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Recently, model-based predictive current control (MB-PCC) has been presented as a good alternative to classical control algorithms in terms of simplicity and performance reliability. However, MB-PCC suffers from the high dependence on system parameters, which may deteriorate its performance under parameters variations. On the other hand, Synchronous Reluctance Motors (SynRMs) are susceptible to suffer from inductances variations due to the magnetic saturation. Accordingly, in this paper a new model-free predictive current control of SynRMs based on a recurrent neural network (RNN-PCC) is developed and proposed. The proposed RNN-PCC relies on the identification of the SynRM currents without considering any parameters. Simulation and experimental results show that both RNN-PCC and MB-PCC have similarly excellent dynamics, while better control performance and tracking errors can be achieved thanks to the proposed RNN-PCC.

Inter-turn short-circuit faults are one of the most (if not the most) harmful electrical machine failures, that if not detected and mitigated at a very incipient stage of development may involve serious consequences, both in terms of repair costs and safety. This paper proposes a novel inter-turn fault diagnostic technique for Synchronous Reluctance Machines based on the Luenberger state observer and currents second-order harmonic. The superiority of the proposed approach over the conventional model-based techniques is the ability to evaluate the severity of the fault as well as the efficiency in detecting faults in early stages and under various operating conditions. The effectiveness of the proposed method is demonstrated and validated through several experimental tests.

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 and 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 the cost function may affect the control performance and tuning it is a complex process. Accordingly, benefiting from the simplicity of the direct control techniques, new direct model predictive flux and power control are proposed in this article, in combination with the direct torque and power control (DTC and DPC), respectively. By defining new DTC and DPC switching tables, only three out of the eight predictions are required to select the best VV for each converter of the BTB topology, which significantly reduces the algorithm computations. To avoid the weighting factor, the electromagnetic torque is simply converted into an equivalent stator flux and only the latter one is considered in the cost function. Experimental results prove that the execution time can be reduced by 26.9%, while the best control performance can be achieved.

This paper presents a field-oriented control (FOC) of a dual star induction generator (DSIG) applied in a grid-connected wind energy conversion system. Currently, the dual star induction machine (DSIM) is increasingly used among multiphase machines. The machine has two star-connections, sharing the same stator offset, by an electrical angle of 30° and fed by two parallel converters. Maximum power point tracking (MPPT) is illustrated in a first stage, in order to extract a maximum of power under fluctuating wind speed. In a second stage, vector control of a DSIG with FOC is described. Finally, voltage oriented control (VOC) is used to ensure the power factor unity on the grid side. The main contribution of the presented paper is the application of a simple architecture of an artificial neural network (ANN) controller in order to improve the robustness and stability of the system, especially against the parameter change. In comparison with the conventional control, which is known by its sensitivity, the proposed neural MPPT with neural FOC (NMPPT-NFOC) presents better performance under normal and abnormal conditions. The robustness and effectiveness of the proposed control has been validated through illustrative simulation results with different functional zones, and for fixed and variable wind speed.

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.

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.

In this article, 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, which considers the performance of the motor both in healthy conditions and under the occurrence of interturn 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 online fault diagnostic technique is explored, demonstrating its potentiality.

Condition monitoring is an essential technology for detecting various incipient defects in electrical machines. Stator inter-turn and rotor broken bar faults are two major faults of the squirrel cage induction motors (SCIMs). Many attempts have been made to provide fault diagnosis systems for detecting the faults individually. This study proposes a novel unified fault diagnosis system for detecting the faults and identifying their exact severities. The system is based on a flexible model of the SCIM and can identify the severities of the faults even if they are presenting simultaneously. Robustness against the motor load change, the unbalance degree of the voltage supply and its distortion are the other merits of the proposed fault diagnosis system. Experimental results under various fault modes and operating conditions demonstrate the effectiveness of the proposed fault identification technique.

This paper addresses the occurrence of stator faults in six-phase induction machines, with a symmetrical winding. A dynamic model of the motor was developed in Matlab/Simulink environment, taking into consideration the occurrence of inter-turn short circuit faults. To validate the proposed approach, simulation results, covering the full range of operation and different fault severity levels, are compared with the motor experimental performance. The obtained voltages and currents present a good agreement with the corresponding experimental results, demonstrating the accuracy of the proposed model.

