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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 18381

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Special Issue Editors

Dr. Moussa Boukhnifer

E-Mail Website
Guest Editor

Université de Lorraine, LCOMS, F-57000 Metz, France
Interests: diagnosis; fault-tolerant control; electrical system, energy management; power electronics
Special Issues, Collections and Topics in MDPI journals

Dr. Larbi Djilali

E-Mail Website
Guest Editor

Faculty of Technology, University of Ciudad del Carmen, Campeche 24130, Mexico
Interests: robust control; neural control; and their applications to renewable power systems; micro-grids; power electronics converters; electrical machines

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit your recent research work to a Special Issue of Energies on “Modeling, Control and Diagnosis of Electrical Machines and Devices”. At present, the growing use of electric machines and drives in more critical applications has driven research on condition monitoring and fault tolerance. The condition monitoring of electrical machines has a very important impact in the field of the electrical systems maintenance, mainly for its potential functions of failure prediction, fault identification, and dynamic reliability estimation. The fault diagnosis of electrical machines and drives has received a great deal of attention due to its benefits in maintenance cost reduction, unscheduled downtime prevention, and in many cases also harm prevention and failure disruption. Fault-tolerant design provides a solution combining fault occurrence conditions, failure detection and location tools, and the reconfiguration of control features. On the other hand, recent advancements in smart technology using artificial intelligence and advanced machine learning capabilities provide new perspectives for meaningful fault diagnostics and fault-tolerant control. These outstanding advancements enhance the performance of condition monitoring and have significant potential for the fault detection of electrical machines and devices.

Given the above premises, this Special Issue aims to highlight recent trends, research and development, applications, solutions, and challenges related to the condition monitoring and fault diagnostics of electrical machines and devices. Topics of interest include, but are not limited to:

Modeling of electrical machines and drives;
Robust control strategies of electrical machines and drives;
Failure detection and diagnosis of electrical machines and drives;
Fault-tolerant control of electrical machines and drive;
Condition monitoring techniques and application in electrical machines and drives;
AI techniques for electrical machine fault diagnosis and fault-tolerant control;
Machine learning techniques for electrical machine fault diagnosis and tolerant control.

Dr. Moussa Boukhnifer
Dr. Larbi Djilali
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

electric machines and drives
condition monitoring
fault diagnosis
fault tolerance
robust control
artificial intelligence
machine learning

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Published Papers (11 papers)

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Editorial

Jump to: Research

4 pages, 151 KiB

Open AccessEditorial

Modeling, Control and Diagnosis of Electrical Machines and Devices

by Moussa Boukhnifer and Larbi Djilali

Energies 2024, 17(10), 2250; https://doi.org/10.3390/en17102250 - 7 May 2024

Viewed by 727

Abstract

Nowadays, the increasing use of electrical machines and devices in more critical applications has driven the research in condition monitoring and fault tolerance [...] Full article

(This article belongs to the Special Issue Modeling, Control and Diagnosis of Electrical Machines and Devices)

Research

Jump to: Editorial

16 pages, 7777 KiB

Open AccessEditor’s ChoiceArticle

Insulation Condition Assessment in Inverter-Fed Motors Using the High-Frequency Common Mode Current: A Case Study

by Mariam Saeed, Daniel Fernández, Juan Manuel Guerrero, Ignacio Díaz and Fernando Briz

Energies 2024, 17(2), 470; https://doi.org/10.3390/en17020470 - 18 Jan 2024

Cited by 3 | Viewed by 1136

Abstract

The use of the common mode current for stator winding insulation condition assessment has been extensively studied. Two main approaches have been followed. The first models the electric behavior of ground-wall insulation as an equivalent

R C

circuit; these methods have been successfully [...] Read more.

R C

circuit; these methods have been successfully applied to high-voltage high-power machines. The second uses the high frequency of the common mode current which results from the voltage pulses applied by the inverter. This approach has mainly been studied for the case of low-voltage, inverter-fed machines, and has not yet reached the level of maturity of the first. One fact noticed after a literature review is that in most cases, the faults being detected were induced by connecting external elements between winding and stator magnetic core. This paper presents a case study on the use of the high-frequency common mode current to monitor the stator insulation condition. Insulation degradation occurred progressively with the machine operating normally; no exogenous elements were added. Signal processing able to detect the degradation at early stages will be discussed. Full article

(This article belongs to the Special Issue Modeling, Control and Diagnosis of Electrical Machines and Devices)

