Outdoor PV system monitoring—input data quality, data imputation and filtering approaches

Lindig, Sascha (author), Louwen, Atse (author), Moser, David (author), Topič, Marko (author)

Photovoltaic monitoring data are the primary source for studying photovoltaic plant behavior. In particular, performance loss and remaining-useful-lifetime calculations rely on trustful input data. Furthermore, a regular stream of high quality is the basis for pro-active operation and management activities which ensure a smooth operation of PV plants. The raw data under investigation are electrical measurements and usually meteorological data such as in-plane irradiance and temperature. Usually, performance analyses follow a strict pattern of checking input data quality followed by the application of appropriate filter, choosing a key performance indicator and the application of certain methodologies to receive a final result. In this context, this paper focuses on four main objectives. We present common photovoltaics monitoring data quality issues, provide visual guidelines on how to detect and evaluate these, provide new data imputation approaches, and discuss common filtering approaches. Data imputation techniques for module temperature and irradiance data are discussed and compared to classical approaches. This work is intended to be a soft introduction into PV monitoring data analysis discussing best practices and issues an analyst might face. It was seen that if a sufficient amount of training data is available, multivariate adaptive regression splines yields good results for module temperature imputation while histogram-based gradient boosting regression outperforms classical approaches for in-plane irradiance transposition. Based on tested filtering procedures, it is believed that standards should be developed including relatively low irradiance thresholds together with strict power-irradiance pair filters.

• Language: English
• Keywords: photovoltaics, solar cells, photovoltaic system performance, photovoltaic system data, data quality, data imputation, data filtering
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2020
• Number of pages: 18 str.
• Numbering: Vol. 13, iss. 19, art. 5099
• PID: 20.500.12556/RUL-134484
• UDC: 621.383.51
• ISSN on article: 1996-1073
• DOI: 10.3390/en13195099
• COBISS.SI-ID: 32452611
• Publication date in RUL: 18.01.2022

Record is a part of a journal

• Title: Energies
• Shortened title: Energies
• Publisher: Molecular Diversity Preservation International
• ISSN: 1996-1073
• COBISS.SI-ID: 518046745

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 01.10.2020

Secondary language

• Language: Slovenian
• Keywords: fotovoltaika, sončne celice