International audience; 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 faulttolerant 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.

International audience; 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.

International audience; This paper introduces a simple and low-cost direct vector control method without current sensors for Permanent Magnet Synchronous Motor (PMSM) drives, using only a single estimator and the measured dc-link voltage, as well as the measured rotor speed for currents reconstruction. The proposed method is based on a new modification of the adaptive state observer to estimate the line currents. A theoretical concept is developed, the modified adaptive state observer is presented. Simulations tests are performed to prove the ability of the proposed scheme to reconstruct stator currents and reproduce the performances of a basic and traditional vector control PMSM drive.

This paper presents a non-isolated DC–DC converter for fuel-cell electric vehicle applications. An H-infinity control, based on mixed sensitivity, applied to a three-phase interleaved boost convert...

Aluminum electrolytic capacitors (AECs) are the most common capacitors used in power electronics. They are frequently used for filtering and storage functions because of their small size, large capacitance, and low price. However, AECs also represent the major cause for power electronics equipment breakdown, mainly due to their wear out through the vaporization of the electrolyte, as a result of both aging and temperature effects. The aging of AECs is expressed by the increase of their equivalent series resistance (ESR) and the decrease of their capacitance ( C ). Therefore, estimating these two parameters can provide a good indicator for a potential capacitor failure. Additionally, these changes in C and ESR parameters strongly influence the ratio between the capacitors voltage ripple and their current ripple. This ratio is dominated by C at low frequencies and by the ESR in the high-frequency range. Hence, this paper suggests a method for online estimating and tracking of both C and ESR using that ratio, which is computed starting from some always existing harmonics at low and high frequencies. The proposed method uses the short time least square Prony's (STLSP) technique to determine and track the amplitudes of the target harmonics. It is shown that compared with the discrete Fourier transform method, the STLSP technique is well suitable for such application, since it has the ability to determine and track the amplitude and frequency of any spectral component, even for noisy and nonstationary signals, using a small number of data samples. This reduces a lot the data storage requirements. Consequently, the proposed method is appropriate for online implementation. The method's effectiveness is proved by simulation and experimental tests using a boost dc–dc converter.

Model predictive current control (MPCC) has recently become a viable alternative for multiphase electric drives, because it easily exploits the inherent advantages of multi-phase machines. However, the prediction in MPCC requires a high number of voltage vectors (VVs), being therefore computationally demanding. In that regard, this paper proposes a computationally efficient MPCC of an asymmetrical six-phase induction machine drive (ASIMD) that reduces the number of VVs used for prediction. By using the characteristics of the deadbeat control (DB), the proposed method obtains a reference voltage vector (RVV), where its position will serve as a reference and integrates the MPCC scheme. Only 4 out of 13 predictions are needed to determine the best VV, dramatically reducing the algorithm computation. Experimental results for a six-phase case study compare the standard MPCC with the suggested method, confirming that deadbeat model predictive current control (DB-MPCC) shows that the execution time can be shortened by 48.8% and successfully improve the motor performance and efficiency.

Motor current signature analysis (MCSA) based detection of rotor cage faults (RFs) in medium-high voltage induction motors has become common in the field due to its low-cost remote monitoring capability. However, low-frequency load oscillations (LOs) in applications that employ speed reduction couplings can induce frequency components in the vicinity of that of RFs. It has been reported that false-positive or -negative RF indications can be produced with the MCSA, if the frequency components produced by RF and LO overlap. There, recently, have been studies on discriminating RF and LO; however, the case where two harmonic components overlap at the same frequency, which is the most difficult and serious case, has not been investigated. In this paper, a spectrum analysis of instantaneous active and reactive power is applied, and it is shown that RF and LO can be separated for the case where the MCSA fails due to the overlap in the two spectral components. An experimental study performed on a 380 V motor with a speed reduction gear shows that RF and LO can be reliably detected and separated for avoiding false RF indications.

