Experimental investigation on minimizing degradation of solar energy generation for photovoltaic module by modified damping systems

Bhore, Chaitanya V.; Andhare, Atul B.; Padole, Pramod M.; Loyte, Akshay; Vincent, J. Sofia; Devarajan, Yuvarajan; Vellaiyan, Suresh

doi:10.1016/j.solener.2022.12.015

Cited by 13 publications

(3 citation statements)

References 42 publications

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“…Their findings highlighted the localization of the greatest strains around topographical features, especially notable after exposure to UV light. Bhore et al [20] conducted a study on PV modules, analyzing modal and harmonic responses with different mounting setups. Their research assists in selecting optimal mounting arrangements to enhance solar energy conversion efficiency and mitigate module degradation due to induced vibrations, including microcracks.…”

Section: Introductionmentioning

confidence: 99%

Integrating machine learning and the finite element method for assessing stiffness degradation in photovoltaic modules

2024

J. Phys.: Condens. Matter

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This study introduces a novel machine learning (ML) method utilizing a stacked auto-encoder network to predict stiffness degradation in photovoltaic (PV) modules with pre-existing cracks. The input data for the training process was derived from numerical simulations, ensuring a comprehensive representation of module behavior under various conditions. The findings highlight the robust predictive capability of the model, as evidenced by its impressive R2 value of 0.961 and notably low root mean square error (RMSE) of 4.02%. These metrics significantly outperform those of other conventional methods, including the convolutional neural network (CNN) with R2 of 0.905 and RMSE of 9.43%, the space vector machine (SVM) with R2 of 0.827 and RMSE of 17.93%, and the random forest (RF) with R2 of 0.899 and RMSE of 11.02%. Moreover, the findings suggest that the predictive dynamics of degradation are affected by the varying weight functions of different input parameters, such as climate temperature, grain size, material effort, and pre-crack size, as the degradation level changes. Furthermore, a geometric analysis reveals model deficiencies where significant overestimations correlate with thicker glass components, while pronounced underestimations are predominantly associated with thinner layers of polycrystalline silicon wafer and Ethylene Vinyl Acetate in the module. As a case study, it demonstrated that to maintain a constant degradation level between 1.30 and 1.32 in a PV module with components featuring consistent geometric attributes, the input parameters must be kept within specific ranges: climate temperature ranging from 33 to 57°C, grain size ranging from 36 to 81 μm, material effort ranging from 0.74 to 0.81, and pre-crack size ranging from 24 to 32 μm. Therefore, this underscores that the ML model not only predicts degradation but also delineates the parameter space required to achieve a consistent output value.

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Section: Introductionmentioning

confidence: 99%

Integrating machine learning and the finite element method for assessing stiffness degradation in photovoltaic modules

2024

J. Phys.: Condens. Matter

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“…These fuels are derived from readily available, renewable waste and shall be acquired from various feedstocks of animal fat and inedible oil groups 4 . Post‐processing fuels possess elevated molecular oxygen and cetane numbers, achieving clean and complete combustion 5 . Existing research proves that these fuels shall be utilized in engines as it needs fewer modifications and exhibits lower HC, Smoke and CO emissions.…”

Section: Introductionmentioning

confidence: 99%

“…4 Post-processing fuels possess elevated molecular oxygen and cetane numbers, achieving clean and complete combustion. 5 Existing research proves that these fuels shall be utilized in engines as it needs fewer modifications and exhibits lower HC, Smoke and CO emissions.…”

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confidence: 99%

Detailed studies on employing fish canning waste as a partial alternative in a research diesel engine: Waste to energy initiation

Devarajan

Munuswamy²,

Ganesan

et al. 2023

Env Prog and Sustain Energy

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This research aims to utilize the energy available from fish canning waste (FCW) as a partial alternative. The fuel is chemically modified to fish canning waste methyl ester (FCWME) by acid‐catalytic transesterification process and blended with diesel at 10%, 20% and 30% volume. The engine utilized in this study is slightly modified by engaging exhaust gas recirculation (EGR) to lower the nitrous oxide (NO) emission of oxygen‐rich fuel blends. Performance tests demonstrated that the FCW/diesel blends show similar patterns to diesel. Higher fuel consumption of 0.01, 0.03 and 0.04 kg/kWh was noted by including FCWME by 10%, 20% and 30% by volume. Power losses of about 1.4%, 1.9% and 2.1% were experienced by blending 10%, 20% and 30% volumes of FCWME with diesel. Conversely, the tailpipe emissions, namely smoke, CO and HC emissions, lowered by 3.7%, 0.017% and 21 ppm, with 117 ppm elevated NO emissions for the FCWME/diesel blends than diesel. The test results revealed that the NO emissions from all test fuels lowered drastically during 10%–30% EGR; 91 ppm reduction in NO emissions was observed at a 30% EGR rate, with a slight increase in other regulated emissions. Considering the encouraging impacts on the environment and improved fuel combustion pattern, FCW biofuel shall be employed as a likely and fractional alternative to diesel fuel.

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Texturing of silicon nitride passivation layers on functional behaviour study of polycrystalline silicon (p-Si) made with plasma enhanced chemical vapour deposition

Rathinavelu,

Upadhyay,

Prabagaran

et al. 2024

J Mater Sci: Mater Electron

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Experimental investigation on minimizing degradation of solar energy generation for photovoltaic module by modified damping systems

Cited by 13 publications

References 42 publications

Integrating machine learning and the finite element method for assessing stiffness degradation in photovoltaic modules

Integrating machine learning and the finite element method for assessing stiffness degradation in photovoltaic modules

Detailed studies on employing fish canning waste as a partial alternative in a research diesel engine: Waste to energy initiation

Texturing of silicon nitride passivation layers on functional behaviour study of polycrystalline silicon (p-Si) made with plasma enhanced chemical vapour deposition

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