The integration of the global Photovoltaic (PV) market with real time data-loggers has enabled large scale PV data analytical pipelines for power forecasting and reliability assessment of PV fleets. Nevertheless, the performance of PV data analysis depends on the quality of PV timeseries data. We propose a novel Spatio-Temporal Denoising Graph Autoencoder STD-GAE framework to impute missing PV Power Data. STD-GAE exploits temporal correlation, spatial coherence, and value dependencies from domain knowledge to recover missing data. It is empowered by two modules. (1) To cope with sparse yet various scenarios of missing data, STD-GAE incorporates a domain-knowledge aware data augmentation module to create plausible variations of missing data patterns. This generalizes STD-GAE to robust imputation over different seasons and environment. (2) STD-GAE nontrivially integrates spatiotemporal graph convolution layers and denoising autoencoder to improve the accuracy of imputation accuracy at PV fleet level. Experimental results on two PV datasets show that STD-GAE can achieve a gain of 43.14% in imputation accuracy and remains less sensitive to missing rate, different seasons, and missing scenarios, compared with state-of-the-art data imputation methods.

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We have studied the degradation of both full-sized modules and minimodules with PERC and Al-BSF cell variations in fields while considering packaging strategies. We demonstrate the implementations of data-driven tools to analyze large numbers of modules and volumes of timeseries data to obtain the performance loss and degradation pathways. This data analysis pipeline enables quantitative comparison and ranking of module variations, as well as mapping and deeper understanding of degradation mechanisms. The best performing module is a half-cell PERC, which shows a performance loss rate (PLR) of −0.27 ± 0.12% per annum (%/a) after initial losses have stabilized. Minimodule studies showed inconsistent performance rankings due to significant power loss contributions via series resistance, however, recombination losses remained stable. Overall, PERC cell variations outperform or are not distinguishable from Al-BSF cell variations.

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