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22 pages, 11235 KiB

Open AccessFeature PaperArticle

Microcontroller-Based Embedded System for the Diagnosis of Stator Winding Faults and Unbalanced Supply Voltage of the Induction Motors

by Przemyslaw Pietrzak, Piotr Pietrzak and Marcin Wolkiewicz

Energies 2024, 17(2), 387; https://doi.org/10.3390/en17020387 - 12 Jan 2024

Cited by 2 | Viewed by 1420

Abstract

Induction motors (IMs) are one of the most widely used motor types in the industry due to their low cost, high reliability, and efficiency. Nevertheless, like other types of AC motors, they are prone to various faults. In this article, a low-cost embedded system based on a microcontroller with the ARM Cortex-M4 core is proposed for the extraction of stator winding faults (interturn short circuits) and an unbalanced supply voltage of the induction motor drive. The voltage induced in the measurement coil by the axial flux was used as a source of diagnostic information. The process of signal measurement, acquisition, and processing using a cost-optimized embedded system (NUCLEO-L476RG), with the potential for industrial deployment, is described in detail. In addition, the analysis of the possibility of distinguishing between interturn short circuits and unbalanced supply voltage was carried out. The effect of motor operating conditions and fault severity on the symptom extraction process was also studied. The results of the experimental research conducted on a 1.5 kW IM confirmed the effectiveness of the developed embedded system in the extraction of these types of faults. Full article

(This article belongs to the Special Issue Modeling, Control and Diagnosis of Electrical Machines and Devices)

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12 pages, 3225 KiB

Open AccessArticle

Effect of Ripple Control on Induction Motors

by Piotr Gnaciński, Marcin Pepliński, Adam Muc, Damian Hallmann and Piotr Jankowski

Energies 2023, 16(23), 7831; https://doi.org/10.3390/en16237831 - 28 Nov 2023

Cited by 2 | Viewed by 992

Abstract

One method for the remote management of electrical equipment is ripple control (RC), based on the injection of voltage interharmonics into the power network to transmit information. The disadvantage of this method is its negative impact on energy consumers, such as light sources, speakers, and devices counting zero crossings. This study investigates the effect of RC on low-voltage induction motors through the use of experimental and finite element methods. The results show that the provisions concerning RC included in the European Standard EN 50160 Voltage Characteristics of Electricity Supplied by Public Distribution Network are imprecise, failing to protect induction motors against excessive vibration. Full article

(This article belongs to the Special Issue Modeling, Control and Diagnosis of Electrical Machines and Devices)

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15 pages, 5302 KiB

Open AccessArticle

Improving Torque Analysis and Design Using the Air-Gap Field Modulation Principle for Permanent-Magnet Hub Machines

by Yuhua Sun, Nicola Bianchi, Jinghua Ji and Wenxiang Zhao

Energies 2023, 16(17), 6214; https://doi.org/10.3390/en16176214 - 27 Aug 2023

Cited by 3 | Viewed by 1279

Abstract

The Double Permanent Magnet Vernier (DPMV) machine is well known for its high torque density and magnet utilization ratio. This paper aims to investigate the torque generation mechanism and its improved design in DPMV machines for hub propulsion based on the field modulation principle. Firstly, the topology of the proposed DPMV machine is introduced, and a commercial PM machine is used as a benchmark. Secondly, the rotor PM, stator PM, and armature magnetic fields are derived and analyzed considering the modulation effect, respectively. Meanwhile, the contribution of each harmonic to average torque is pointed out. It can be concluded that the 7th-, 12th-, 19th- and 24th-order flux density harmonics are the main source of average torque. Thanks to the multi-working harmonic characteristics, the average torque of DPMV machines has significantly increased by 31.8% compared to the counterpart commercial PM machine, while also reducing the PM weight by 75%. Thirdly, the auxiliary barrier structure and dual three-phase winding configuration are proposed from the perspective of optimizing the phase and amplitude of working harmonics, respectively. The improvements in average torque are 9.9% and 5.4%, correspondingly. Full article

(This article belongs to the Special Issue Modeling, Control and Diagnosis of Electrical Machines and Devices)

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18 pages, 5968 KiB

Open AccessArticle

Impact of Inter-Turn Short Circuit in Excitation Windings on Magnetic Field and Stator Current of Synchronous Condenser under Unbalanced Voltage

by Junqing Li, Chengzhi Zhang, Yuling He, Xiaodong Hu, Jiya Geng and Yapeng Ma

Energies 2023, 16(15), 5695; https://doi.org/10.3390/en16155695 - 29 Jul 2023

Cited by 1 | Viewed by 1339

Abstract

Inter-turn short circuit in the excitation windings of synchronous condensers is a common fault that directly impacts their normal operation. However, current fault analysis and diagnosis of synchronous condensers primarily rely on voltage-balanced conditions, while research on short-circuit faults under unbalanced voltage conditions is limited. Therefore, this paper aims to analyze the fault characteristics of inter-turn short circuits in the excitation windings of synchronous condensers under unbalanced grid voltage. Mathematical models were developed to represent the air gap flux density and stator parallel currents for four operating conditions: normal operation and inter-turn short circuit fault under balanced voltage, as well as a process without fault and with inter-turn short circuit fault under unbalanced voltage. By comparing the harmonic content and amplitudes, various aspects of the fault mechanism of synchronous condensers were revealed, and the operating characteristics under different conditions were analyzed. Considering the four aforementioned operating conditions, finite element simulation models were created for the TTS-300-2 synchronous condenser in a specific substation as a case study. The results demonstrate that the inter-turn short circuit fault in the excitation windings under unbalanced voltage leads to an increase in even harmonic currents in the stator parallel currents, particularly the second and fourth harmonics. This validates the accuracy of the theoretical analysis findings. Full article

