Stability analysis and robust damping of multiresonances in distributed-generation-based islanded microgrids

International audienc

Adaptive Reference Trajectory for Power Quality Enhancement in Three-Phase Four-Wire Standalone Power Supply Systems with Nonlinear and Unbalanced Loads

International audienc

Improving Auto-Encoders' self-supervised image classification using pseudo-labelling via data augmentation and the perceptual loss

11 pages, 17 figures, 5 tablesIn this paper, we introduce a novel method to pseudo-label unlabelled images and train an Auto-Encoder to classify them in a self-supervised manner that allows for a high accuracy and consistency across several datasets. The proposed method consists of first applying a randomly sampled set of data augmentation transformations to each training image. As a result, each initial image can be considered as a pseudo-label to its corresponding augmented ones. Then, an Auto-Encoder is used to learn the mapping between each set of the augmented images and its corresponding pseudo-label. Furthermore, the perceptual loss is employed to take into consideration the existing dependencies between the pixels in the same neighbourhood of an image. This combination encourages the encoder to output richer encodings that are highly informative of the input's class. Consequently, the Auto-Encoder's performance on unsupervised image classification is improved both in termes of stability and accuracy becoming more uniform and more consistent across all tested datasets. Previous state-of-the-art accuracy on the MNIST, CIFAR-10 and SVHN datasets is improved by 0.3%, 3.11% and 9.21% respectively

Multi-Resonant based Output Voltage Control of Autonomous Distributed Generators

The distributed or decentralized generation electricity constitutes the central stone of various recent energy models, such as the intelligent electrical supply networks. The quality of energy in this type of structure depends primarily on the strategy of control adopted, in order to guarantee proper operation according to the international standards. The strategy of control proposed in this paper uses two control loops. An internal control loop aims at deadening the phenomena of resonance while ensuring the required control dynamic for fast disturbance rejection. As for the external control loop, a multi-resonant composed of a stabilizing state feedback that ensure an asymptotic tracking of the voltage reference with a weak rate of harmonic distortion. Finally, experimental results are presented to show the performances and feasibility of the proposed control strategy

Multi-Resonant based Output Voltage Control of Autonomous Distributed Generators

The distributed or decentralized generation electricity constitutes the central stone of various recent energy models, such as the intelligent electrical supply networks. The quality of energy in this type of structure depends primarily on the strategy of control adopted, in order to guarantee proper operation according to the international standards. The strategy of control proposed in this paper uses two control loops. An internal control loop aims at deadening the phenomena of resonance while ensuring the required control dynamic for fast disturbance rejection. As for the external control loop, a multi-resonant composed of a stabilizing state feedback that ensure an asymptotic tracking of the voltage reference with a weak rate of harmonic distortion. Finally, experimental results are presented to show the performances and feasibility of the proposed control strategy

Improving Auto-Encoders' self-supervised image classification using pseudo-labelling via data augmentation and the perceptual loss

11 pages, 17 figures, 5 tablesIn this paper, we introduce a novel method to pseudo-label unlabelled images and train an Auto-Encoder to classify them in a self-supervised manner that allows for a high accuracy and consistency across several datasets. The proposed method consists of first applying a randomly sampled set of data augmentation transformations to each training image. As a result, each initial image can be considered as a pseudo-label to its corresponding augmented ones. Then, an Auto-Encoder is used to learn the mapping between each set of the augmented images and its corresponding pseudo-label. Furthermore, the perceptual loss is employed to take into consideration the existing dependencies between the pixels in the same neighbourhood of an image. This combination encourages the encoder to output richer encodings that are highly informative of the input's class. Consequently, the Auto-Encoder's performance on unsupervised image classification is improved both in termes of stability and accuracy becoming more uniform and more consistent across all tested datasets. Previous state-of-the-art accuracy on the MNIST, CIFAR-10 and SVHN datasets is improved by 0.3%, 3.11% and 9.21% respectively

Frequency adaptive proportional + multi-resonant output voltage control strategy for parallel operating distributed generation units

International audienc

An Improved Direct Torque Control with an Advanced Broken-Bar Fault Diagnosis for Induction Motor Drives

This paper presents an advanced strategy combining fuzzy logic and artificial neural networks (ANNs) for direct torque control (DTC) and broken-bar fault diagnosis in induction motors. More specifically, a fuzzy-based controller is used to simultaneously minimize the stator flux and the electromagnetic torque ripples. A neural switching table is then proposed to achieve the interface inverter control. Besides, a closed-loop broken-bar fault detection strategy based on the Hilbert technique (HT) with the discrete wavelet transform (DWT) and ANNs is proposed. The fault detection is performed by analyzing the induction motor’s stator current by using the combined techniques HT-DWT. The effect of a broken-bar fault on the machine varies according to the number and position of the broken bars. The neural detector was used in order to identify the number of broken bars through only one current measurement. The effectiveness of the developed control has been verified using MATLAB/Simulink and real-time simulation in OPAL-RT 4510. Obtained results show improved performances in terms of torque ripple minimization and stator current quality, evaluated, respectively, at 43.75% and 41.26% as well as a rigorous motor health monitoring