Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
4 results
Search Results
Item Investigation of performance and technical assessments of hybrid source electric vehicles under different locations and driving conditions(Taylor and Francis Ltd., 2024) Sidharthan P, V.; Kashyap, Y.Sustainable transportation is a significant concept followed by nations implementing Nationally Determined Contributions (NDCs) that reduce emissions and adapt to climate change impacts. Electric vehicle (EV) adoption has accelerated; however, a trade-off exists between EV adoption and EV batteries-Battery charging from the grid (conventional energy sources) and e-wastes from retired batteries deposited in landfills. Thus, EVs associated with renewable energy sources (RES) are an alternate solution. This paper proposes a hybrid source electric vehicle (HSEV) with a high energy-dense supercapacitor (SC) as the primary source and PV energy as the secondary source. An energy management algorithm (EMA) with a modified controller is implemented in a Matlab/Simulink environment. Analysis of HSEV under varying locations (Australia, India, and Scotland), driving profiles (WLTP class-1, IDC, and ECE), and driving times (daytime, nighttime) highlights the importance of the proposed EMA. Grid charging instants are reduced to 3 times per month in Australia under WLTP class-1 cycle employing PV energy. Moreover, SC degradation is least compared to the lithium-ion battery in a BEV (Battery Electric Vehicle), hence avoiding the chances of maintenance and replacements. The proposed HSEV exhibits improved performance compared to BEVs of a similar type under different locations, driving, and environmental conditions. © 2023 Taylor & Francis Group, LLC.Item Ray-Tracing modeling for urban photovoltaic energy planning and management(Elsevier Ltd, 2024) Kosmopoulos, P.; Dhake, H.; Kartoudi, D.; Tsavalos, A.; Koutsantoni, P.; Katranitsas, A.; Lavdakis, N.; Mengou, E.; Kashyap, Y.The traditional Radiative Transfer Modelling solutions for Solar Energy monitoring and forecasting often provide outputs for a single point location or an area location. However, for high resolution representation of areas these solutions suffer due to low simulation speeds. This approach makes it difficult for decision-makers to accurately estimate the solar potential of the administrative area and plan installations accordingly. In this direction, the study introduces three-dimensional Ray-Tracing based radiative modeling which is a high-speed area-based solution for solar energy monitoring. The three-dimensional ray-tracing was simulated by using advanced graphic creation platforms and cloud computing in conjunction with satellite data of the clouds, aerosols, building shadows effects and three-dimensional representations of the city using Cesium 3D tiles and Unreal Engine ®. The entire system was developed in a hybrid model to be exploited by urban planners for solar PV installations and by electricity distribution system operators for energy management and efficient incorporation of the produced energy into the regular and smart grids. This study implements and analyses this Ray-Tracing model for solar photovoltaic energy potential estimation at a rooftop level for the city of Athens, Greece. The total rooftop exploitable area in Athens was found to be close to 34 km2, which is able to massively host distributed PVs followed by almost 4.3 TWh of annually produced energy, whilst Penteli (a Municipality in Athens) possessed a potential of 96.8 GWh with an exploitable area of just 0.8 km2. This amount of energy, in a hypothetical full coverage scenario, is able to provide for 48.7% of Athens's total energy requirement. Similar year-long simulations were conducted using the EU's largest rooftop solar installation at Stavros Niarchos Foundation Cultural Center and randomly selected rooftops having solar installations in different municipalities in Athens. With these estimated solar potential values, the gross savings in natural gas consumption and hence the CO2 equivalent emissions can be computed. With the current estimated solar potential of Athens, the analyzed savings accounted of nearly 2.43 billion euros and 18 MT CO2 equivalent emissions. These computed annual savings are capable of covering installation costs for nearly 100,000 new solar installations. The end-product of this study is the development of a solar cadastre web tool which will support the decision-makers in the energy transition policies and the solar PV penetration into the urban environment and eventually drive the effort into renewable energy transition across the globe. © 2024 The Author(s)Item MSSEAG-UNet: A Novel Deep Learning Architecture for Cloud Segmentation in Fisheye Sky Images and Solar Energy Forecast(Institute of Electrical and Electronics Engineers Inc., 2025) Kumar, A.; Kashyap, Y.; Sharma, K.; Vittal, K.; Shubhanga, K.N.This study analyzes sky images captured using a ground-based fisheye camera, aiming to address the challenge of accurately segmenting clouds, which is difficult due to their fuzzy and indistinct boundaries and uneven lighting conditions. Accurate segmentation of clouds in ground-based sky images is crucial for accurate solar energy forecasting. Motivated by these challenges, this article has proposed a novel deep learning architecture called multispatial squeeze-and-excite attention gated U-Net (MSSEAG-UNet) for cloud segmentation in ground-based fisheye sky images. The proposed architecture integrates a multispatial convolutional (MS-CNN) block and squeeze-and-excitation (SE) blocks in the encoder path to improve multiscale feature extraction (MFF) and recalibrate feature maps, while an attention block is incorporated in the decoder path to emphasize key cloud features. The segmentation performance of the MSSEAG-UNet is compared with five benchmark models, and results show that the proposed model outperforms than all benchmarks models. Furthermore, the segmented cloud images produced by the MSSEAG-UNet are used to calculate the cloud percentage, which is then integrated with the original sky images using a multicolumn convolutional model for global horizontal irradiance (GHI) forecast. GHI forecast is conducted for 15-, 30-, and 60-min ahead timesteps, with the best results achieved for the 60-min forecast, yielding mean absolute error (MAE), mean square error (mse), and RMSE values of 6.245%, 0.683%, and 8.265%, respectively. These results highlight the effectiveness of the proposed approach in improving both cloud segmentation accuracy and short-term solar irradiance forecasting. © 1980-2012 IEEE.Item Design and development of a ground-based kite steer controller for kite-based wind power generation(Springer Science and Business Media Deutschland GmbH, 2025) Castelino, R.V.; Kumar, P.; Kashyap, Y.Kite Power Systems, a class of Airborne Wind Energy Systems (AWES), are capable of harvesting high-altitude wind energy using tethered kites, offering substantial material and efficiency advantages over traditional wind turbines. This paper introduces a novel ground-based Kite Steer Controller (KSC), pivotal for optimizing kite trajectory and power generation. The proposed KSC incorporates a Roll-Pitch-Zone control method, enabling precise steering in figure-of-eight trajectories while maintaining operational efficiency under varying wind conditions, including turbulence. Unlike prior approaches, this study emphasizes a detailed force analysis of control lines, revealing that control forces account for 23% of total aerodynamic forces, and the KSC consumes only 20% of the total power generated during a cycle. Experimental field tests with a 12 m2 Leading Edge Inflatable kite validate the system’s performance, demonstrating robust control capabilities under both steady and turbulent winds. This research advances global efforts in renewable airborne wind energy by presenting a scalable, energy-efficient solution for autonomous kite control, addressing critical challenges in AWES design and deployment. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.