(This article belongs to the Special Issue Modeling, Control and Diagnosis of Electrical Machines and Devices)

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13 pages, 2208 KiB

Open AccessArticle

Torque Ripple Minimization of Variable Reluctance Motor Using Reinforcement Dual NNs Learning Architecture

by Hamad Alharkan

Energies 2023, 16(13), 4839; https://doi.org/10.3390/en16134839 - 21 Jun 2023

Cited by 4 | Viewed by 1183

Abstract

The torque ripples in a switched reluctance motor (SRM) are minimized via an optimal adaptive dynamic regulator that is presented in this research. A novel reinforcement neural network learning approach based on machine learning is adopted to find the best solution for the tracking problem of the SRM drive in real time. The reference signal model which minimizes the torque pulsations is combined with tracking error to construct the augmented structure of the SRM drive. A discounted cost function for the augmented SRM model is described to assess the tracking performance of the signal. In order to track the optimal trajectory, a neural network (NN)-based RL approach has been developed. This method achieves the optimal tracking response to the Hamilton–Jacobi–Bellman (HJB) equation for a nonlinear tracking system. To do so, two neural networks (NNs) have been trained online individually to acquire the best control policy to allow tracking performance for the motor. Simulation findings have been undertaken for SRM to confirm the viability of the suggested control strategy. Full article

(This article belongs to the Special Issue Modeling, Control and Diagnosis of Electrical Machines and Devices)

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23 pages, 9427 KiB

Open AccessArticle

Analytical Modeling, Analysis and Diagnosis of External Rotor PMSM with Stator Winding Unbalance Fault

by Ahmed Belkhadir, Remus Pusca, Driss Belkhayat, Raphaël Romary and Youssef Zidani

Energies 2023, 16(7), 3198; https://doi.org/10.3390/en16073198 - 1 Apr 2023

Cited by 6 | Viewed by 2102

Abstract

Multiple factors and consequences may lead to a stator winding fault in an external rotor permanent magnet synchronous motor that can unleash a complete system shutdown and impair performance and motor reliability. This type of fault causes disturbances in operation if it is not recognized and detected in time, since it might lead to catastrophic consequences. In particular, an external rotor permanent magnet synchronous motor has disadvantages in terms of fault tolerance. Consequently, the distribution of the air-gap flux density will no longer be uniform, producing fault harmonics. However, a crucial step of diagnosis and controlling the system condition is to develop an accurate model of the machine with a lack of turns in the stator winding. This paper presents an analytical model of the stator winding unbalance fault represented by lack of turns. Here, mathematical approaches are used by introducing a stator winding parameter for the analytical modeling of the faulty machine. This model can be employed to determine the various quantities of the machine under different fault levels, including the magnetomotive force, the flux density in the air-gap, the flux generated by the stator winding, the stator inductances, and the electromagnetic torque. On this basis, a corresponding link between the fault level and its signature is established. The feasibility and efficiency of the analytical approach are validated by finite element analysis and experimental implementation. Full article

(This article belongs to the Special Issue Modeling, Control and Diagnosis of Electrical Machines and Devices)

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30 pages, 4232 KiB

Open AccessArticle

Advanced Torque Ripple Minimization of Synchronous Reluctance Machine for Electric Vehicle Application

by Olaoluwa Demola Aladetola, Mondher Ouari, Yakoub Saadi, Tedjani Mesbahi, Moussa Boukhnifer and Kondo Hloindo Adjallah

Energies 2023, 16(6), 2701; https://doi.org/10.3390/en16062701 - 14 Mar 2023

Cited by 9 | Viewed by 2856

Abstract

The electric machine and the control system determine the performance of the electric vehicle drivetrain. Unlike rare-earth magnet machines such as permanent magnet synchronous machines (PMSMs), synchronous reluctance machines(SynRMs) are manufactured without permanent magnets. This allows them to be used as an alternative to rare-earth magnet machines. However, one of the main drawbacks of this machine is its high torque ripple, which generates significant acoustic noise. The most typical method for reducing this torque ripple is to employ an optimized structural design or a customized control technique. The objective of this paper is the use of a control approach to minimize the torque ripple effects issue in the SynRM. This work is performed in two steps: Initially, the reference current calculation bloc is modified to reduce the torque ripple of the machine. A method for calculating the optimal reference currents based on the stator joule loss is proposed. The proposed method is compared to two methods used in the literature, the FOC and MTPA methods. A comparative study between the three methods based on the torque ripple rate shows that the proposed method allows a significant reduction in the torque ripple. The second contribution to the minimization of the torque ripple is to propose a sliding mode control. This control suffers from the phenomenon of “Chattering” which affects the torque ripple. To solve this problem, a second-order sliding mode control is proposed. A comparative study between the different approaches shows that the second-order sliding mode provides the lowest torque ripple rate of the machine. Full article

