J.JAYAKUMAR

@karunya.edu

Professor
Karunya Institute of Technology and Sciences



              

https://researchid.co/jayakumar23

RESEARCH INTERESTS

Power system
Renewable Energy
Machine Learning
Artificial Intelligence
Optimization

FUTURE PROJECTS

Development of Smart Grid Test bed system

Developing 5 bus system


Applications Invited
Knowledge in smart grid
75

Scopus Publications

643

Scholar Citations

13

Scholar h-index

19

Scholar i10-index

Scopus Publications

  • Fractional order sliding mode control for power quality improvement in the distribution system
    Khammampati R. Sreejyothi, P. Venkatesh Kumar, and J. Jayakumar

    Institute of Advanced Engineering and Science
    This paper presents fractional order sliding mode control (FOSMC) based distribution system compensator (DSTATCOM) for power quality improvement in the distribution system. The three-phase two-level inverter-based voltage source converter (VSI) with DC-link capacitor is used as DSTACOM. In this paper, the FOSMC-based DSTATCOM improves supply current harmonics, load balancing, and reactive power and reduces THD. The sinusoidal pulse width modulation (SPWM) is generating gating pulses for VSI. The performance of the presented system is verified in MATLAB/Simulink software. The simulations are verified source voltage, current and load current as well as compensating current. The FOSMC has maintained a constant supply current when connecting non-linear load. The hardware results are also presented in the manuscript. The hardware results are supply current, voltage, compensating current, and load current.

  • A Hybrid Intelligent Controller for Extended-Range Electric Vehicles
    Jayakumar Jayaraj, Dakka Obulesu, Hemaprabha Govindaraj, Francisxavier Thomas, Nagalingam Rajeswaran, Chilakala Rami Reddy, Abdullah S. Algarni, Abdullah Alwabli and Saeed Faisal Malky


    A smart battery electric vehicle control framework is proposed in this paper. The specific controller empowers ceaseless observation and management of the battery's state with the scope of extending the vehicle's driving range under varying temperature and driving pattern conditions. The proposed method utilizes an incorporated scheme for dealing with a crossover energy stockpiling framework to expand a battery's lifespan while further ensuring its smooth activity.

  • The hybrid solar energized back-to-back high voltage direct current modular converter for distributed networks
    Karunakar Thadkapally, Francisxavier Thomas Josh, Jeyaraj Jency Joseph, and Jayaraj Jayakumar

    Institute of Advanced Engineering and Science
    High voltage direct current (HVDC) transmission is flexible towards the power control (produced by solar or wind) and can be transported over thousands of kilo meters with minimal losses over the high voltage alternative current (HVAC). It allows solar power to be integrated into the current power grid on a large scale. The author view in this article aims at providing an overview of methods used to integrate HVDC and solar systems. MATLAB/Simulink is used to simulate the solar power integration with HVDC transmission link. This article emphaises solar energy and grid integration, which results in quality and controlled electricity to the grid. Further the simulation studies are compared with real time data between the stations Pugalur AC grid (high solar energy region) and Thrissur AC grid (low solar energy region). Obtained results from the simulation, voltage and currents and power quality stresses the superiority towards the solar integration. The comparison studies enumerate the need to go situation for HVDC technology during the penetration of solar voltaic penetration into the utility network.

  • An Improved MPPT Approach Using Artificial Neural Network for PV Grid System
    Blessy A Rahiman, J. Jayakumar and R. Meenal


    Nowadays, in transportation systems, the clean energy aspect of solar photovoltaic (PV) energy is becoming more and more popular. However, the nonlinear environmental dependence of solar PV is its main drawback. Voltage management and effective Maximum Power Point Tracking (MPPT) techniques are essential to maximize the power produced from PV systems. This proposed work aims to integrate an Artificial Neural Network (ANN) based MPPT for PV-tied grid systems with a Boost converter. The fluctuating DC voltage from the PV panels is converted by the proposed Boost converter into a stable and appropriate voltage level for grid integration with high efficiency and low Total Harmonic Distortion (THD). Additionally, this work uses the ANN-based MPPT technique to track the PV system's optimal power, leading to better tracking accuracy and faster convergence. The single phase VSI converts the DC input to AC output for power supply to the grid system with the aid of a PI controller. The MATLAB/Simulink is used to implement the entire proposed system, and a comparison is made with the existing topologies (MPPT, P&O Based MPPT) to demonstrate the significance of the implemented work.


