Monitoring and Diagnosis

Photovoltaics Monitoring and Diagnosis

Monitoring and Diagnosis

Curriculum 3

The PhD candidate will afford studies that are related to all the approaches to the monitoring and diagnosis of photovoltaic systems, with relation to all their parts, from the modules to the supporting structures, to wirings and connection components, up to power electronics and electrical energy storage systems.

The methodologies to use will be inevitably based on a number of competences, which will be given to all the student through the common part of the education plan. Indeed, interdisciplinary knowledge will be needed to have a holistic view of the approaches used in actual and future applications. Image based diagnostic, in the infrared and visible ranges, will need a glance to algorithms for image processing and to issues related to images acquisition through drones. A solid background on Artificial Intelligence (AI) is build up in order to apply powerful data driven approaches to images as well as to electrical, atmospheric, operational data acquired on the modules, on electronics, on switching converters, on the power plant more in general.

Studies on model-based approaches will also be covered, these ones needing a transversal and multi-disciplinary basis related to multi-physics approaches that include electrical and thermal aspects. The competences needed will be provided in the common part of the education plan and they will be related to all the parts of the photovoltaic plant, including the modules and the balance of system.

Approaches based on the analysis of the electrical quantities measured on the photovoltaic plant will be studied, both in the time and in the frequency domain. Model-based and data-driven methods will be exploited also for studying innovative methodologies exploiting digital twins.

Operation-and-Maintenance (O&M) strategies will be afforded to guarantee the highest possible power production along the system lifetime and to extend its Remaining Useful Life (RUL). Prognostic approaches and mitigation actions will be investigated and their feedback over control strategies to improve the RUL will be a topic of interest for the students.

The off-line and on-line implementations of methods and algorithms will be the subject of an in-depth analysis, with a special emphasis on the features offered by modern embedded systems and architectures, to exploit edge-computing features and cloud facilities.