(This article belongs to the Special Issue Modeling, Control and Diagnosis of Electrical Machines and Devices)

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27 pages, 18649 KiB

Open AccessFeature PaperArticle

A New Bearing Fault Detection Strategy Based on Combined Modes Ensemble Empirical Mode Decomposition, KMAD, and an Enhanced Deconvolution Process

by Yasser Damine, Noureddine Bessous, Remus Pusca, Ahmed Chaouki Megherbi, Raphaël Romary and Salim Sbaa

Energies 2023, 16(6), 2604; https://doi.org/10.3390/en16062604 - 9 Mar 2023

Cited by 9 | Viewed by 1955

Abstract

In bearing fault diagnosis, ensemble empirical mode decomposition (EEMD) is a reliable technique for treating rolling bearing vibration signals by dividing them into intrinsic mode functions (IMFs). Traditional methods used in EEMD consist of identifying IMFs containing the fault information and reconstructing them. However, an incorrect selection can result in the loss of useful IMFs or the addition of unnecessary ones. To overcome this drawback, this paper presents a novel method called combined modes ensemble empirical mode decomposition (CMEEMD) to directly obtain a combination of useful IMFs containing fault information. This is without needing to pass through the processes of IMF selection and reconstruction, as well as guaranteeing that no defect information is lost. Owing to the small signal-to-noise ratio, this makes it difficult to determine the fault information of a rolling bearing at the early stage. Therefore, improving noise reduction is an essential procedure for detecting defects. The paper introduces a robust process for extracting rolling bearings defect information based on CMEEMD and an enhanced deconvolution technique. Firstly, the proposed CMEEMD extracts all combined modes (CMs) from adjoining IMFs decomposed from the raw fault signal by EEMD. Then, a selection indicator known as kurtosis median absolute deviation (KMAD) is created in this research to identify the combination of the appropriate IMFs. Finally, the enhanced deconvolution process minimizes noise and improves defect identification in the identified CM. Analyzing real and simulated bearing signals demonstrates that the developed method shows excellent performance in extracting defect information. Compared results between selecting the sensitive IMF using kurtosis and selecting the sensitive CM using the proposed KMAD show that the identified CM contains rich fault information in many cases. Furthermore, our comparisons revealed that the enhanced deconvolution approach proposed here outperformed the minimum entropy deconvolution (MED) approach for improving fault pulses and the wavelet de-noising method for noise suppression. Full article

(This article belongs to the Special Issue Modeling, Control and Diagnosis of Electrical Machines and Devices)

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19 pages, 2552 KiB

Open AccessArticle

Neural Inverse Optimal Control of a Regenerative Braking System for Electric Vehicles

by Jose A. Ruz-Hernandez, Larbi Djilali, Mario Antonio Ruz Canul, Moussa Boukhnifer and Edgar N. Sanchez

Energies 2022, 15(23), 8975; https://doi.org/10.3390/en15238975 - 28 Nov 2022

Cited by 6 | Viewed by 2052

Abstract

This paper presents the development of a neural inverse optimal control (NIOC) for a regenerative braking system installed in electric vehicles (EVs), which is composed of a main energy system (MES) including a storage system and an auxiliary energy system (AES). This last one is composed of a supercapacitor and a buck–boost converter. The AES aims to recover the energy generated during braking that the MES is incapable of saving and using later during the speed increase. To build up the NIOC, a neural identifier has been trained with an extended Kalman filter (EKF) to estimate the real dynamics of the buck–boost converter. The NIOC is implemented to regulate the voltage and current dynamics in the AES. For testing the drive system of the EV, a DC motor is considered where the speed is controlled using a PID controller to regulate the tracking source in the regenerative braking. Simulation results illustrate the efficiency of the proposed control scheme to track time-varying references of the AES voltage and current dynamics measured at the buck–boost converter and to guarantee the charging and discharging operation modes of the supercapacitor. In addition, it is demonstrated that the proposed control scheme enhances the EV storage system’s efficacy and performance when the regenerative braking system is working. Furthermore, the mean squared error is calculated to prove and compare the proposed control scheme with the mean squared error for a PID controller. Full article

(This article belongs to the Special Issue Modeling, Control and Diagnosis of Electrical Machines and Devices)

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