  • Random forest machine learning algorithm based seasonal multi-step ahead short-term solar photovoltaic power output forecasting
    Sravankumar Jogunuri, Josh F.T, Albert Alexander Stonier, Geno Peter, Jayakumar Jayaraj, Jaganathan S, Jency Joseph J and Vivekananda Ganji


    To maintain grid stability, the energy levels produced by sources within the network must be equal to the energy consumed by customers. In current times, achieving energy balance mainly involves regulating the electrical energy sources, as consumption is typically beyond the control of grid operators. For improving the stability of the grid, accurate forecasting of photovoltaic power output from largely integrated solar photovoltaic plant connected to grid is required. In the present study, to improve the forecasting accuracy of the forecasting models, onsite measurements of the weather parameters and the photovoltaic power output from the 20 kW on‐grid were collected for a typical year which covers all four seasons and evaluated the random forest techniques and other techniques like deep neural networks, artificial neural networks and support vector regression (reference in this study). The simulation results show that the proposed random forest technique for the forecasting horizon of 15 and 30 min is performing well with 49% and 50% improvements in the accuracy respectively over reference model for the study location 22.78°N, 73.65°E, College of Agricultural Engineering and Technology, Anand Agricultural University, Godhra, India.

  • Fuzzy Logic Controlled Current-Mode Switch Mode Power Supply with Enhanced Steady State Response


  • Brain Tumor Classification and Identification using PSO and ANFIs
    Jayashree S. Awati and Mahesh Kumbhar

    FOREX Publication
    Fast Computer-Aided Diagnostic Systems (CAD) have become instrumental in diagnosing diseases. Brain tumors, in particular, pose a significant health challenge. Traditional tumor detection methods relied on radiologists and biopsy, which are time-consuming and detrimental to patients. Early detection is crucial for effective treatment. This system leverages image processing, SWARM intelligence, and Support Vector Machines (SVMs) to detect and classify brain tumors swiftly and accurately. Image processing encompasses preprocessing, segmentation, and feature extraction, with the Particle Swarm Optimization (PSO) method optimizing feature selection. SVMs identify tumor types. While various techniques exist for tumor detection, none achieve 100% accuracy. This system is engineered to provide precise detection.

  • Predictive analysis, diagnosis of COVID-19 through computational screening and validation with spectro photometrical approach
    J. Jayakumar, Arunraj Ebanesar, and Sneha Gautam

    Springer Science and Business Media LLC

  • Time-Series based Household Electricity Consumption Forecasting
    Anita Philips and J. Jayakumar

    IEEE
    Data analytics using machine learning technologies when applied to the energy consumption data can provide valuable inputs for maintaining the perfect supply demand balance in a smart electrical grid system. In particular, the accurate predictions of energy consumption for future periods of time aids significantly in cost-cutting and energy saving for utility companies. Making use of the popular method of time-series forecasting and the Artificial Neural Networks (ANN) models, here in this paper, one of the variants of the Recurrent Neural Networks (RNN) model, the Long Short Term Memory (LSTM) model is applied for household electricity consumption forecasting. Real datasets from consumption building are used for experimenting the model and applied through Tensorflow platform with the keras functions in Python. The results obtained show significantly accurate values in predicting future consumption derived from models training with actual values of current consumption. Hence, this work provides yet another proof that the LSTM machine learning forecasting methods can be efficiently applied for household electricity forecasting.

