Evaluation Of Photovoltaic Module Performance Using Novel Data-Driven I-V Feature Extraction And Suns-V Oc Determined From Outdoor Time-Series I-V Curves

Keywords

photovoltaic cells; power system reliability; solar panels; statistical learning

Abstract

This paper presents an alternative method to extract performance parameters including the maximum power point (PMP

), short-circuit current (ISC), open-circuit voltage (VOC), shunt resistance (Rsh), series resistance (RS), and fill factor (FF) from time-series I-V curves of PV Modules under real-world exposure conditions. Moreover, 'steps' in I-V curves are also extracted, which is considered as a sign for module mismatch and physical damage. To better quantify performance losses and distinguish loss mechanisms, Suns-VOC psuedo IV curves are also constructed from aforesaid real-world data, based on the measured ISC, VOC pairs during each week, and performance losses are calculated from the difference in P MP of initial and degraded I-V and pseudo I-V curves corrected with various algorithms.

Publication Date

11-26-2018

Publication Title

2018 IEEE 7th World Conference on Photovoltaic Energy Conversion, WCPEC 2018 - A Joint Conference of 45th IEEE PVSC, 28th PVSEC and 34th EU PVSEC

Number of Pages

778-783

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/PVSC.2018.8547772

Socpus ID

85059916463 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85059916463

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