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18 pages, 8964 KiB

Open AccessArticle

Design of a Multi-Robot System for Wind Turbine Maintenance

by Josef Franko, Shengzhi Du, Stephan Kallweit, Enno Duelberg and Heiko Engemann

Energies 2020, 13(10), 2552; https://doi.org/10.3390/en13102552 - 18 May 2020

Cited by 35 | Viewed by 8828

Abstract

The maintenance of wind turbines is of growing importance considering the transition to renewable energy. This paper presents a multi-robot-approach for automated wind turbine maintenance including a novel climbing robot. Currently, wind turbine maintenance remains a manual task, which is monotonous, dangerous, and [...] Read more.

The maintenance of wind turbines is of growing importance considering the transition to renewable energy. This paper presents a multi-robot-approach for automated wind turbine maintenance including a novel climbing robot. Currently, wind turbine maintenance remains a manual task, which is monotonous, dangerous, and also physically demanding due to the large scale of wind turbines. Technical climbers are required to work at significant heights, even in bad weather conditions. Furthermore, a skilled labor force with sufficient knowledge in repairing fiber composite material is rare. Autonomous mobile systems enable the digitization of the maintenance process. They can be designed for weather-independent operations. This work contributes to the development and experimental validation of a maintenance system consisting of multiple robotic platforms for a variety of tasks, such as wind turbine tower and rotor blade service. In this work, multicopters with vision and LiDAR sensors for global inspection are used to guide slower climbing robots. Light-weight magnetic climbers with surface contact were used to analyze structure parts with non-destructive inspection methods and to locally repair smaller defects. Localization was enabled by adapting odometry for conical-shaped surfaces considering additional navigation sensors. Magnets were suitable for steel towers to clamp onto the surface. A friction-based climbing ring robot (SMART— Scanning, Monitoring, Analyzing, Repair and Transportation) completed the set-up for higher payload. The maintenance period could be extended by using weather-proofed maintenance robots. The multi-robot-system was running the Robot Operating System (ROS). Additionally, first steps towards machine learning would enable maintenance staff to use pattern classification for fault diagnosis in order to operate safely from the ground in the future. Full article

(This article belongs to the Special Issue Maintenance Management of Wind Turbines)

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13 pages, 3241 KiB

Open AccessArticle

A New Approach for Fault Detection, Location and Diagnosis by Ultrasonic Testing

by Fausto Pedro García Marquez and Carlos Quiterio Gómez Muñoz

Energies 2020, 13(5), 1192; https://doi.org/10.3390/en13051192 - 5 Mar 2020

Cited by 59 | Viewed by 4692

Abstract

Wind turbine blades are constantly submitted to different types of particles such as dirt, ice, etc., as well as all the different environmental parameters that affect the behaviour and efficiency of the energy generation system. These parameters can cause faults to the wind [...] Read more.

Wind turbine blades are constantly submitted to different types of particles such as dirt, ice, etc., as well as all the different environmental parameters that affect the behaviour and efficiency of the energy generation system. These parameters can cause faults to the wind turbine blades, modifying their behaviour due, for example, to the turbulence. A new method is presented in this paper based on cross-correlations to determine the presence of delamination in the blades. The experiments were conducted in two real wind turbine blades to analyse the fault and non-fault blades using ultrasonic guided waves. Finally, the energy analysis of the signal based on wavelet transforms allowed to determine energies abrupt changes in the correlation of the signals and to locate the faults. Full article

(This article belongs to the Special Issue Maintenance Management of Wind Turbines)

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20 pages, 5762 KiB

Open AccessArticle

Reducing Operational Costs of Offshore HVDC Energy Export Systems Through Optimized Maintenance

by Jan Frederick Unnewehr, Hans-Peter Waldl, Thomas Pahlke, Iván Herráez and Anke Weidlich

Energies 2020, 13(5), 1146; https://doi.org/10.3390/en13051146 - 3 Mar 2020

Viewed by 3652

Abstract

For the grid connection of offshore wind farms today, in many cases a high-voltage direct current (HVDC) connection to the shore is implemented. The scheduled maintenance of the offshore and onshore HVDC stations makes up a significant part of the operational costs of [...] Read more.