  • loT based Patient Monitoring System towards Improving quality of life for Patients with Mild Respiratory Failure
    S. Vijai, J. Jayakumar, P. S. Hency Jose, K. Rajasekaran, and P. A. Christu Raj

    Institution of Engineering and Technology


  • ADVANCEMENTS IN PIEZOELECTRIC ENERGY HARVESTING FOR A SUSTAINABLE DEVELOPMENT: A COMPREHENSIVE REVIEW OF ENVIRONMENTAL PREDICTION METHODS


  • SUSTAINABLE ENERGY DEVELOPMENT PREDICTION OF ENERGY HARVESTING SYSTEM WITH AN ADAPTIVE HIERARCHICAL RECURRENT NETWORK AND BIODYNAMIC FUSION OPTIMISATION ALGORITHM


  • Voice Enabled Deep Learning Based Image Captioning Solution for Guided Navigation
    Senthil Kumar A, Selvaraj Kesavan, Jayakumar J, Ananda Kumar K S, and Prasad Maddula

    IEEE
    The use of technology to assist visually impaired individuals is crucial in addressing the global issue of vision impairment. Worldwide more than billion people suffer from a vision impairment that should have been avoided or is yet unaddressed. According to the statistics, there is a significant need for solutions that can help those who are visually impaired, mainly in the middle- and low-income countries where the vision impairment population is higher. It is anticipated that population expansion and ageing will increase the likelihood that more people may get vision impairment. The efficientnetB3 deep learning algorithm will be used in this project to caption images for blind people. so, they can learn about object identification, distance, and position. This has been accomplished by utilizing advanced picture captioning techniques, efficient net B3 algorithms, and tokenization approaches, where the computer learns the scenes with various captions. The computer recognizes and forecasts any image that is acquired using the camera. The significant objects are also anticipated, and the camera's distances are determined. Following the prediction, the user receives an audio output that can be used to determine the object's position and distance. Hence, with the aid of this research, we give the blind artificial eyesight that can give them confidence when they move on their own. The aim is to step forward in addressing the global issue of vision impairment. The use of technology to assist visually impaired individuals is crucial in providing them with the tools they need to navigate their environment and live their lives with greater ease. By utilizing advanced algorithms and image captioning techniques, the quality of life can be improved for people worldwide who are affected by vision impairment. The intension is to develop an artificial vision for vision impaired people by detecting real time objects, distance and the position of it from the person using Audio Output and to develop a model for image captioning to predict the captions.

  • A comprehensive review on electric vehicles: charging and control techniques, electric vehicle-grid integration
    P. H. Femy and J. Jayakumar

    Walter de Gruyter GmbH
    Abstract Energy consumption in the field of transportation comes next to industrial consumption worldwide. If transportation is completely powered by renewable energy, the utilization of fossil fuels can be drastically reduced, which will result in a lesser amount of greenhouse gas emissions. Electric vehicles (EVs) can act as an alternative to make transportation pollution-free. Large-scale usage of EVs causes high electricity demand on the supply system. This problem can be overcome by utilizing renewable energy sources (RESs) for Electric Vehicle charging. Due to the unpredictability of RESs, coordinating EV charging with other loads and renewable generation is problematic. By using EVs as energy units, power fluctuations in the electric grid can be compensated. This paper presents a summary of recent research in the domain of integration of electric vehicles (EVs) to the smart grid. Electric vehicles-smart grid integrated systems face several issues related to communication, grid infrastructure and control in the future power system. Smart grid technologies are summarized in Section 2. The existing research articles in this area are classified into two based on the purpose: EVs integration into the electric grid and Vehicle to grid services. Finally, the research gaps and future scope of incorporating electric vehicles with renewable energy sources and the Smart grid are highlighted.

