Special Issue "Intelligent Condition Monitoring of Wind Power Systems"

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

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editors

Dr. Xiandong Ma
Website
Guest Editor
Engineering Department, 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
Dr. Sinisa Durovic
Website
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 diagnostics

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 papers will be 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 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

  • 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 (3 papers)

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Research

Open AccessArticle
Global Sliding-Mode Suspension Control of Bearingless Switched Reluctance Motor under Eccentric Faults to Increase Reliability of Motor
Energies 2020, 13(20), 5485; https://doi.org/10.3390/en13205485 - 20 Oct 2020
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 [...] Read more.
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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Open AccessArticle
An End-to-End, Real-Time Solution for Condition Monitoring of Wind Turbine Generators
Energies 2020, 13(18), 4817; https://doi.org/10.3390/en13184817 - 15 Sep 2020
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 [...] Read more.
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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Open AccessArticle
Managing Wind Power Generation via Indexed Semi-Markov Model and Copula
Energies 2020, 13(16), 4246; https://doi.org/10.3390/en13164246 - 17 Aug 2020
Cited by 1
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 [...] Read more.
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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