Browsing by Author "Singh, V."

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A modified point estimate-based probabilistic load flow approach for improving tail accuracy in wind-integrated power systems
(Elsevier Ltd, 2025) Singh, V.; Moger, T.; Jena, D.
Modern power systems confront risks, including demand variations and forced outages of traditional generators. Moreover, the extensive grid integration of new energy generation has exacerbated the uncertainty because of its intermittent nature. The Hong's three-point estimation method (3PEM) for performing probabilistic load flow (PLF) is commonly used to cope with power system uncertainties; however, it has poor tail accuracy. To overcome this issue, the basic 3PEM is modified by adding a new pair of tail points. This modified 3PEM (MH3PEM) is equivalent to 5PEM but utilize reduced order moments. Also, a hybrid Hong-Harr PEM approach is proposed to efficiently deal with a mixture of independent and correlated input variables. The input variables’ correlation is modeled using the Nataf transformation. The proposed approaches are tested on wind farm-integrated 24-bus and 72-bus equivalent systems, and their findings are compared with the fundamental PEM schemes. Utilizing the Monte-Carlo simulation as a reference, the MH3PEM provides the most accurate results with a low computational burden. © 2025 Elsevier B.V.
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. Â© 2022, Springer Nature Switzerland AG.
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. Â© 2021 IEEE.
Deflection Surface Analysis of Thin Plate Structures Using Regression Technique
(Springer Science and Business Media Deutschland GmbH, 2023) Singh, V.; Prashanth, M.H.
Deflection analysis of any structure is of common interest while designing the structure. Analysis for deflection in thin plate structures is generally done with the help of Kirchhoffâ€™s plate theory, also known as classical plate theory. Kirchhoffâ€™s plate theory helps in developing a fourth-order partial deflection equation relating the deflection of a plate to the loading condition on the plate and the material or bending rigidity of the plate. The present study focuses on the analysis of a simply supported thin concrete plate that is subject to uniform loading over the entire plate area. The deflection surface of the plate is developed using Navierâ€™s double trigonometric Fourier series. Regression analysis is done to understand how various plate parameters like material rigidity, loading on the plate, and area of the plate could affect the magnitude of deflection of the plate. Also, the effect of the mentioned plate parameters on the magnitude of bending moments, twisting moments, and shear forces acting on the plate is studied. Regression modelling is used to achieve the same. Statistical metrics like R-squared error, Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are used to check the efficiency of regression analysis. In the present study, the regression modelling technique is also used to solve the fourth-order partial differential equation. Â© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Explicating fog computing key research challenges and solutions
(CRC Press, 2021) Martin, J.P.; Singh, V.; Chandrasekaran, K.; Kandasamy, A.
[No abstract available]
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. Â© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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. Â© 2022, Springer Nature Switzerland AG.
Large Power System Stability Analysis Using a FOSS-based tool: SciLab/Xcos
(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.
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. Â© 2018 IEEE.
Maximum entropy based probabilistic load flow for assessing input uncertainties and line outages in wind-integrated power systems
(Elsevier Ltd, 2025) Singh, V.; Moger, T.; Jena, D.
The swift expansion of distributed generation, particularly from photovoltaics and wind turbines, poses a formidable challenge to conventional probabilistic load flow (PLF) methods. This paper addresses the urgent need for a robust and efficient PLF approach by investigating a maximum entropy (ME) based probabilistic density function (PDF) approximation, utilizing advanced cumulant arithmetic from linearized power flow formulation. The ME-PLF method notably enhances the accuracy of output PDFs under extensive uncertainties, such as load demand fluctuations and disturbances in network branches. Unlike the Gram–Charlier expansion (GCE) reconstruction method, ME-PLF effectively eliminates the issue of erroneously obtaining negative values in the tail regions of the PDFs. Additionally, the fundamental cumulant method (CM) is refined to better model dependencies between wind power generators (WPGs) and loads. The simulations are conducted using the MATLAB programming software. Results from practical test systems have been validated against those obtained using the Monte Carlo simulation method. The suggested method has been proven to be highly effective due to its preciseness and reduced computational effort. © 2025 Elsevier B.V.
