Faculty Publications

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Publications by NITK Faculty

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    Explicating fog computing key research challenges and solutions
    (CRC Press, 2021) Martin, J.P.; Singh, V.; Chandrasekaran, K.; Kandasamy, A.
    [No abstract available]
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    Probabilistic Steady-State Analysis of Power Systems Integrated with Renewable Generations
    (CRC Press, 2022) Singh, V.; Moger, T.; Jena, D.
    [No abstract available]
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    Large Power System Stability Analysis Using a FOSS-based tool: SciLab/Xcos
    (Institute of Electrical and Electronics Engineers Inc., 2018) Singh, V.; Navada, H.G.; Shubhanga, K.N.
    This paper describes the usage of an open-source tool namely Scilab-package for development of a multi-machine small-signal stability programme. It is shown that the package has enough computational capabilities to carry out large power system analysis. Analytical and time-domain simulation results obtained for a well-known 4-machine, 10-bus, 10-machine, 39-bus and 50-machine, l45-bus power systems demonstrate that Scilab/Xcos can be an alternate open-source tool to conventional proprietary software. © 2018 IEEE.
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    Quantum Machine Learning: A Review and Current Status
    (Springer Science and Business Media Deutschland GmbH, 2021) Mishra, N.; Kapil, M.; Rakesh, H.; Anand, A.; Mishra, N.; Warke, A.; Sarkar, S.; Dutta, S.; Gupta, S.; Prasad Dash, A.; Gharat, R.; Chatterjee, Y.; Roy, S.; Raj, S.; Kumar Jain, V.; Bagaria, S.; Chaudhary, S.; Singh, V.; Maji, R.; Dalei, P.; Behera, B.K.; Mukhopadhyay, S.; Panigrahi, P.K.
    Quantum machine learning is at the intersection of two of the most sought after research areas—quantum computing and classical machine learning. Quantum machine learning investigates how results from the quantum world can be used to solve problems from machine learning. The amount of data needed to reliably train a classical computation model is evergrowing and reaching the limits which normal computing devices can handle. In such a scenario, quantum computation can aid in continuing training with huge data. Quantum machine learning looks to devise learning algorithms faster than their classical counterparts. Classical machine learning is about trying to find patterns in data and using those patterns to predict further events. Quantum systems, on the other hand, produce atypical patterns which are not producible by classical systems, thereby postulating that quantum computers may overtake classical computers on machine learning tasks. Here, we review the previous literature on quantum machine learning and provide the current status of it. © 2021, Springer Nature Singapore Pte Ltd.
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    Comparative Evaluation of Basic Probabilistic Load Flow Methods with Wind Power Integration
    (Institute of Electrical and Electronics Engineers Inc., 2021) Singh, V.; Moger, T.; Jena, D.
    The unprecedented penetration of distributed energy resources (DERs) such as wind power generations (WPGs) poses tremendous challenges for for the planning and maintenance of power systems due to their intermittent and uncertain nature. This paper mainly focuses on comparing basic probabilistic load flow (PLF) techniques when WPGs are integrated into the existing power grid. Considering loads and WPGs as random inputs, the performance of the cumulant method (CM) and point estimation method (PEM) are analyzed with respect to Monte-Carlo method for higher precision and less computational time. Case-studies are carried out on sample 10-bus and SR 72-bus equivalent systems. Simulation results demonstrated that 2n+1 PEM provides the best performance when dealing with high level of uncertainty associated with input variables. © 2021 IEEE.
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    Extracting Emotion Quotient of Viral Information Over Twitter
    (Springer Science and Business Media Deutschland GmbH, 2022) Kumar, P.; Reji, R.E.; Singh, V.
    In social media platforms, a viral information or trending term draws attention, as it asserts potential user content towards topic/terms and sentiment flux. In real-time sentiment analysis, this viral information deliver potential insights, as encompass sentiment and co-located ranges of emotions be useful for the analysis and decision support. A traditional sentiment analysis tool generates the level of predefined sentiments over social media content for the defined duration and lacks in the extraction of emotional impact created by the same. In these settings, it is a multifaceted task to estimate precisely the emotional quotient viral information creates. The proposed novel algorithm aims, to (i) extract the sentiment and co-located emotions quotient of viral information and (ii) utilities for comprehensive comparison on co-occurring viral informations, and sentiment analysis over Twitter text data. The generated emotion quotients and micro-sentiment reveals several valuable insight of a viral topic and assists in decision support. A use-case analysis over real-time extracted data asserts significant insights, as generated sentiments and emotional effects reveals co-relations caused by viral/trending information. The algorithm delivers an efficient, robust, and adaptable solution for the sentiment analysis also. © 2022, Springer Nature Switzerland AG.
