Faculty Publications
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Publications by NITK Faculty
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Item PV Power Prediction and Investigation for Movable Solar PV Module(Institute of Electrical and Electronics Engineers Inc., 2022) Manohar, K.A.; Bairwa, B.; Yaragatti, U.R.; Raghu, C.N.In this paper, A moving solar panel's power output, and solar irradiation output are compared to a fixed solar panel experimentally. The solar module's performance was first measured while it was in a fixed position and then dual axis solar tracker is employed to rotate the solar module to track the sun in two axes while the appropriate measurements were taken. solar modules using solar trackers will increase the output of annual solar irradiation that incident on the module surface compared to a fixed solar module at an optimum tilt angle. A moving solar module produces higher power output than a fixed module. Thus, it produces higher power output which means it utilizes the most of available solar radiation effectively. © 2022 IEEE.Item Solar Irradiation Forecast Enhancement Using Hybrid Architecture(Institute of Electrical and Electronics Engineers Inc., 2023) Chiranjeevi, M.; Karlamangal, S.; Moger, T.; Jena, D.Power balancing at the grid is much more involved process due to the fact that solar power generation is primarily weather dependent, as it is relied on solar irradiation, which is very volatile and unpredictable. Accurate solar irradiation forecasting can significantly increase the performance of solar power plants. This research is motivated by the current advancements in deep learning (DL) models and its practical use in the green energy field. The proposed model combines two DL architectures: convolutional neural network (CNN) and long short-term memory (LSTM). The effectiveness of the same is analysed by comparing with recurrent neural network (RNN) family architectures. The RNN family models are Long Short Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and Bi-directional GRU (Bi-GRU). The simulations are conducted on a publicly available data set from Desert Knowledge Australia Solar Centre (DKASC), Australia. A meteorological station across the Northern Territory (NT Solar resource) collects high resolution solar and climate data from Darwin location, which is used for the experiment. From the results, it is evident that each of the bidirectional model outperform its unidirectional equivalent architectures. However, the hybrid network (CNN-LSTM) outperforms all the individual models as per the error metric analysis. © 2023 IEEE.Item Preprocessing Techniques of Solar Irradiation Data(Institute of Electrical and Electronics Engineers Inc., 2023) Chiranjeevi, M.; Karlamangal, S.; Moger, T.; Jena, D.; Agarwal, A.Solar energy being abundant, non-exhaustive, environmentally friendly attracts the people attention towards the alternate renewable energy. High-quality time series data is essential for producing an accurate estimate of solar power generation. In most cases, the plethora of information hidden in time series data cannot be accessed. Common issues with time series include outliers, noise, missing data, and a lack of order in the timestamps itself that impair forecasting accuracy. So, preprocessing of the input data is a mandate in order to achieve a precise and dependable forecast. This study proposes various pre-processing techniques to improve the performance of the forecasting accuracy. The different ways to handle the missing values and outliers detection by sliding window method and box plots are presented in this study. The solar irradiation data collected from solar center Alice Springs, Australia used for validation of the preprocessing results. The efficacy of the proposed method in detecting the missing values and outliers is effective from the obtained results. © 2023 IEEE.Item Solar assisted photocatalytic degradation of organic pollutants in the presence of biogenic fluorescent ZnS nanocolloids(Elsevier Ltd, 2019) Uddandarao, P.; Hingnekar, T.A.; Mohan Balakrishnan, R.M.; Rene, E.R.The main aim of this study was to ascertain the photocatalytic degradation of organic pollutants present in aqueous phase using fluorescent biogenic ZnS nanocolloids produced from an endophytic fungus Aspergillus flavus. The degradation studies were carried out using different organic pollutants such as methyl violet (MV), 2,4-dichlorophenoxyacetic acid (2,4-D) and paracetamol (PARA) for 120 min, 270 min and 240 min, respectively, at pH varying from 3.0 to 11.0. The results from this study indicate that the degradation efficiency of ZnS nanocolloids for MV, 2,4-D and PARA were 87%, 33% and 51%, respectively, at the optimum concentration of 100 mg/L of the tested organic pollutants. At different time intervals, the samples were analyzed for their chemical oxygen demand (COD) and total organic carbon (TOC) contents. The reduction of COD and TOC were 78% and 74% for MV at 120 min; 55.5% and 57.2% for 2,4-D at 270 min and 47.6% and 44.5% for PARA at 240 min, respectively. The degradation pathway was determined based on the mass spectrum and the intermediates formed; in addition, the interaction between organic pollutants and nanocolloids was also elucidated based on atomic force microscopy (AFM) and fluorescence spectrum. © 2019 Elsevier LtdItem Solar Irradiation Prediction Hybrid Framework Using Regularized Convolutional BiLSTM-Based Autoencoder Approach(Institute of Electrical and Electronics Engineers Inc., 2023) Chiranjeevi, M.; Karlamangal, S.; Moger, T.; Jena, D.Solar irradiance prediction is an essential subject in renewable energy generation. Prediction enhances the planning and management of solar installations and provides several economic benefits to energy companies. Solar irradiation, being highly volatile and unpredictable makes the forecasting task complex and difficult. To address the shortcomings of the traditional approaches, this research