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. Â© 2022 IEEE.
Modified matrix of ZnO prismoid structures for improved photocatalytic activity: A theoretical and experimental insight
(Elsevier B.V., 2024) Manohar, A.; Kompa, A.; Christopher, B.; Shil, S.; Rao, K.; Udayshankar, N.K.; Mahesha, M.G.; Singh, V.; U, U.
Currently, the world needs low-cost and high-performance photocatalysts to degrade the carcinogenic pollutant from water. In the present work, a modified ZnO matrix using Mg as a dopant has been reported with theoretical and experimental results to highlight its structure and functions on photocatalytic activity. A versatile chemical co-precipitation technique was employed to get the Mg-ZnO nanostructures. Structural characterization by high-resolution transmission electron microscopy (HRTEM) and X-ray diffraction (XRD) studies show the formation of hexagonal wurtzite structures with no impurity phases. Optical studies confirm the formation of ZnO with intrinsic defects after modifying the matrix, which agrees with the band structure calculations computed using density functional theory (DFT). Mg-modified ZnO introduced intrinsic defects like vacancies and interstitials that have a great impact on applications like photocatalysis. Based on these supporting results we employed prepared samples for dye degradation, which performed well (80% degradation efficiency) in a short period of UV irradiation. This could be a promising technology for environmental remediation. © 2023 Elsevier B.V.
Novel application of graphite-talc hybrid nanoparticle enriched cutting fluid in turning operation
(Elsevier Ltd, 2021) Singh, V.; Sharma, A.K.; Sahu, R.K.; Katiyar, J.K.
In this study, the influence of hybrid nanocutting fluid (both graphite and talc nanoparticles dispersed in a base fluid) in turning of Titanium alloy grade 5. The hybrid nanocutting fluid was developed by the blending of graphite and talc nanoparticles in a constant volumetric proportion (50:50) in pure coconut oil as a base fluid. The prepared hybrid nanocutting fluid has been investigated for its tribological behaviour using a pin-on-disc machine. The Gray relational analysis (GRA) is applied as a conservative approach in the optimization of process variables of Titanium alloy with multiple performance characteristics. The turning performance of the hybrid nanocutting fluid is compared with that of pure coconut oil in terms of cutting force and surface roughness. From the Gray relational grade analysis, it is obtained that the feed rate has a larger influence on responses as compared to cutting speed and nanoparticle concentration as well. By the application of hybrid nanocutting fluid, it is obtained a significant reduction in cutting force and surface roughness compared to pure coconut oil by 21.19 % and 18.9 %, respectively. © 2020 The Society of Manufacturing Engineers
Probabilistic Load Flow Approach Combining Cumulant Method and K-Means Clustering to Handle Large Fluctuations of Stochastic Variables
(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, V.; Moger, T.; Jena, D.
The modern electrical power system faces various uncertainties, including load fluctuations, forced outages of conventional generators, network branches. Furthermore, the rising penetration of wind power generation introduces additional uncertainty, causing difficulties in power system planning, operation. This paper uses an analytical probabilistic load flow approach to account for all such uncertainties. The random branch outages are simulated using the fictional powers injections into the relevant nodes. A fuzzy method is used to perform contingency sequencing to avoid masking mistakes that might occur when utilizing performance index-based sequencing methods. The sparse Jacobian inverse is eliminated to preserve storage space, accelerate the computation. A modified Cumulant method is used in conjunction with the K-means clustering process to deal with the substantial fluctuations of the input variables. In the proposed approach, the correlated samples are generated using inverse Nataf transformation. These correlated samples are clustered using K-means clustering. The Cumulant method is applied within each cluster, total probability law is used to integrate each cluster's findings. The proposed PLF is tested on 24-bus, 259-bus wind integrated equivalent systems. Compared with the Monte-Carlo simulation, the proposed PLF yields computationally efficient, more accurate findings. © 1972-2012 IEEE.
