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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 February 2023) | Viewed by 31053
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Special Issue Editors

Dr. Xiandong Ma

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Guest Editor

School of Engineering, Lancaster University, Lancaster LA1 4YW, UK
Interests: renewable energy system; distributed energy generation; wind power system; condition monitoring; fault diagnosis; fault prognostics; signal processing; data mining; artificial intelligence; computational intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Sinisa Durovic

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Guest Editor

Department of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK
Interests: power conversion devices in renewable power generation; condition monitoring; intelligent diagnostics; wind turbine generator systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mohamed Benbouzid

E-Mail Website
Guest Editor

Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France
Interests: fault detection and diagnosis; failure prognosis; cyberattack detection; fault-resilient control; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to a Special Issue of Energies on the subject area of “Intelligent Condition Monitoring of Wind Power Systems”. Wind turbines, both onshore and offshore, represent a major and rapidly growing form of renewable and sustainable energy generation. Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. These uncertainties can affect not only the operational performance but also the integrity of the wind power generation system under service conditions. Condition monitoring (CM) continues to play an important role in achieving reliable and economic operation of wind turbines. Recent developments in artificial intelligence (AI) and computational intelligence (CI) techniques have received considerable attention in the CM area, indicating promising application potential. It is essential that intelligent CM techniques utilizing AI and CI are developed to improve detection robustness and increase confidence of diagnosis and prognosis. This could enable CM to become more capable at the detection and diagnosis of faults and become better at autonomous prediction of operational state and failures of key components and the system as a whole, with as little human intervention as possible.

The aim of this Special Issue is to collect and disseminate novel, intelligent, and autonomous condition monitoring techniques and their potential applications for wind power systems. Topics of interest for this Special Issue include but are not limited to:

Development of condition monitoring systems including sensor systems
Modeling and condition monitoring of electric machines and drives/wind power generation systems
Power conversion system reliability
Power electronic condition monitoring
Condition monitoring of the interconnection/HVDC electronics
Performance analysis of wind turbines and their connections
Condition-based operation and maintenance strategies
Physics-based modeling and data-driven modeling
Signal processing and data mining
AI- and CI-enabled techniques and applications

Dr. Xiandong Ma
Dr. Sinisa Durovic
Prof. Dr. Mohamed Benbouzid
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 power system
wind turbine drivetrain
electrical machine
power electronics
predictive condition monitoring
fault diagnosis and prognostics
physics-based models
data-driven-based models
data mining
artificial intelligence techniques
computational intelligence techniques
deep learning
machine learning

Published Papers (12 papers)

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Research

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13 pages, 3131 KiB

Open AccessArticle

A Comparative Analysis on the Variability of Temperature Thresholds through Time for Wind Turbine Generators Using Normal Behaviour Modelling

by Alan Turnbull, James Carroll and Alasdair McDonald

Energies 2022, 15(14), 5298; https://doi.org/10.3390/en15145298 - 21 Jul 2022

Cited by 4 | Viewed by 1337

Abstract

Data-driven normal behaviour models have gained traction over the last few years as a convenient way of modelling turbine operational health to detect anomalies. By leveraging high-dimensional operational relationships, temperature thresholds can be automatically calculated based on each individual turbine unique operating envelope, in theory minimising false alarms and providing more reliable diagnostics. The aim of this work is to provide further insight into practical uses and limitations of implementing normal behaviour temperature models in practice, to inform practitioners, as well as assist in improving wind turbine generator fault detection systems. Results suggest that, on average, as little as two months of data are adequate to produce stable temperature alarm thresholds, with the worst case example requiring approximately 200–290 days of data depending on the component and desired convergence criteria. Full article

(This article belongs to the Special Issue Intelligent Condition Monitoring of Wind Power Systems)

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21 pages, 5475 KiB

Open AccessArticle

A Multi-Turbine Approach for Improving Performance of Wind Turbine Power-Based Fault Detection Methods

by Usama Aziz, Sylvie Charbonnier, Christophe Berenguer, Alexis Lebranchu and Frederic Prevost

