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Advanced Fault Detection, Diagnosis and Prognosis in a Context of...
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Advanced Fault Detection, Diagnosis and Prognosis in a Context of Renewable Power Generation

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 8171

Special Issue Editors

Dr. Ryad Zemouri

E-Mail Website
Guest Editor
Centre de Recherche d’Hydro-Québec (CHRQ), Dir. Recherche et Innovation - Production, 1800, Boul. Lionel-Boulet, Varennes, Quebec City, QC J3X 1S1, Canada
Interests: hydro-generator diagnosis and prognosis; deep learning and artificial intelligence; signal and image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Mélanie Lévesque

E-Mail Website
Guest Editor
Centre de Recherche d’Hydro-Québec (CHRQ), Dir. Recherche et Innovation - Production, 1800, Boul. Lionel-Boulet, Varennes, Quebec City, QC J3X 1S1, Canada
Interests: hydro-generator diagnosis and prognosis; measurement and analysis of partial discharges; development of visual inspection methodology; dissection for post-mortem analysis and quality control
Dr. Arezki Merkhouf

E-Mail Website
Guest Editor
Centre de Recherche d’Hydro-Québec (CHRQ), Dir. Recherche et Innovation - Production, 1800, Boul. Lionel-Boulet, Varennes, Quebec City, QC J3X 1S1, Canada
Interests: generator and motor design and optimization; generator acoustics and vibrations; analytical and numerical simulations of large rotating electrical machines; numerical and analytical computation of electromagnetic fields

Special Issue Information

Dear Colleagues,

Deregulation of the electricity market, as well as the emergence of renewable energy systems in recent years, has introduced new operating rules, several daily starts and stops, spin-no-load availability, and electrical machines pushed to their limits. To take advantage of this new reality, the short-term benefits must not be outweighed by a reduction in reliability or expected equipment life. The availability and reliability of power generation equipment are both key features that are driving utilities to implement advanced fault detection, and diagnostic and prognostic methods to move from a systematic maintenance policy to a condition-based maintenance (CBM).

In this context of Prognosis and Health Management (PHM), numerous data from various sources are collected and a large part of it is not labeled. Therefore, more and more data-driven PHM models are used instead of physical-based models. However, designing a data-driven model depends strongly on the quality of the data, the availability of degradation patterns, and a judicious exploitation of human expertise. How to optimally extract physical knowledge from human experts to guide the learning of these models with limited degradation data? How to combine different types of signals to optimize the recognition of active degradation physical states and, thus, improve the diagnosis of the whole system? How to take advantage of the physical-based learning and numerical simulation models to generate failure patterns for the diagnosis, as well as their propagation patterns for the prognosis?

In this Special Issue devoted to Advanced Fault Detection, Diagnosis and Prognosis in a context of renewable power generation, we invite practical as well as academic research and review paper to contribute and share their valuable experience.

Topic

(1) Early fault detection methods;

(2) Diagnosis and Prognosis methods;

(3) Fusion of different data sources to optimize the diagnosis and prognosis;

(4) Synthetic data generation and data augmentation to generate failure patterns;

(5) Deep learning optimization by exploiting the expert’s knowledge.

Dr. Ryad Zemouri
Dr. Mélanie Lévesque
Dr. Arezki Merkhouf
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. Electronics 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 2400 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

  • fault detection
  • diagnosis
  • prognosis
  • deep learning
  • condition-based-maintenance
  • hydro-generators
  • wind-turbine
  • renewable power
  • generation system

Published Papers (6 papers)