For the grid connection of offshore wind farms today, in many cases a high-voltage direct current (HVDC) connection to the shore is implemented. The scheduled maintenance of the offshore and onshore HVDC stations makes up a significant part of the operational costs of the connected wind farms. The main factor for the maintenance cost is the lost income from the missing energy yield (indirect maintenance costs). In this study, we show an in-depth analysis of the used components, maintenance cycles, maintenance work for the on- and offshore station, and the risks assigned in prolonging the maintenance cycle of the modular multilevel converter (MMC). In addition, we investigate the potential to shift the start date of the maintenance work, based on a forecast of the energy generation. Our findings indicate that an optimized maintenance design with respect to the maintenance behavior of an HVDC energy export system can decrease the maintenance-related energy losses (indirect maintenance costs) for an offshore wind farm to almost one half. It was also shown that direct maintenance costs for the MMC (staff costs) have small effect on the total maintenance costs. This can be explained by the fact that the additional costs for maintenance staff are two orders of magnitude lower than the revenue losses during maintenance. Full article

(This article belongs to the Special Issue Maintenance Management of Wind Turbines)

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18 pages, 1645 KiB

Open AccessArticle

Evaluation of Anomaly Detection of an Autoencoder Based on Maintenace Information and Scada-Data

by Marc-Alexander Lutz, Stephan Vogt, Volker Berkhout, Stefan Faulstich, Steffen Dienst, Urs Steinmetz, Christian Gück and Andres Ortega

Energies 2020, 13(5), 1063; https://doi.org/10.3390/en13051063 - 29 Feb 2020

Cited by 17 | Viewed by 4461

Abstract

The usage of machine learning techniques is widely spread and has also been implemented in the wind industry in the last years. Many of these techniques have shown great success but need to constantly prove the expectation of functionality. This paper describes a [...] Read more.

The usage of machine learning techniques is widely spread and has also been implemented in the wind industry in the last years. Many of these techniques have shown great success but need to constantly prove the expectation of functionality. This paper describes a new method to monitor the health of a wind turbine using an undercomplete autoencoder. To evaluate the health monitoring quality of the autoencoder, the number of anomalies before an event has happened are to be considered. The results show that around 35% of all historical events that have resulted into a failure show many anomalies. Furthermore, the wind turbine subsystems which are subject to good detectability are the rotor system and the control system. If only one third of the service duties can be planned in advance, and thereby the scheduling time can be reduced, huge cost saving potentials can be seen. Full article

(This article belongs to the Special Issue Maintenance Management of Wind Turbines)

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16 pages, 3819 KiB

Open AccessArticle

Research on the Fault Characteristic of Wind Turbine Generator System Considering the Spatiotemporal Distribution of the Actual Wind Speed

by Xiaoling Sheng, Shuting Wan, Kanru Cheng and Xuan Wang

Energies 2020, 13(2), 356; https://doi.org/10.3390/en13020356 - 10 Jan 2020

Cited by 2 | Viewed by 2564

Abstract

A reliable fault monitoring system is one of the conditions that must be considered in the design of large wind farms today. The most important factor for the fault monitoring should be the accurate diagnosis criteria with sensitive fault characteristics. Most of the [...] Read more.

A reliable fault monitoring system is one of the conditions that must be considered in the design of large wind farms today. The most important factor for the fault monitoring should be the accurate diagnosis criteria with sensitive fault characteristics. Most of the current fault diagnosis criteria are obtained based on the average wind speed at the center of the hub which is not in accord with the actual wind condition in nature. So, this paper utilizes an equivalent wind speed (EWS), which can describe the actual wind speed spatiotemporal distribution on the rotor disk area considering the effects of wind shear and tower shadow, to analyze the common mechanical and electrical faults again. Firstly, the EWS model applicable to the 3-blade wind turbines is introduced; then the new fault characteristics of the wind turbine rotor aerodynamic imbalance and the stator winding asymmetry are theoretically analyzed based on the EWS model; finally, the simulation platform is built in Matlab/Simulink for comparison and the simulation result is well consistent with the theory analysis. The aim of this research is to find more accurate fault characteristics and help promoting the healthy development of wind power industry. Full article

(This article belongs to the Special Issue Maintenance Management of Wind Turbines)

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18 pages, 1740 KiB

Open AccessArticle

Assessment of Early Stopping through Statistical Health Prognostic Models for Empirical RUL Estimation in Wind Turbine Main Bearing Failure Monitoring

by Jürgen Herp, Niels L. Pedersen and Esmaeil S. Nadimi

Energies 2020, 13(1), 83; https://doi.org/10.3390/en13010083 - 23 Dec 2019

Cited by 14 | Viewed by 3466

Abstract

Details about a fault’s progression, including the remaining-useful-lifetime (RUL), are key features in monitoring, industrial operation and maintenance (O&M) planning. In order to avoid increases in O&M costs through subjective human involvement and over-conservative control strategies, this work presents models to estimate the [...] Read more.