Energies 2022, 15(8), 2806; https://doi.org/10.3390/en15082806 - 12 Apr 2022

Cited by 1 | Viewed by 1478

Abstract

The relationship between wind speed and the power produced by a wind turbine is expressed by its power curve. Power curves are commonly used to monitor the production performance of a wind turbine by asset managers to ensure optimal production. They can also be used as a tool to detect faults occurring on a wind turbine when the fault causes a decrease in performance. However, the wide dispersion of data generally observed around the reference power curve limits the detection performance of power curve-based techniques. Fault indicators, such as residuals, which measure the difference between the actual power produced and the expected power, are largely affected by this dispersion. To increase the detection performance of power-based fault detection methods, a hybrid solution of mono-multi-turbine residual generation is proposed in this paper to reduce the influence of the power curve dispersion. A new simulation framework, modeling the effect of wind nature (turbulent/laminar) on the wind turbine performance, is also proposed. This allows us to evaluate and compare the performances of two fault detection methods in their multi-turbine implementation. The results show that the application of a multi-turbine approach to a basic residual generation method significantly improves its detection performance and makes it as efficient as a more complex method. Full article

(This article belongs to the Special Issue Intelligent Condition Monitoring of Wind Power Systems)

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29 pages, 2851 KiB

Open AccessArticle

Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection

by Camila Correa-Jullian, Sergio Cofre-Martel, Gabriel San Martin, Enrique Lopez Droguett, Gustavo de Novaes Pires Leite and Alexandre Costa

Energies 2022, 15(8), 2792; https://doi.org/10.3390/en15082792 - 11 Apr 2022

Cited by 8 | Viewed by 3126

Abstract

Driven by the development of machine learning (ML) and deep learning techniques, prognostics and health management (PHM) has become a key aspect of reliability engineering research. With the recent rise in popularity of quantum computing algorithms and public availability of first-generation quantum hardware, it is of interest to assess their potential for efficiently handling large quantities of operational data for PHM purposes. This paper addresses the application of quantum kernel classification models for fault detection in wind turbine systems (WTSs). The analyzed data correspond to low-frequency SCADA sensor measurements and recorded SCADA alarm logs, focused on the early detection of pitch fault failures. This work aims to explore potential advantages of quantum kernel methods, such as quantum support vector machines (Q-SVMs), over traditional ML approaches and compare principal component analysis (PCA) and autoencoders (AE) as feature reduction tools. Results show that the proposed quantum approach is comparable to conventional ML models in terms of performance and can outperform traditional models (random forest, k-nearest neighbors) for the selected reduced dimensionality of 19 features for both PCA and AE. The overall highest mean accuracies obtained are 0.945 for Gaussian SVM and 0.925 for Q-SVM models. Full article

(This article belongs to the Special Issue Intelligent Condition Monitoring of Wind Power Systems)

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20 pages, 3419 KiB

Open AccessArticle

Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

by Conor McKinnon, James Carroll, Alasdair McDonald, Sofia Koukoura and Charlie Plumley

Energies 2021, 14(20), 6601; https://doi.org/10.3390/en14206601 - 13 Oct 2021

Cited by 12 | Viewed by 1915

Abstract

Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed. Full article

(This article belongs to the Special Issue Intelligent Condition Monitoring of Wind Power Systems)

