Fiche individuelle
Mahamad EL IAALI | ![]() | |
Titre | Doctorant | |
Equipe | Réseaux | |
Téléphone | +33 (0)3-XX-XX-XX-XX | |
mohamad.el-iaali@master.centralelille.fr | ||
Site personnel | http://linkedin.com/in/mohamad-el-iaali-485b7a197 | |
Publications |
ACT Conférence internationale avec acte |
---|
[1] Machine Learning Methods for State Estimation in Sparse Distribution Networks 2025 IEEE Conference on Artificial Intelligence (CAI), 07/2025, URL, Abstract EL IAALI Mahamad, RAZI Reza, BRUYERE Antoine, FRANCOIS Bruno, SOARES Joao, VALE Zita |
The widespread deployment of renewable energy sources and the increasing uptake of electric vehicles contribute to greater complexity in managing power flows within distribution grids. Understanding system dynamics and implementing control actions are crucial for effective grid management. While smart meters, accurate sensors, communication networks facilitate measurements at specific voltage nodes, obtaining measurements at all nodes across a network is costly and, in some cases, impractical. In response to these challenges, this paper firstly compares different state estimation methods based on available measurements and secondly, explains why an artificial neural network-based state estimation method is the most effective. The proposed method utilizes data-driven techniques to deliver precise estimations of voltages and currents within distribution networks, offering valuable insights into flexibility management and control processes. This approach also considers the phase angle effect of voltage and holds significant promise in improving awareness of the situation for power system operators, enhancing their understanding of evolving grid dynamics. The robustness and effectiveness of this method are validated through missing data cases, demonstrating its adaptability to diverse operational conditions. |
[2] A Novel Cascading Artificial Neural Networks for Enhanced Distribution Network State Estimation 2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON), 07/2024, URL, Abstract EL IAALI Mahamad, RAZI Reza, BRUYERE Antoine, FRANCOIS Bruno, SOARES Joao |
The growing integration of renewable energy sources and the widespread adoption of electric vehicles add complexity to power flows in distribution systems. Understanding the system dynamics and implementing control actions are imperative for effective grid management. While measurements in specific nodes are facilitated by smart meters, accurate sensors, communication networks, and electric vehicles, it is costly and, in some cases, impossible to obtain measurements at all nodes. In response to these challenges, this paper introduces a machine learning-based state estimation method, utilizing available measurements and estimating others. By harnessing data-driven techniques, the proposed method aims to provide accurate estimations of voltages and currents in distribution networks, offering valuable insights into flexibilities management and control processes. Specific case study is considered, confirming the efficiency of the suggested state estimation approach and providing insights into its flexibility across a range of scenarios. In addition to the mentioned benefits, the results validate the proposed method, demonstrating a small margin of error. This approach holds significant promises in improving situational awareness for power system operators and enhancing their understanding of the evolving grid dynamics. |
[3] Distribution Network Digital Twin: a Basic Machine Learning based Voltage Estimato 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 12/2023, URL, Abstract EL IAALI Mahamad, RAZI Reza, BRUYERE Antoine, FRANCOIS Bruno, SOARES Joao |
The increasing integration of renewable energy sources, and the widespread use of electric vehicles have introduced greater complexity and dynamics in power flows in distribution systems. With bidirectional power flows enhancing the observability of the grid becomes essential to identify critical feeders. In this context, data-driven methods offer a promising approach to enhance situational awareness for power system operators and improve their understanding of the system behavior under variabilities. This paper proposes a voltage estimation method based on a Machine Learning algorithm to address this challenge. By leveraging data-driven techniques, the proposed method aims to provide accurate and efficient voltage estimations in distribution networks, contributing to the effective management and control of modern power systems. |
ACN Conférence nationale avec acte |
[1] Un réseau de neurones artificiels pour l'estimation rapide de l'état d'un réseau de distribution de basse tension Conférence des Jeunes Chercheurs en Génie Electrique (JCGE), 06/2024, Abstract EL IAALI Mahamad, RAZI Reza, BRUYERE Antoine, FRANCOIS Bruno, SOARES Joao |
L'intégration croissante des sources d'énergie renouvelables et l'adoption généralisée des véhicules électriques
complexifient les flux d'énergie dans les réseaux de distribution. Il est impératif de comprendre la dynamique de ces flux et de
mettre en oeuvre des actions de contrôle pour une gestion efficace du réseau. Bien que les mesures dans les réseaux électriques
soient facilitées par les compteurs intelligents et les réseaux de communication, il est coûteux et, dans certains cas, impossible
d'obtenir des mesures précises à tous les noeuds d'un réseau en temps réel. En réponse à ces défis, cet article présente une
méthode d'estimation de l'état basée sur un réseau neuronal artificiel, qui utilise les mesures remontées par les bornes de
recharge de véhicules électriques et celles au poste source. La méthode proposée vise à fournir des estimations précises des
tensions et des courants dans les réseaux de distribution, offrant ainsi des informations précieuses pour le pilotage des
flexibilités. Cette approche intègre l'effet de l'angle des tensions, améliorant la compréhension de la dynamique du réseau par
les opérateurs électriques. Elle est validée par les résultats obtenus avec plusieurs études de cas. |
Le L2EP recrute
Dernières actualités
- Best Paper Award – IEEE PowerTech 2025
- Prix Galileo Ferraris Contest, juin 2025
- International EMR Summer School 2025 , July 8-11
- Soutenance HDR, Ronan GERMAN, 8 Juillet 2025
- Power electronic converters on transmission system, Summer School, July 8-11
- Séminaire, Dr Nishant Kumar, 24 Juin 2025,
- Séminaire, « Shared Experiences » on Publications, 18 juin 2025
- Soutenance de Thèse, Soufiane GHAFIRI, 6 mai 2025
- Comité scientifique Énergie Électrique 4.0, 25 avril 2025
- Soutenance de Thèse, Xuyang LU, 7 Avril 2025