EU PVSEC Programme Online
EU PVSEC 2020, 7 - 11 September 2020
Presentation: 5CV.3.19 Quantitative Assessment of the Power Loss of Silicon PV Modules by IR Thermography and Its Practical Application in the Field
Type: Visual
Date: Wednesday, 9th September 2020
15:15 - 16:45
Author(s): J. Denz, C. Buerhop-Lutz, C. Camus, I. Kruse, T. Pickel, B. Doll, J. Hauch, C.J. Brabec
Presenter / Speaker: B. Doll, FAU Erlangen Nürnberg, Erlangen, Germany
Event: Conference Conference
Session: 5CV.3 Operation, Performance and Maintenance of PV Systems
Type(s) of Access:  Conference Registration
Topic: 5. 3 Operation, Performance and Maintenance of PV Systems
Keywords: Infrared Imaging, PV Module, UAV, Reliability
Summary / Abstract: On the road to more photovoltaic (PV) energy production, high-throughput and non-disruptive maintenance routines are needed to ensure a reliable long-term performance of PV plants. Additionally, a method that allows a quantitative assessment of silicon PV module performance solely by measuring cell temperatures via infrared thermography (IR) is more easily applicable in a high-throughput fashion using e.g. unmanned aerial vehicles (UAVs) than measuring each module individually. We introduced a mathematical framework to determine power losses of modules from IR images. The method makes use of the fact that, in a steady state closed system, energy that is not converted into electrical power has to be dissipated as heat. In this contribution, we reduce this method to practice by analysing the electrical performance of four PV modules with temperature anomalies at two different sites, which are equipped with a power monitoring system by the company Sunsniffer for validation. We present an analytical expression for the power-temperature-relationship, and discuss influences of convection and how to correct for them. We find that power predictions often deviate less than 3% from the monitoring data and confirm our theoretical considerations, allowing power loss to be quantified. Cases with larger deviations show that some influences are yet to be better understood and more data is needed.