Special Issue "Analysis for Electrical Machines Monitoring"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electrical Power and Energy System".

Deadline for manuscript submissions: 31 October 2019.

Special Issue Editors

Prof. Dr. Adam Glowacz
E-Mail Website
Guest Editor
Department of Automatic, Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland
Interests: machine; fault diagnosis; pattern recognition; signal processing; signal analysis; image processing; computer science; automatic
Special Issues and Collections in MDPI journals
Prof. Dr. Jose Alfonso Antonino Daviu
E-Mail Website
Guest Editor
Universitat de València: VALENCIA, Spain
Interests: electric motors; fault diagnosis; transient analysis; signal processing; wavelet analysis; infrared thermography; time-frequency transforms
Special Issues and Collections in MDPI journals

Special Issue Information

Dear colleague,

This Special Issue invites original research papers that report on the state-of-the-art and recent advancements in electrical machines monitoring. Today, electrical machines are widely applied in modern industry. Research concerning electrical machines and drives condition monitoring is constantly progressing. The increasing importance of these energy conversion devices and their widespread use in uncountable applications have motivated significant research efforts. The scope of this Special Issue encompasses applications in condition monitoring, fault diagnosis, and the reliability of electrical machines: induction, synchronous, DC, commutator motors, transformers, electric generators, AC and DC generators, etc. Analysis of electrical voltages, currents, and acoustic and vibration signals are also within the scope of this Special Issue.

Prof. Adam Glowacz
Prof. Grzegorz Królczyk
Prof. Jose Alfonso Antonino Daviu
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

  • electrical machines
  • condition monitoring
  • fault diagnosis
  • measurement
  • reliability
  • analysis

Published Papers (7 papers)

