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Laadjal Khaled was born in Tebessa, Algeria, in 1991. He received the BSc, MSc, and Ph.D. degrees in Electrical Engineering from the University of Biskra, in 2013, 2015, and 2018. In January 2015, he joined the CISE—Electromechatronic Systems Research Centre (http://www.cise.ubi.pt), University of Beira Interior, Covilhã, Portugal, where he has been a Ph.D. Researcher since 2020. He is currently a Postdoctoral Researcher at CISE— Electromechatronic Systems Research Centre (http://www.cise.ubi.pt), University of Beira Interior, Covilhã, Portugal. His research interests are related to condition monitoring and fault diagnosis in power electronics systems, energy storage system components and AC machines. Antonio J. Marques Cardoso received the Dipl. Eng., Dr. Eng., and Habilitation degrees from the University of Coimbra, Coimbra, Portugal, in 1985, 1995, and 2008, respectively, all in Electrical Engineering. From 1985 until 2011 he was with the University of Coimbra, Coimbra, Portugal, where he was Director of the Electrical Machines Laboratory. Since 2011, he has been with the University of Beira Interior (UBI), Covilhã, Portugal, where he is Full Professor at the Department of Electromechanical Engineering and Director of CISE - Electromechatronic Systems Research Centre (http://cise.ubi.pt). He was Vice-Rector of UBI (2013-2014). His current research interests are in fault diagnosis and fault tolerance in electrical machines, power electronics, and drives.
The availability maximization is a goal for any organization because the equipment downtime implies high non-production costs and, additionally, the abnormal stopping and restarting usually imply loss of product’s quality. In this way, a method for predicting the equipment’s health state is vital to maintain the production flow as well as to plan maintenance intervention strategies. This paper presents a maintenance prediction approach based on sensing data managed by Hidden Markov Models (HMM). To do so, a diagnosis of drying presses in a pulp industry is used as case study, which is done based on data collected every minute for three years and ten months. This paper presents an approach to manage a multivariate analysis, in this case merging the values of sensors, and optimizing the observable states to insert into a HMM model, which permits to identify three hidden states that characterize the equipment’s health state: “Proper Function”, “Alert state”, and “Equipment Failure”. The research described in this paper demonstrates how an equipment health diagnosis can be made using the HMM, through the collection of observations from various sensors, without information of machine failures occurrences. The approach developed demonstrated to be robust, even the complexity of the system, having the potential to be generalized to any other type of equipment.
Alexandre Martins; Inácio Fonseca; José Torres Farinha; João Reis; António J. Marques Cardoso. Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study. Applied Sciences 2021, 11, 7685 .
AMA StyleAlexandre Martins, Inácio Fonseca, José Torres Farinha, João Reis, António J. Marques Cardoso. Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study. Applied Sciences. 2021; 11 (16):7685.
Chicago/Turabian StyleAlexandre Martins; Inácio Fonseca; José Torres Farinha; João Reis; António J. Marques Cardoso. 2021. "Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study." Applied Sciences 11, no. 16: 7685.
Supercapacitors (SCs), or ultracapacitors, due to their attractive features, such as, high power density, long life cycle, etc., have received much attention from the transportation sector. SCs can be used as an additional energy storage system (ESS) in combination with lithium-ion batteries to enhance the performance of electric vehicles (EVs) in dynamic states, including acceleration and regenerative braking modes of operation. Online accurate estimation of SCs' state of health (SoH) and state of energy (SoE) is essential for an efficient energy management and real-time condition monitoring in EV applications. The accuracy of the estimation of the SoE and SoH is based on the model's efficiency, which ensures that in order to minimize the impact of aging, model parameters should be defined in real time. Nevertheless, because the SC model is obviously nonlinear and broad in scale, online identification of the parameters estimation is usually difficult. In this paper, a generalized SC model of high accuracy and good robustness is proposed. The classification of the estimation methodologies for SoH and SoE of SC will be very helpful in choosing the appropriate method for the development of reliable and secure ESS and an energy management strategy for EVs.
Khaled Laadjal; Antonio J. Marques Cardoso. A review of supercapacitors modeling, SoH, and SoE estimation methods: Issues and challenges. International Journal of Energy Research 2021, 1 .
AMA StyleKhaled Laadjal, Antonio J. Marques Cardoso. A review of supercapacitors modeling, SoH, and SoE estimation methods: Issues and challenges. International Journal of Energy Research. 2021; ():1.
