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A Model-based predictive current control (MBPCC) has recently become a powerful advanced control technology in industrial drives. However, MBPCC relies on the knowledge of the system model and parameters, being, therefore, very sensitive to parameters errors. In the case of the synchronous reluctance motor (SynRM), where the parameters vary due to its ferromagnetic structure and nonlinear magnetic properties, MBPCC performance would suffer significantly. Accordingly, in this paper, a Grey Wolf Optimizer based model-free predictive current control (GW-MFPCC) of SynRM is proposed, to skip all the effects of the model dependency and parameters uncertainty. The proposed method predicts the stator current through tracking the minimum cost function using the grey wolf optimizer. The proposed GW-MFPCC scheme is compared to MBPCC, and its effectiveness is evaluated and confirmed by experimental results. [ABSTRACT FROM AUTHOR]

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 article 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. [ABSTRACT FROM AUTHOR]

Interturn 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 article proposes a novel interturn fault diagnostic technique for synchronous reluctance machines based on the Luenberger state observer and current's 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. [ABSTRACT FROM AUTHOR]

In recent years, multiphase machines have been presented as a good alternative to the classical three-phase machines due to their ability to operate under fault-tolerant conditions. However, the transition from pre- to post-fault control needs a reliable fault diagnostics algorithm. Accordingly, this article presents a reliable diagnostics approach that allows the detection and identification of multiple open-switches and open-phase faults in power converters fed six-phase induction motors, including adjacent and nonadjacent phases. The proposed method provides normalized numerical signatures from the measured output currents without requiring either tuning effort or detection thresholds. The reliability and robustness of the proposed algorithm are proven through experimental tests. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Fault detection and fault accommodation, as well as fault tolerance, are important issues in closed-loop controlled three-phase converters. For such systems, power semiconductors and sensors are the most affected components by the occurrence of failures. Therefore, fault diagnosis and remedial operating strategies involving such components are highly required. In this paper, a new reliable approach is proposed, which aims to diagnose current sensor faults and power semiconductor open-circuit faults in permanent magnet synchronous motor drives. The proposed fault diagnosis scheme uses only the measured currents, which makes it suitable for real-time implementation. The performance of the proposed technique is analyzed through experiments. [ABSTRACT FROM AUTHOR]

Fault tolerance in wind turbines is considered crucial to increase their reliability and availability levels. This paper presents a fault-tolerant direct-drive permanent magnet synchronous generator (PMSG) using new direct control techniques, with the ability to handle power semiconductor open-circuit 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, through a triode for alternating current (TRIAC). The main contribution of this paper consists of the development of an alternative direct torque control and direct power control schemes for both machine-side converter and grid-side converter, respectively. Moreover, a reliable fault diagnostics algorithm without requiring additional sensors is also integrated, providing the information required to instantaneously trigger fault-tolerant remedial strategies. Simulation and experimental results are presented to validate the effectiveness of the proposed fault-tolerant PMSG drive. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motors (PMSMs) drives using three-phase voltage-source inverters (VSIs) are currently used in many industrial applications. The reliability of VSIs is one of the most important factors to improve the reliability and availability levels of the drive. Accordingly, this paper presents a robust fault diagnostic method for multiple insulated gate bipolar transistors (IGBTs) open-circuit faults and current sensor faults in three-phase PMSM drives. The proposed observer-based algorithm relies on an adaptive threshold for fault diagnosis. Current sensor and open-circuit faults can be distinguished and the faulty sensors and/or power semiconductors are effectively isolated. The proposed technique is robust to machine parameters and load variations. Several simulation and experimental results using a vector-controlled PMSM drive are presented, showing the diagnostic algorithm robustness against false alarms and its effectiveness in both IGBTs and current sensors fault diagnosis. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

This paper presents a fault tolerant control FTC strategy of Permanent Magnet Synchronous Motor PMSM drive. Position sensor fault detection, isolation and reconfiguration are presented for control scheme devoted for PMSM. MRAS estimator and Luenberger observer are used in order to generate residuals for detection the defect, and then the estimator is used to reconfigure the drive and to conserve the performances of system in order to increase the reliability and to ensure the continuity of the process. The validity of the proposed strategy has been verified by the simulation. [ABSTRACT FROM AUTHOR]

Model predictive current control (MPCC) has recently become a powerful advanced control technology in industrial drives. However, current prediction in MPCC requires a high number of voltage vectors (VVs) synthesizable by the converter, thus being computationally demanding. Accordingly, in this paper, a computationally efficient MPCC of synchronous reluctance motors (SynRMs) that reduces the number of VVs used for prediction is proposed. By making the most of the simplicity of hysteresis current control (HCC) and integrating it with the MPCC scheme, only four out of eight predictions are needed to determine the best VV, dramatically reducing algorithm computations. The experimental results show that the execution time can be shortened by 20% while maintaining the highest control efficiency. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]