Performance comparison between optimization algorithms for asymmetrical cascaded multilevel inverter control

This paper discusses the hardware and control system design of the asymmetric cascade multilevel inverter. The asymmetric cascaded multilevel inverter structure is adopted to minimize bridges, gate drive circuits and DC power source number. Therefore, the proposed structure is able to generate higher voltage at higher speed with low switching losses and high efficiency. Selective Harmonic Elimination (SHE) based on Newton-Raphson (N-R) algorithm is developed to calculate a switching angle for a range of modulation index to control asymmetric cascade multilevel inverter. Simulation results prove that Newton-Raphson technique is more effective than genetic algorithm (G-A) and equal calculated switching angles method (ECSA). A comparison between the algorithm performance for 9-level asymmetric cascade H-bridge inverter control was evaluated and experimentally tested on FPGA-based prototypes

Improving the classification of a nanocomposite using nanoparticles based on a meta-analysis study, recurrent neural network and recurrent neural network Monte-Carlo algorithms

This paper may be the first meta-analysis that presents a comprehensive synthesis of scientific works spanning the last five years, focusing on methodologies and results related to the analysis of nanocomposite using nanoparticles. The primary objective is to identify the optimal algorithm using software information and leading to better classification methodology. Specifically, this study comes up with the advantages and the drawbacks of the most used algorithms and proposes an enhancement and performance of Recurrent Neural Networks based on Long Short Term Memory (LSTM) neurons. Besides, a comparison of Deep Learning methods for the classification of polymeric nanoparticles, with polypropylene serving as a case study will be implemented. Experiment comparisons are conducted to assess with one physical property, later expanded to four properties and finally to eight properties. Neural networks, including Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Recurrent Neural Networks-Monte Carlo, are employed for simulations. The evaluation criteria encompass accuracy, calculation time, mean square error (MSE) and other metrics. The findings contribute to the selection of an optimal algorithm for the analysis of polymeric nanoparticles, emphasizing the potential of Deep Learning methodologies, particularly Recurrent Neural Networks Monte Carlo, in advancing classification accuracy and efficiency

Review of the PV Electricity Production Estimate under the Effect of Climatic Disturbances and Sunspots by Using Deep-Learning Tools

The mathematical models used in the estimation of GHI on the Earth's surface are inconvenient because they always assume that the sky clarity index is constant. Hence, these models are often confronted with long-period empirical ground measurements that may exceeds 11 years. The impact of cloud cover on an electric power generation site is a very critical parameter for installing a solar power plant and evaluating its productivity. The state of knowledge about the sun influence, the greenhouse effect on climate change, and cloud occurrence can’t be described in a mathematical or numerical model.Therefore, in this paper, we propose the use of Deep-Learning techniques to predict any site’s productivity by analyzing its potential insolation. We also suggest the analysis of the ground and satellite- based measurements collected over 30 years. We propose the estimation of future climate change affecting cloud cover

inversion à gauche des système chaotique

International audienceWhen chaotic systems is used as support for secure data transmission, singularity of observability and left invertibility is an important problem. In this paper, data secure transmission is analyzed with respect to the observability and left invertibility concept. Moreover, in order to overcome observability and left invert-ibility singularities, a immersion technique is proposed. The use of high order sliding mode observer on a well known Qi circuit, allows to highlight the well founded of the proposed analysis and method.Quand les systèmes chaotiques sont utilisés comme support pour la transmission sécurisée de données, la singularité de l'observabilité et de l'inversion à gauche sont un problème important. Dans cet article, la transmission sécurisée de données est analysée par rapport au concept d'observabilité et d'inversion à gauche. De plus, afin de surmonter les singularités d'observabilité et d'inversion, une technique d'immersion est proposée. L'utilisation d'observateur en mode glissant d'ordre supérieur sur le circuit chaotique de Qi , permet de mettre en évidence le bien fondé de l'analyse et de la méthode proposées