Modelling and Passivity-based Control of a Non-isolated DC-DC Converter in a Fuel Cell System

This paper presents the model of a fuel cell and the design and simulation of a cascade of two DC-DC converters. First, a detailed mathematical model of fuel cell is presented and simulated. Then, a nonlinear model of the whole controlled system is developed and a robust nonlinear controller of currents is synthesized using a passivity-based control. A formal analysis based on Lyapunov stability and average theory is developed to describe the control currents loops performances. A classical PI controller is used for the voltages loops. The simulation models have been developed and tested in the MATLAB/SIMULINK. Simulated results are displayed to validate the feasibility and the effectiveness of the proposed strategy

Stochastic Brennan–Schwartz Diffusion Process: Statistical Computation and Application

In this paper, we study the one-dimensional homogeneous stochastic Brennan–Schwartz diffusion process. This model is a generalization of the homogeneous lognormal diffusion process. What is more, it is used in various contexts of financial mathematics, for example in deriving a numerical model for convertible bond prices. In this work, we obtain the probabilistic characteristics of the process such as the analytical expression, the trend functions (conditional and non-conditional), and the stationary distribution of the model. We also establish a methodology for the estimation of the parameters in the process: First, we estimate the drift parameters by the maximum likelihood approach, with continuous sampling. Then, we estimate the diffusion coefficient by a numerical approximation. Finally, to evaluate the capability of this process for modeling real data, we applied the stochastic Brennan–Schwartz diffusion process to study the evolution of electricity net consumption in Morocco.This research was funded by LAMSAD from “Fonds propres de l’Université Hassan I” (Morroco) and FQM-147 from “Plan Andaluz de Investigaciòn” (Spain)

Modified T-type topology of three-phase multi-level inverter for photovoltaic systems

In this article, a three-phase multilevel neutral-point-clamped inverter with a modified t-type structure of switches is proposed. A pulse width modulation (PWM) scheme of the proposed inverter is also developed. The proposed topology of the multilevel inverter has the advantage of being simple, on the one hand since it does contain only semiconductors in reduced number (corresponding to the number of required voltage levels), and no other components such as switching or flying capacitors, and on the other hand, the control scheme is much simpler and more suitable for variable frequency and voltage control. The performances of this inverter are analyzed through simulations carried out in the MATLAB/Simulink environment on a three-phase inverter with 9 levels. In all simulations, the proposed topology is connected with R-load or RL-load without any output filter

Power Quality Improvement using a New DPC Switching Table for a Three-Phase SAPF

This research focuses on the analysis and design of robust direct power control (DPC) for a shunt active power filter (SAPF). The study proposes a novel switching table design based on an analysis of the impact of inverter switching vectors on the derivatives of instantaneous reactive and active powers. The goal is to reduce the number of commutations by eliminating null vectors while maintaining the desired DC-bus voltage using a PI regulator-based anti windup technique. Additionally, a robust PLL structure-based band pass multivariate filter (BPMVF) is utilized to enhance the network voltage. The research demonstrates the effectiveness of the suggested power control through extensive simulation results, showing high performance in both transient and steady-state conditions. The proposed approach offers the advantages of sinusoidal network current, and unitary power factor, and eliminates the need for current regulators and coordinate transformations or PWM generators. Further research directions could explore the practical implementation and real-world performance of this technique in power systems

Robust Voltage Vector-Controlled Three-Phase SAPF-based BPMVF and SVM for Power Quality Improvement

The multiplication of nonlinear loads leads to significant degradation of the energy quality, thus the interconnection network is subject to being polluted by the generation of harmonic components and reactive power, which causes a weakening efficiency, especially for the power factor. In three-phase systems, they can cause imbalances by causing excessive currents at the neutral. This research treats the operation of robust voltage-oriented control (VOC) for a shunt active power filter (SAPF). The main benefit of this technique is to guarantee a decoupled control of the active and reactive input currents, as well as the input reference voltage. To sustain the DC voltage, a robust PI-structure-based antiwindup is inserted to ensure active power control. Besides, a robust phase-locked loop (PLL)-based bandpass multivariable filter (BPMVF) is used to improve the network voltage quality. Furthermore, a space vector modulation (SVM) is designed to replace the conventional one. A sinusoidal network current and unitary power factor are achieved with fewer harmonics. The harmonics have been reduced from 27.98% to 1.55% which respects the IEEE 519-1992 standard. Expanded simulation results obtained from the transient and steady-state have demonstrated the high performance of the suggested control scheme

The use of machine learning in the Internet of Things

In the Internet of Things and wireless sensor networks period, a large number of connected objects and seeing bias are devoted to collecting, transferring, and inducing a huge quantum of data for a wide variety of fields and operations. To effectively run these complex networks of connected objects, there are several challenges like topology changes, link failures, memory constraints, interoperability, network traffic, content, scalability, network operation, security, and sequestration to name many. therefore, overcoming these challenges and exploiting them to support this technological outbreak would be one of the most pivotal tasks of the ultramodern world. Recently, the development of Artificial Intelligence(AI) led to the emergence of Machine Learning (ML), which has become the crucial enabler to figure out results and literacy models in an attempt to enhance the quality of service parameters of Internet of Things and wireless sensor networks. By learning from one gest, ML ways aim to resolve issues in the Internet of Things and wireless sensor networks and fields by erecting algorithmic models. In this paper, we’re going to punctuate the most abecedarian generalities of ML orders and Algorithms. We start by furnishing a thorough overview of the Internet of Things and wireless sensor network technologies. We also bandy the vital part of ML ways in driving up the elaboration of these technologies. also, as the crucial donation of this paper, a new taxonomy of ML algorithms is handed. We also epitomize the major operations and exploration challenges that abused ML ways in the WSN and IoT. ultimately, we dissect the critical issues and list some unborn exploration direction

The use of machine learning in the Internet of Things

In the Internet of Things and wireless sensor networks period, a large number of connected objects and seeing bias are devoted to collecting, transferring, and inducing a huge quantum of data for a wide variety of fields and operations. To effectively run these complex networks of connected objects, there are several challenges like topology changes, link failures, memory constraints, interoperability, network traffic, content, scalability, network operation, security, and sequestration to name many. therefore, overcoming these challenges and exploiting them to support this technological outbreak would be one of the most pivotal tasks of the ultramodern world. Recently, the development of Artificial Intelligence(AI) led to the emergence of Machine Learning (ML), which has become the crucial enabler to figure out results and literacy models in an attempt to enhance the quality of service parameters of Internet of Things and wireless sensor networks. By learning from one gest, ML ways aim to resolve issues in the Internet of Things and wireless sensor networks and fields by erecting algorithmic models. In this paper, we’re going to punctuate the most abecedarian generalities of ML orders and Algorithms. We start by furnishing a thorough overview of the Internet of Things and wireless sensor network technologies. We also bandy the vital part of ML ways in driving up the elaboration of these technologies. also, as the crucial donation of this paper, a new taxonomy of ML algorithms is handed. We also epitomize the major operations and exploration challenges that abused ML ways in the WSN and IoT. ultimately, we dissect the critical issues and list some unborn exploration direction