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Research

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Open AccessArticle
Anti-Interference and Location Performance for Turn-to-Turn Short Circuit Detection in Turbo-Generator Rotor Windings
Energies 2019, 12(7), 1378; https://doi.org/10.3390/en12071378 - 10 Apr 2019
Cited by 1
Abstract
Online and location detection of rotor winding inter-turn short circuits are an important direction in the field of fault diagnosis in turbo-generators. This area is facing many difficulties and challenges. This study is based on the principles associated with the U-shaped detection coil [...] Read more.
Online and location detection of rotor winding inter-turn short circuits are an important direction in the field of fault diagnosis in turbo-generators. This area is facing many difficulties and challenges. This study is based on the principles associated with the U-shaped detection coil method. Compared with dynamic eccentricity faults, the characteristics of the variations in the main magnetic field after a turn-to-turn short circuit in rotor windings were analyzed and the unique characteristics were extracted. We propose that the degree of a turn-to-turn short circuit can be judged by the difference in the induction voltage of the double U-shaped detection coils mounted on the stator core. Here, the faulty slot position was determined by the local convex point formed by the difference in the induced voltage. Numerical simulation was used here to determine the induced voltage characteristics in the double U-shaped coils caused by the turn-to-turn short circuit fault. We analyzed the dynamic eccentricity fault as well as combined the fault of a turn-to-turn short circuit and dynamic eccentricity. Finally, we demonstrate the positive anti-interference performance associated with this fault detection method. This new online detection method is satisfactory in terms of sensitivity, speed, and positioning, and overall performance is superior to the traditional online detection methods. Full article
(This article belongs to the Special Issue Analysis for Electrical Machines Monitoring)
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Open AccessArticle
A New Machine-Learning Prediction Model for Slope Deformation of an Open-Pit Mine: An Evaluation of Field Data
Energies 2019, 12(7), 1288; https://doi.org/10.3390/en12071288 - 03 Apr 2019
Abstract
Effective monitoring of the slope deformation of an open-pit mine is essential for preventing catastrophic collapses. It is a challenging task to accurately predict slope deformation. To this end, this article proposed a new machine-learning method for slope deformation prediction. Ground-based interferometric radar [...] Read more.
Effective monitoring of the slope deformation of an open-pit mine is essential for preventing catastrophic collapses. It is a challenging task to accurately predict slope deformation. To this end, this article proposed a new machine-learning method for slope deformation prediction. Ground-based interferometric radar (GB-SAR) was employed to collect the slope deformation data from an open-pit mine. Then, an ensemble learner, which aggregated a set of weaker learners, was proposed to mine the GB-SAR field data, delivering a slope deformation prediction model. The evaluation of the field data acquired from the Anjialing open-pit mine demonstrates that the proposed slope deformation model was able to precisely predict the slope deformation of the monitored mine. The prediction accuracy of the super learner was superior to those of all the independent weaker learners. Full article
(This article belongs to the Special Issue Analysis for Electrical Machines Monitoring)
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Open AccessArticle
Study and Stability Analysis of Leading Phase Operation of a Large Synchronous Generator
Energies 2019, 12(6), 1047; https://doi.org/10.3390/en12061047 - 18 Mar 2019
Abstract
This paper proposes a two-dimensional finite element mathematical model taking the mf-15 simulation generator in the dynamic model laboratory as the research object. The electromagnetic performance of the motor under no-load and rated load was analyzed to verify the correctness of the model. [...] Read more.
This paper proposes a two-dimensional finite element mathematical model taking the mf-15 simulation generator in the dynamic model laboratory as the research object. The electromagnetic performance of the motor under no-load and rated load was analyzed to verify the correctness of the model. By using the method of field-circuit coupling combined with simulation analysis, the limit value of the generator’s phase-in depth is determined, and the variation law of the electric and magnetic field distribution with the depth of the generator’s established Leading phase operation is analyzed, as well as the influence of the phase-in operation on the system’s operation stability. The results obtained provide a reference for the phase-in operation analysis of large generators. Full article
(This article belongs to the Special Issue Analysis for Electrical Machines Monitoring)
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Open AccessArticle
An Improved LSSVM Model for Intelligent Prediction of the Daily Water Level
Energies 2019, 12(1), 112; https://doi.org/10.3390/en12010112 - 29 Dec 2018
Cited by 2
Abstract
Daily water level forecasting is of significant importance for the comprehensive utilization of water resources. An improved least squares support vector machine (LSSVM) model was introduced by including an extra bias error control term in the objective function. The tuning parameters were determined [...] Read more.
Daily water level forecasting is of significant importance for the comprehensive utilization of water resources. An improved least squares support vector machine (LSSVM) model was introduced by including an extra bias error control term in the objective function. The tuning parameters were determined by the cross-validation scheme. Both conventional and improved LSSVM models were applied in the short term forecasting of the water level in the middle reaches of the Yangtze River, China. Evaluations were made with both models through metrics such as RMSE (Root Mean Squared Error), MAPE (Mean Absolute Percent Error) and index of agreement (d). More accurate forecasts were obtained although the improvement is regarded as moderate. Results indicate the capability and flexibility of LSSVM-type models in resolving time sequence problems. The improved LSSVM model is expected to provide useful water level information for the managements of hydroelectric resources in Rivers. Full article
(This article belongs to the Special Issue Analysis for Electrical Machines Monitoring)
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Open AccessArticle
Experimental Study for the Assessment of the Measurement Uncertainty Associated with Electric Powertrain Efficiency Using the Back-to-Back Direct Method
Energies 2018, 11(12), 3536; https://doi.org/10.3390/en11123536 - 19 Dec 2018
Cited by 2
Abstract
Brushless electric motors are used intensively in the industrial automation sector due to the motors low inertia and fast response. According to the International Electrotechnical Commission, IEC 60034-2-1, the efficiency of a three-phase electric machine (excluding machines for traction vehicles) can be determined [...] Read more.
Brushless electric motors are used intensively in the industrial automation sector due to the motors low inertia and fast response. According to the International Electrotechnical Commission, IEC 60034-2-1, the efficiency of a three-phase electric machine (excluding machines for traction vehicles) can be determined by direct or indirect techniques. In the case of small traction motors (<10 kW), direct methods are used extensively by manufacturers, even if no standard has been published or scheduled by the IEC. In this paper, we evaluated the accuracy of the (direct) back-to-back method for the estimation of the energy performance of a 3 kW brushless AC electric motor used in a light electric vehicle. We measured the efficiencies of a pair of motors and inverters, as well as the overall efficiency of the entire power train. The results showed that the methodology was sufficiently accurate and comparable with other indirect methods available in existing literature. Moreover, we developed a Simulink model that used the powertrain efficiency map as the input to perform the simulation of a standard urban driving cycle. The simulation was run 500 times to calculate the probability density function associated with the total range of the vehicle, considering the uncertainty of the efficiency that was determined experimentally. The simulation results confirmed the low deviation of the distribution standard compared to the average value of the range of the vehicle. Full article
(This article belongs to the Special Issue Analysis for Electrical Machines Monitoring)
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Open AccessArticle
Magnetization-Dependent Core-Loss Model in a Three-Phase Self-Excited Induction Generator
Energies 2018, 11(11), 3228; https://doi.org/10.3390/en11113228 - 21 Nov 2018
Cited by 1
Abstract
Steady-state, transient, as well as dynamic analyses of self-excited induction generators (SEIGs) are generally well-documented. However, in most of the documented studies, core losses have been neglected or inaccurately modeled. This paper is concerned with the accurate modeling of core losses in SEIG [...] Read more.
Steady-state, transient, as well as dynamic analyses of self-excited induction generators (SEIGs) are generally well-documented. However, in most of the documented studies, core losses have been neglected or inaccurately modeled. This paper is concerned with the accurate modeling of core losses in SEIG analysis. The core loss is presented as a function related to the level of saturation. This relation is determined experimentally and integrated into a nonlinear model of the SEIG. The nonlinear model is solved using a mathematical optimization scheme to obtain the performance parameters of the SEIG. A new set of curves describing accurate behavior of the SEIG parameters is produced and presented in this paper. The computed parameters of the model are validated experimentally, and the agreement attained demonstrates the functionality and accuracy of the proposed core-loss model. Full article
(This article belongs to the Special Issue Analysis for Electrical Machines Monitoring)
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Review