Details about a fault’s progression, including the remaining-useful-lifetime (RUL), are key features in monitoring, industrial operation and maintenance (O&M) planning. In order to avoid increases in O&M costs through subjective human involvement and over-conservative control strategies, this work presents models to estimate the RUL for wind turbine main bearing failures. The prediction of the RUL is estimated from a likelihood function based on concepts from prognostics and health management, and survival analysis. The RUL is estimated by training the model on run-to-failure wind turbines, extracting a parametrization of a probability density function. In order to ensure analytical moments, a Weibull distribution is assumed. Alongside the RUL model, the fault’s progression is abstracted as discrete states following the bearing stages from damage detection, through overtemperature warnings, to over overtemperature alarms and failure, and are integrated in a separate assessment model. Assuming a naïve O&M plan (wind turbines are run as close to failure as possible without regards for infrastructure or supply chain constrains), 67 non run-to-failure wind turbines are assessed with respect to their early stopping, revealing the potential RUL lost. These are turbines that have been stopped by the operator prior to their failure. On average it was found that wind turbines are stopped 13 days prior to their failure, accumulating 786 days of potentially lost operations across the 67 wind turbines. Full article

(This article belongs to the Special Issue Maintenance Management of Wind Turbines)

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20 pages, 5088 KiB

Open AccessArticle

Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection

by Tongke Yuan, Zhifeng Sun and Shihao Ma

Energies 2019, 12(22), 4224; https://doi.org/10.3390/en12224224 - 6 Nov 2019

Cited by 32 | Viewed by 4360

Abstract

The fault diagnosis and prediction technology of wind turbines are of great significance for increasing the power generation and reducing the downtime of wind turbines. However, most of the current fault detection approaches are realized by setting a single alarm threshold. Considering the [...] Read more.

The fault diagnosis and prediction technology of wind turbines are of great significance for increasing the power generation and reducing the downtime of wind turbines. However, most of the current fault detection approaches are realized by setting a single alarm threshold. Considering the complicated working conditions of wind farms, such methods are prone to ignore the fault, send out a false alarm, or leave insufficient troubleshooting time. In this work, we propose a gearbox fault prediction approach of wind turbines based on the supervisory control and data acquisition (SCADA) data. A stacking model composed of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBOOST) was constructed as the normal behavior model to describe the normal conditions of the wind turbines. We used the Mahalanobis distance (MD) instead of the residual to measure the deviation of the current state from the normal conditions of the turbines. By inputting the MD series into the proposed change-point detection algorithm, we can obtain the change point at which the fault symptom begins to appear, and thus achieving the fault prediction of the gearbox. The proposed approach is validated on the historical data of 5 wind turbines in a wind farm, which proves its effectiveness to detect the fault in advance. Full article

(This article belongs to the Special Issue Maintenance Management of Wind Turbines)

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24 pages, 2832 KiB

Open AccessArticle

Optimal Preventive Maintenance of Wind Turbine Components with Imperfect Continuous Condition Monitoring

by Ahmed Raza and Vladimir Ulansky

Energies 2019, 12(19), 3801; https://doi.org/10.3390/en12193801 - 8 Oct 2019

Cited by 25 | Viewed by 4083

Abstract

Among the different maintenance techniques applied to wind turbine (WT) components, online condition monitoring is probably the most promising technique. The maintenance models based on online condition monitoring have been examined in many studies. However, no study has considered preventive maintenance models with [...] Read more.

Among the different maintenance techniques applied to wind turbine (WT) components, online condition monitoring is probably the most promising technique. The maintenance models based on online condition monitoring have been examined in many studies. However, no study has considered preventive maintenance models with incorporated probabilities of correct and incorrect decisions made during continuous condition monitoring. This article presents a mathematical model of preventive maintenance, with imperfect continuous condition monitoring of the WT components. For the first time, the article introduces generalized expressions for calculating the interval probabilities of false positive, true positive, false negative, and true negative when continuously monitoring the condition of a WT component. Mathematical equations that allow for calculating the expected cost of maintenance per unit of time and the average lifetime maintenance cost are derived for an arbitrary distribution of time to degradation failure. A numerical example of WT blades maintenance illustrates that preventive maintenance with online condition monitoring reduces the average lifetime maintenance cost by 11.8 times, as compared to corrective maintenance, and by at least 4.2 and 2.6 times, compared with predetermined preventive maintenance for low and high crack initiation rates, respectively. Full article

(This article belongs to the Special Issue Maintenance Management of Wind Turbines)

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19 pages, 2931 KiB

Open AccessArticle

A Context-Aware Oil Debris-Based Health Indicator for Wind Turbine Gearbox Condition Monitoring

by Kerman López de Calle, Susana Ferreiro, Constantino Roldán-Paraponiaris and Alain Ulazia

Energies 2019, 12(17), 3373; https://doi.org/10.3390/en12173373 - 2 Sep 2019

Cited by 24 | Viewed by 3831

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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20 pages, 3364 KiB