Conventional Finite-Control-Set Model Predictive Torque Control (FCS-MPTC) suffers from the high computational burden and the weighting factor tuning effort, which increase the flux and torque ripples and have a negative impact on the drive performance. Accordingly, this paper presents an enhanced FCS-MPTC for Permanent Magnet Synchronous Motor (PMSM) drives with reduced computational effort and without the use of weighting factors. Based on the use of a new 2-D lookup table (LUT) of Direct Torque Control (DTC) and different from prior method using eight predicted voltage vectors in the cost function, only three voltage vectors are predicted and evaluated, which reduce notably the computational effort. Furthermore, with the aim to eliminate the weighting factors in the cost function, the torque and flux vector references are converted into an equivalent angle between the stator and rotor flux vectors, which decreases the current and flux ripples and improves the control performance. Simulation results are presented to show the effectiveness of the proposed control method.

Under abnormal electrical supply conditions, induction motors can easily reach an overheating condition. A continuous operation under thermal stress significantly decrements the motors useful lifetime. In this paper a short-time thermal transient analysis, based on a Lumped Parameter Thermal Network (LPTN), is performed in order to evaluate the thermal behaviour of an asynchronous machine operating under supply voltage unbalance. Three types of supply unbalance are considered in this paper: unbalanced voltage amplitude, unbalanced phase, and a combination of both. The analytical model is used to estimate the temperature in different points of the stator. This model presents a reasonable compromise between simplicity, accuracy, and rapidity. The obtained results are validated using an experimental prototype.

Aluminum electrolytic capacitors (AECs) are widely used in the DC link of power converters, such as the three-phase interleaved boost converter (IBC) which offers many advantages over the conventional amplifier converter. IBCs can operate both in closed- or open-loop systems and any fault in the converter components can reduce significantly the overall system performance. AECs failures represent the major cause for power electronics equipment breakdown, mainly due to both aging and temperature effects involving significant increase on their equivalent series resistance (ESR) parameter. This reveals the importance of developing robust control strategies that allow evaluating the electrolytic capacitor state condition and keeping, at the same time, the performance of the converter controller, even under a capacitor fault occurrence. For that purpose, a fault detection and control strategy is proposed in this paper, for a three-phase interleaved boost converter application. The control task of this strategy uses a mixed sensitivity $\mathbf{H}\infty$ controller to ensure fast dynamics and robustness even under failure mode operation, while the fault detection task uses the Short Time Least Square Prony's (STLSP) method to perform an online estimation of the ESR parameter, allowing a continuous evaluation of the electrolytic capacitor state condition, and consequently avoiding a total system failure. The effectiveness of the proposed strategy is verified by several experimental tests.

Significant changes may occur in the thermal behaviour of a directly grid-fed induction machine when subjected to unbalances in the voltage supply. This paper studies and analyses the thermal behaviour of a low power, three-phase, squirrel-cage induction motor, connected in star configuration with floating neutral point, when subjected to different levels of unbalanced voltage. The dependence of the thermal motor behaviour on the severity level of the unbalance is studied and analysed. In addition to amplitude unbalances, this paper focuses on the effects of phase unbalances, as well, which were not addressed in detail in previous published studies. Moreover, situations of mixed unbalance, where amplitude and phase unbalances occur simultaneously, are also studied. The Finite Element Method (FEM) was used to simulate the thermal behaviour of the machine. The experimental setup consists of a three-phase programmable AC power supply, suitable to precisely emulate unbalance conditions that may occur in real-scale power systems, supplying a 2.2 kW induction motor. Experimental data were acquired resorting to Resistance Temperature Detectors (RTDs) PT100, placed in the machine phase whose supply current value changed the most. Finally, the simulation results are verified and critically discussed through experimentally obtained results.

International audience; This paper proposes a fault-tolerant control technique against current sensors failure in direct torque controlled induction motors drives. Several important aspects are covered in the proposed algorithm, such as the detection of sensors failure, the isolation of faulty sensors, and the reconfiguration of the control system by a correct estimation. A logic circuit ensures fault detection by analyzing the residual signal between the measured and estimated quantities, while a single modified Luenberger observer performs the task of estimating the line currents, even when all the sensors are in the faulty state. In addition, a decision logic circuit isolates the erroneous signal and simultaneously selects the appropriate estimated current signal. The proposed scheme is tested on MATLAB/Simulink environment and experimentally validated in a laboratory prototype mainly containing a dS1104 card and 4 kW induction motor.

