An international research team has come up with a new methodology for performance and reliability analysis that can purportedly bridge the qualitative-quantitative gap that exists in current operation and maintenance (O&M) practices for PV power plants.
The new methodology combines a series of data quality routines (DQRs) to ensure data fidelity by detecting and reconstructing invalid data through a sequence of filtering stages and inference techniques.
The scientists – from the University of Cyprus, Sandia National Laboratories, Solarcentury, and Gantner Instruments GmbH – said the methodology is based on quantifiable criteria from IEC 61724, which is the common standard for PV system performance monitoring. It can minimize existing process gaps that are presented in an ambiguous or qualitative manner.
“Such gaps can be translated into different ways depending on the PV performance analyst and can be one of the main sources of bias and inconsistency,” the academics said. “Therefore, data quality routines (DQRs) that operate on measurements were developed, and each step of the methodology is described in a quantitative manner based on detailed analyses and not arbitrary assumptions.”
The initial step in the seven-stage process involves the use of data statistics to determine the recording interval and the reporting period for PV performance and reliability analysis. In the second step, the results are analyzed to find time-stamp gaps, repetitive entries, duplicate records, and synchronization issues between meteorological and electrical data. The third step is implemented by applying daylight filters to the data collected in the time series, after removing the repetitive and duplicate time-stamp records.
The academics claim that PV plant operators will be able to reconstruct missing data rates below 10%.
“At higher missing data rates (between 15% and 40%), the application of listwise deletion is not recommended, since absolute percentage error (APE) up to 62.01% was observed,” they explained. “For missing data rates higher than 10%, data inference techniques are recommended.”
They described the data quality routines in “Data processing and quality verification for improved photovoltaic performance and reliability analytics,” which was recently published in Progress in Photovoltaics.
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