Computer Vision Method for Extracting an Induced Electroluminescence Signal from Photovoltaic Modules in Daylight Conditions Using Drone-Captured Images

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Electroluminescence (EL) imaging is a powerful technique for evaluating the condition of photovoltaic (PV) modules and individual cells. While drones are a cheap and practical imaging medium, they present a challenge in terms of image stabilization. Flying during daylight hours has the advantage of being safer, cheaper, and camera-focus is easier to maintain. The main drawback is that sunlight introduces sufficient background noise to dominate the EL signal. We present a method for automatically tracking and rectifying a PV module in a stack of EL images captured by drone in daylight, and subsequently rasterizing the EL signal (S/N < 0.1). The method combines feature detection with direct corner alignment, and is applicable to any type of drone-based PV-inspection. To extract the EL signal, a stabilized image stack is analyzed depth-wise. Background noise is calculated and subtracted, and a Fast Fourier Transform (FFT) analysis is performed to form an EL amplitude map of the module. The analysis is validated by examining adjacent frequencies and by comparison with stationary EL. Results show promising image quality that fares well compared to stationary EL.
Original languageEnglish
Title of host publicationProceedings of 37th European Photovoltaic Solar Energy Conference and Exhibition
Publication date2020
Pages1573-1579
ISBN (Print)3-936338-73-6
DOIs
Publication statusPublished - 2020
Event37th European Photovoltaic Solar Energy Conference and Exhibition - Virtual event
Duration: 7 Sep 202011 Sep 2020
Conference number: 37
https://www.photovoltaic-conference.com/

Conference

Conference37th European Photovoltaic Solar Energy Conference and Exhibition
Number37
LocationVirtual event
Period07/09/202011/09/2020
Internet address

Keywords

  • Electroluminescence
  • Daylight
  • Drone
  • Evaluation
  • Analysis

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