Advanced direct torque control based on neural tree controllers for induction motor drives.

This paper introduces a novel direct torque control approach based on the decision tree (T-DTC), employing artificial neural networks that are effectively trained to enhance accuracy and robustness. The main objective of T-DTC is the substantial reduction of flux and torque ripples inherent in the conventional DTC, ensuring effective control of the induction motor. The conventional hysteresis controllers for stator flux and electromagnetic torque are replaced by two advanced controllers named M5 Prime model trees. Additionally, the traditional switching table is substituted with a novel decision tree table utilizing the classifier algorithm 4.5. The effectiveness of the proposed T-DTC strategy is demonstrated through simulation in MATLAB/Simulink and validated in real-time using an HIL platform based on OPAL-RT OP 5600 and Virtex 6 FPGA ML605. The results obtained demonstrate a notable improvement compared to existing techniques in the literature.

Wavelet packet and fuzzy logic theory for automatic fault detection in induction motor.

In this paper, a method based on the application of the fuzzy logic technique to diagnose the fault of broken rotor bars in an induction machine has been proposed. Through the decomposition into a wavelet packet, we can detect, identify and prognosis failures in all operating conditions of the machine. The energy calculations for each level of decomposition are richer with the necessary information for fault diagnosis. The latter can be used as input to an intelligent diagnostic system based on fuzzy logic for the detection and classification of the broken bars faults. The advantage of this method is the use of a single current sensor. Indeed, we can detect online, the fault and the number of broken bars with a variable load. The obtained results are very satisfactory. Some results were verified by simulations under MATLAB/Simulink and validated experimentally via dSPACE 1104 card.

Analysis and experimental evaluation of shunt active power filter for power quality improvement based on predictive direct power control.

This paper discusses the use of the concept of classical and predictive direct power control for shunt active power filter function. These strategies are used to improve the active power filter performance by compensation of the reactive power and the elimination of the harmonic currents drawn by non-linear loads. A theoretical analysis followed by a simulation using MATLAB/Simulink software for the studied techniques has been established. Moreover, two test benches have been carried out using the dSPACE card 1104 for the classic and predictive DPC control to evaluate the studied methods in real time. Obtained results are presented and compared in this paper to confirm the superiority of the predictive technique. To overcome the pollution problems caused by the consumption of fossil fuels, renewable energies are the alternatives recommended to ensure green energy. In the same context, the tested predictive filter can easily be supplied by a renewable energy source that will give its impact to enhance the power quality.