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Research

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18 pages, 5010 KiB  
Article
Advanced Fault-Detection Technique for DC-Link Aluminum Electrolytic Capacitors Based on a Random Forest Classifier
by Acácio M. R. Amaral, Khaled Laadjal and Antonio J. Marques Cardoso
Electronics 2023, 12(12), 2572; https://doi.org/10.3390/electronics12122572 - 07 Jun 2023
Cited by 6 | Viewed by 1000
Abstract
In recent years, significant technological advances have emerged in renewable power generation systems (RPGS), making them more economical and competitive. On the other hand, for the RPGS to achieve the highest level of performance possible, it is important to ensure the healthy operation [...] Read more.
In recent years, significant technological advances have emerged in renewable power generation systems (RPGS), making them more economical and competitive. On the other hand, for the RPGS to achieve the highest level of performance possible, it is important to ensure the healthy operation of their main building blocks. Power electronic converters (PEC), which are one of the main building blocks of RPGS, have some vulnerable components, such as capacitors, which are responsible for more than a quarter of the failures in these converters. Therefore, it is of paramount importance that the design of fault diagnosis techniques (FDT) assess the capacitor’s state of health so that it is possible to implement predictive and preventive maintenance plans in order to reduce unexpected stoppage of these systems. One of the most commonly used capacitors in power converters is the aluminum electrolytic capacitor (AEC) whose aging manifests itself through an increase in its equivalent series resistance (ESR). Several advanced intelligent techniques have been proposed for assessing AEC health status, many of which require the use of a current sensor in the capacitor branch. However, the introduction of a current sensor in the capacitor branch imposes practical restrictions; in addition, it introduces unwanted resistive and inductive effects. This paper presents an FDT based on the random forest classifier (RFC), which triggers an alert mechanism when the DC-link AEC reaches its ESR threshold value. The great advantage of the proposed solution is that it is non-invasive; therefore, it is not necessary to introduce any sensor inside the converter. The validation of the proposed FDT will be carried out using several computer simulations carried out in Matlab/Simulink. Full article
(This article belongs to the Special Issue Advanced Fault Detection, Diagnosis and Prognosis in a Context of Renewable Power Generation)
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15 pages, 4263 KiB  
Article
A Current Spectrum-Based Algorithm for Fault Detection of Electrical Machines Using Low-Power Data Acquisition Devices
by Bilal Asad, Hadi Ashraf Raja, Toomas Vaimann, Ants Kallaste, Raimondas Pomarnacki and Van Khang Hyunh
Electronics 2023, 12(7), 1746; https://doi.org/10.3390/electronics12071746 - 06 Apr 2023
Cited by 1 | Viewed by 1495
Abstract
An algorithm to improve the resolution of the frequency spectrum by detecting the number of complete cycles, removing any fractional components of the signal, signal discontinuities, and interpolating the signal for fault diagnostics of electrical machines using low-power data acquisition cards is proposed [...] Read more.
An algorithm to improve the resolution of the frequency spectrum by detecting the number of complete cycles, removing any fractional components of the signal, signal discontinuities, and interpolating the signal for fault diagnostics of electrical machines using low-power data acquisition cards is proposed in this paper. Smart sensor-based low-power data acquisition and processing devices such as Arduino cards are becoming common due to the growing trend of the Internet of Things (IoT), cloud computation, and other Industry 4.0 standards. For predictive maintenance, the fault representing frequencies at the incipient stage are very difficult to detect due to their small amplitude and the leakage of powerful frequency components into other parts of the spectrum. For this purpose, offline advanced signal processing techniques are used that cannot be performed in small signal processing devices due to the required computational time, complexity, and memory. Hence, in this paper, an algorithm is proposed that can improve the spectrum resolution without complex advanced signal processing techniques and is suitable for low-power signal processing devices. The results both from the simulation and practical environment are presented. Full article
(This article belongs to the Special Issue Advanced Fault Detection, Diagnosis and Prognosis in a Context of Renewable Power Generation)
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25 pages, 38445 KiB  
Article
Fault Diagnosis of PMSM Stator Winding Based on Continuous Wavelet Transform Analysis of Stator Phase Current Signal and Selected Artificial Intelligence Techniques
by Przemyslaw Pietrzak and Marcin Wolkiewicz
Electronics 2023, 12(7), 1543; https://doi.org/10.3390/electronics12071543 - 24 Mar 2023
Cited by 2 | Viewed by 1723
Abstract
High efficiency, high reliability and excellent dynamic performance have been key aspects considered in recent years when selecting motors for modern drive systems. These features characterize permanent magnet synchronous motors (PMSMs). This paper presents the application of continuous wavelet transform (CWT) and artificial [...] Read more.
High efficiency, high reliability and excellent dynamic performance have been key aspects considered in recent years when selecting motors for modern drive systems. These features characterize permanent magnet synchronous motors (PMSMs). This paper presents the application of continuous wavelet transform (CWT) and artificial intelligence (AI) techniques to the detection and classification of PMSM stator winding faults. The complex generalized Morse wavelet used for CWT analysis of three different diagnostic signals—the stator phase current, its envelope and the space vector module—is used to extract the symptoms most sensitive to the interturn short circuits (ITSCs) at the incipient stage of the damage. The effectiveness of automatic stator winding fault classification is compared for three selected ML algorithms: multilayer perceptron, support vector machine and k-nearest neighbors. The effect of the ML models’ hyperparameters on their accuracy is also verified. The high effectiveness of the proposed methodology is confirmed by the results of the experimental verification carried out for different load torque levels and supply voltage frequency values. Full article
(This article belongs to the Special Issue Advanced Fault Detection, Diagnosis and Prognosis in a Context of Renewable Power Generation)
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22 pages, 6974 KiB  
Article
Experimental Analysis of the Current Sensor Fault Detection Mechanism Based on Neural Networks in the PMSM Drive System
by Kamila Jankowska and Mateusz Dybkowski
Electronics 2023, 12(5), 1170; https://doi.org/10.3390/electronics12051170 - 28 Feb 2023
Cited by 6 | Viewed by 1694
Abstract
In this paper, a current sensor fault detection mechanism based on multilayer perceptron (MLP) in a permanent magnet synchronous motor (PMSM) drive system is presented. The solution for the PMSM was previously described and tested only in simulation studies. The described application allows [...] Read more.
In this paper, a current sensor fault detection mechanism based on multilayer perceptron (MLP) in a permanent magnet synchronous motor (PMSM) drive system is presented. The solution for the PMSM was previously described and tested only in simulation studies. The described application allows the detection of basic faults (lack of signal, gain error, signal noise) in current sensors and the indication of the phase (A or B) in which the fault occurred. The work is focused on the analysis of the fault detector but also presents the possibilities of their classification. The work mainly presents experimental research for different values of speed during the load and regenerative mode. In addition to the study of various operating conditions of the drive system, the detector efficiency was also verified for three neural structures with a different number of neurons in the hidden layers. The work also presents simulation tests (in Matlab Simulink software) for the additional conditions of the drive system for the same neural structures as in the experimental studies. The results obtained during offline and online faults detection with the use of the DS1103 controller are presented. Full article
(This article belongs to the Special Issue Advanced Fault Detection, Diagnosis and Prognosis in a Context of Renewable Power Generation)
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17 pages, 1430 KiB  
Article
Fault Diagnosis Strategy for a Standalone Photovoltaic System: A Residual Formation Approach
by Zaheer Alam, Malak Adnan Khan, Zain Ahmad Khan, Waleed Ahmad, Imran Khan, Qudrat Khan, Muhammad Irfan and Grzegorz Nowakowski
Electronics 2023, 12(2), 282; https://doi.org/10.3390/electronics12020282 - 05 Jan 2023
Cited by 1 | Viewed by 1348
Abstract
The search for sustainability and green energy, in electricity production, has lead many researchers to study and improve photovoltaic (PV) systems. The PV systems, being a combination of power electronic modules and PV array, have high tendency of faults in sensors, switches, and [...] Read more.
The search for sustainability and green energy, in electricity production, has lead many researchers to study and improve photovoltaic (PV) systems. The PV systems, being a combination of power electronic modules and PV array, have high tendency of faults in sensors, switches, and passive devices. Thus, a reliable fault diagnosis (FD) scheme plays a significant role in protecting PV systems. In this article, a sliding mode observer (SMO)-based FD scheme is presented to figure out the sensor faults in a standalone PV system. The proposed FD scheme makes use of residual formation which in turn helps in detection of faults on the basis of a defined threshold. In addition to the functionality of fault detection, the SMO provides the benefit of reduction in number of sensors required in the PV system. This feature can be utilized as software redundancy in fault-tolerant control (FTC). The test bench, standalone PV system, is equipped with a buck–boost converter for maximum power transfer (MPT) to the connected load. Moreover, the FD scheme is backed by a back-stepping (BS) analogy-based control scheme for extraction of maximum power from the PV panel. The simulations are performed in the MATLAB/Simulink platform under varying environmental conditions (temperature and irradiance) and resistive load. These simulations, subjected to varying operating conditions, confirm the efficacy, in terms of robustness, chattering (oscillations about the reference), and steady-state error, of the proposed scheme. Full article
(This article belongs to the Special Issue Advanced Fault Detection, Diagnosis and Prognosis in a Context of Renewable Power Generation)
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Review