  • Liver Tumor Classification Using Optimal Opposition-Based Grey Wolf Optimization
    Reshma Jose, Shanty Chacko, J. Jayakumar, and T. Jarin

    World Scientific Pub Co Pte Ltd
    Image processing plays a significant role in various fields like military, business, healthcare and science. Ultrasound (US), Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the various image tests used in the treatment of the cancer. Detecting the liver tumor by these tests is a complex process. Hence, in this research work, a novel approach utilizing a deep learning model is used. That is Deep Belief Network (DBN) with Opposition-Based Learning (OBL)-Grey Wolf Optimization (GWO) is used for the classification of liver cancer. This process undergoes five major processes. Initially, in pre-processing the color contrast is improved by Contrast Limited Adaptive Histogram Equalization (CLAHE) and the noise is removed by Wiener Filtering (WF). The liver is segmented by adaptive thresholding following pre-processing. Following that, the kernelizedFuzzy C Means (FCM) method is used to segment the tumor area. The form, color, and texture features are then extracted during the feature extraction process. Finally, these traits are categorized using DBN, and OBL-GWO is employed to enhance system performance. The entire evaluation is done on Liver Tumor Segmentation (LiTS) benchmark dataset. Finally, the performance of the proposed DBN-OBL-GWO is compared to other models and their achievements are proved. The proposed DBN-OBL-GWO achieves a better accuracy of 0.995, precision of 0.948 and false positive rate (FPR) of 0.116, respectively.

  • Identification of Power Leakage and Protection of Over Voltage in Residential Buildings
    Chitra S, Jayakumar J, Venkateshkumar P, Shanty Chacko, and Sivabalan

    FOREX Publication
    In many residential buildings the electrical wires of individual houses are laid in the same conduit pipe and some mistakes could be made in identifying similar coloured wires when they are laid in same conduit pipe. Most of the faults are caused by the neutral interconnection in the wiring system. Usually neutral wires are connected to neutral bus within the panel board or switchboard, and are "bonded" to earth ground. In our secondary distribution, tree system of supply is mostly utilized. The voltage of each phase to neutral will be maintained at rated value even during the unbalanced load conditions. If neutral wire connection is poor the voltage at each phase will be different from one another, such an isolated neutral point is called floating neutral and the voltage of the point is always changing. This is the reason for over voltage causing damage to appliance’s which should be protected. In this paper, a smart system that identifies power leakage and provides over voltage protection to the residential building is proposed.

  • Analysis of reactive power loadability and management of flexible alternating current transmission system devices in a distribution grid using whale optimization algorithm
    Honey Baby, Jayaraj Jayakumar, Mobi Mathew, Mohamed G. Hussien, and Nallapaneni Manoj Kumar

    Institution of Engineering and Technology (IET)

  • Regression Based Predictive Machine Learning Model for Pervasive Data Analysis in Power Systems
    Dr. K. Sasikala, Dr. J. Jayakumar, Dr. A. Senthil Kumar, Dr. Shanty Chacko, and Dr. Hephzibah Jose Queen

    FOREX Publication
    The main aim of this paper is to highlight the benefits of Machine Learning in the power system applications. The regression-based machine learning model is used in this paper for predicting the power system analysis and Economic analysis results. In this paper, Predictive ML models for two modified IEEE 14-bus and IEEE-30 bus systems, integrated with renewable energy sources and reactive power compensative devices are proposed and developed with features that include an hour of the day, solar irradiation, wind velocity, dynamic grid price, and system load. An hour-wise input database for the model development is generated from monthly average data and hour-wise daily curves with normally distributed standard deviations. A very significant Validation technique (K Fold cross validation technique) is explained. Correlation between Input and output variable using spearman’s correlation analysis using Heat maps. Followed by the Multiple Linear Regression based Training and testing of the Modified IEEE 14 and IEEE30 Bus systems for base load case, 10% and 20% load increment with the 5-fold cross validation is also presented. Comparative analysis is performed to find the best fit ML Model for our research.

