Researcher at GeePs and Professor at U-PSay
She is the leader of the activities “Advanced characterizations in real conditions” and “soft integration
of PV in smart-grid” at GeePs. She has supervised 13 Ph.D. thesis and several Master thesis. She is an
author/co-author of more than 100 papers and international communications, co-owner of 3 patents
in PV. She is an expert for ANR evaluations, regular reviewer for IEEE journal (occasional for some
other journals in the field of PV), and Editorial Board Member of the MDPI journal “Sustainability”.
She teaches at the University of Paris-Saclay in energy conversion, electronics, optronics, and
instrumentation. She was responsible for 2 professional undergraduate degrees in Energy Efficiency
in Buildings (creation) and in Maintenance.
Abstract: Fault Detection and Diagnosis applied to photovoltaic power plants
There is an increasing interest both in academic or industry for health monitoring of photovoltaic (PV) power lants. The main reasons are safety issues and the loss of income due to faults or failures. In a PV power plant, on the DC side, the fault can affect a single cell, a module or a string. The fault effect or signature can be detectable or not, depending on the available information, the fault severity and the fault diagnosis method. From the abundant literature, there is a diversity of approaches based on different input data (array I-V characteristic, array or string maximum power point, module level power point, infrared images, etc. . . ), different techniques (image processing, neural network, etc. . . ) depending on fault types (mismatch, short-circuit, open-circuit, etc. . . ). From the application point of view, it is not obvious to identify what would be the most efficient method to implement a condition-based maintenance that is now recognised as the most cost effective method. Therefore, we propose in this work from the analysis of the publications in 2017 to classify the fault detection and diagnosis methods through a framework defined in 4 steps: modelling, pre-processing, features extraction and features analysis.