Faculty Publications

Now showing 1 - 5 of 5

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%. Â© 2020 Elsevier Ltd. All rights reserved.
Effect of Residual Resist on Performance of Single-Mode 1× 4 Optical Splitter in Photosensitive Polymer
(2010) Singhal, R.; Satyanarayan, M.N.; Pal, S.
Polymer residues are generally left in the Y-junctions of the conventional splitters. Besides increased insertion loss, the Y-junction residue results in asymmetric distribution of power at device outputs. An analysis of the device performance in the presence of junction residue is presented and a design to overcome the non-uniformities in output power distribution brought about by the presence of the residue is proposed. © Taylor & Francis Group, LLC.
Output power loss of photovoltaic panel due to dust and temperature
(International Journal of Renewable Energy Research, 2017) Tripathi, A.K.; Mangalpady, M.; Murthy, C.S.N.
Due to increase in power consumption and greenhouse problem all over the world, an alternative source is necessary for generating clean and environmental friendly electric power. In this regard, solar energy could be a good choice of power generation, since the cost of solar panels decreasing rapidly in the past few years. Moreover, solar energy has also become more efficient as compared to other source of energy systems. The performance of solar photovoltaic (PV) panel depends on the incoming light to panel surface and it is governed by environmental parameters, mainly dust and temperature. Dust shading creates a barrier in the path of incoming sun light, which reduces the amount of sunlight falling on photovoltaic panel surface, and hence power output and performance of panel reduces significantly. The increase in temperature above maximum power point temperature results in power output loss of panel. This paper presents the phenomena of performance degradation of PV panel due to dust shading and temperature.
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.
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

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