Open AccessArticle

Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks

by Meng Li and Shuangxin Wang

Energies 2019, 12(17), 3256; https://doi.org/10.3390/en12173256 - 23 Aug 2019

Cited by 12 | Viewed by 3761

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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13 pages, 5124 KiB

Open AccessArticle

Hierarchical Fault-Tolerant Control using Model Predictive Control for Wind Turbine Pitch Actuator Faults

by Donggil Kim and Dongik Lee

Energies 2019, 12(16), 3097; https://doi.org/10.3390/en12163097 - 12 Aug 2019

Cited by 7 | Viewed by 3185

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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19 pages, 1612 KiB

Open AccessArticle

Use of Markov Decision Processes in the Evaluation of Corrective Maintenance Scheduling Policies for Offshore Wind Farms

by Helene Seyr and Michael Muskulus

Energies 2019, 12(15), 2993; https://doi.org/10.3390/en12152993 - 3 Aug 2019

Cited by 13 | Viewed by 5326

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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15 pages, 3285 KiB

Open AccessArticle

An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach

by Jianjun Chen, Weihao Hu, Di Cao, Bin Zhang, Qi Huang, Zhe Chen and Frede Blaabjerg

Energies 2019, 12(14), 2764; https://doi.org/10.3390/en12142764 - 18 Jul 2019

Cited by 39 | Viewed by 4255

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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17 pages, 2985 KiB

Open AccessArticle

Framework for Managing Maintenance of Wind Farms Based on a Clustering Approach and Dynamic Opportunistic Maintenance

by Juan Izquierdo, Adolfo Crespo Márquez, Jone Uribetxebarria and Asier Erguido

Energies 2019, 12(11), 2036; https://doi.org/10.3390/en12112036 - 28 May 2019

Cited by 8 | Viewed by 3340

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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13 pages, 223 KiB

Open AccessArticle

A Decision-making Model for Corrective Maintenance of Offshore Wind Turbines Considering Uncertainties

by Sathishkumar Nachimuthu, Ming J. Zuo and Yi Ding

Energies 2019, 12(8), 1408; https://doi.org/10.3390/en12081408 - 12 Apr 2019

Cited by 26 | Viewed by 3999

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)

15 pages, 2020 KiB

Open AccessArticle

Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis

by ASM Shihavuddin, Xiao Chen, Vladimir Fedorov, Anders Nymark Christensen, Nicolai Andre Brogaard Riis, Kim Branner, Anders Bjorholm Dahl and Rasmus Reinhold Paulsen

Energies 2019, 12(4), 676; https://doi.org/10.3390/en12040676 - 20 Feb 2019

Cited by 162 | Viewed by 17634

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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16 pages, 4489 KiB

Open AccessArticle

Fault Simulation and Online Diagnosis of Blade Damage of Large-Scale Wind Turbines

by Feng Gao, Xiaojiang Wu, Qiang Liu, Juncheng Liu and Xiyun Yang

Energies 2019, 12(3), 522; https://doi.org/10.3390/en12030522 - 7 Feb 2019

Cited by 10 | Viewed by 3197

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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19 pages, 1476 KiB

Open AccessArticle

MIDAS: A Benchmarking Multi-Criteria Method for the Identification of Defective Anemometers in Wind Farms

by Arkaitz Rabanal, Alain Ulazia, Gabriel Ibarra-Berastegi, Jon Sáenz and Unai Elosegui

Energies 2019, 12(1), 28; https://doi.org/10.3390/en12010028 - 22 Dec 2018

Cited by 28 | Viewed by 3999

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

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, 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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20 pages, 2702 KiB

Open AccessArticle

Pitch Angle Misalignment Correction Based on Benchmarking and Laser Scanner Measurement in Wind Farms

by Unai Elosegui, Igor Egana, Alain Ulazia and Gabriel Ibarra-Berastegi

Energies 2018, 11(12), 3357; https://doi.org/10.3390/en11123357 - 1 Dec 2018

Cited by 23 | Viewed by 8378

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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15 pages, 5143 KiB

Open AccessArticle

Ice Detection Model of Wind Turbine Blades Based on Random Forest Classifier

by Lijun Zhang, Kai Liu, Yufeng Wang and Zachary Bosire Omariba

Energies 2018, 11(10), 2548; https://doi.org/10.3390/en11102548 - 25 Sep 2018

Cited by 62 | Viewed by 4912

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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A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines

by Pinjia Zhang and Delong Lu

Energies 2019, 12(14), 2801; https://doi.org/10.3390/en12142801 - 20 Jul 2019

Cited by 51 | Viewed by 7578

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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