Faculty Publications

Now showing 1 - 4 of 4

Case study of a hybrid (Wind and solar) power plant
(2011) Jaralikar, S.M.; Mangalpady, M.
The paper highlights the urgency of utilizing and promoting use of non conventional sources, particularly the wind and solar energy, so as to control the environmental pollution, such as ozone layer depletion, deforestation, loss of biodiversity, global warming etc. As a case study, the various performance factors of a 10 kW hybrid (wind and solar) power plant, which is having 60:40 power generation share of wind power to solar power were analysed. The study shows that there is mismatch between the designed and actual plant load factor (PLF), as well as the power generation share of the wind and solar power plant. It was also found that the plant utilization factor (PUF) was poor and that there is very less scope for installation of solar tracking system. Based on the detailed analysis of obtained results, certain recommendations were made for streamlining and optimizing the power generation capacity, and also for better utilization of generated power.
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.
Development and Evaluation of Dust Cleaning System for a Solar PV Panel
(University of Kuwait, 2022) Tripathi, A.K.; Mangalpady, M.; Ray, S.; Rao, N.R.N.V.; Vamshi Krishna, S.; Durgesh Nandan, D.
The most promising application of solar energy is the conversion of solar energy into electrical energy by using solar photovoltaic (PV) panel. The performance of solar based PV panel is definitely influenced by the amount of solar radiation, which are reaching on the panel surface. Since the solar PV panels are operating in open atmosphere dust particles get deposited on their surfaces and most of the times they have to work in this condition. These deposited dust particles create a layer of dust particles over the panel surface which prevents the 100% penetration of solar radiation into the panel surface. Therefore, proper cleaning of the panel surface becomes very necessary. In order to improve the performance of the PV panel an automatic microcontroller driven dust cleaning technique is developed which is capable of removing the accumulated dust particles from the PV panel surface. Moreover, an experimental study has been performed to analyse the efficiency of this developed techn ique. The developed cleaning system showed an improvement of 27.98% in the output power of PV panel when compared to the dusty panel. © 2022 University of Kuwait. All rights reserved.
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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