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    An Adaptive Algorithm for Emotion Quotient Extraction of Viral Information Over Twitter Data
    (Springer Science and Business Media Deutschland GmbH, 2022) Kumar, P.; Reji, R.E.; Singh, V.
    In social media platforms, a viral information or trending term draws attention, as it asserts the impact of user content towards topic/terms. In real-time sentiment analysis, these viral terms could deliver potential insights for the analysis and decision support. A traditional sentiment analysis tool generates the level of predefined sentiments over social media content for the defined duration and lacks in the extraction of emotional impact created by the same. In these settings, it is a multifaceted task to estimate precisely the emotional quotient viral information creates. A novel algorithm is proposed, to (i) extract the sentiment and emotions quotient of current viral information over twitter, (ii) compare co-occurring trending/viral information, (iii) in-depth analysis of potential Twitter text data. The generated emotion quotients and micro-sentiment reveals several valuable insight of a viral/trending topic and assists in decision support. A use-case analysis over real-time extracted data asserts significant insights, as generated sentiments and emotional effects reveals co-relations caused by viral/trending information. The algorithm delivers an efficient, robust, and adaptable solution for the sentiment analysis also. © 2022, Springer Nature Switzerland AG.
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    Extracting Emotion and Sentiment Quotient of Viral Information Over Twitter
    (Springer Science and Business Media Deutschland GmbH, 2022) Kumar, P.; Reji, R.E.; Singh, V.
    In social media platforms, viral or trending information are consumed for several decision-making, as they harness the information flux. In apt to this, millions of real-time users often consumed the data co-located to these virilities. Thus, encompass sentiment and co-located emotions, could be utilized for the analysis and decision support. Traditionally, sentiment tool offers limited insights and lacks in the extraction of emotional impact. In these settings, estimation of emotion quotient becomes a multifaceted task. The proposed novel algorithm aims, to (i) extract the sentiment and co-located emotions quotient of viral information and (ii) utilities for comprehensive comparison on co-occurring viral information, and sentiment analysis over Twitter data. The emotion and micro-sentiment reveals several valuable insight of a viral topic and assists in decision support. A use-case analysis over real-time extracted data asserts significant insights, as generated sentiments and emotional effects reveals co-relations caused by viral/trending information. The algorithm delivers an efficient, robust, and adaptable solution for the sentiment analysis also. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Probabilistic Load Flow Considering Load and Wind Power Uncertainties using Modified Point Estimation Method
    (Institute of Electrical and Electronics Engineers Inc., 2022) Singh, V.; Moger, T.; Jena, D.
    Nowadays, renewable energy sources (REs) are increasingly integrated into electrical power networks. Among many REs, wind energy has emerged as a prominent source of electricity. However, rising wind power penetration has increased the system's net generation variability. Consequently, the ability to monitor and simulate the behavior of wind power generation (WPG) in detail is critical. Furthermore, the wind speed or wind power output of different wind farms can be highly interdependent and may not follow Normal distribution. This study proposes a probabilistic load flow (PLF) technique for modeling normally distributed loads and non-normally distributed WPG based on the modified point estimation method (PEM). This modification allows modeling dependent input random variables as a function of many independent ones using the Nataf transformation. By utilizing the findings of the Monte-Carlo method as a reference, the usefulness of the suggested technique is tested by conducting case studies on a 24-bus equivalent system of the Indian Southern region power grid. Simulation results indicate that the modified PEM can easily handle the correlation and have high processing efficiency. © 2022 IEEE.
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    Modified Cumulant based Probabilistic Load Flow Considering Correlation between Loads and Wind Power Generations
    (Institute of Electrical and Electronics Engineers Inc., 2022) Singh, V.; Moger, T.; Jena, D.
    With the growing use of wind sources, power system analysis should consider the variation of wind power and the correlation among wind farms. In this paper, the Cumulant method (CM) for performing probabilistic load flow (PLF) analysis is modified to account for the correlation between random input variables. Considering the dependence between loads and wind power generations (WPGs), the modified CM models the dependent variables as a function of many independent ones using the Nataf transformation. The effectiveness of the suggested method is verified by performing case studies on a 24-bus equivalent system of the Indian southern region power grid. Furthermore, relative error values in reference with the Monte-Carlo simulation (MCS) method are analyzed. © 2022 IEEE.