Engineering topics

Wieser, Raymond J.

Publications and source records attributed to Wieser, Raymond J..

6 records

DOE OSTI2023

Rear-Side Irradiance Simulation of Field PV Modules

Assessing the durability of photovoltaic (PV) module backsheet is critical to increasing module lifetime. Historically, laboratory-based accelerated testing has insufficient in predicting large-scale failures of commercial polymeric materials. Additionally, there is growing concern that standard condition tests do not reflect non-uniformities in field exposure, and that certain modules experience more severe degradation due to their location. Anisotropy in field exposures is installation-dependent and reflects different levels of exposure to irradiance due to mounting geometry, ground surface albedo, and climatic zone. "Bificial_Radiance" [1], simulates the amount of reflected irradiance on the Polyethylene Naphthalate (PEN) backsheet of a PV array located in Maryland. In our work, site specific weather data are gathered for the entire length of exposure for the modules. The total full-spectrum dose for the incident irradiance on the backsheet was then determined. The simulation results are integrated with historical field survey data to better understand real orld outdoor degradation.

backsheet↗

DOE OSTI2023

Rear-Side Irradiance Simulation of Field PV Modules: Preprint

Assessing the durability of photovoltaic (PV) module backsheet is critical to increasing module lifetime. Historically, laboratory-based accelerated testing has insufficient in predicting large-scale failures of commercial polymeric materials. Additionally, there is growing concern that standard condition tests do not reflect non-uniformities in field exposure, and that certain modules experience more severe degradation due to their location. Anisotropy in field exposures is installation dependent and reflects different levels of exposure to irradiance due to mounting geometry, ground surface albedo, and climatic zone. "Bificial Radiance" [1], simulates the amount of reflected irradiance on the Polyethylene Naphthalate (PEN) backsheet of a PV array located in Maryland. In our work, site specific weather data are gathered for the entire length of exposure for the modules. The total full-spectrum dose for the incident irradiance on the backsheet was then determined. The simulation results are integrated with historical field survey data to better understand realworld outdoor degradation.

backsheet↗

DOE OSTI2023

Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Missing Photovoltaic Data Imputation

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 long-term reliability assessment of PV fleets. Nevertheless, the performance of PV data analysis heavily depends on the quality of PV timeseries data. This paper proposes a novel Spatio-Temporal Denoising Graph Autoencoder (STD-GAE) framework to impute missing PV Power Data. STDGAE 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 that creates 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 (to recover local missing data by observed “neighboring” PV plants) and denoising autoencoder (to recover corrupted data from augmented counterpart) to improve the accuracy of imputation accuracy at PV fleet level. We have evaluated our proposed model on two realworld PV datasets. Experimental results 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 such as MIDA and LRTC-TNN.

Fan, Yangxin↗

DOE OSTI2023

Field retrieved photovoltaic backsheet survey from diverse climate zones: Analysis of degradation patterns and phenomena

Understanding the impact of climate stressors on photovoltaic (PV) backsheet degradation in real-use conditions is critical to improve the accelerated testing exposures, extend the backsheet lifetime, and increase the confidence in PV reliability. Here, in this work, a total of 33 PV module backsheets were retrieved from six climatic zones worldwide with 2 - 28 years of exposure. These modules included five types of backsheet air-side materials (or outer layer): poly(vinylidene fluoride) (PVDF), poly(tetrafluoroethylene-co-hexafluoropropylene-co-vinylidene fluoride) (THV), poly(vinyl fluoride) (PVF), poly(ethylene terephthalate) (PET), and polyamide (PA). Attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) was used to identify air-side materials. The degradation induced color change, gloss loss, and chemical material changes analyzed using optical microscopy, differential scanning calorimetry (DSC), scanning electron microscopy (SEM), colorimetry (yellowness index (YI)), and gloss measurements. PVDF, THV, and PVF air-side layer backsheets, in particular PVF, had minimal degradation in the air-side layer appearance and chemical structures after exposure in different climatic zones. The PET air-side backsheets exhibited obvious color increase (22.55 YI units after about 9 years exposure) and the PA/PA/PA backsheets showed large gloss loss (up to 76.4 %) relative to the unexposed backsheets. Severe cracks between cells that penetrated through the entire thickness of backsheets are observed on PA/PA/PA backsheets after 4-6 years of exposure in 6 climatic zones. The current indoor exposure standards were not sufficient to identify this degradation type. However, fluoropolymer based PV backsheets showed lower levels of degradation predictors and increased climatic resistance. Specific samples (PVF) showed little change from baseline after 28 years of outdoor exposure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Spatiotemporal Modeling of Real World Backsheets Field Survey Data: Hierarchical (Multilevel) Generalized Additive Models

Assessing photovoltaic module backsheet durability is critical to increasing module lifetime. Laboratory-based accelerating testing has recently failed to predict large scale failures of widely adopted polymeric materials. Additionally, there is a growing concern on characterizing the non-uniformity of field exposure. Therefore, data from field surveys are critical to assess the performance of component lifetimes. Using a documented field survey protocol, 19 field surveys were conducted. The focus of this survey strategy is to investigate spatial continuity in degradation modes. By combining field survey data with real-time satellite weather data, stressor / response models have been trained. Generalized additive Models (GAM) model was created to predict the value of degradation based on measured predictors. Two different GAM constructions were testing using different implementations of basis splines. The model includes variables on the environmental stressors of the system and the location of each measurement in the PV mounting structure. The incorporation of hierarchical structure into the models allowed for material specific degradation rates, while maintaining the assumption of a global trend. The model performed well with an adjusted R2 of 0.975 for yellowness index prediction.

backsheet↗

DOE OSTI2022

Spatiotemporal Modeling of Real World Backsheets Field Survey Data: Hierarchical (Multilevel) Generalized Additive Models: Preprint

Assessing photovoltaic module backsheet durability is critical to increasing module lifetime. Lab based accelerating testing has recently failed to predict large scale failures of widely adopted polymeric materials. Field surveyed data is critical to assess the performance of component lifetime. Using a documented field survey protocol, 13 field surveys where conducted. Each measurement is encoded with it's spatial location in respect to the other modules. By combining field survey data on degradation predictors with real time satellite weather data, data-driven predictive models of backsheet degradation were trained. LOESS models were constructed to investigate the spatial dependence of measurements. It was found that micro-climatic effects like treelines, ground surface changes, and elevation changes effected the magnitude and variance of the measurements. A GAM model was created to predict the value of degradation based on measured predictors. The model includes variables on the climate of the system and the location of each measurement in the PV mounting structure. The model performed well with an adj:R2 of 0:95 for yellowness index prediction. The model was cross-validated using k-folds.

backsheet↗