Data processing and quality verification for improved photovoltaic performance and reliability analytics

Abstract

Data integrity is crucial for the performance and reliability analysis of photovoltaic (PV) systems, since actual in‐field measurements commonly exhibit invalid data caused by outages and component failures. The scope of this paper is to present a complete methodology for PV data processing and quality verification in order to ensure improved PV performance and reliability analyses. Data quality routines (DQRs) were developed to ensure data fidelity by detecting and reconstructing invalid data through a sequence of filtering stages and inference techniques. The obtained results verified that PV performance and reliability analyses are sensitive to the fidelity of data and, therefore, time series reconstruction should be handled appropriately. To mitigate the bias effects of 10% or less invalid data, the listwise deletion technique provided accurate results for performance analytics (exhibited a maximum absolute percentage error of 0.92%). When missing data rates exceed 10%, data inference techniques yield more accurate results. The evaluation of missing power measurements demonstrated that time series reconstruction by applying the Sandia PV Array Performance Model yielded the lowest error among the investigated data inference techniques for PV performance analysis, with an absolute percentage error less than 0.71%, even at 40% missing data rate levels. The verification of the routines was performed on historical datasets from two different locations (desert and steppe climates). The proposed methodology provides a set of standardized analytical procedures to ensure the validity of performance and reliability evaluations that are performed over the lifetime of PV systems.

International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology)

“…If instead a larger share of data is missing, data imputation is the recommended approach, although many different imputation techniques are existing. A recent study by Livera et al 13 proposes a unified methodology for data processing, quality verification, and reconstruction. It was shown that PLR studies are sensitive to invalid or missing data rate.…”

A hybrid methodology for distribution level photovoltaic power production forecasting verified at the distribution system of Cyprus

“…The quality of the acquired utility-scale PV system datasets was analysed in order to verify the applicability of the selected plants as reference PV systems for the up-scaling process. A DQR that operates on the acquired datasets was developed in order to ensure data validity for the model development and performance assessment [52]. More specifically, the developed DQR comprises of statistical algorithms capable of detecting missing (or erroneous) data, duplicate records, outliers and outages along with data filtering and imputation techniques that can be applied to identify data anomalies and reconstruct the dataset by filtering out detected erroneous data.…”

Comparative Analysis of Change-Point Techniques for Nonlinear Photovoltaic Performance Degradation Rate Estimations

“…Furthermore, spatial and temporal variability in the actual temperature of the array could lead to seasonality in the performance metric, especially if the wind direction varies seasonally and affects spatial patterns of array temperature. Moreover, with respect to data quality, integrity and processing, the Rd calculation can also be influenced by other factors such as missing data [10], sensor drift [11], filtering criteria [12], temporal aggregation [13], etc. Therefore, data handling and quality assurance is crucial in an Rd pipeline [10].…”

“…Moreover, with respect to data quality, integrity and processing, the Rd calculation can also be influenced by other factors such as missing data [10], sensor drift [11], filtering criteria [12], temporal aggregation [13], etc. Therefore, data handling and quality assurance is crucial in an Rd pipeline [10].…”