Chicago/Turabian StyleKhaled Laadjal; Antonio J. Marques Cardoso. 2021. "A review of supercapacitors modeling, SoH, and SoE estimation methods: Issues and challenges." International Journal of Energy Research , no. : 1.
Predictive maintenance is very important in industrial plants to support decisions aiming to maximize maintenance investments and equipment’s availability. This paper presents predictive models based on long short-term memory neural networks, applied to a dataset of sensor readings. The aim is to forecast future equipment statuses based on data from an industrial paper press. The datasets contain data from a three-year period. Data are pre-processed and the neural networks are optimized to minimize prediction errors. The results show that it is possible to predict future behavior up to one month in advance with reasonable confidence. Based on these results, it is possible to anticipate and optimize maintenance decisions, as well as continue research to improve the reliability of the model.
Balduíno Mateus; Mateus Mendes; José Farinha; António Cardoso. Anticipating Future Behavior of an Industrial Press Using LSTM Networks. Applied Sciences 2021, 11, 6101 .
AMA StyleBalduíno Mateus, Mateus Mendes, José Farinha, António Cardoso. Anticipating Future Behavior of an Industrial Press Using LSTM Networks. Applied Sciences. 2021; 11 (13):6101.
Chicago/Turabian StyleBalduíno Mateus; Mateus Mendes; José Farinha; António Cardoso. 2021. "Anticipating Future Behavior of an Industrial Press Using LSTM Networks." Applied Sciences 11, no. 13: 6101.
Lithium-ion batteries are the most used these days for charging electric vehicles (EV). It is important to study the aging of batteries because the deterioration of their characteristics largely determines the cost, efficiency, and environmental impact of electric vehicles, especially full-electric ones. The estimation of batteries’ state-condition is also very important for improving energy efficiency, lengthening the life cycle, minimizing costs and ensuring safe implementation of batteries in electric vehicles. However, batteries with large temporal variables and non-linear characteristics are often affected by random factors affecting the equivalent internal resistance (EIR), battery state of charge (SoC), and state of health (SoH) in EV applications. The estimation of batteries’ parameters is a complex process, due to its dependence on various factors such as batteries age and ambient temperature, among others. A good estimate of SoC and internal resistance leads to long battery life and disaster prevention in the event of a battery failure. The classification of estimation methodologies for internal parameters and the charging status of batteries will be very helpful in choosing the appropriate method for the development of a reliable and secure battery management system (BMS) and an energy management strategy for electric vehicles.
Khaled Laadjal; Antonio Cardoso. Estimation of Lithium-Ion Batteries State-Condition in Electric Vehicle Applications: Issues and State of the Art. Electronics 2021, 10, 1588 .
AMA StyleKhaled Laadjal, Antonio Cardoso. Estimation of Lithium-Ion Batteries State-Condition in Electric Vehicle Applications: Issues and State of the Art. Electronics. 2021; 10 (13):1588.
Chicago/Turabian StyleKhaled Laadjal; Antonio Cardoso. 2021. "Estimation of Lithium-Ion Batteries State-Condition in Electric Vehicle Applications: Issues and State of the Art." Electronics 10, no. 13: 1588.
This paper presents the analytical calculation of the heat transfer coefficient of a complex housing shape of a Totally Enclosed Fan-Cooled (TEFC) industrial machine when it works below 20% of its nominal speed or close to stall. Therefore, passive cooling is dominant, and most of the heat is extracted by the combination of natural convection and radiation phenomena. Under these conditions, the area-based composite approach was used for the development of the analytical calculation method. A test rig using a TEFC Synchronous Reluctance Motor (SynRM) was constructed, and the collected experimental data was used to validate the proposed analytical method successfully.
Payam Shams Ghahfarokhi; Andrejs Podgornovs; Ants Kallaste; Antonio Cardoso; Anouar Belahcen; Toomas Vaimann; Bilal Asad; Hans Tiismus. Determination of Heat Transfer Coefficient from Housing Surface of a Totally Enclosed Fan-Cooled Machine during Passive Cooling. Machines 2021, 9, 120 .
AMA StylePayam Shams Ghahfarokhi, Andrejs Podgornovs, Ants Kallaste, Antonio Cardoso, Anouar Belahcen, Toomas Vaimann, Bilal Asad, Hans Tiismus. Determination of Heat Transfer Coefficient from Housing Surface of a Totally Enclosed Fan-Cooled Machine during Passive Cooling. Machines. 2021; 9 (6):120.