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. Â© 2022 IEEE.
Probabilistic Load Flow for Wind Integrated Power System Considering Node Power Uncertainties and Random Branch Outages
(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, V.; Moger, T.; Jena, D.
This paper proposes an analytical probabilistic load flow (PLF) approach that considers conventional generator outages, load variability, and random branch outages. The branch outages are modeled as 0-1 distributions of fictitious power injections at the appropriate nodes. The distribution of state variables and line power flows is then obtained using a combined Cumulant and Gram-Charlier series expansion approach. The proposed PLF performs contingency sequencing with fuzzy logic to eliminate random line checking and avoid masking mistakes faced by performance index-based algorithms. The Jacobian inverse calculation in the traditional Cumulant method is eliminated to conserve storage space and speed up the computation using the Gauss-Jordan method. The correlations among loads and wind power generations has been modeled using the Nataf transformation process. Results of 24-bus and 259-bus equivalent systems of the Indian southern and western power grids are analyzed and validated with those obtained using the Monte Carlo simulation method. The suggested method's efficacy is justified by its accuracy and low computational burden. © 2010-2012 IEEE.
Probabilistic Steady-State Analysis of Power Systems Integrated with Renewable Generations
(CRC Press, 2022) Singh, V.; Moger, T.; Jena, D.
[No abstract available]
PUF-Based Ownership Transfer Using Blockchain
(Springer Science and Business Media Deutschland GmbH, 2025) Cunha, T.B.D.; Manjappa, M.; Singh, V.; Anand, A.
Counterfeiting of electronic components in the branded products is one of the most important and difficult issues to deal with in national/international markets along with the trusted ownership transfer of the product. Today we have to trust an individual while buying a product believing that the product is not tampered. But, we do not have any trusted source which can back this claim. This creates a lot of speculation in the market. For a long time RFID tags were used to find the anti counterfeits in the supply chain, but the problem with the RFID tag is that they can be cloned and hence the authenticity of the tags over the network is questionable. Hence, in order to counter this, we are leveraging blockchain technology to build a novel ownership transfer protocol where the ownership transfer mechanism is secured and authenticated using Physically Unclonable Functions (PUF). The genuinity of the product is checked by PUF by using Challenge Response check during the ownership transfer. Further, the ownership transfer history of the particular product is also maintained in the blockchain which helps the buyer to get more details on the product. The proposed blockchain architecture also provides a temporary ownership transfer option for the owners during servicing or leasing. The proposed architecture is implemented in ethereum blockchain platform and tested for its efficiency. The architecture is found to be promising. Â© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
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 areasâ€”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. Â© 2021, Springer Nature Singapore Pte Ltd.
Simulation of Radio over Fiber System for Microwave Signal Generation using Sub- Carrier Modulation Technique
(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, V.; Meena, K.S.R.; Singh, M.
Nowadays, Radio over fiber (RoF) technology is used in a variety of applications, including wireless communication systems, satellite communication systems, and radar systems. In this paper, we reported a sub-carrier modulation with Amplitude Shift Keying (SCM-ASK) system which supports the transmission of various signals across a single-mode fibre with enhanced encryption efficiency. The quality of the received signal is usually poor in RoF systems, a number of factors may contribute to this problem, such as high bit error rates (BER), low Q-factor values, and the receiver may not be working in a high data rate network. To overcome this, the Q-factor needs to be raised while BER should be brought down to assured values. Using Opti-System software, SCM-ASK is investigated at various channel spacings and fibre lengths. Also, the RoF system performance is analyzed in terms of BER, Q-factor, and the eye diagram patterns. Â© 2023 IEEE.