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13 pages, 1709 KiB

Open AccessArticle

Fault Diagnosis Method for Wind Turbine Gearboxes Based on IWOA-RF

by Mingzhu Tang, Zixin Liang, Huawei Wu and Zimin Wang

Energies 2021, 14(19), 6283; https://doi.org/10.3390/en14196283 - 02 Oct 2021

Cited by 5 | Viewed by 1724

Abstract

A fault diagnosis method for wind turbine gearboxes based on undersampling, XGBoost feature selection, and improved whale optimization-random forest (IWOA-RF) was proposed for the problem of high false negative and false positive rates in wind turbine gearboxes. Normal samples of raw data were subjected to undersampling first, and various features and data labels in the raw data were provided with importance analysis by XGBoost feature selection to select features with higher label correlation. Two parameters of random forest algorithm were optimized via the whale optimization algorithm to create a fitness function with the false negative rate (FNR) and false positive rate (FPR) as evaluation indexes. Then, the minimum fitness function value within the given scope of parameters was found. The WOA was controlled by the hyper-parameter

α

to optimize the step size. This article uses the variant form of the sigmoid function to alter the change trend of the WOA hyper-parameter α from a linear decline to a rapid decline first and then a slow decline to allow the WOA to be optimized. In the initial stage, a larger step size and step size change rate can make the model progress to the optimization target faster, while in the later stage of optimization, a smaller step size and step size change rate allows the model to more accurately find the minimum value of the fitness function. Finally, two hyper-parameters, corresponding to the minimum fitness function value, were substituted into a random forest algorithm for model training. The results showed that the method proposed in this paper can significantly reduce the false negative and false positive rates compared with other optimization classification methods. Full article

(This article belongs to the Special Issue Intelligent Condition Monitoring of Wind Power Systems)

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17 pages, 3161 KiB

Open AccessArticle

Objective Analysis of Corrosion Pits in Offshore Wind Structures Using Image Processing

by Waseem Khodabux and Feargal Brennan

Energies 2021, 14(17), 5428; https://doi.org/10.3390/en14175428 - 31 Aug 2021

Cited by 3 | Viewed by 1757

Abstract

Corrosion in the marine environment is a complex mechanism. One of the most damaging forms of corrosion is pitting corrosion, which is difficult to design and inspect against. In the North Sea, multiple offshore wind structures have been deployed that are corroding from the inside out. One of the most notable corrosion mechanisms observed is pitting corrosion. This study addresses the lack of information both in the literature and the industry standards on the pitting corrosion profile for water depth from coupons deployed in the North Sea. Image processing was therefore conducted to extract the characteristics of the pit, which were defined as pit major length, minor length, area, aspect ratio, and count. The pit depth was measured using a pit gauge and the maximum pit depth was found to be 1.05 mm over 111 days of exposure. The goal of this paper is to provide both deterministic models and a statistical model of pit characteristics for water depth that can be used by wind farm operators and researchers to inform and simulate pits on structures based on the results of a real field experiment. As such, these models highlight the importance of adequate corrosion protection. Full article

(This article belongs to the Special Issue Intelligent Condition Monitoring of Wind Power Systems)

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15 pages, 27566 KiB

Open AccessArticle

Wind Turbine Generator Controller Signals Supervised Machine Learning for Shaft Misalignment Fault Detection: A Doubly Fed Induction Generator Practical Case Study

by Ahmed Al-Ajmi, Yingzhao Wang and Siniša Djurović

Energies 2021, 14(6), 1601; https://doi.org/10.3390/en14061601 - 13 Mar 2021

Cited by 4 | Viewed by 2545

Abstract

With a continued strong increase in wind generator applications, the condition monitoring of wind turbine systems has become ever more important in ensuring the availability and reduced cost of produced power. One of the key turbine conditions requiring constant monitoring is the generator shaft alignment, which if compromised and untreated can lead to catastrophic system failures. This study explores the possibility of employing supervised machine learning methods on the readily available generator controller loop signals to achieve detection of shaft misalignment condition. This could provide a highly noninvasive and low-cost solution for misalignment monitoring in comparison with the current misalignment monitoring field practice that relies on invasive and costly drivetrain vibration analysis. The study utilises signal datasets measured on a dedicated doubly fed induction generator test rig to demonstrate that high consistency and accuracy recognition of shaft angular misalignment can be achieved through the application of supervised machine learning on controller loop signals. The average recognition accuracy rate of up to 98.8% is shown to be attainable through analysis of a key feature subset of the stator flux-oriented controller signals in a range of operating speeds and loads. Full article