Chicago/Turabian StylePayam Shams Ghahfarokhi; Andrejs Podgornovs; Ants Kallaste; Antonio Cardoso; Anouar Belahcen; Toomas Vaimann; Bilal Asad; Hans Tiismus. 2021. "Determination of Heat Transfer Coefficient from Housing Surface of a Totally Enclosed Fan-Cooled Machine during Passive Cooling." Machines 9, no. 6: 120.
Vanja Ambrožič; Antonio J. Marques Cardoso; Gerasimos Rigatos. Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives. IET Electric Power Applications 2021, 15, 795 -798.
AMA StyleVanja Ambrožič, Antonio J. Marques Cardoso, Gerasimos Rigatos. Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives. IET Electric Power Applications. 2021; 15 (7):795-798.
Chicago/Turabian StyleVanja Ambrožič; Antonio J. Marques Cardoso; Gerasimos Rigatos. 2021. "Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives." IET Electric Power Applications 15, no. 7: 795-798.
In the above article [1] , Figs. 3 and 19 are modified versions of Fig. 18 from the article. Although this source was cited in our article as [6], the captions for these figures are missing the appropriate citation/reference.
Payam Shams Ghahfarokhi; Andrejs Podgornovs; Ants Kallaste; Antonio J. Marques Cardoso; Anouar Belahcen; Toomas Vaimann; Hans Tiismus; Bilal Asad. Corrections to “Opportunities and Challenges of Utilizing Additive Manufacturing Approaches in Thermal Management of Electrical Machines”. IEEE Access 2021, 9, 62532 -62532.
AMA StylePayam Shams Ghahfarokhi, Andrejs Podgornovs, Ants Kallaste, Antonio J. Marques Cardoso, Anouar Belahcen, Toomas Vaimann, Hans Tiismus, Bilal Asad. Corrections to “Opportunities and Challenges of Utilizing Additive Manufacturing Approaches in Thermal Management of Electrical Machines”. IEEE Access. 2021; 9 ():62532-62532.
Chicago/Turabian StylePayam Shams Ghahfarokhi; Andrejs Podgornovs; Ants Kallaste; Antonio J. Marques Cardoso; Anouar Belahcen; Toomas Vaimann; Hans Tiismus; Bilal Asad. 2021. "Corrections to “Opportunities and Challenges of Utilizing Additive Manufacturing Approaches in Thermal Management of Electrical Machines”." IEEE Access 9, no. : 62532-62532.
Artificial intelligence algorithms and vibration signature monitoring are recurrent approaches to perform early bearing damage identification in induction motors. This approach is unfeasible in most industrial applications because these machines are unable to perform their nominal functions under damaged conditions. In addition, many machines are installed at inaccessible sites or their housing prevents the setting of new sensors. Otherwise, current signature monitoring is available in most industrial machines because the devices that control, supply and protect these systems use the stator current. Another significant advantage is that the stator phases lose symmetry in bearing damaged conditions and, therefore, are multiple independent sources. Thus, this paper introduces a new approach based on fractional wavelet denoising and a deep learning algorithm to perform a bearing damage diagnosis from stator currents. Several convolutional neural networks extract features from multiple sources to perform supervised learning. An information fusion (IF) algorithm then creates a new feature set and performs the classification. Furthermore, this paper introduces a new method to achieve positive unlabeled learning. The flattened layer of several feature maps inputs the fuzzy c-means algorithm to perform a novelty detection instead of clusterization in a dynamic IF context. Experimental and on-site tests are reported with promising results.
Andre Barcelos; Antonio Cardoso. Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms. Energies 2021, 14, 2509 .
AMA StyleAndre Barcelos, Antonio Cardoso. Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms. Energies. 2021; 14 (9):2509.
Chicago/Turabian StyleAndre Barcelos; Antonio Cardoso. 2021. "Current-Based Bearing Fault Diagnosis Using Deep Learning Algorithms." Energies 14, no. 9: 2509.
