The main aim of the proposal is the development of a commercial product that will assist in maintaining an optimal level of operation of PV plants. In particular, the proposed project is concerned with advancing the field of the automatic identification of performance loss, degradation and failures in monitored PV plants and their classification into various fault types and degradation mechanisms, which are manifested in the field.Specifically, innovative methodologies based on machine learning and statistical analysis will be developed for identifying performance losses and failures in PV plants without disrupting their operation. Such methodologies have the potential to contribute greatly to new standards on PV performance, degradation and reliability.
The scientific and technological objectives of the project are to:
The ultimate technological objective of the proposed work is to extend the capabilities of the Raycatch product. Raycatch is an AI diagnostics technology for solar energy, on a mission to revolutionize the PV market by enabling automated management of solar assets.
The extended tool will include data quality routines, an optimized methodology for the estimation of degradation rate and appropriate algorithms for automatic identification and uninterrupted monitoring of PV systems. As such, the early identification and classification of failures and performance loss mechanisms will be achieved. Therefore, corrective actions will be able to be taken by the corresponding owners or operators in order to safeguard the PV performance and minimize the investment risks.