Impact of Missing Data on the Estimation of Photovoltaic System Degradation Rate

Livera, Andreas; Phinikarides, Alexander; Makrides, George; Georghiou, George E.

doi:10.1109/pvsc.2017.8366442

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Table I presents the name plate ratings and measured parameters of the mc-Si PV modules analyzed in this study. Missing data due to outages, sensor and acquisition failures are normally experienced problems in the field [12], which this setup also suffered. Basic data screening is applied to remove outliers and faulty readings before advancing with the analysis.…”

Section: Methodsmentioning

confidence: 99%

Performance assessment of field deployed multi-crystalline PV modules in Nordic conditions

Paudyal

Imenes

2019

2019 IEEE 46th Photovoltaic Specialists Conference (PVSC)

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This paper presents an investigation of data monitoring quality and evaluation of performance degradation of four different multi-crystalline silicon (mc-Si) photovoltaic (PV) modules installed in the higher latitude conditions in southern Norway. Degradation of each module has been investigated in terms of degradation of short-circuit current (ISC), open-circuit voltage (VOC), fill factor (FF) and maximum power (PMPP). The analysis for the period of monitoring data from 2014 to 2018 show no considerable module degradation compared to the standard degradation rate of all parameters. The statistical analysis of I SC shows an average degradation of 0.17% for all modules. Spectral corrections were applied to I SC and P MPP , in addition to temperature and irradiance corrections. Among the parameters, FF and ISC show slight degradation based on the yearly average method. Infrared images were used for validation of findings, but due to the unavailability of images from the initial installation period, the image results are inconclusive. Performance ratio plots based on corrected I SC values show very stable performances over the five-year period. The results suggest that PV modules in cold conditions may undergo lower degradation compared to typical degradation rates experienced in other regions.

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Section: Methodsmentioning

confidence: 99%

Performance assessment of field deployed multi-crystalline PV modules in Nordic conditions

Paudyal

Imenes

2019

2019 IEEE 46th Photovoltaic Specialists Conference (PVSC)

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“…15 Different data inference techniques have been proposed based on statistical procedures, parametric models, empirical, and machine learning approaches. 11,12,15,18,19 Numerous studies propose data imputation with simulated data, 11,20 mean or median imputation, 21 optimally weighted average imputation, 22 multivariate imputation by chain equations (MICEs), 23 linear interpolation (LI), 14 k-nearest neighbors (k-NN) imputation, 15 last observation carried forward (LOCF), seasonal decomposition (SD), 24 bootstrapping, 21 and random forest (RF). 23 Dataset reconstruction is important for ensuring that the significant features of the time series are preserved and not lost due to reductions in the dimensionality.…”

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confidence: 99%

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

Livera

Theristis

Koumpli³

et al. 2020

Progress in Photovoltaics

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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.

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Evaluation of Missing Data Imputation Methods for an Enhanced Distributed PV Generation Prediction

Sundararajan

2019

Advances in Intelligent Systems and Computing

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Impact of Missing Data on the Estimation of Photovoltaic System Degradation Rate

Cited by 4 publications

References 6 publications

Performance assessment of field deployed multi-crystalline PV modules in Nordic conditions

Performance assessment of field deployed multi-crystalline PV modules in Nordic conditions

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

Evaluation of Missing Data Imputation Methods for an Enhanced Distributed PV Generation Prediction

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