Expert Quality Control of Solar Radiation Ground Data Sets

Abstract

Ground-based radiation measurements are required for all large solar projects and for evaluating the accuracy of solar radiation models and datasets. Ground data almost always contain low-quality periods caused by instrumental issues, logging errors, or maintenance deficiencies. Therefore, quality control (QC) is needed to detect and eventually flag or exclude such suspicious or erroneous data before any subsequent analysis. The few existing automatic QC methods are not perfect, thus expert visual inspection of the data is still required. In this work, we present a harmonized QC procedure, which is a combination of various available methods, including some that include an expert visual inspection. In the framework of IEA PVPS Task 16, these tests are applied to 161 world stations that are equipped with various radiometer models, and are candidates for an ongoing benchmark of irradiance datasets derived from satellite or weather models. Because the implementation of these methods by experts, and their subsequent decisions, might lead to different QC results, the independently obtained results from nine evaluators are compared for two test sites. The QC results are found similar and more stringent than purely automated tests, even though some deviations exist due to differences in manual flagging.

Automating Quality Control of Irradiance Data with a Comprehensive Analysis for Southern Africa

“…Data-driven techniques such as power plant maintenance and operations, forecasting, and designing require good-quality irradiance measurements. Ground-based data contains periods, even if brief, of erroneous data caused by various reasons [3]. Quality control (QC) of large irradiance datasets is therefore instrumental in ensuring the continuing growth of PV systems.…”

“…Quality control of large irradiance datasets is a critical data-processing technique that ensures common errors are flagged and removed from the dataset. There have been significant publications on various QC methodologies [1,3,[8][9][10][11][12][13]. The Baseline Surface Radiation Network (BSRN) recommendations by [10] are a popular QC methodology and have been used as a proposed QC methodology in various works [3,[13][14][15][16].…”

“…There have been significant publications on various QC methodologies [1,3,[8][9][10][11][12][13]. The Baseline Surface Radiation Network (BSRN) recommendations by [10] are a popular QC methodology and have been used as a proposed QC methodology in various works [3,[13][14][15][16]. Most of the research previously mentioned includes a form of user review, which can become timeconsuming as dataset sizes increase.…”

A network of all sky imagers (ASI) enabling accurate and high-resolution very short-term forecasts of solar irradiance

“…Figure 4: Keogram for ASI at station PVAMM showing concatenated vertical stripes of each sky image during a full day.The meteorological measurements are provided as 1-min averaged and quality-controlled values.Availability and quality of the data is optimized by a highquality instrumentation, strong mobile connection at each location and regular maintenance visits. Data quality checks based on existing standards are performed on 1-min averaged data[10]. Figure5shows a comprehensive evaluation of irradiance measurements at station OLUOL.…”

“…Figure 5: Comprehensive quality control (QC) of irradiance measurements at station OLUOL (located at University of Oldenburg) from April 2019 -August 2022. QC description and method from[10] …”