(This article belongs to the Special Issue Intelligent Condition Monitoring of Wind Power Systems)

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16 pages, 3673 KiB

Open AccessArticle

Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region

by Minh-Quang Tran, Yi-Chen Li, Chen-Yang Lan and Meng-Kun Liu

Energies 2020, 13(24), 6559; https://doi.org/10.3390/en13246559 - 11 Dec 2020

Cited by 9 | Viewed by 2221

Abstract

A novel concept of wind farm fault detection by monitoring the wind speed in the wake region is proposed in this study. A wind energy dissipation model was coupled with a computational fluid dynamics solver to simulate the fluid field of a wind turbine array, and the wind velocity and direction in the simulation were exported for identifying wind turbine faults. The 3D steady Navier–Stokes equations were solved by using the cell center finite volume method with a second order upwind scheme and a

k - ε

turbulence model. In addition, the wind energy dissipation model, derived from energy balance and Betz’s law, was added to the Navier–Stokes equations’ source term. The simulation results indicate that the wind speed distribution in the wake region contains significant information regarding multiple wind turbine faults. A feature selection algorithm specifically designed for the analysis of wind flow was proposed to reduce the number of features. This algorithm proved to have better performance than fuzzy entropy measures and recursive feature elimination methods under a limited number of features. As a result, faults in the wind turbine array could be detected and identified by machine learning algorithms. Full article

(This article belongs to the Special Issue Intelligent Condition Monitoring of Wind Power Systems)

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38 pages, 15571 KiB

Open AccessArticle

Global Sliding-Mode Suspension Control of Bearingless Switched Reluctance Motor under Eccentric Faults to Increase Reliability of Motor

by Pulivarthi Nageswara Rao, Ramesh Devarapalli, Fausto Pedro García Márquez and Hasmat Malik

Energies 2020, 13(20), 5485; https://doi.org/10.3390/en13205485 - 20 Oct 2020

Cited by 13 | Viewed by 2416

Abstract

Bearingless motor development is a substitute for magnetic bearing motors owing to several benefits, such as nominal repairs, compactness, lower cost, and no need for high-power amplifiers. Compared to conventional motors, rotor levitation and its steady control is an additional duty in bearingless switched reluctance motors when starting. For high-speed applications, the use of simple proportional integral derivative and fuzzy control schemes are not in effect in suspension control of the rotor owing to inherent parameter variations and external suspension loads. In this paper, a new robust global sliding-mode controller is suggested to control rotor displacements and their positions to ensure fewer eccentric rotor displacements when a bearingless switched reluctance motor is subjected to different parameter variations and loads. Extra exponential fast-decaying nonlinear functions and rotor-tracking error functions have been used in the modeling of the global sliding-mode switching surface. Simulation studies have been conducted under different testing conditions. From the results, it is shown that rotor displacements and suspension forces in X and Y directions are robust and stable. Owing to the proposed control action of the suspension phase currents, the rotor always comes back rapidly to the center position under any uncertainty. Full article

(This article belongs to the Special Issue Intelligent Condition Monitoring of Wind Power Systems)

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18 pages, 5495 KiB

Open AccessArticle

An End-to-End, Real-Time Solution for Condition Monitoring of Wind Turbine Generators

by Adrian Stetco, Juan Melecio Ramirez, Anees Mohammed, Siniša Djurović, Goran Nenadic and John Keane