  • SRF Theory-Based PI Controller Applied to Micro Grid Interfaced with hybrid sources for Power Quality Improvement
    Khammampati R Sreejyothi, P. Venkatesh Kumar, and J. Jayakumar

    IEEE
    The importance of the Micro grid is increasing day to day because of reducing transmission cost, In comparison to the grid, microgrids have more renewable energy sources and it is easier to spot faults. If the main grid goes down, the microgrid can keep consumers comfortable by supplying power to homes and businesses for a while. The microgrid is best suitable in hilly areas and remote locations. Microgrid delivers important loads with a high-quality, consistent energy supply. This paper presents a Power quality improvement in Micro Grid used SRF theory. The microgrid is the interconnection of hybrid sources and load. The hybrid sources are PV cell, Fuel Cell, and Super Capacitor. By integrating these small sources, microgrids are implemented in generating the electrical power at load demand. The voltage and reactive power support to the external grid are examined using rDSTATCOM deployed at various locations throughout the microgrid. The simulation results were verified in MATLAB/SIMULINK software.

  • Operating Cost Analysis of Microgrid Including Renewable Energy Sources and a Battery Under Dynamic Pricing
    Hephzibah Jose Queen, J. Jayakumar, and T. J. Deepika

    Springer Singapore

  • Comparative techno-economic analysis of power system with and without renewable energy sources
    Hephzibah Jose Queen, Jayakumar J., and Deepika T. J.

    Institute of Advanced Engineering and Science
    <p>The primary aim of this work is to feature the advantages of integrating natural source of energy from the solar and wind to the prevailing electric power systems. Two types of analysis are carried out in two test systems (standard and modified test systems) and the outcome of the test systems are compared. The two analyses are technical analysis and economic analysis. The stability of the voltage is analyzed under technical analysis and the price of energy consumed from the electric grid is calculated and analyzed under the economic analysis. Dynamic hourly load data, hourly solar radiation, hourly wind velocity, and dynamic electricity prices are considered for the standard IEEE system and modified test system (with the integration of RES). Voltage stability index (L-Index) and price of the electricity consumed from electric grid are found for standard test system and the outcome is compared with the outcome of modified test systems. MATLAB coding is done for techno-economic analysis for both test systems. It is inferred from the outcome that the integration of renewable energy sources fairly contributes to the economic benefit of the system by lowering the power purchased from the grid and enhance the stability of the system.</p>

  • A Review of Different Configurations and Control Techniques for DSTATCOM in the Distribution system
    Khammampati R Sree Jyothi, P. Venkatesh Kumar, and J. JayaKumar

    EDP Sciences
    This paper presents a review of DSTATCOM Topologies and power quality control Techniques. The used topologies are Three-phase Three-wire and Three-phase four-wire and control techniques are Instantaneous reactive power theory(IRP), Synchronous Reference Frame Theory(SRF), Model Predictive Control (MPC), Sliding mode control(SMC), Adaptive Neuro-fuzzy interface systems(ANFIS) and Artificial intelligence based controllers. These control techniques are used to mitigate the reactive power compensation, load balancing, Neutral current compensation, harmonics reduction and maintains the Total harmonic Distortion in IEEE519 standards. Performance investigated in Single-phase Distribution systems by connecting with STATCOM and without DSTATCOM in MATLAB/SIMULINK

  • A Review on the Feasibility of Deployment of Renewable Energy Sources for Electric Vehicles under Smart Grid Environment
    Manjinder Singh and Harpreet Kaur

    FOREX Publication
    Filling dead pixels or eliminating unwanted things is typically preferred within the applications of remotely sensed images. In proposed article, a competent image imprinting technique is demonstrated to resolve this drawback, relied nonlocal total variation. Initially remotely sensed images are effected by ill posed inverse problems i.e. image destripping, image de-noising etc. So it is required to use regularization technique to makes these problems well posed i.e. NLTV method, which is the combination of nonlocal operators and total variation model. Actually this method can make use of the good features of non-local operators for textured images and total variation method in edge preserving for color images. To optimize the proposed variation model, an Ant Colony Optimization algorithm is used in order to get similarity with the original image. And evaluate the outcomes of proposed technique with the existing technique i.e. MNLTV optimized by Bregmanized-operator-splitting algorithm which is a prediction based method. The investigation of all outcomes confirms the efficacy of this rule.

