A predictive torque and flux control algorithm for the synchronous reluctance machine is presented in this paper. The algorithm realizes a voltage space phasor preselection, followed by the computation of the switching instants for the optimum switching space phasor, with the advantages of inherently constant switching frequency and time equidistant implementation on a DSP-based system. The criteria on how to choose the appropriate voltage space phasor depend on the state of the machine and the deviations of torque and flux at the end of the cycle. In order to obtain an appropriate model of the machine, it has been developed on a d-q frame of coordinates attached to the rotor and takes into account the magnetic saturation in both d-q axes and the cross-saturation phenomenon between both axes. Therefore, high-performance torque control is achieved, and the torque ripple is reduced. Simulated and experimental results using a DSP/field-programmable-gate-array-based control and a commercially available machine show the validity of the proposed control scheme

Induction motor is a ubiquitous machine. In industrial settings, online monitoring of motors' health status in order to schedule maintenance operations with the goal of damage prevention has become an essential necessity. Broken rotor bar is one of the most common failures in the rotor of a squirrel cage motor. Motor current signature analysis (MCSA) has become a popular method for the detection of this failure because of its high reliability. Recent works have performed the MCSA with a combination of different signal processing techniques to identify the presence of broken bars. In this paper, the MCSA is done with empirical mode decomposition from which a set of intrinsic mode functions (IMFs) is obtained. The extracted features from two of the obtained IMFs form the basis of the proposed classification criterion; these are the samples between zero crossings (SBZCs) and the time between successive zero crossings (TSZCs). The standard deviation from the SBZCs and the TSZCs is used as a classification feature. Experimental results using our method show high accuracy in the detection of a broken and a half-broken rotor bar.

This paper introduces a particle swarm optimization (PSO) algorithm that uses experimental measurements for the identification of the direct ($d$ ) and quadrature ($q$ ) stator inductances and the stator resistance of permanent-magnet synchronous machines (PMSMs). Strategies for the identification of stator resistance and flux linkage of permanent magnets are also presented. The developed PSO algorithm has the advantages of fast and stable convergence characteristics, and it is relatively easy to implement; moreover, neither voltage source inverters nor closed-loop control strategies are needed. In this paper, the experimental results obtained by PSO are compared with other ones obtained through a current decay test to demonstrate the effectiveness of the presented method. A 32-bit C2000 real-time microcontroller and a commercial surface-mounted PMSM have been used to carry out the experimental measurements.

this paper introduces the comparison of four predictive torque control schemes for a permanent magnet synchronous machine (PMSM). The first method is the finite set model predictive control (FS-MPC). In FS-MPC the optimal switching state is selected based on the evaluation and minimization of a cost function for all possible voltage space vectors (VSV's) of the inverter. The second method performs a simplified FS-MPC (SFS-MPC) where the selection and evaluation of the possible VSV's are reduced to only three. The third method is based on the principle of predictive direct torque control (PDTC), where the duty cycle of the switching state is optimized for application in the inverter. Finally, a method that combines FS-MPC and PDTC named model predictive torque control (MPTC) is presented. This paper introduces the methodology and the results of a compressive comparison of the four predictive schemes based on different criterions. The control schemes are implemented on a field programmable gate array and are applied to a PMSM. Experimental results are presented to validate the presented comparison and discussion.

As an enhancement of a predictive direct torque control (PDTC) scheme for synchronous reluctance machines (SynRMs), an encoderless PDTC scheme that is able to operate at very low and zero speeds is introduced in this paper. At very low speeds, the angular position of the rotor can be estimated by injecting test voltage signals (TVSs) to detect the spatial orientations of existing position-dependent rotor anisotropies. The TVS produces transient stator current changes, so that stator current derivatives can be estimated in a digital way. In a SynRM, the stator current derivatives contain useful information that can be used to estimate the angular position of the rotor. The obtained information is processed by means of a quadrature phase-locked loop observer to extract the angular position of the rotor without detriments in phase and frequency. Following this strategy, no extra hardware, special current transducers, or connections are needed in comparison with a standard drive with an encoder. Based on the proposed strategy, an encoderless PDTC scheme for SynRMs is implemented. Experimental results using a digital signal processor and a field-programmable gate array embedded in the same board verify the effectiveness of the proposed control scheme.

Broken bars detection on induction motors has been a topic of interest in recent years. Its detection is important due to the fact that the failure is silent and the consequences it produces as power consumption increasing, vibration, introduction of spurious frequencies in the electric line, among others, can be catastrophic. In this paper, the use of motor current signature analysis and mathematical morphology to detect broken bars on induction motors under different mechanical load condition is analyzed. The proposed algorithm first identifies the motor load and then the motor condition. The statistical analysis of several tests under different motor loads (100%, 75%, 50%, and 25%) and motor condition (healthy, one broken bar, and two broken bars) is presented. The proposed method has been implemented in a field programmable gate array, to be used in real-time online applications. The algorithm obtained in average a 95% accuracy of failure detection.