the effectiveness of the pitch angle calibration method to yield Annual Energy Production increase. Full article
(This article belongs to the Special Issue Maintenance Management of Wind Turbines)
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Open AccessArticle
Ice Detection Model of Wind Turbine Blades Based on Random Forest Classifier
Energies 2018, 11(10), 2548; https://doi.org/10.3390/en11102548 - 25 Sep 2018
Cited by 2
Abstract
When wind turbine blades are icing, the output power of a wind turbine tends to reduce, thus informing the selection of two basic variables of wind speed and power. Then other features, such as the degree of power deviation from the power curve [...] Read more.
When wind turbine blades are icing, the output power of a wind turbine tends to reduce, thus informing the selection of two basic variables of wind speed and power. Then other features, such as the degree of power deviation from the power curve fitted by normal sample data, are extracted to build the model based on the random forest classifier with the confusion matrix for result assessment. The model indicates that it has high accuracy and good generalization ability verified with the data from the China Industrial Big Data Innovation Competition. This study looks at ice detection on wind turbine blades using supervisory control and data acquisition (SCADA) data and thereafter a model based on the random forest classifier is proposed. Compared with other classification models, the model based on the random forest classifier is more accurate and more efficient in terms of computing capabilities, making it more suitable for the practical application on ice detection. Full article
(This article belongs to the Special Issue Maintenance Management of Wind Turbines)
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Review

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Open AccessReview
A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines
Energies 2019, 12(14), 2801; https://doi.org/10.3390/en12142801 - 20 Jul 2019
Cited by 2
Abstract
Wind power, as a renewable energy for coping with global climate change challenge, has achieved rapid development in recent years. The breakdown of wind turbines (WTs) not only leads to high repair expenses but also may threaten the stability of the whole power [...] Read more.
Wind power, as a renewable energy for coping with global climate change challenge, has achieved rapid development in recent years. The breakdown of wind turbines (WTs) not only leads to high repair expenses but also may threaten the stability of the whole power grid. How to reduce the operation and the maintenance (O&M) cost of wind farms is an obstacle to its further promotion and application. To provide reliable condition monitoring and fault diagnosis (CMFD) for WTs, this paper presents a comprehensive survey of the existing CMFD methods in the following three aspects: energy flow, information flow, and integrated O&M system. Energy flow mainly analyzes the characteristics of each component from the angle of energy conversion of WTs. Information flow is the carrier of fault and control information of WT. At the end of this paper, an integrated WT O&M system based on electrical signals is proposed. Full article
(This article belongs to the Special Issue Maintenance Management of Wind Turbines)
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