Analysis on the Features of NOCT and NMOT Tests With Photovoltaic Module

Bae, Joon-Hak; Kim, Deok-Young; Shin, Junhwa; Lee, Song-Eun; Kim, Ki-Chai

doi:10.1109/access.2020.3017372

Cited by 9 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a wind speed of 5 m/s with a length of 0.5 m and ambient temperature 25 • C, the suggested model yields 23 W/m K in contrast to available convective heat transfer coefficient of Equations ( 6) and ( 7) that where led to 24 W/m K and 18 W/m K, respectively. Calculating the NOCT temperature according to Equations ( 2) and ( 12) with an assumption of L = 1 m display T C,NOCT = 28.53 + 20 = 48.53 • C which remarkably matches the experimental report for NOCT Tempearure with 45 ± 5 [19,36].…”

Section: Noct Condition and Convection Model Of Wind Speedsupporting

confidence: 76%

Wind Velocity and Forced Heat Transfer Model for Photovoltaic Module

Hassanian,

Yeganeh,

Riedel

2024

Fluids

View full text Add to dashboard Cite

This study proposes a computational model to define the wind velocity of the environment on the photovoltaic (PV) module via heat transfer concepts. The effect of the wind velocity and PV module is mostly considered a cooling effect. However, cooling and controlling the PV module temperature leads to the capability to optimize the PV module efficiency. The present study applied a nominal operating cell temperature (NOCT) condition of the PV module as a reference condition to determine the wind velocity and PV module temperature. The obtained model has been examined in contrast to the experimental heat transfer equation and outdoor PV module performance. The results display a remarkable matching of the model with experiments. The model’s novelty defines the PV module temperature in relation to the wind speed, PV module size, and various ambient temperatures that were not included in previous studies. The suggested model could be used in PV module test specification and provide analytical evaluation.

show abstract

Section: Noct Condition and Convection Model Of Wind Speedsupporting

confidence: 76%

Wind Velocity and Forced Heat Transfer Model for Photovoltaic Module

Hassanian,

Yeganeh,

Riedel

2024

Fluids

View full text Add to dashboard Cite

show abstract

“…Although the detailed study of the photovoltaic (PV) module temperature should include heat-transfer phenomena analysis [16,17], which would lead to transient temperature models, the alternative, analytical steady-state temperature models have been extensively used to determine the PV module temperature in PV plants. These straightforward models only need simple meteorological variables as an input, and led to acceptable degree of accuracy in the prediction of electrical PV performance in PV plants [18]. Some of the proposed models were found to determine the module temperature with an accuracy comparable to the cell-to-cell temperature differences typically encountered within a module [15,19].…”

Section: Background and Objectivementioning

confidence: 98%

“…Different works have assessed NOCT and NMOT models, generally considering openrack-mounted modules [18,32], but some of them addressing related BIPV configurations also. For instance, Davis et al [33] concluded that the NOCT model underpredicted the module temperatures and found temperature differences of up to 20 degrees between insulated and non-insulated vertically mounted modules.…”

Section: Background and Objectivementioning

confidence: 99%

Assessment of PV Module Temperature Models for Building-Integrated Photovoltaics (BIPV)

et al. 2022

View full text Add to dashboard Cite

This paper assesses two steady-state photovoltaic (PV) module temperature models when applied to building integrated photovoltaic (BIPV) rainscreens and curtain walls. The models are the Ross and the Faiman models, both extensively used for PV modules mounted on open-rack support structures in PV plants. The experimental setups arrange the BIPV modules vertically and with different backside boundary conditions to cover the mounting configurations under study. Data monitoring over more than a year was the experimental basis to assess each model by comparing simulated and measured temperatures with the help of four different metrics: mean absolute error, root mean square error, mean bias error, and coefficient of determination. The performance ratio of each system without the temperature effect was calculated by comparing the experimental energy output with the energy output determined with the measured temperatures. This parameter allowed the estimation of the PV energy with the predicted temperatures to assess the suitability of each temperature model for energy-prediction purposes. The assessment showed that the Ross model is the most suitable for predicting the annual PV energy in rainscreen and curtain-wall applications. Highlighted is the importance of fitting the model coefficients with a representative set of in situ monitored data. The data set should preferably include the inner (backside) temperature, i.e., the air chamber temperature in ventilated façades or the indoor temperature in curtain walls and windows.

show abstract

“…The PV produces heat when electricity is produced. This is based on the normal operating cell temperature (NOCT) test conducted at 800 W/m 2 and 20°C ambient temperature [6]. The PV temperature on average is around 45°C, which is 25°C different from the surrounding.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Configurations for Photovoltaic-Thermoelectric Generator Cogeneration System

Ayop,

Tan,

Md Ayob

et al. 2023

Jurnal Teknologi

View full text Add to dashboard Cite

Photovoltaic (PV) converts solar energy to electrical energy directly. During this process, the PV produce energy losses in the form of heat energy. To improve the system's efficiency, this heat energy is converted into electrical energy using the thermoelectric generator (TEG). The PV and TEG have a nonlinear current-voltage characteristic and it requires a power converter with maximum power point tracking (MPPT) to properly extract the energy. There are several configurations of power converters available for the PV-TEG cogeneration system (PTCS). Nonetheless, the literature that compares the performance of the configurations is unavailable. This paper compares 4 configurations for the PTCS, which include the separate boost full MPPT (SBFM), separate boost partial MPPT, series source boost MPPT, and PV boost MPPT with series TEG. The boost converter with perturb and observe MPPT method is used for all configurations to ensure a fair comparison. The results show that SBFM can efficiently extract the energy from both PV and TEG up to 98.5%. The other configurations can efficiently extract energy from the PV (more than 98.5%). However, the energy extracts from the TEG have a low efficiency down to 87%. Overall, the configuration chosen for the PTCS affects the efficiency of the system.

show abstract

Analysis on the Features of NOCT and NMOT Tests With Photovoltaic Module

Cited by 9 publications

References 12 publications

Wind Velocity and Forced Heat Transfer Model for Photovoltaic Module

Wind Velocity and Forced Heat Transfer Model for Photovoltaic Module

Assessment of PV Module Temperature Models for Building-Integrated Photovoltaics (BIPV)

Comparative Study of Configurations for Photovoltaic-Thermoelectric Generator Cogeneration System

Contact Info

Product

Resources

About