In critical industrial applications fault diagnosis and fault tolerance are considered key features, in order to ensure the required reliability and availability levels. In this context, this paper proposes a new and effective diagnostic algorithm for power semiconductors open‐circuit faults, in three‐phase, two‐level, voltage‐source inverter‐fed permanent magnet synchronous machine (PMSM). The proposed method is based on the analysis of the errors between the reference currents and the PMSM stator predictive currents. Hence, for each phase of the PMSM two fault diagnostic variables have been defined, which allow the diagnosis of both single and multiple open‐circuit faults. The fuzzy logic approach is applied to the fault diagnostic variables in order to identify the faulty power switches. The method is experimentally validated on a model predictive controlled (MPC) permanent magnet synchronous motor (PMSM) drive, which shows the effectiveness of the fault diagnosis algorithm with a high robustness regarding the operating point and parameter variations of the PMSM drive system.
Badii Gmati; Imed Jlassi; Sejir Khojet El Khil; Antonio J. Marques Cardoso. Open‐switch fault diagnosis in voltage source inverters of PMSM drives using predictive current errors and fuzzy logic approach. IET Power Electronics 2021, 14, 1059 -1072.
AMA StyleBadii Gmati, Imed Jlassi, Sejir Khojet El Khil, Antonio J. Marques Cardoso. Open‐switch fault diagnosis in voltage source inverters of PMSM drives using predictive current errors and fuzzy logic approach. IET Power Electronics. 2021; 14 (6):1059-1072.
Chicago/Turabian StyleBadii Gmati; Imed Jlassi; Sejir Khojet El Khil; Antonio J. Marques Cardoso. 2021. "Open‐switch fault diagnosis in voltage source inverters of PMSM drives using predictive current errors and fuzzy logic approach." IET Power Electronics 14, no. 6: 1059-1072.
Model predictive fault‐tolerant current control (MPFTCC) of permanent magnet synchronous generator (PMSG) drives can make a valuable contribution to improving the reliability and availability levels of wind turbines, because back‐to‐back (BTB) converters are prone to failure. However, MPFTCC suffers from excessive computational burden, because the BTB converter is treated as one system where all feasible voltage vectors (VVs) are used for prediction and evaluation. Accordingly, a computationally efficient MPFTCC algorithm for a PMSG drive is developed and proposed with the ability to handle insulated‐gate bipolar transistor open‐circuit faults. The candidate VVs of both machine‐ and grid‐side converters are separately predicted and evaluated, which significantly reduces calculation effort. The proposed reconfigurable converter is a five‐leg power converter with a common leg that connects the generator first phase to the grid three‐phase, ensuring proper postfault reconfiguration of the grid‐side inverter. Moreover, a three‐switch rectifier is adopted to achieve fault tolerance of the PMSG‐side rectifier. Performance of the considered MPFTCC strategies is evaluated by experimental means.
Imed Jlassi; Antonio J. Marques Cardoso. Open‐circuit fault‐tolerant operation of permanent magnet synchronous generator drives for wind turbine systems using a computationally efficient model predictive current control. IET Electric Power Applications 2021, 15, 837 -846.
AMA StyleImed Jlassi, Antonio J. Marques Cardoso. Open‐circuit fault‐tolerant operation of permanent magnet synchronous generator drives for wind turbine systems using a computationally efficient model predictive current control. IET Electric Power Applications. 2021; 15 (7):837-846.
Chicago/Turabian StyleImed Jlassi; Antonio J. Marques Cardoso. 2021. "Open‐circuit fault‐tolerant operation of permanent magnet synchronous generator drives for wind turbine systems using a computationally efficient model predictive current control." IET Electric Power Applications 15, no. 7: 837-846.
The main purpose of this paper is to develop a high-level performance, and low-cost current sensorless control strategy for Induction Motor (IM) drives. Therefore, a new phase-current regeneration method, for current sensorless vector control in induction motor drives is introduced. The idea is based on the reconfiguration of the Luenberger adaptive observer for currents estimation, using the information provided by the dc-link voltage sensor. The basis of the proposed control and the theoretical study of the modified adaptive observer are presented. Several simulation and experimental tests were performed on an induction motor of 1.1 kW working under different operating conditions. The obtained results prove and testify the relevance, workability, and practicability of the suggested currents sensorless vector control strategy.
Younes Azzoug; Mohamed Sahraoui; Remus Pusca; Tarek Ameid; Raphaël Romary; Antonio J. Marques Cardoso. High-performance vector control without AC phase current sensors for induction motor drives: Simulation and real-time implementation. ISA Transactions 2021, 109, 295 -306.