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34 pages, 698 KiB  
Review
On the Use of Indirect Measurements in Virtual Sensors for Renewable Energies: A Review
by Abderraouf Benabdesselam, Quentin Dollon, Ryad Zemouri, Francis Pelletier, Martin Gagnon and Antoine Tahan
Electronics 2024, 13(8), 1545; https://doi.org/10.3390/electronics13081545 - 18 Apr 2024
Viewed by 304
Abstract
In the dynamic landscape of renewable energy, the primary goal continues to be the enhancement of competitiveness through the implementation of cutting-edge technologies. This requires a strategic focus on reducing energy costs and maximizing system performance. Within this framework, the continuous online monitoring [...] Read more.
In the dynamic landscape of renewable energy, the primary goal continues to be the enhancement of competitiveness through the implementation of cutting-edge technologies. This requires a strategic focus on reducing energy costs and maximizing system performance. Within this framework, the continuous online monitoring of assets is essential for efficient operations, by conducting measurements that describe the condition of various components. However, the execution of these measurements can present technical and economic obstacles. To overcome these challenges, the implementation of indirect measurement techniques emerges as a viable solution. By leveraging measurements obtained in easily accessible areas, these methods enable the estimation of quantities in regions that would otherwise be inaccessible. This approach improves the monitoring process’s efficiency and provides previously unattainable information. Adopting indirect measurement techniques is also cost-effective, allowing the replacement of expensive sensors with existing infrastructure, which cuts down on installation costs and labor. This paper offers a detailed state-of-the-art review by providing an in-depth examination and classification of indirect measurement techniques and virtual sensing methods applied in the field of renewable energies. It also identifies and discusses the existing challenges and limitations within this topic and explores potential future developments. Full article
(This article belongs to the Special Issue Advanced Fault Detection, Diagnosis and Prognosis in a Context of Renewable Power Generation)
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