RECENT SCHOLAR PUBLICATIONS

  • Fractional order sliding mode control for power quality improvement in the distribution system
    KR Sreejyothi, PV Kumar, J Jayakumar
    International Journal of Applied 13 (2), 408-414 2024

  • An Improved MPPT Approach Using Artificial Neural Network for PV Grid System
    BA Rahiman, J Jayakumar, R Meenal
    2024 International Conference on Recent Advances in Electrical, Electronics 2024

  • The hybrid solar energized back-to-back high voltage direct current modular converter for distributed networks
    K Thadkapally, FT Josh, JJ Joseph, J Jayakumar
    Bulletin of Electrical Engineering and Informatics 13 (1), 88-97 2024

  • An Efficient ARIA-RSA-SHA256 Hybridized Encryption Algorithm for Metering Data in Smart Grid Network Systems
    A Philips, J Jayakumar
    International Journal of Renewable Energy Research (IJRER) 13 (4), 1467-1480 2023

  • Brain Tumor Classification and Identification using PSO and ANFIs
    JS Awati, M Kumbhar
    International Journal of Electrical and Electronics Research 11 (4), 1039-1043 2023

  • Voice Enabled Deep Learning Based Image Captioning Solution for Guided Navigation
    S Kesavan, J Jayakumar, AK KS, P Maddula
    2023 International Conference on Network, Multimedia and Information 2023

  • Design and Implementation of Remote Controlled High Temperature Short Time (HTST) pasteurization system for Healthy Milk production
    C Marimuthu, J Jayakumar
    Preprints 2023

  • Time-Series based Household Electricity Consumption Forecasting
    A Philips, J Jayakumar
    2023 11th International Conference on Smart Grid (icSmartGrid), 1-15 2023

  • NEURAL NETWORK BASED DC-DC CONVERTER FOR ELECTRIC VEHICLE APPLICATION WITH PV PANEL
    JJ Joseph, R Meenal, J Jayakumar, FT Josh, S Chacko, ...
    Journal of Survey in Fisheries Sciences 10 (3S), 1291-1299 2023

  • A comprehensive review on electric vehicles: charging and control techniques, electric vehicle-grid integration
    PH Femy, J Jayakumar
    Energy Harvesting and Systems 10 (1), 1-14 2023

  • ADVANCEMENTS IN PIEZOELECTRIC ENERGY HARVESTING FOR A SUSTAINABLE DEVELOPMENT: A COMPREHENSIVE REVIEW OF ENVIRONMENTAL PREDICTION METHODS
    C MARIMUTHU, V MANIKANDAN, J JAYAKUMAR
    Journal of Environmental Protection and Ecology 24 (8), 2786-2795 2023

  • SUSTAINABLE ENERGY DEVELOPMENT PREDICTION OF ENERGY HARVESTING SYSTEM WITH AN ADAPTIVE HIERARCHICAL RECURRENT NETWORK AND BIODYNAMIC FUSION OPTIMISATION ALGORITHM
    C MARIMUTHU, V MANIKANDAN, J JAYAKUMAR
    Journal of Environmental Protection and Ecology 24 (8), 2796-2805 2023

  • PERFORMANCE, ENVIRONMENT, ACTUATORS, AND SENSORS MODEL TO PORTRAY AN INTELLIGENT AGENT MODEL
    GD Saxena, ST Kumbhar, D Sasikala, K Jagadeesh, J Jayakumar, ...
    NeuroQuantology 21 (1), 499 2023

  • Speech Development, Speech Delay And Speech Defects In Children
    RV Shinde, N Mohamed Imtiaz, K Sasikala, JJ Joseph, P Nagabushanam, ...
    Journal of Pharmaceutical Negative Results, 8237-8243 2022

  • Liver Tumor Classification using Optimal Opposition-Based Grey Wolf Optimization
    R Jose, S Chacko, J Jayakumar, T Jarin
    International Journal of Pattern Recognition and Artificial Intelligence 36 2022

  • Analysis of reactive power loadability and management of flexible alternating current transmission system devices in a distribution grid using whale optimization algorithm
    H Baby, J Jayakumar, M Mathew, MG Hussien, NM Kumar
    IET Renewable Power Generation 2022

