Abstract
The deployment of PV systems continues showing amazing growth worldwide. The use of smart monitoring platforms together with sophisticated communication devices helps maximizing the energy yield in real-time, trigger alarms in case of failures, and track the performance evolution of PV components (mainly PV inverters and PV modules). In the frame of the H2020 TRUST-PV project [1], an expected result is a context-sensitive PV plant component benchmarking based on monitoring data from over 6 GW of PV plants under operation and Big Data analytics, which will be directly linked to the Cost Priority Number (CPN) methodology [2]., which is further developed within the TRUST-PV project To achieve those ambitious goals, 3E’s SynaptiQ monitoring platform is being explored. This platform has over 8000 PV plants connected to it, working with over 70 PV inverter manufacturers, and over 200 PV module manufacturers (up to December 2020)[3]. The comprehensive detail of information of each component, and monitoring data at different electrical levels (sub string, string, inverter, PV plant), together with an organized data architecture, enables the exploitation of the data for a component-benchmarking of PV modules and PV inverters. In this work, we first explore one of the largest databases with real PV plants connected globally (3E SynaptiQ Solar). Then, big data approaches are discussed to finally evaluate technical strategies towards a commercial component-benchmarking for different components of PV plants. The goal of this component-benchmarking tool is to support technical decisions of different stakeholders (manufacturers, designers, operators, financial entities, etc.), improve the assumptions taken in PV yield and degradation modelling, inverter reliability, and the maintenance of a large database with operational Key Performance Indicators (KPIs) coming from real-world PV plants in different climate zones globally.