AMA StyleYounes Azzoug, Mohamed Sahraoui, Remus Pusca, Tarek Ameid, Raphaël Romary, Antonio J. Marques Cardoso. High-performance vector control without AC phase current sensors for induction motor drives: Simulation and real-time implementation. ISA Transactions. 2021; 109 ():295-306.
Chicago/Turabian StyleYounes Azzoug; Mohamed Sahraoui; Remus Pusca; Tarek Ameid; Raphaël Romary; Antonio J. Marques Cardoso. 2021. "High-performance vector control without AC phase current sensors for induction motor drives: Simulation and real-time implementation." ISA Transactions 109, no. : 295-306.
The additive manufacturing approach is considered a new manufacturing technology method and is evolving dynamically in recent years. It is advancing and achieving as the key enabling technology in a wide range of applications, from medical sciences to the aerospace and automotive industries. This novel approach opens a new path to overcome the conventional manufacturing problems and challenges by providing more design freedom, new ranges of materials, lightweight and complex geometries. According to demands metrics such as lightweight and high power density motors. This offers clear motivation to develop the advanced thermal management method with new materials and a novel additive manufacturing (AM) approach. The paper aims to provide a comprehensive review of all the attempts in various electrical machines' thermal management methods using AM method. It considers the opportunities and challenges that designers are facing while implementing these approaches. Finally, the authors make some comments/forecasts on how the AM could improve the performance and manufacturability of the future thermal management system of electrical machines.
Payam Shams Ghahfarokhi; Andrejs Podgornovs; Ants Kallaste; Antonio J. Marques Cardoso; Anouar Belahcen; Toomas Vaimann; Hans Tiismus; Bilal Asad. Opportunities and Challenges of Utilizing Additive Manufacturing Approaches in Thermal Management of Electrical Machines. IEEE Access 2021, 9, 36368 -36381.
AMA StylePayam Shams Ghahfarokhi, Andrejs Podgornovs, Ants Kallaste, Antonio J. Marques Cardoso, Anouar Belahcen, Toomas Vaimann, Hans Tiismus, Bilal Asad. Opportunities and Challenges of Utilizing Additive Manufacturing Approaches in Thermal Management of Electrical Machines. IEEE Access. 2021; 9 ():36368-36381.
Chicago/Turabian StylePayam Shams Ghahfarokhi; Andrejs Podgornovs; Ants Kallaste; Antonio J. Marques Cardoso; Anouar Belahcen; Toomas Vaimann; Hans Tiismus; Bilal Asad. 2021. "Opportunities and Challenges of Utilizing Additive Manufacturing Approaches in Thermal Management of Electrical Machines." IEEE Access 9, no. : 36368-36381.
Three-phase induction motors are considered to be the workhorse of industry. Therefore, induction motor faults are not only the cause of users’ frustrations but they also drive up the costs related to unexpected breakdowns, repair actions, and safety issues. One of the most critical faults in three-phase induction motors is related to the occurrence of inter-turn short circuits, due to its devastating consequences. The topic of inter-turn short-circuit faults in three-phase induction motors has been discussed over recent decades by several researchers. These studies have mainly dealt with early fault detection to avoid dramatic consequences. However, they fall short of addressing the potential burnout of the induction motor before the detection step. Furthermore, the cumulative action played by an inevitable degree of unbalanced supply voltages may exacerbate such consequences. For that reason, in deep detail, this paper delves into the thermal analysis of the induction motor when operating under these two harsh conditions: unbalanced supply voltages and the presence of the most incipient type of inter-turn short-circuit condition—a short-circuit between two turns only. In this work, the finite element method has been applied to create the faulty scenarios, and a commercial software (Flux2D) has been used in order to simulate the electromagnetic and thermal behavior of the machine for various degrees of severity of the aforementioned faulty modes. The obtained results confirm that the diagnostic tools reported in the literature might not be effective, failing to warrant the required lead time so that suitable actions can be taken to prevent permanent damage to the machine.
Amel Adouni; Antonio J. Marques Cardoso. Thermal Analysis of Low-Power Three-Phase Induction Motors Operating under Voltage Unbalance and Inter-Turn Short Circuit Faults. Machines 2020, 9, 2 .
AMA StyleAmel Adouni, Antonio J. Marques Cardoso. Thermal Analysis of Low-Power Three-Phase Induction Motors Operating under Voltage Unbalance and Inter-Turn Short Circuit Faults. Machines. 2020; 9 (1):2.
