Identifying snow in photovoltaic monitoring data for improved snow loss modeling and snow detection

Øgaard, Mari Benedikte; Aarseth, Bjørn Lupton; Skomedal, Åsmund; Riise, Heine Nygard; Sartori, Sabrina; Selj, Josefine

doi:10.1016/j.solener.2021.05.023

Cited by 25 publications

(7 citation statements)

References 26 publications

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“…During snowfall periods, the PV system power production is reduced, while the module and ambient temperature measurements are lower than the typical operating temperature ranges. It is worth noting here that this is a seasonally repeated performance loss that affects all three electrical parameters (current, voltage and hence power) 70 . Additionally, when snow sheds, an increase in the recorded voltage measurements is initially observed followed by a stepwise reduction 70 …”

Section: Methodsmentioning

confidence: 96%

“…It is worth noting here that this is a seasonally repeated performance loss that affects all three electrical parameters (current, voltage and hence power). 70 Additionally, when snow sheds, an increase in the recorded voltage measurements is initially observed followed by a stepwise reduction. 70 The changepoint_prior_scale setting of FBP is then readjusted to estimate R D and to detect soiling and cleaning events.…”

Section: The Seasonal Hybrid Extreme Studentized Deviates (S-h-esd)mentioning

confidence: 99%

“…70 Additionally, when snow sheds, an increase in the recorded voltage measurements is initially observed followed by a stepwise reduction. 70 The changepoint_prior_scale setting of FBP is then readjusted to estimate R D and to detect soiling and cleaning events. All quantitative metrics, specifications, and models used for differentiating fault types from loss mechanisms in PV systems are summarized in Figure 2 and Table 1 (presented in Section 2.3).…”

Section: The Seasonal Hybrid Extreme Studentized Deviates (S-h-esd)mentioning

confidence: 99%

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Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems

Livera

Theristis

Micheli

et al. 2022

Progress in Photovoltaics

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Fault detection and classification in photovoltaic (PV) systems through real-time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend-based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change-point (CP) models.Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.

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Section: Methodsmentioning

confidence: 96%

Section: The Seasonal Hybrid Extreme Studentized Deviates (S-h-esd)mentioning

confidence: 99%

Section: The Seasonal Hybrid Extreme Studentized Deviates (S-h-esd)mentioning

confidence: 99%

See 1 more Smart Citation

Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems

Livera

Theristis

Micheli

et al. 2022

Progress in Photovoltaics

View full text Add to dashboard Cite

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“…[22,[42][43][44][45][46][47][48] Photovoltaics (PVs) have been deployed significantly in these regions due to their reduced cost; however, due to ice precipitations, using PV panels has produced annual power losses from 15% to 34%. [42,44,49] Snow-related energy losses account for power losses of 35% (annual) and 70% (snow season) for PV modules with a tilt angle of 0°(Calumet, MI, USA); these losses can slightly be reduced to 30% (annual) and 60% (snow season), respectively, with a tilt angle of 35°. [45] Further, precipitated snow creates an albedo effect, reflecting a significant amount of solar radiation into the atmosphere.…”

Section: Deicing and Onsite Power Production Using Aewmentioning

confidence: 99%

Field‐Induced Transparent Electrode‐Integrated Transparent Solar Cells and Heater for Active Energy Windows: Broadband Energy Harvester

Patel

Kim

2023

Advanced Science

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Invisible power generation by natural and artificial light enables sustainability by onsite‐power deployment, lower cost, and minimal burden on the built environment. However, dark, opaque photovoltaics limit light utilization in a transparent way. Herein, it is proposed that the active energy window (AEW) invisibly features power production, providing higher freedom for onsite power generators in window objects without limiting human vision. The AEW has a transparent photovoltaic (TPV) for onsite power and a transparent heater (TH) to remove the effects of shadows from snow and recover the power lost. Moreover, a heating function is applied to remove the effects of weathering related to snow. The proposed prototype integrates a TPV‐TH, offering ultraviolet (UV)‐blocking, daylighting, thermal comfort, and onsite power with a power conversion efficiency of 3% (AM1.5G). Field‐induced transparent electrodes are applied to the TPV‐TH and designed considering the AEW. Owing to these electrodes, the AEW ensure a wide field‐of‐view without optical dead zones, ensuring see‐through vision. The first TPV‐TH integration is performed into a 2 cm2‐window that generates onsite power of 6 mW and has an average visible transmittance of ≈39%. It is believed that light can be utilized with comfort through the AEW in self‐sustainable buildings and vehicles.

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“…[16] The snow model suggested by Marion et al, [17] which is implemented in the simulation tools SAM and pvlib, has been shown to perform better than other available models. [18] The model is, however, not very well tested for ground-mounted PV systems, and it is possible that not all influential effects are included in the model, but we believe that the model is the best available tool for giving an indication of the snow losses for our location. To estimate monthly snow losses, the implementation of the Marion et al model described by Øgaard et al [19] was used.…”

Section: Energy Technology Components At Charging/filling Stationmentioning

confidence: 99%

Hybrid PV Systems and Colocalization of Charging and Filling Stations for Electrification of Road Transport Sector

et al. 2022

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Electrification of the road transport sector likely includes both battery electric (BEV) and hydrogen fuel cell electric vehicles (FCEV). Integration of energy carriers is described as a route forward for efficient integration of renewable energy. The objective of this work is to determine cost‐efficiency improvements with co‐localization of BEV and FCEV stations, and how this impacts optimal sizing of the photovoltaic (PV) production and battery storage. Grid‐connected co‐localized charging/filling stations, situated north of Oslo, Norway, are modeled in HOMER Pro and HOMER Grid. PV production is modeled using PVsyst and a snow loss model to analyze the effect of snow shading on PV production. Demand data for BEV and FCEV are synthesized based on historical traffic data (year 2015–2019) to represent three different cases of BEV/FCEV distribution. Results indicate that co‐localization, i.e., the integration of energy carriers for BEV and FCEV, leads to a marginal cost‐efficiency improvement of 0.1–1.4%, depending on BEV/FCEV distribution and cost assumptions. Co‐localization shows greater benefits for the integration of locally produced renewable power. Due to co‐localization, the cost‐optimal PV capacity is either increased or PV power export is reduced. Stationary batteries are also observed to cost‐efficiently perform peak shaving in a future scenario.

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Identifying snow in photovoltaic monitoring data for improved snow loss modeling and snow detection

Cited by 25 publications

References 26 publications

Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems

Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems

Field‐Induced Transparent Electrode‐Integrated Transparent Solar Cells and Heater for Active Energy Windows: Broadband Energy Harvester

Hybrid PV Systems and Colocalization of Charging and Filling Stations for Electrification of Road Transport Sector

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