Induction motors airgap-eccentricity fault detection has attracted the interest of many researchers in the last decades. The discrete Fourier transform (DFT) has been widely used for that purpose. However, DFT can only give meaningful information for stationary signals. In addition, DFT requires a long acquisition time in order to get a high-frequency resolution. In this paper, an efficient time-domain technique based on a modified Prony's method for the airgap-eccentricity fault detection in induction motors is presented in order to overcome the DFT problems. Using this technique, the stator current and apparent power—as processed signals—are divided into short overlapped time windows and each one is analyzed by the least squares Prony's method. The proposed technique allows tracking the frequency and amplitude of the airgap-eccentricity fault characteristic component with a very high accuracy using only few data samples. A fault severity factor based on the amplitude of the estimated component is defined. The effectiveness and accuracy of the proposed method are verified through the presented simulation and experimental results.

This paper presents a fault-tolerant permanent magnet synchronous generator (PMSG) drive for wind turbine systems, with the ability to handle power semiconductor open-circuit (O-C) faults in the full-scale back-to-back converter. The fault-tolerant topology consists of a five-leg converter, with a shared leg connected to a generator phase and to its corresponding grid phase. The main contribution of this paper consists on the development of an alternative vector-control-based pulse-width-modulation (PWM) control scheme for both machine-side converter (MSC) and grid-side converter (GSC), allowing to reduce the dc-bus voltage ratings. Moreover, a reliable fault diagnosis algorithm is also integrated, providing the information required to instantaneously trigger fault-tolerant remedial strategies, without any additional sensors. Simulation and experimental results are presented to validate the effectiveness of the proposed fault-tolerant PMSG drive.

In this paper the occurrence of stator faults in line-start permanent magnet synchronous motors are analyzed. A dynamic model was developed in Matlab/Simulink and on-line fault diagnostic techniques are explored. To validate the presented approach, simulation results are compared with the motor experimental performance.

A finite-control-set model predictive current control (FCS-MPCC) including saturation and iron losses of synchronous reluctance motors (SynRMs) is introduced in this paper. The SynRM iron losses' resistance and the direct/quadrature inductances are integrated into the main control system as lookup tables, where the values are chosen depending on the inputs. The motor's performance, under steady-state and transient conditions, of the proposed predictive scheme is analyzed and compared to that of a predictive scheme that do not take saturation and iron losses into account. Simulation and experimental results of a predictive-controlled 2.2 kW SynRM are presented. Comparison of both predictive schemes leads to the conclusion that the proposed control scheme has much smaller current and torque ripples.

A mathematical model and simulation of a directly coupled water pumping system employing a photovoltaic-fed Synchronous Reluctance Motor (SynRM) is presented in this paper.

It has recently been shown that spectrum analysis of instantaneous power can provide sensitive online detection of rotor faults in induction motors compared with current, torque, speed, or vibration spectrum analysis. However, it was reported that monitoring of the twice slip frequency, $2sf_{s}$ , components induced by the fundamental component can produce false rotor fault alarms due to asymmetry in the rotor or low frequency load oscillations. In this paper, the rotor fault components induced in the power spectrum by the stator fifth and seventh space harmonics are derived to evaluate their immunity to false alarms. It is shown that the $(6 - 8s)f_{s}$ component can provide reliable detection of rotor faults under cases where existing methods produce false alarms. An experimental study performed on custom built rotor samples shows that the new components are capable of detecting rotor faults immune to false alarms produced by rotor axial air ducts, rotor anisotropy, and low frequency load oscillations for cases where existing methods fail. The components derived in this paper can also be applied to vibration, speed, torque, or acoustic monitoring for reliable detection of rotor faults.

In order to increase the reliability and availability of wind turbines, condition monitoring and fault diagnosis are considered crucial means to achieve these goals. In this context, direct drives wind turbines based on permanent-magnet synchronous generators (PMSGs) with full-scale power converters are an emerging and promising technology. However, several statistical studies point out that power converters are a significant contributor to the overall failure rate of modern wind turbines. Accordingly, this paper presents a new algorithm for multiple open-circuit faults diagnosis in full-scale back-to-back converters, applied in PMSG drives used for wind turbine systems. The proposed method is based on a Luenberger observer and on an adaptive threshold, which can guarantee a reliable diagnosis independently of the drive operating conditions. Several simulation and experimental results using a PMSG drive with a full-scale converter are presented, showing the diagnostic algorithm effectiveness and robustness against false alarms for both generator- and grid-side converters.