Chicago/Turabian StyleAmel Adouni; Antonio J. Marques Cardoso. 2020. "Thermal Analysis of Low-Power Three-Phase Induction Motors Operating under Voltage Unbalance and Inter-Turn Short Circuit Faults." Machines 9, no. 1: 2.
As one of the most important assets of the industry, it is crucial to fully characterise all failure modes showing potential to degrade the normal operation of induction motors (IMs). One of the failure modes which lacks detailed knowledge and proper diagnostic tools is the inter‐turn short‐circuit (SC) fault. Given the severity of such failure mode, it is pivotal to ensure that incipient fault symptoms are correctly identified, thus preventing critical damages to the IM. Unfortunately, the state‐of‐the‐art does not provide enough data to confirm whether the available diagnostic tools act out in due time to avoid permanent damage to the faulty IM. To evaluate the impacts of this failure mode in the temperature of the stator windings of an IM, this paper presents the results obtained from two alternative thermal models of the same IM, resorting to the lumped parameter thermal network method and to the finite elements method. The results confirm that diagnostic tools reported in the literature might not be effective, failing to correctly diagnose an inter‐turn SC fault and to warrant the lead time required to take actions suitable to prevent permanent damage to the IM.
Fernando Bento; Amel Adouni; Ananias C. P. Muxiri; Davide S. B. Fonseca; Antonio J. Marques Cardoso. On the risk of failure to prevent induction motors permanent damage, due to the short available time‐to‐diagnosis of inter‐turn short‐circuit faults. IET Electric Power Applications 2020, 15, 51 -62.
AMA StyleFernando Bento, Amel Adouni, Ananias C. P. Muxiri, Davide S. B. Fonseca, Antonio J. Marques Cardoso. On the risk of failure to prevent induction motors permanent damage, due to the short available time‐to‐diagnosis of inter‐turn short‐circuit faults. IET Electric Power Applications. 2020; 15 (1):51-62.
Chicago/Turabian StyleFernando Bento; Amel Adouni; Ananias C. P. Muxiri; Davide S. B. Fonseca; Antonio J. Marques Cardoso. 2020. "On the risk of failure to prevent induction motors permanent damage, due to the short available time‐to‐diagnosis of inter‐turn short‐circuit faults." IET Electric Power Applications 15, no. 1: 51-62.
Faults in aluminum electrolytic capacitors (AECs) are classified as the major cause for power electronics equipment breakdown, mainly due to AECs wear out through the vaporization of the electrolyte, as a result of both aging and temperature effects. Therefore, the online estimation of these two parameters can provide valuable and timely information that allows detecting any potential capacitor failure. Changes in C and ESR parameters influence the relationship between the capacitor voltage and current ripples. This ratio is dominated by C at low frequencies, and by ESR in the high-frequency range. The aim of this paper is to propose an algorithm allowing a continue estimation and tracking of both parameters (ESR and C). This algorithm should be simple, fast, and suitable for online implementation. Therefore, this paper introduces the use of the Short-Time Fourier Transform (STFT) technique to determine and track C and ESR values, starting from some always-existing harmonics at low and high frequencies. The STFT is based on applying the Discrete Fourier Transform (DFT) algorithm on a sliding window, which makes it more appropriate for online implementation. The effectiveness of the proposed method is proved by simulation and laboratory experiments, using a boost DC-DC converter
Khaled Laadjal; Mohamed Sahraoui; Antonio J. Marques Cardoso. On-Line Fault Diagnosis of DC-Link Electrolytic Capacitors in Boost Converters Using the STFT Technique. IEEE Transactions on Power Electronics 2020, 36, 6303 -6312.
AMA StyleKhaled Laadjal, Mohamed Sahraoui, Antonio J. Marques Cardoso. On-Line Fault Diagnosis of DC-Link Electrolytic Capacitors in Boost Converters Using the STFT Technique. IEEE Transactions on Power Electronics. 2020; 36 (6):6303-6312.
Chicago/Turabian StyleKhaled Laadjal; Mohamed Sahraoui; Antonio J. Marques Cardoso. 2020. "On-Line Fault Diagnosis of DC-Link Electrolytic Capacitors in Boost Converters Using the STFT Technique." IEEE Transactions on Power Electronics 36, no. 6: 6303-6312.