developed a hybrid resilient architecture for an enhanced solar irradiation forecast by employing a long short-term memory (LSTM) autoencoder, convolutional neural network (CNN), and the Bi-directional Long Short Term Memory (BiLSTM) model with grid search optimization. The suggested hybrid technique is comprised of two parts: feature encoding and dimensionality reduction using an LSTM autoencoder, followed by a regularized convolutional BiLSTM. The encoder is tasked with extracting the key features in order to deduce the input into a compact latent representation. The decoder network then predicts solar irradiance by analyzing the encoded representation's attributes. The experiments are conducted on three publicly available data sets collected from Desert Knowledge Australia Solar Centre (DKASC), National Solar Radiation Database (NSRDB), and Hawaii Space Exploration Analog and Simulation (HI-SEAS) Habitat. The analysis of univariate and multivariate-multi step ahead forecasting performed independently and it is compared with the conventional approaches. Several benchmark forecasting models and three performance metrics are utilized to validate the hybrid approach's prediction performance. The results show that the proposed architecture outperforms benchmark models in accuracy. © 2013 IEEE.Item An innovative competence square technique for PV array reconfiguration under partial shading conditions(Taylor and Francis Ltd., 2024) Kumar, D.; Raushan, R.The power generated by the photovoltaic (PV) array reduces drastically due to irregular solar irradiation. Shading pattern, shaded area, and array configuration are the main causes of reduction in power generation. The electrical characteristics of the modules in the array possess several peaks due to the fact that they produce distinct row currents and have various peaks in their electrical characteristics. Therefore, an appropriate technique is needed to minimize the row current difference that leads to maximum power generation at a higher level. This can be achieved by array reconfiguration; therefore, an Innovative Competence Square technique is proposed in this paper. The proposed technique disperses the shade effectively over the entire array by displacing the modules. The MATLAB simulation was run under three distinct shading conditions to validate the proposed technique’s performance, and it has been compared to the Competence Square technique and TCT interconnection. The technique’s benefits can be seen in terms of increased power generation, FF, and reduced mismatch losses. © 2023 Informa UK Limited, trading as Taylor & Francis Group.Item Solar irradiation forecast enhancement using clustering based CNN-BiLSTM-attention hybrid architecture with PSO(Taylor and Francis Ltd., 2024) Chiranjeevi, M.; Madyastha, A.; Maurya, A.K.; Moger, T.; Jena, D.Accurate solar irradiation forecasting is essential for optimising solar energy use. This paper presents a novel forecasting approach: the ‘Clustering-based CNN-BiLSTM-Attention Hybrid Architecture with PSO’. It combines clustering, attention mechanisms, Convolutional Neural Networks (CNN), Bidirectional Long-Short Term Memory (BiLSTM) networks, and Particle Swarm Optimisation (PSO) into a unified framework. Clustering categorises days into groups, improving predictive capabilities. The CNN-BiLSTM model captures spatial and temporal features, identifying complex patterns. PSO optimises the hybrid model’s hyperparameters, while an attention mechanism assigns probability weights to relevant information, enhancing performance. By leveraging spatial and temporal patterns in solar data, the proposed model improves forecasting accuracy in univariate and multivariate analyses with multi-step predictions. Extensive tests on real-world datasets from various locations show the model’s effectiveness. For example, with NASA power data, the model achieves a Mean Absolute Error (MAE) of 24.028 W/m2, Root Mean Square Error (RMSE) of 43.025 W/m2, and an R2 score of 0.984 for 1-hour ahead forecasting. The results show significant improvements over conventional methods. © 2024 Informa UK Limited, trading as Taylor & Francis Group.Item An adaptive modeling for bifacial solar module levelized cost and performance analysis for mining application(John Wiley and Sons Ltd, 2024) Shiva Kumar, B.S.; Kunar, B.M.; Murthy, C.S.N.Power density and efficiency typically dominate design approaches for power electronics. However, cost optimality is in no way guaranteed by these strategies. A design framework that minimizes the (i) levelized cost of electricity (LCOE), (ii) collection of light, and (iii) irradiance of the generation system is proposed as a solution to this flaw. From an improvement of the swarm behavior optimization model to get a minimum LCOE of solar panel, we design to optimize height, tilt angle, azimuth angle, and some parameters to solve the objective function and LCOE improvement problem to obtain the optimal design problem. In adaptive salp swarm optimization (ASSO), this change's proposed model producer swarm behavior is regarded as an adaptive process that keeps the algorithm from prematurely converging during exploration. The proposed algorithm's performance was confirmed using benchmark test functions, and the results were compared with those of the salp swarm optimization (SSO) and other efficient optimization algorithms. LCOE condition as far as “land-related cost” and “module-related cost” demonstrates that the optimal design of bifacial farms is determined by the interaction of these parameters. This proposed model can be used to evaluate visibility on building surfaces that are suitable for mining applications like crushing. Experimentation results show Minimum LCOE AS 0.05 (€/Kw)minimum irradiance and collection light as 336.23(w/m2) and 83.02%n proposed framework model. The swarm optimization method is contrasted with the optimal parameters derived from a conventional solver. © 2023 John Wiley & Sons Ltd.