The knowledge of the broken rotor bar characteristic frequencies and amplitudes has a great importance for all related diagnostic methods. The monitoring of motor faults requires a high resolution spectrum to separate different frequency components. The discrete Fourier transform (DFT) has been widely used to achieve these requirements. However, DFT can give meaningful information only for stationary harmonics which cannot be guaranteed in real cases. In addition, a long data sequence is necessary for DFT to get high frequency resolution. Nevertheless, the signals are time varying, and the steady-state conditions can be lost for a long time acquisition. As a solution for these problems, this paper proposes an efficient time-domain technique based on a modified Prony method for the estimation of the frequencies/amplitudes of broken rotor bar faults. Using this technique, the stator current is divided into short overlapped time windows, and each one is analyzed by the least squares Prony method. The proposed technique provides a linear time–frequency/amplitude representation with high frequency resolution and adjustable time resolution. It is shown that this technique allows tracking the frequencies and amplitudes of the sidebands around the fundamental frequency component with a very high accuracy. The efficiency of the proposed method is verified by simulation and experimental tests.

A new approach for current sensor fault detection and isolation (FDI) for permanent magnet synchronous motor (PMSM) drives is presented. Contrary to the classical approaches for sensors fault diagnosis, based on residual generation through observers or parity equations, the proposed technique uses the average normalised machine-phase currents. The main advantages of this approach are that it does not need any information about the PMSM or inverter models, and it involves low tuning efforts, that make it suitable for real-time implementation with good reliability. The good performance and the robustness of the proposed FDI approach are illustrated through experimental results.

Power converters are complex devices subject to failures that can lead to undesired stops. For this reason, many fault-tolerant topologies and strategies have been deeply studied, mainly focusing on improving the electromechanical behavior of the reconfigured drive after the fault. However, it is also very important to evaluate the possible increase of power losses. In this paper, a power loss analysis is performed on a current-controlled three-phase inverter supplying a permanent-magnet synchronous motor (just chosen as an example), for all the operating modes, starting from the fault occurrence until the faulty operation mode is established. This is particularly important because the effectiveness of a fault-tolerant control strategy depends on the possibility to maintain a continuous operation. The latter is ensured only if there is no such increment of the losses that the survival of the component is at risk. The simulations are validated by means of experimental tests, and the results are discussed. The method can be easily applied to other topologies and reconfiguration strategies.

In this paper a new on-line fault diagnostics technique of the occurrence of stator faults in an 8/6 Switched Reluctance Motor is proposed. To validate the proposed approach a dynamic model was developed in Matlab/Simulink environment, based on the utilization of a specific lookup table for each phase, obtained with FEMM.

Predicting a localized defect on a rolling bearing during the degradation process before a complete failure is crucial to prevent system failures, unscheduled downtimes and substantial loss of productivity. During this process, impulses associated with the fault are weak, nonstationary or time-frequency varying, and contaminated by noises, which render the problem of extracting these impulses very difficult. This work investigates the effectiveness of common signal processing techniques on predicting incipient faults, e.g. Fast Fourier transform, Short-Time Fourier transform, Wavelet transform. It was found that an adaptive filter is required to enhance and reconstruct the signals during the degradation process, and a combination of adaptive filter and Morlet wavelet transform is necessary in order to effectively detect a localized defect on rolling element bearings during degradation. The proposed method was applied to analyze vibration signals collected from a run-to-failure test of drivetrain. The analysis shows that the frequency associated with a bearing defect can be well identified in the early stage or during degradation.