In the case of failure of one or more components of a drive system, the emergency shutdown of the system is not always the best way to act. Therefore, simultaneous reconfiguration of the drive control strategy is mandatory to enable an uninterrupted operation to cater for the catastrophic failure. In this context, this paper presents a current sensors fault-tolerant control method for induction motor drives, based on vector control and currents estimation. Several important issues are considered in the proposed method, namely, the detection of sensors failure, isolation of the faulty sensors, and reconfiguration of the control system by proper currents estimation. A new adaptation of the Luenberger observer is proposed and used to perform the task of stator currents estimation. Furthermore, a developed logic circuit is used to detect the faulty current sensors and isolate them with simultaneous generation of logic impulses allowing switching to a proper estimation. The proposed fault-tolerant control strategy is firstly tested in MATLAB/Simulink environment in order to illustrate its high-performance. Then, several experimental tests are carried out on a 1.1 kW three-phase induction motor to validate the theoretical results and to confirm the effectiveness of the proposed algorithm.
Younes Azzoug; Mohamed Sahraoui; Remus Pusca; Tarek Ameid; Raphaël Romary; Antonio J. Marques Cardoso. Current sensors fault detection and tolerant control strategy for three-phase induction motor drives. Electrical Engineering 2020, 1 -18.
AMA StyleYounes Azzoug, Mohamed Sahraoui, Remus Pusca, Tarek Ameid, Raphaël Romary, Antonio J. Marques Cardoso. Current sensors fault detection and tolerant control strategy for three-phase induction motor drives. Electrical Engineering. 2020; ():1-18.
Chicago/Turabian StyleYounes Azzoug; Mohamed Sahraoui; Remus Pusca; Tarek Ameid; Raphaël Romary; Antonio J. Marques Cardoso. 2020. "Current sensors fault detection and tolerant control strategy for three-phase induction motor drives." Electrical Engineering , no. : 1-18.
In this study, a novel trigeneration system is conceived to produce heat and electricity and to provide cooling for the health treatments and touristic facilities of a spa, based on the natural hot water and solar sources. The power generation components, individually considered, are commercially available ones, but their novel combination and the complex power flow management represented a challenge. The proposed system is composed of a low-temperature driven adsorption chiller, thermally activated by a low enthalpy geothermal source, and by hybrid photovoltaic/thermal panels. In this way, multiple objectives are achieved: produce electricity and thermal energy by renewable sources; optimise the use of different renewable sources (geothermal and solar); use the energy available for free from a geothermal source also during summer (otherwise wasted) to produce a cooling effect, and in so doing, avoiding the huge electricity consumption of conventional air conditioning units in summer; reduce the temperature of the fluids released to the environment (in a natural reserve); reduce the CO2 emissions by 45% with respect to the present configuration, limiting the global warming. The mathematical models were experimentally validated using a pilot plant built on purpose, and the performance of the whole system was analysed and discussed.
Cristina Moscatiello; Chiara Boccaletti; Aderito Neto Alcaso; Carlos A. Figueiredo Ramos; Antonio J. Marques Cardoso. Trigeneration system driven by the geothermal and solar sources. IET Renewable Power Generation 2020, 14, 2340 -2347.
AMA StyleCristina Moscatiello, Chiara Boccaletti, Aderito Neto Alcaso, Carlos A. Figueiredo Ramos, Antonio J. Marques Cardoso. Trigeneration system driven by the geothermal and solar sources. IET Renewable Power Generation. 2020; 14 (13):2340-2347.
Chicago/Turabian StyleCristina Moscatiello; Chiara Boccaletti; Aderito Neto Alcaso; Carlos A. Figueiredo Ramos; Antonio J. Marques Cardoso. 2020. "Trigeneration system driven by the geothermal and solar sources." IET Renewable Power Generation 14, no. 13: 2340-2347.
Direct model predictive control (DMPC) of permanent magnet synchronous generators with full-scale back-to-back (BTB) converters, plays an important role in improving the power quality injected to the grid, to comply with grid codes. However, DMPC suffers from the excessive computational burden, since all the feasible voltage vectors (VVs) of the BTB converter are used for the prediction and evaluation. Moreover, the weighting factor in cost function may affect the control performance, and tuning it is a complex process. Accordingly, and benefiting from the simplicity of direct control techniques, new direct model predictive flux and power control (DMPFC and DMPPC) are proposed in this paper, in combination with direct torque and power control (DTC and DPC), respectively. By defining new DTC and DPC switching tables, only 3 out of the 8 predictions are required to select the best VV for each converter of the BTB topology, which significantly reduce the algorithm computations. To avoid the weighting factor, the electromagnetic torque is simply converted into an equivalent stator flux, and only this latter is considered in the cost function. Experimental results prove that the execution time can be reduced by 26.9 %, while best control performance can be achieved.
