Faculty Publications

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Author: search.filters.author.Tripathi, A.K.Subject: search.filters.subject.Solar radiation

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    Evaluation of solar PV panel performance under humid atmosphere
    (Elsevier Ltd, 2020) Tripathi, A.K.; Ray, S.; Mangalpady, A.; Prasad, S.
    The main aim of this paper is to study the effects of humidity on the PV panel. In this paper, the panel performance was studied in the laboratory under varied humid atmosphere. The PV performance parameters were computed by measuring its output voltage and current, amount of solar radiation incident on the panel's surface and its surface temperature by varying humidity levels artificially in the laboratory. From the studies it was observed that with rising humidity levels, solar insolation and panel power output decrease. With an increment of 50.15% in the humidity level, the panel power output reduces by 34.22%. Moreover, it was found that due to the increase in humidity from 65.40% to 98.20% the panel temperature got lowered by 11.40%. © 2020 Elsevier Ltd. All rights reserved.
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    Analysis on photovoltaic panel temperature under the influence of solar radiation and ambient temperature
    (Institute of Electrical and Electronics Engineers Inc., 2021) Tripathi, A.K.; Ray, S.; Mangalpady, M.
    The generation of electrical energy from solar energy is one of the most promising utilization of solar energy technology and it can be achieved by the application of solar photovoltaic (PV) panel. In this paper an experimental study has been conducted to examine the effect of solar radiation and ambient temperature on the surface temperature of the solar photovoltaic panel. With the help of experimental measurements, a multi-linear regression model is developed relating the three quantities. The developed model validated with the actual measured values shows good accuracy with small values of root mean square error. During the study, the recordedvalue of maximum panel temperature was 78.50°C for the atmospheric condition which having solar radiation of 1140 W/m2 and ambient temperature of 36°C. The developed relation and subsequent outcomes of the study will help the PV panel designers and manufacturers incomprehending the effects of atmospheric parameters on the temperature of the photovoltaic panel. © 2021 IEEE
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    Output power enhancement of solar PV panel using solar tracking system
    (Bentham Science Publishers B.V. P.O. Box 294 Bussum 1400 AG, 2019) Tripathi, A.K.; Mangalpady, M.; Murthy, C.S.N.
    Solar Photovoltaic (PV) energy conversion has gained much attention nowadays. The output power of PV panel depends on the condition under which the panel is working, such as solar radiation, ambient temperature, dust, wind speed and humidity. The amount of falling sunlight on the panel surface (i.e., solar radiation) directly affects its output power. In order to maximize the amount of falling sunlight on the panel surface, a solar tracking PV panel system is introduced. This paper describes the design, development and fabrication of the solar PV panel tracking system. The designed solar tracking system is able to track the position of the sun throughout the day, which allows more sunlight falling on the panel surface. The experimental results show that there was an enhancement of up to a 64.72% in the output power of the PV panel with reference to the fixed orientation PV panel. Further, this study also demonstrates that the full load torque of the tracking system would be much higher than the obtained torque, which is required to track the position of the sun. This propounds, that the proposed tracking system can also be used for a higher capacity PV power generation system. © 2019 Bentham Science Publishers.
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    Performance of a PV panel under different shading strengths
    (Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2019) Tripathi, A.K.; Mangalpady, M.; Murthy, C.S.N.
    Solar radiation and the surface temperature of a PV panel are the two key parameters that play an important role in the performance of the PV panel. The shading on the panel surface reduces the solar radiation falling on its surface, thus degrading the panel performance. Moreover, the effect of shading is also influenced by the panel configuration. The performance of the PV panel under different levels of shading strength and panel configuration is the main focus of this study. It was reported that, due to 50% shading of a single cell, the reduction in maximum power output was 25.71%. Similarly, the reduction in maximum power output was 70.27% with 50% shading of the panel surface. Further, in this study, the reduction in the PV panel power output was reported as 16.54% and 6.03% for the series and parallel configuration, under the same level of shading condition. © 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group.
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    Advancing solar PV panel power prediction: A comparative machine learning approach in fluctuating environmental conditions
    (Elsevier Ltd, 2024) Tripathi, A.K.; Mangalpady, M.; Elumalai, P.V.; Karthik, K.; Khan, S.A.; Asif, M.; Koppula, K.S.
    Solar photovoltaic (PV) panels play a crucial role in sustainable energy generation, yet their power output often faces uncertainties due to dynamic weather conditions. In this study, a comparative machine learning approach is introduced, utilizing multivariate regression (MR), support vector machine regression (SVMR), and Gaussian regression (GR) techniques for precise solar PV panel power prediction. The investigation into the impact of environmental factors—solar radiation, ambient temperature, and relative humidity—on PV panel output reveals the superior predictive capabilities of SVMR models. With a mean squared error (MSE) of 0.038, a mean absolute error (MAE) of 0.17, and an R2 value of 0.99, SVMR outperforms GR and MR models. Conversely, Gaussian regression demonstrates comparatively weaker performance, yielding an R2 of 0.88, an MSE of 0.49, and an MAE of 0.63. This research underscores the reliability and enhanced accuracy of the proposed SVMR model in forecasting solar PV panel output. The outcomes presented herein carry significant implications for promoting the widespread adoption of PV panels in electricity generation, particularly in challenging environmental conditions. The findings offer valuable insights into optimizing solar PV deployment, ultimately contributing to the expansion of solar power generation in the national energy landscape. Moreover, the comparative analysis provides insights into how anticipated PV power generation can adapt to varying weather conditions, encompassing factors such as temperature, humidity, and solar radiation. © 2024 The Authors