Aluminum electrolytic capacitors (AECs) are the most common capacitors used in power electronics. They are frequently used for filtering and storage functions because of their small size, large capacitance and low price. However, AECs also represent the major cause for power electronics equipment breakdown, mainly due to their wear out through the vaporization of the electrolyte, as a result of both aging and temperature effects. The aging of AECs is expressed by the increase of their equivalent series resistance (ESR) and the decrease of their capacitance (C). Therefore, estimating these two parameters can provide a good indicator for a potential capacitor failure. Additionally, these changes in C and ESR parameters strongly influence the ratio between the capacitors voltage ripple and their current ripple. This ratio is dominated by C at low frequencies and by the ESR in the high frequency range. Hence, this paper suggests a method for on-line estimating and tracking of both C and ESR using that ratio, which is computed starting from some always existing harmonics at low and high frequencies. The proposed method uses the Short Time Least Square Prony's (STLSP) technique to determine and track the amplitudes of the target harmonics. The STLSP technique is well suited for such application, since it has the ability to determine and track the amplitude and frequency of any spectral component, even for noisy and non-stationary signals, using a small number of data samples. This reduces a lot the calculation time and the storage requirements. Consequently, the proposed method is appropriate for online implementation. The method's effectiveness is proved by simulation and experimental tests using a boost DC-DC converter.

The widespread use of electric drives has led to an increasing interest in their condition monitoring and fault diagnostics. With respect to power-switch open-circuit fault diagnosis, the majority of the proposed voltage-based approaches require additional hardware, increasing the system cost. As a result, industry acceptance may be low. This paper proposes a new voltage-based approach-without additional sensors-for open-circuit fault diagnosis in closed-loop controlled pulsewidth-modulation (PWM) ac voltage source converters, by using the information contained in the reference voltages available from the control system. The robustness against false alarms of the proposed technique is improved by employing a voltage observer, in order to estimate the actual converter voltages. It has been proven that the algorithm is insensitive to parameter errors; thus, its effectiveness is not compromised. In order to assess the applicability of the diagnostic technique, it is integrated into the control system of a permanent-magnet-synchronous-machine drive, with regenerative capabilities, together with three of the most widely used PWM control strategies: hysteresis current control, direct torque control, and space vector modulation. The technique performance is analyzed in detail by means of several experimental results.

In this paper, the instantaneous active and reactive power signature analyses are presented for stator fault diagnosis in operating squirrel cage three-phase induction motors either directly connected to the mains or fed from inverters. Both simulation and experimental results are presented to show the effectiveness and the merits of the proposed approach that offers the possibility to detect this type of fault and to discriminate it from other abnormality conditions responsible for the same motor behavior by comparing the signature analyses of the two considered quantities.

Fault-tolerant permanent magnet synchronous generator (PMSG) drives for wind turbine applications play a major role in improving reliability and availability levels, since power converters are very prone to fail. In this paper, a fault-tolerant converter with the ability to handle power switch open-circuit faults is addressed. The main concern of the proposed converter topology is the minimization of the hardware requirements, leading to a low increase of the system cost. First, the employed fault diagnostic technique does not require additional measurements, nor high computational effort. Secondly, the circuit topology reconfiguration implies a minimum number of extra components as well as minimal oversizing of the standard ones. Accordingly, a four-switch three-phase converter with the dc bus midpoint connected to the transformer neutral point and a three-switch three-phase rectifier are adopted for post-fault operation of the grid- and PMSG-side converters, respectively. Vector control strategies are proposed for both converters under analysis, focusing the issues of capacitor voltages balancing and torque ripple minimization. The performance of the proposed fault-tolerant PMSG drive is analyzed by means of experimental results.

—In this article, a method based on the application of the discrete wavelet transform to the instantaneous reactive power signal, for diagnosing the occurrence of broken rotor bars in induction motors operating under time-varying load conditions, is presented. This method is based on the decomposition of the instantaneous reactive power signal, from which wavelet approximation and detail coefficients are extracted. The energy evaluation of known bandwidths permits to define a fault severity factor. This method has been tested through the simulation of an induction motor using a mathematical model based on the winding-function approach. These simulation results are complemented by experimental tests conducted on an induction motor with several faulty rotors that can be interchanged and both simulation and experimental results have shown the effectiveness of the proposed method for broken rotor bars diagnosis in induction motors even under time-varying load conditions.

Fault-tolerant permanent magnet synchronous generator drives might play an important role in improving the reliability and availability levels of the modern wind turbines. However, the acceptance by the wind turbine industry will be low if its implementation cannot be accomplished in a cost-effective way. In this paper, a low-cost fully integrated fault-tolerant converter with the ability to handle power switch open-circuit faults is proposed. The two topologies proposed for post-fault operation of the grid- and PMSG-side converters are a five-leg converter with a shared leg connected to a phase of the generator and to the transformer neutral point and a three-switch three-phase rectifier, respectively. Both do not require the oversizing of any component of the conventional system in order to operate within a limited range. Moreover, the fault diagnostic technique does not require additional measurements, and the integration into the drive controller of all diagnostic and control algorithms can be performed without great increase of the computational effort. Control strategies are proposed for the considered converter topologies, and their performance is analyzed by means of experimental results.