Imed Jlassi; Antonio J. Marques Cardoso. Enhanced and Computationally Efficient Model Predictive Flux and Power Control of PMSG Drives for Wind Turbine Applications. IEEE Transactions on Industrial Electronics 2020, 68, 6574 -6583.
AMA StyleImed Jlassi, Antonio J. Marques Cardoso. Enhanced and Computationally Efficient Model Predictive Flux and Power Control of PMSG Drives for Wind Turbine Applications. IEEE Transactions on Industrial Electronics. 2020; 68 (8):6574-6583.
Chicago/Turabian StyleImed Jlassi; Antonio J. Marques Cardoso. 2020. "Enhanced and Computationally Efficient Model Predictive Flux and Power Control of PMSG Drives for Wind Turbine Applications." IEEE Transactions on Industrial Electronics 68, no. 8: 6574-6583.
The inter-turn faults are one of the most (if not the most) challenging electrical machine failures to detect online and at incipient severity stages. Past works and experience have shown that this specific fault type will lead to a ground fault and consequently a catastrophic failure of the machine in a very small amount of time. Past works have proposed techniques and methodologies to confront this dangerous fault. However, a common feature is most past efforts is the high level of severity, limited by external resistors to avoid machine breakdowns. Scenarios like the above are not of much use in industry because they lead to the development of diagnostic techniques insensitive to the real fault severity levels that are non-catastrophic and that are by their nature very low. This is the motivation behind this work, which challenges the existing background in this field and offers a reliable solution, which may be adopted in industry. The paper studies induction motors with incipient inter-turn fault severity with many well-known techniques. The experimental results prove many methods unreliable and insensitive to low level inter-turn fault detection. Finally, the authors propose a novel method that relies on the monitoring of the stray flux at three positions of the machine.
Konstantinos N. Gyftakis; Antonio J. Marques Cardoso. Reliable Detection of Stator Interturn Faults of Very Low Severity Level in Induction Motors. IEEE Transactions on Industrial Electronics 2020, 68, 3475 -3484.
AMA StyleKonstantinos N. Gyftakis, Antonio J. Marques Cardoso. Reliable Detection of Stator Interturn Faults of Very Low Severity Level in Induction Motors. IEEE Transactions on Industrial Electronics. 2020; 68 (4):3475-3484.
Chicago/Turabian StyleKonstantinos N. Gyftakis; Antonio J. Marques Cardoso. 2020. "Reliable Detection of Stator Interturn Faults of Very Low Severity Level in Induction Motors." IEEE Transactions on Industrial Electronics 68, no. 4: 3475-3484.
In this paper the occurrence of stator faults in line-start permanent magnet synchronous motors is analyzed. A dynamic model of the motor was developed in Matlab/Simulink, that considers the performance of the motor both in healthy conditions and under the occurrence of inter-turn short-circuit faults, at an early stage of development. To validate the presented approach, simulation results, covering the full range of operation and different fault severity levels, are compared with the motor experimental performance. The output of the proposed model, namely voltages and currents, present a good agreement with experimental results, demonstrating the accuracy of the proposed model. Additionally, an on-line fault diagnostic technique is explored, demonstrating its potentiality.
Davide Fonseca; Carlos M. C. Santos; Antonio J. Marques Cardoso. Stator Faults Modeling and Diagnostics of Line-Start Permanent Magnet Synchronous Motors. IEEE Transactions on Industry Applications 2020, 56, 2590 -2599.
AMA StyleDavide Fonseca, Carlos M. C. Santos, Antonio J. Marques Cardoso. Stator Faults Modeling and Diagnostics of Line-Start Permanent Magnet Synchronous Motors. IEEE Transactions on Industry Applications. 2020; 56 (3):2590-2599.
Chicago/Turabian StyleDavide Fonseca; Carlos M. C. Santos; Antonio J. Marques Cardoso. 2020. "Stator Faults Modeling and Diagnostics of Line-Start Permanent Magnet Synchronous Motors." IEEE Transactions on Industry Applications 56, no. 3: 2590-2599.