Energies 2020, 13(18), 4817; https://doi.org/10.3390/en13184817 - 15 Sep 2020

Cited by 1 | Viewed by 2149

Abstract

Data-driven wind generator condition monitoring systems largely rely on multi-stage processing involving feature selection and extraction followed by supervised learning. These stages require expert analysis, are potentially error-prone and do not generalize well between applications. In this paper, we introduce a collection of end-to-end Convolutional Neural Networks for advanced condition monitoring of wind turbine generators. End-to-end models have the benefit of utilizing raw, unstructured signals to make predictions about the parameters of interest. This feature makes it easier to scale an existing collection of models to new predictive tasks (e.g., new failure types) since feature extracting steps are not required. These automated models achieve low Mean Squared Errors in predicting the generator operational state (40.85 for Speed and 0.0018 for Load) and high accuracy in diagnosing rotor demagnetization failures (99.67%) by utilizing only raw current signals. We show how to create, deploy and run the collection of proposed models in a real-time setting using a laptop connected to a test rig via a data acquisition card. Based on a sampling rate of 5 kHz, predictions are stored in an efficient time series database and monitored using a dynamic visualization framework. We further discuss existing options for understanding the decision process behind the predictions made by the models. Full article

(This article belongs to the Special Issue Intelligent Condition Monitoring of Wind Power Systems)

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21 pages, 1724 KiB

Open AccessArticle

Managing Wind Power Generation via Indexed Semi-Markov Model and Copula

by Guglielmo D’Amico, Giovanni Masala, Filippo Petroni and Robert Adam Sobolewski

Energies 2020, 13(16), 4246; https://doi.org/10.3390/en13164246 - 17 Aug 2020

Cited by 9 | Viewed by 2270

Abstract

Because of the stochastic nature of wind turbines, the output power management of wind power generation (WPG) is a fundamental challenge for the integration of wind energy systems into either power systems or microgrids (i.e., isolated systems consisting of local wind energy systems only) in operation and planning studies. In general, a wind energy system can refer to both one wind farm consisting of a number of wind turbines and a given number of wind farms sited at the area in question. In power systems (microgrid) planning, a WPG should be quantified for the determination of the expected power flows and the analysis of the adequacy of power generation. Concerning this operation, the WPG should be incorporated into an optimal operation decision process, as well as unit commitment and economic dispatch studies. In both cases, the probabilistic investigation of WPG leads to a multivariate uncertainty analysis problem involving correlated random variables (the output power of either wind turbines that constitute wind farm or wind farms sited at the area in question) that follow different distributions. This paper advances a multivariate model of WPG for a wind farm that relies on indexed semi-Markov chains (ISMC) to represent the output power of each wind energy system in question and a copula function to reproduce the spatial dependencies of the energy systems’ output power. The ISMC model can reproduce long-term memory effects in the temporal dependence of turbine power and thus understand, as distinct cases, the plethora of Markovian models. Using copula theory, we incorporate non-linear spatial dependencies into the model that go beyond linear correlations. Some copula functions that are frequently used in applications are taken into consideration in the paper; i.e., Gumbel copula, Gaussian copula, and the t-Student copula with different degrees of freedom. As a case study, we analyze a real dataset of the output powers of six wind turbines that constitute a wind farm situated in Poland. This dataset is compared with the synthetic data generated by the model thorough the calculation of three adequacy indices commonly used at the first hierarchical level of power system reliability studies; i.e., loss of load probability (LOLP), loss of load hours (LOLH) and loss of load expectation (LOLE). The results will be compared with those obtained using other models that are well known in the econometric field; i.e., vector autoregressive models (VAR). Full article

(This article belongs to the Special Issue Intelligent Condition Monitoring of Wind Power Systems)

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Review

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33 pages, 3921 KiB

Open AccessReview

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

by Mohamed Benbouzid, Tarek Berghout, Nur Sarma, Siniša Djurović, Yueqi Wu and Xiandong Ma

Energies 2021, 14(18), 5967; https://doi.org/10.3390/en14185967 - 20 Sep 2021

Cited by 23 | Viewed by 5532

Abstract

Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms. Full article

(This article belongs to the Special Issue Intelligent Condition Monitoring of Wind Power Systems)

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