In the past decades, climate concern has been growing and electric energy has been identified as one of the key players in the energy transition. Meanwhile, the status of multilevel converters has evolved from ‘new’ to ‘established’ concept with industrial products in each power decade from 1W to 10MW. 

Choosing the best semiconductor has thus become more and more difficult because the choice is not guided by the voltage and current to handle: devices with a reduced voltage rating or with a reduced current rating can be respectively connected in series or parallel to build multilevel converters with improved performances: better efficiency, higher power density and/or reduced cost. 

To help designers make the right decision, specific methods can be used and design tools have been developed. This includes unified formalism for series-, parallel- and series-parallel- multilevel converters to allow analytic models for pre-design, unified simulation models for easy comparison of simulated waveforms, and modular approach of prototype construction. 

In this talk, design tools of different levels of complexity and accuracy that can be used to aid the designer at different stages of the design process will be presented. 

With the advancement of AI (Artificial Intelligence), DL (Deep Learning) methods have achieved outstanding performance and great success in a variety of real-world applications. Thanks to the versatility of DL, an increasing number of researchers are attracted to adopting it as a solution approach in diverse fields ranging from renewable energy production to bioinformatics. However, the performance of DL heavily depends on many factors, such as the learning model architecture and the hyper-parameters’ values. Moreover, the possible interactions between these different factors make the design of an effective DL model a complex task for the human practitioner. Recent research suggests EAs (Evolutionary Algorithms), such as GAs (Genetic Algorithms) and SI (Swarm Intelligence) algorithms, as effective candidate methods for the induction and tuning of highly effective DL models. This has been not only motivated but also justified by the ability of EAs to intelligently sample very large and complex search spaces of candidate DL models. Thanks to the prominent results observed and reported in various application domains, a new research field named EDL (Evolutionary Deep Learning) appeared in the machine learning area. The goal of this talk is to introduce the fundamentals of the EDL field by detailing its different components and merits. Eventually, several avenues for future research will be exposed, such as ETL (Evolutionary Transfer Learning) and HW-NAS (Hardware-Aware Neural Architecture Search).

Finally, this talk will be concluded with a set of promising applications of EDL in the green energy sector.

The production of electricity from photovoltaic (PV) energy is a fast-growing area of research in academia and industry. This trend has been boosted by the massive deployment of large-scale grid-connected photovoltaic power plants, which has stimulated research efforts regarding reliability and cost-effectiveness. However, photovoltaic systems are subject to various types of malfunction, whether temporary or permanent, and these malfunctions can have a significant impact on system performance and availability. In this context, meticulous monitoring and fault diagnosis of photovoltaic (PV) systems is essential to ensure the long-term reliability and sustainable operation of the entire PV system. Up to now, PV system fault detection and diagnosis can be classified into three main groups: process history based methods, quantitative model based methods and signal and image processing based methods. This talk will cover current practices and new trends in fault detection and diagnosis in grid connected PV systems, including the emergence of deep and machine learning approaches.

Thierry A. Meynard

Keynote 1

CNRS Research Director at the LAPLACE(*) and Fellow of the IEEE, also involved in several industry-related activities.

Slim Bechikh

Keynote 2

Full Professor with the University of Carthage, FSEG-Nabeul, Tunisia ; Research Director within the SMART laboratory, ISG-Tunis, University of Tunis, Tunisia; and Senior Member of the IEEE.


Keynote 3

Full professor in the Department of Electrical Engineering at Mohamed BoudiaFUniversity in M'sila, Algeria, and head of the 'Micro-Grids' team at the Electrical Engineering Laboratory (LGE).

Thierry A. Meynard graduated from the Ecole Nationale Supérieure d’Electrotechnique, d’Electronique, d’Hydraulique de Toulouse in 1985, became a Doctor of the Institut National Polytechnique de Toulouse, France, in 1988 and was then an invited researcher at the Université du Québec à Trois Rivières, Canada, in 1989. He joined the CNRS (Centre National de la Recherche Scientifique) as a full-time researcher in 1990, was Head of the Static Converter Group from 1994 to 2001. From 2010 to 2018 he has been associate director of the national program 3DPHI (3-Dimensional Power Hybrid Integration). He is now Directeur de Recherches CNRS at the LAPLACE(*) and Fellow of the IEEE, but in parallel he has been also involved in several industry-related activities.   

Thierry A. Meynard has been part-time consultant with Cirtem from 2000 to 2016. In 2016 he co-founded and became scientific advisor at the company Power Design Technologies that develops PowerForge, the software for design of 2- and multi-level power converters later acquired by Gamma Technologies. Since January 2020, he is also acting as an independant consultant to transfer more innovation into industrial products. 

His main research interests are related to series and parallel multicell converters, magnetic components and the development of design tools for power electronics. 

T.A. Meynard is co-inventor of several topologies of multilevel converter used by ABB, Alstom, Cirtem, General Electric, Schneider Electric : ‘Flying capacitor’, ‘Stacked MultiCell’, ‘5LANPC’, ‘AC/AC chopper’, xPlexed choppers….

(*) Laboratoire Plasma et Conversion d’Energie, UMR CNRS n° 5213, B.P. 7122, 2, rue Camichel, 31071 Toulouse Cedex 7 FRANCE

Slim Bechikh (SMIEEE) received the B.Sc., M.Sc., Ph.D., and Habilitation degrees in Business Informatics from the University of Tunis, ISG-Tunis, Tunisia, in 2006, 2008, 2013, and 2015, respectively. He is currently Full Professor with the University of Carthage, FSEG-Nabeul, Tunisia.

He is also a Research Director within the SMART laboratory, ISG-Tunis, University of Tunis, Tunisia. He published over 100 papers in peer-reviewed journals and refereed conferences. His current research interests include evolutionary optimization, machine learning, deep neural networks, business analytics, and SBSE. Dr. Bechikh was a recipient of the Best Paper Award of the ACM SAC-2010 in Switzerland. He supervised the Tunisian best national Doctoral thesis in ICT for the year 2019, which earned a presidential prize in scientific research and technology. He was promoted to the grade of IEEE Senior Member by August 2021.

He is Associate Editor for IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION and SWARM AND EVOLUTIONARY COMPUTATION. He serves as reviewer for over 80 international journals in artificial intelligence and its applications.


Prof. Aissa Chouder received his engineering and magister degrees in electronics from the Université Ferhat Abbas, Sétif, Algeria, in 1991 and 1999 respectively, and his Phd in electronic engineering from the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, in 2010. 

He is currently full professor in the Department of Electrical Engineering at Mohamed BoudiaFUniversity in M'sila, Algeria, and head of the 'Micro-Grids' team at the Electrical Engineering Laboratory (LGE).  He has co-authored more than 150 articles in international journals and conference proceedings. His research focuses on modeling of power electronics, control of renewable energy systems and AC and DC microgrids.