Fast Fourier transform (FFT) analysis has been successfully used for fault diagnosis in induction machines. However, this method does not always provide good results for the cases of load torque, speed and voltages variation, leading to a variation of the motor-slip and the consequent FFT problems that appear due to the non-stationary nature of the involved signals. In this paper, the discrete wavelet transform (DWT) of the apparent-power signal for the airgap-eccentricity fault detection in three-phase induction motors is presented in order to overcome the above FFT problems. The proposed method is based on the decomposition of the apparent-power signal from which wavelet approximation and detail coefficients are extracted. The energy evaluation of a known bandwidth permits to define a fault severity factor (FSF). Simulation as well as experimental results are provided to illustrate the effectiveness and accuracy of the proposed method presented even for the case of load torque variations.

Reliability and availability levels are crucial aspects for assessing the economic viability of wind energy conversion systems. Therefore, fault-tolerant systems can make a valuable contribution. This paper presents a fault-tolerant permanent-magnet synchronous generator (PMSG) drive employing new direct control techniques. For postfault operation, a direct power control (DPC) of a four-switch three-phase converter and a direct torque control of a three-switch three-phase rectifier are proposed. Switching tables are theoretically formulated for both control techniques. Two alternative tables are obtained for the DPC of the grid-side converter, permitting the choice between implementation simplicity and enhanced performance. All necessary reconfigurations to handle open-circuit faults are triggered by a reliable fault diagnostic method, which has a low computational demand, without requiring additional measurements. Experimental results are presented, demonstrating the feasibility of the proposed fault-tolerant PMSG drive.

Condition monitoring and fault diagnosis are currently considered crucial means to increase the reliability and availability of wind turbines and, consequently, to reduce the wind energy cost. With similar goals, direct-drive wind turbines based on permanent magnet synchronous generators (PMSGs) with full-scale power converters are an emerging and promising technology. Numerous studies show that power converters are a significant contributor to the overall failure rate of modern wind turbines. In this context, open-circuit fault diagnosis in the two power converters of a PMSG drive for wind turbine applications is addressed in this paper. A diagnostic method is proposed for each power converter, allowing real-time detection and localization of multiple open-circuit faults. The proposed methods are suitable for integration into the drive controller and triggering remedial actions. In order to prove the reliability and effectiveness of the proposed fault diagnostic methods, several simulation and experimental results are presented.

Three-phase inverters are currently utilized in an enormous variety of industrial applications, including variable-speed ac drives. However, due to their complexity and exposure to several stresses, they are prone to suffer critical failures. Accordingly, this paper presents a novel diagnostic algorithm that allows the real-time detection and localization of multiple power switch open-circuit faults in inverter-fed ac motor drives. The proposed method is quite simple and just requires the measured motor phase currents and their corresponding reference signals, already available from the main control system, therefore avoiding the use of additional sensors and hardware. Several experimental results using a vector-controlled permanent-magnet synchronous motor drive are presented, showing the diagnostic algorithm effectiveness, its relatively fast detection time, and its robustness against false alarms.

[EN] The Park's Vector Approach (PVA), together with its variations, has been one of the most widespread diagnostic methods for electrical machines and drives. Regarding the broken rotor bars fault diagnosis in induction motors, the common practice is to rely on the width increase of the Park's Vector (PV) ring and then apply some more sophisticated signal processing methods. It is shown in this paper that this method can be unreliable and is strongly dependent on the magnetic poles and rotor slot numbers. To overcome this constraint, the novel Filtered Park's/Extended Park's Vector Approach (FPVA/FEPVA) is introduced. The investigation is carried out with FEM simulations and experimental testing. The results prove to satisfyingly coincide, whereas the proposed advanced FPVA method is desirably reliable. (C) 2017 Elsevier Ltd. All rights reserved., The authors acknowledge the support of the Portuguese Foundation for Science and Technology under Project No. SFRH/BSAB/118741/2016, and also the support of the Spanish 'Ministerio de Economia y Competitividad' (MINECO) and FEDER program in the framework of the 'Proyectos I+D del Subprograma de Generacion de Conocimiento, Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia' (ref: DPI2014-52842-P).

