In the present work, we introduce a novel control strategy to optimize the use of a PV-STATCOM connected to the distribution network in the case of the recent Moroccan grid code. The STATCOM is implemented between a PV array module and the PCC to the distribution network, which is typical of a standard structure in presence of renewable generators. The PV-STATCOM is proposed to achieve dynamic support for the distribution grid in the event of disturbances, faults and to ensure power factor correction while injecting active power. The control strategy is based on a PI controller widely used in such power electronics applications. The PV STATCOM is not yet used in Moroccan electrical network and the proposed system overcomes network uncertainties, grid disturbances and significantly improves the power quality at the point of common coupling PCC while absorbing and providing reactive power to the network in an acceptable reversing time. The proposed PV-STATCOM ensures dynamic voltage support requirements and current THD mitigation according to the constraints of the Moroccan GRID CODE document. The simulation is performed using MATLAB Simulink software and shows best results in simulation while respecting the technical limits imposed by local regulations.

Tremendous Research work is devoted to advancing state of art in intelligent transportation systems, with the ultimate objective of maximizing the beneficial effect of the transportation sector on the environment. One of the most critical parts of balancing energy consumption and resource exploitation in sustainable development is the introduction of decarbonized transportation in the form of electric cars. This article discusses the results of a study that evaluates the energy needed to power a battery electric vehicle (BEV) when the driver switches to the comfort ride option. We have modeled a Battery electric vehicle, thermal management system, and Adaptive Cruise (ACC) blocks using MATLAB/Simulink. To evaluate our developed model, we have conducted simulation tests under a variety of settings, including route and temperature data from Moroccan roads.

The manual programming of Programmable Logic Controllers (PLCs) is a time-consuming and error-prone task, particularly for complex systems. Researchers have proposed various techniques, such as the modular, decentralized, hierarchical, and distributed approaches, which all fall under the Supervisory Control Theory (SCT) whose main goal is to generate a supervisor that ensures that the controller's behavior satisfies the specifications. While these techniques have demonstrated efficiency in systems of low complexity, high-complexity systems and probabilistic ones remain a major challenge. In this paper, we propose a novel pipeline that relies solely on Artificial Intelligence (AI)-techniques, namely, Reinforcement Learning (RL) and Natural Language Processing (NLP) techniques, including Named Entity Recognition (NER) and Large Language Models (LLMs). As a preliminary result, we demonstrate the potential of the proposed pipeline using the recent ChatGPT model by making a sample dataset in this context and generating Structured Text code from specifications presented in a natural language format. Finally, we outline future directions toward a fully automated AI-based PLC programming tool.

Generally, public lighting networks needs nearly 40% of the world's energy, which is why it has become important to work to promote more efficient, environmentally friendly and, above all, less energy consuming lighting. In this paper, we will develop two algorithms based on deep artificial neural networks with feed forward backpropagation that aim to adapt the electrical power that feeds the LED lighting system taking into consideration two parameters, namely the road flow and the weather conditions. To do this, we will start by modeling the lighting system with the stateflow tool, after that, we will create the two ANN s that will be trained by real data of the road flow and nebulosity which will allow us to study the impact of the two controllers on the LED lighting system. A comparison will be made at the end of the paper to deduce the most optimal algorithm.

In this paper, we propose a Time-based estimator method to reconfigure manufacturing systems in case of sensor faults detection. The main idea is to replace the information lost by a timed one. In non-faulty behavior, each sensor is defined through two events: activation and deactivation. Our contribution is to define an estimator model of each event based on different clocks to maintain the same desired behavior of the system in a faulty mode (reconfigured).

International audience

The first method that was developed to deal with the SLAM problem is based on the extended Kalman filter, EKF SLAM. However this approach cannot be applied to a large environments because of the quadratic complexity and data association problem. The second approach to address the SLAM problem is based on the Rao-Blackwellized Particle filter FastSLAM, which follows a large number of hypotheses that represent the different possible trajectories, each trajectory carries its own map, its complexity increase logarithmically with the number of landmarks in the map. In this paper we will present the result of an implementation of the FastSLAM 2.0 on an open multimedia applications processor, based on a monocular camera as an exteroceptive sensor. A parallel implementation of this algorithm was achieved. Results aim to demonstrate that an optimized algorithm implemented on a low cost architecture is suitable to design an embedded system for SLAM applications.