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Open AccessReview
A Comprehensive Review of Winding Short Circuit Fault and Irreversible Demagnetization Fault Detection in PM Type Machines
Energies 2018, 11(12), 3309; https://doi.org/10.3390/en11123309 - 27 Nov 2018
Abstract
Contemporary research has shown impetus in the diagnostics of permanent magnet (PM) type machines. The manufacturers are now more interested in building diagnostics features in the control algorithms of machines to make them more salable and reliable. A compact structure, exclusive high-power density, [...] Read more.
Contemporary research has shown impetus in the diagnostics of permanent magnet (PM) type machines. The manufacturers are now more interested in building diagnostics features in the control algorithms of machines to make them more salable and reliable. A compact structure, exclusive high-power density, high torque density, and efficiency make the PM machine an attractive option to use in industrial applications. The impact of a harsh operational environment most often leads to faults in PM machines. The diagnosis and nipping of such faults at an early stage have appeared as the prime concern of manufacturers and end users. This paper reviews the recent advances in fault diagnosis techniques of the two most frequently occurring faults, namely inter-turn short fault (ITSF) and irreversible demagnetization fault (IDF). ITSF is associated with a short circuit in stator winding turns in the same phase of the machine, while IDF is associated with the weakening strength of the PM in the rotor. A detailed literature review of different categories of fault indexes and their strengths and weaknesses is presented. The research trends in the fault diagnosis and the shortcomings of available literature are discussed. Moreover, potential research directions and techniques applicable for possible solutions are also extensively suggested. Full article
(This article belongs to the Special Issue Analysis for Electrical Machines Monitoring)
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