This paper presents a novel technique based on evolutionary algorithms for identifying severity of ball bearing faults in three-phase induction motors. The proposed technique can be used to determine if the induction motor bearings are still in healthy condition or there is some defect at their inner or outer raceway. These defects cause basic motions on the air gap distribution that can be recognized as temporary air gap eccentricity. The particle swarm optimization algorithm is used to estimate severity of the resultant temporary air gap eccentricity. At this end, a rather exact model of the induction motor is required with the proposed bearing faults at variable severities. Winding function approach is extended to develop such model. This is a non-invasive technique because it uses only the line voltages and line currents of the stator. Other advantages of this method include: insensitive to unbalanced supply voltage, load level variation and temperature variation. The proposed technique is evaluated using simulation data obtained for a 2.2 kW three-phase induction machine. The simulation results demonstrate the effectiveness of the proposed technique for the diagnostics of the bearing faults.

This paper presents a performance analysis of a synchronous reluctance motor (SynRM) drive under different fault-tolerant modulation strategies. The analysis comprises different single power switches short-circuit faults in a three-level NPC converter controlled by a space vector modulation (SVM) technique. The comparison of the normal and two post-fault operating conditions is based on the analysis of certain parameters such as the motor efficiency, power factor, electromagnetic torque, currents RMS values, total waveform distortion values, and total waveform oscillation values, resulting from the computational simulations.

This paper presents a new fault diagnostic technique applied to switched reluctance motor drives, based on the analysis of the power converter supply current. A fault is detected when the measured amplitude of the dc bus current differs from its expected amplitude, assuming normal operating conditions. The information about phase currents amplitudes and the control commands of all power switches permit to easily estimate the amplitude of the power converter supply current, since an asymmetric bridge converter is used. Simulation and experimental results are presented. Open- and short-circuit fault occurrences in the converter power switches are considered and analyzed. The proposed technique can early detect these fault occurrences and can also identify the affected motor phase. In almost all situations, the faulty element is also identified. An early fault diagnosis, with an accurate fault identification, is of a paramount importance since it permits the early adoption of fault-tolerant procedures that minimize the fault impact on the machine operation.

Rotor electrical faults are common types of faults in squirrel cage induction motors (IMs) and have been investigated in detail under steady state conditions for direct mains-fed machines. Motor current signature analysis (MCSA) has been widely reported in the literature as a suitable fault diagnostic technique for directly-supplied IMs. However, when an induction motor is fed from a frequency converter composed of a rectifier, DC link filter and a PWM inverter, its characteristic signals (currents, voltages, electromagnetic torque, etc.) become very noisy. This paper presents an experimental analysis of a damaged squirrel cage induction motor supplied from an industrial frequency converter at different speed and load levels. The main purpose is to experimentally study the ability of the MCSA to diagnose the occurrence of broken rotor bars under these operating conditions.

This paper deals with the use of the instantaneous active and reactive power signature analyses and their derived signals-the instantaneous power factor and phase angle - for discriminating broken rotor bars and airgap eccentricity conditions (rotor faults) from mechanical load oscillation effects in operating three-phase squirrel cage induction motors. Both simulation and experimental results are presented to show the effectiveness and the merits of the proposed approach that offers the possibility to distinguish between the three conditions by comparing the signature analyses of two different quantities for each abnormality condition.

This paper presents a new diagnostic technique for the diagnosis of open-circuit faults in matrix converters operating with an Optimum Alesina-Venturini modulation scheme. Compared to previous diagnostic techniques developed for matrix converters, the technique proposed in this paper has the major advantage of not requiring the installation of any additional sensors in the converter besides the ones already present for control purposes. Hence, it can be easily integrated into the control platform of the converter at no additional cost. In addition to the diagnostic approach, a new fault-tolerant switching strategy is also presented so that after the appearance of an open-circuit fault, the matrix converter can continue to run although with degraded performance, until a repair action can be taken. Several simulation and experimental results are presented, demonstrating the effectiveness and applicability of the developed techniques.