A measurement platform for photovoltaic performance analysis in environments with ultra-low energy harvesting potential

Masoudinejad, Mojtaba; Emmerich, Jan; Kossmann, Dominik; Riesner, Andreas; Roidl, Moritz; Hompel, Michael ten

doi:10.1016/j.scs.2015.09.006

Cited by 14 publications

(4 citation statements)

References 6 publications

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“…Not only does this coating add a layer of protection against uncontrolled light, but it also prohibits light reflections inside the cabinet that can cause heterogeneous lighting. Although similar physical structure for the measurement of indoor light has been suggested in [17,19], they address issues on providing a homogeneous lighting when the light intensity has to be changed.…”

Section: Measurement Platformmentioning

confidence: 99%

Data-Sets for Indoor Photovoltaic Behavior in Low Lighting Conditions

Masoudinejad

2020

Data

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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.

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Section: Measurement Platformmentioning

confidence: 99%

Data-Sets for Indoor Photovoltaic Behavior in Low Lighting Conditions

Masoudinejad

2020

Data

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“…Since the harvested energy and all measurement parameters are small in value, a lifelike analysis under such values requires very accurate measurements. Therefore, a roughly complex measurement platform was used which is explained with more details in [6] and [7] .…”

Section: Measurement Platformmentioning

confidence: 99%

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

Masoudinejad

Kamat

Emmerich

et al. 2015

2015 3rd International Renewable and Sustainable Energy Conference (IRSEC)

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This paper presents a gray box model of a photovoltaic (PV) cell working under low illuminance artificial indoor lighting conditions. A mathematical model of PV cells with single and two diode configuration is used. Additionally a platform for the measurement of the PV cells within the indoor environment is presented. An optimization problem is formulated to tune the internal parameters of the mathematical models according to the collected data from measurement platform. Fluorescent and halogen lights are used as two common indoor lighting sources.The fine-tuned mathematical model with calculated parameters is implied to evaluate the performance of the gray box model compared to the measured data.Finally, to avoid using multiple models for different environmental conditions, regression techniques are used to generalize the model only based on the irradiance.

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“…“Energy harvesting is defined as the transfer process by which energy source is acquired from the ambient energy, stored in energy storage element and powered to the target systems” (Kunwar et al , 2020). The theory of energy harvesting is: gathering energy from surroundings and offering technological solutions such as solar energy harvesting, wind energy collection and vibration energy harvesting (Pode, 2020; Masoudinejad et al , 2016).…”

Section: Introductionmentioning

confidence: 99%

Neural network-based estimation of lighting condition in indoor environment with improved brain storm algorithm

Patil¹,

Goudar²,

Kharadkar³

2021

JEDT

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Purpose For decades, continuous research work is going on to maximize the power harvested from the sun; however, there is only a limited analysis on exploiting the microwatt output power from indoor lightings. Microelectronic system has power demand in the µW range, and therefore, indoor photovoltaics would be appropriate for micro-energy harvesting appliances. “Energy harvesting is defined as the transfer process by which energy source is acquired from the ambient energy, stored in energy storage element and powered to the target systems”. The theory of energy harvesting is: gathering energy from surroundings and offering technological solutions such as solar energy harvesting, wind energy collection and vibration energy harvesting. “The solar cell or photovoltaic cell (PV), is a device that converts light into electric current using the photoelectric effect”. Factors such as light source, temperature, circuit connection, light intensity, angle and height can manipulate the functions of PV cells. Among these, the most noticeable factor is the light intensity that has a major impact on the operations of solar panels. Design/methodology/approach This paper aims to design an enhanced prediction model on illuminance or irradiance by an optimized artificial neural network (ANN). The input attributes or the features considered here are temperatures, maxim, TSL, VI, short circuit current, open-circuit voltage, maximum power point (MPP) voltage, MPP current and MPP power, respectively. To enhance the performance of the prediction model, the weights of ANN are optimally tuned by a new self-improved brain storm optimization (SI-BSO) model. Findings The superiority of the implemented work is compared and proved over the conventional models in terms of error analysis and prediction analysis. Accordingly, the presented approach was analysed and its superiority was proved over other conventional schemes such as ANN, ANN-Levenberg–Marquardt (LM), adaptive-network-based fuzzy inference system (ANFIS) and brainstorm optimization (BSO). In addition, analysis was held with respect to error measures such as mean absolute relative error (MARE), mean square root error (MSRE), mean absolute error and mean absolute percentage error. Moreover, prediction analysis was also performed that revealed the betterment of the presented model. More particularly, the proposed ANN + SI-BSO model has attained minimal error for all measures when compared to the existing schemes. More particularly, on considering the MARE, the adopted model for data set 1 was 23.61%, 48.12%, 79.39% and 90.86% better than ANN, ANN-LM, ANFIS and BSO models, respectively. Similarly, on considering data set 2, the MSRE of the implemented model was 99.87%, 70.69%, 99.57% and 94.74% better than ANN, ANN-LM, ANFIS and BSO models, respectively. Thus, the enhancement of the presented ANN + SI-BSO scheme has been validated effectively. Originality/value This work has established an improved illuminance/irradiance prediction model using the optimization concept. Here, the attributes, namely, temperature, maxim, TSL, VI, Isc, Voc, Vmpp, Impp and Pmpp were given as input to ANN, in which the weights were chosen optimally. For the optimal selection of weights, a novel ANN + SI-BSO model was established, which was an improved version of the BSO model.

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A measurement platform for photovoltaic performance analysis in environments with ultra-low energy harvesting potential

Cited by 14 publications

References 6 publications

Data-Sets for Indoor Photovoltaic Behavior in Low Lighting Conditions

Data-Sets for Indoor Photovoltaic Behavior in Low Lighting Conditions

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

Neural network-based estimation of lighting condition in indoor environment with improved brain storm algorithm

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