An Online Novel Two-Layered Photovoltaic Fault Monitoring Technique Based Upon the Thermal Signatures

Abstract

The share of photovoltaic (PV) power generation in the energy mix is increasing at a rapid pace with dramatically increasing capacity addition through utility-scale PV power plants globally. As PV plants are forecasted to be a major energy generator in the future, their reliable operation remains of primary concern due to a possibility of faults in a tremendously huge number of PV panels involved in power generation in larger plants. The precise detection of nature and the location of the faults along with a prompt remedial mechanism is deemed crucial for smoother power plant operation. The existing fault diagnostic methodologies based on thermal imaging of the panels as well as electrical parameters through inverter possess certain limitations. The current article deals with a novel fault diagnostic technique based on PV panel electrical parameters and junction temperatures that can precisely locate and categorize the faults. The proposed scheme has been tested on a 1.6 kW photovoltaic system for short circuit, open circuit, grounding, and partial shading faults. The proposed method showed improved accuracy compared to thermal imaging on panel scale fault detection, offering a possibility to adapt to the PV plant scale.

Fault Prediction and Early-Detection in Large PV Power Plants Based on Self-Organizing Maps

“…In particular, the recent development of key enabling technologies and paradigms, most notably Internet-of-Things (IoT)-environments and machine learning algorithms to handle massive quantities of data, have been recently applied to monitoring the functioning of PV systems. A large number of scientific papers have been written to describe how they can be effectively used to timely detect possible malfunctions through the assessment of an indicator performance, and significant works on the topic include papers [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. Besides, similar strategies have been also presented in works that tackle wind farms, see [ 20 , 21 ] with the objective of identifying equipment level failures, while in this case fewer works can be found for the counterpart for PV plants [ 22 ].…”