3rd International Conference on Electronic Engineering and Renewable Energy 2022

Special Session 3: Machine learning and deep learning methods for power optimization, fault detection and diagnosis of solar photovoltaic systems

In 2020, the global photovoltaic capacity reached about 760 GWp corresponding to several millions of photovoltaic (PV) systems installed worldwide. Thus, the operation and maintenance activities of such plants are today important for a great number of professionals working in this solar sector. As a single fault occurring in one of the components of the PV system can seriously compromise its performance and yield, the capability to detect, localize, isolate, and fix the fault is crucial in order to avoid unnecessary loss of money and safety issues. A number of international standards, equipment and monitoring systems have been developed and commercialized, but most of them are expensive or at a prototype level. Another issue related to the commercial products is related to the fact that these are usually closed source and thus unlikely able to adapt to different location and operation conditions. In the direction of power optimization, fault detection and diagnosis for PV systems a great research effort is then expected in the future, and already the number of paper published in this field is increasing very rapidly. This special issue is aims at attracting scholars and professionals working in the photovoltaic sector, and is focused (but not limited to) on the following topics:

  • Automatic photovoltaic monitoring and supervision systems;
  • Smart photovoltaic monitoring systems;
  • IoT-based remote sensing of photovoltaic systems ;
  • AI techniques based-fault diagnosis of photovoltaic systems;
  • Fault tolerance in photovoltaic systems ;
  • Fault prediction and diagnosis approaches ;
  • Machine learning methods to assist energy system optimization;
  • New techniques for fault detection and diagnosis ;
  • Fault localization techniques in photovoltaic systems;
  • Real-time implementation of fault diagnosis techniques;
  • Challenges and future directions in real-time applications;
  • Machine and deep learning in fault diagnosis of photovoltaic systems.

Guest Editors:

Dr. Adel Mellit:

Renewable energy laboratory, Faculty of Sciences and Technology, Jijel University, Algeria.

Dr. Sahbi Boubaker:

University of Jeddah, Saudi Arabia.

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