A gray box modeling of a photovoltaic cell under low illumination in materials handling application

Masoudinejad, Mojtaba; Kamat, Manjunath; Emmerich, Jan; Hompel, Michael ten; Sardesai, Saskia

doi:10.1109/irsec.2015.7455081

Cited by 13 publications

(8 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Simulink Design Optimization Tool (SDO) was used for parameter identification. Masoudinejad et al [63] investigated the use of a serial GB model of a photovoltaic (PV) cell for low illuminance indoor lighting conditions in a material handling and warehousing. Internal parameters were identified using a least squares method.…”

Section: Materials Processing and Energy Materialsmentioning

confidence: 99%

“…"ANN and GA", "LS cost function", "EKF", "non-linear fitting model" [58][59][60] Water treatment estimation and control aeration tank "serial", "parallel", "combined" "ammonium and nitrate concentration", "dissolution rate" "EKF", "ANN" [61][62][63][64] Material processing and energy materials estimation and control "return materials authorization process", "thermotronic system", "photo-voltaic cell" serial temperature "fuzzy membership function", "SDO", "LS" Table 3. GB modeling application in industrial robots and miscellaneous cases.…”

Section: Paper Industrymentioning

confidence: 99%

See 1 more Smart Citation

Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

et al. 2020

View full text Add to dashboard Cite

Virtual sensors, or soft sensors, have greatly contributed to the evolution of the sensing systems in industry. The soft sensors are process models having three fundamental categories, namely white-box (WB), black-box (BB) and gray-box (GB) models. WB models are based on process knowledge while the BB models are developed using data collected from the process. The GB models integrate the WB and BB models for addressing the concerns, i.e., accuracy and intuitiveness, of industrial operators. In this work, various design aspects of the GB models are discussed followed by their application in the process industry. In addition, the changes in the data-driven part of the GB models in the context of enormous amount of process data collected in Industry 4.0 are elaborated.

show abstract

Section: Materials Processing and Energy Materialsmentioning

confidence: 99%

Section: Paper Industrymentioning

confidence: 99%

Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

et al. 2020

View full text Add to dashboard Cite

show abstract

“…While the two-diode model has been shown to have superior performance in outdoor conditions, conflicting results on its performance have been presented in some of previous studies. For instance, results presented by Masoudinejad et al [9] demonstrated lower performance of the two-diode model when compared to the one-diode model. Moreover, differences in the effects on PV cell performances and losses in indoor lighting conditions have been shown [17], [18].…”

Section: Introductionmentioning

confidence: 96%

A Comparison of One- and Two-Diode Model Parameters at Indoor Illumination Levels

Bader

Oelmann

2020

IEEE Access

View full text Add to dashboard Cite

Indoor photovoltaic (PV) application gains in attraction for low-power electronic systems, which requires accurate methods for performance predictions in indoor environments. Despite this, the knowledge on the performance of commonly used photovoltaic device models and their parameter estimation techniques in these scenarios is very limited. Accurate models are an essential tool for conducting feasibility analyses and component dimensioning for indoor photovoltaic systems. In this paper, we therefore conduct a comparison of the one-and two-diode models with parameters estimated based on two well-known methods. We evaluate the models' performance on datasets of photovoltaic panels intended for indoor use, and illumination conditions to be expected in indoor environments lit by artificial light sources. The results demonstrate that the one-diode model outperforms the two-diode model with respect to the estimation of the overall I-V characteristics. The two-diode model results instead in lower maximum power point errors. Both models show a sensitivity to initial conditions, such as the selection of the diode ideality factor, as well as the curve form of the photovoltaic panel to be modeled, which has not been acknowledged in previous research. INDEX TERMS Indoor photovoltaics, energy harvesting, photovoltaic cell models, one-diode model, twodiode model, parameter estimation.

show abstract

“…Photovoltaic (PV) systems under solar lighting have a long history dating back to the 18th century [1]. Long-term research has led to the maturity of this type of energy harvesting with different fields of application [2]: from power production and heating [3] to outer space energy supply [4]. Due to growing enthusiasm for the Internet of Things and Industry 4.0 revolution [5], a new application domain has been opened in recent years to supply embedded devices and cyber-physical systems [6].…”

Section: Introductionmentioning

confidence: 99%

“…Even Standard Testing Condition (STC) AM1.5 is proposed as a part of the IEC60904-3 standard [16], explaining a specific lighting situation to enable comparison of PV modules' performances. In contrast, there are a limited number of works on the indoor performance and analysis of small scale PV modules [2]. In addition to smaller physical size of indoor PV device than a typical solar module, light absorbed by them has a different spectrum [17].…”

Section: Introductionmentioning

confidence: 99%

Data-Sets for Indoor Photovoltaic Behavior in Low Lighting Conditions

Masoudinejad

2020

Data

Self Cite

View full text Add to dashboard Cite

Analysis of voltage–current behavior of photovoltaic modules is a critical part of their modeling. Parameter identification of these models demands data from them, measured in realistic environments. In spite of advancement in modeling methodologies under solar lighting, few analyses have been focused on indoor photovoltaics. Lack of accurate and reproducible data as a major challenge in this field is addressed here. A high accuracy measurement setup for evaluation and analysis of indoor photovoltaic modules is explained. By use of this system, different modules are measured under diverse environmental conditions. These measurements are structured in data-sets that can be used for either analysis of physical environment effects and modeling or development of specific parameter identification methods in low light intensity conditions.

show abstract

A gray box modeling of a photovoltaic cell under low illumination in materials handling application

Cited by 13 publications

References 20 publications

Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data

A Comparison of One- and Two-Diode Model Parameters at Indoor Illumination Levels

Data-Sets for Indoor Photovoltaic Behavior in Low Lighting Conditions

Contact Info

Product

Resources

About