@inproceedings{40dcfeeaeb3c471b9b5e2b76bcaf9a76,
title = "An Online Degradation Condition Evaluation Method for Solar Photovoltaic Panels",
abstract = "Solar PV panels are the center equipment of photovoltaic power systems. Condition monitoring is the key technique to predicting the health status and increasing the availability of PV panels. This paper proposes the Feature Fingerprint Intervals (FFI) method to monitor the degradation conditions of PV panels. The proposed approach is sensitive to the degradation mechanisms of PV panels. The prediction indicator, the amplitude of impedance spectroscopy, at FFI will be acquired directly from impedance spectroscopy while performing the online monitoring of PV panels. The health status of PV panels can be predicted based on the variation of the amplitude values of impedance spectroscopy at the FFI compared with the reference values. The proposed approach can support the accurate condition monitoring of PV panels.",
keywords = "condition monitoring, feature fingerprint intervals, impedance spectroscopy, solar PV panels",
author = "Xize Dai and Hosani, {Khalifa Hassan Ai} and Sumanti, {Ameena Ai} and Moursi, {Mohamed Ei} and Huai Wang",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE PES Conference on Innovative Smart Grid Technologies - Middle East, ISGT Middle East 2023 ; Conference date: 12-03-2023 Through 15-03-2023",
year = "2023",
doi = "10.1109/ISGTMiddleEast56437.2023.10078559",
language = "British English",
series = "2023 IEEE PES Conference on Innovative Smart Grid Technologies - Middle East, ISGT Middle East 2023 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE PES Conference on Innovative Smart Grid Technologies - Middle East, ISGT Middle East 2023 - Proceedings",
address = "United States",
}