  • SRF Theory-Based PI Controller Applied to Micro Grid Interfaced with hybrid sources for Power Quality Improvement
    KR Sreejyothi, PV Kumar, J Jayakumar
    2022 8th International Conference on Advanced Computing and Communication 2022

  • Regression Based Predictive Machine Learning Model for Pervasive Data Analysis in Power Systems
    K Sasikala, J Jayakumar, AS Kumar, S Chacko, H Jose
    Int. J. Electr. Electron. Res 10 (3), 550-556 2022

  • Operating cost analysis of microgrid including renewable energy sources and a battery under dynamic pricing
    HJ Queen, J Jayakumar, TJ Deepika
    Proceedings of International Conference on Power Electronics and Renewable 2022

  • Comparative techno-economic analysis of power system with and without renewable energy sources
    HJ Queen, J Jayakumar, TJ Deepika
    Indonesian Journal of Electrical Engineering and Computer Science 24 (3 2021

MOST CITED SCHOLAR PUBLICATIONS

  • [Retracted] Conceptual Implementation of Artificial Intelligent based E‐Mobility Controller in smart city Environment
    J Jayakumar, B Nagaraj, S Chacko, P Ajay
    Wireless Communications and Mobile Computing 2021 (1), 5325116 2021
    Citations: 182

  • FPGA implementation of AES algorithm for high throughput using folded parallel architecture
    K Rahimunnisa, P Karthigaikumar, S Rasheed, J Jayakumar, ...
    Security and Communication Networks 7 (11), 2225-2236 2014
    Citations: 56

  • PSP: Parallel sub-pipelined architecture for high throughput AES on FPGA and ASIC
    K Rahimunnisa, P Karthigaikumar, NA Christy, SS Kumar, J Jayakumar
    Central European Journal of Computer Science 3, 173-186 2013
    Citations: 47

  • Power generation enhancement with hybrid thermoelectric generator using biomass waste heat energy
    AA Angeline, J Jayakumar, LG Asirvatham, JJ Marshal, S Wongwises
    Experimental Thermal and Fluid Science 85, 1-12 2017
    Citations: 41

  • Performance prediction of hybrid thermoelectric generator with high accuracy using artificial neural networks
    AA Angeline, LG Asirvatham, DJ Hemanth, J Jayakumar, S Wongwises
    Sustainable Energy Technologies and Assessments 33, 53-60 2019
    Citations: 31

  • Torque modeling of switched reluctance motor using LSSVM-DE
    SS Kumar, J Jayakumar
    Neurocomputing 211, 117-128 2016
    Citations: 18

  • Nonlinear modeling of a switched reluctance motor using LSSVM-ABC
    JE Stephen, SS Kumar, J Jayakumar
    Acta Polytechnica Hungarica 11 (6), 143-158 2014
    Citations: 16

  • Power generation from combusted “syngas” using hybrid thermoelectric generator and forecasting the performance with ann technique
    AA Angeline
    Journal of Thermal Engineering 4 (4), 2149-2168 2018
    Citations: 14

  • Performance analysis of lithium batteries
    VM Dileepan, J Jayakumar
    2017 International Conference on Innovations in Electrical, Electronics 2017
    Citations: 14

  • Machine learning-based predictive techno-economic analysis of power system
    HJ Queen, J Jayakumar, TJ Deepika, KVSM Babu, SP Thota
    IEEE Access 9, 123504-123516 2021
    Citations: 13

  • Performance analysis of (Bi2Te3-PbTe) hybrid thermoelectric generator
    AA Angeline, J Jayakumar, LG Asirvatham
    International Journal of Power Electronics and Drives Systems 8 (2) 2017
    Citations: 13

  • Torque modeling of switched reluctance motor using LSSVM-DE
    J Evangeline S, S Suresh Kumar, J Jayakumar
    Neurocomputing 211 (C), 117-128 2016
    Citations: 13

  • Modelling and control of MIMO gasifier system during coal quality variations
    XA Mary, L Sivakumar, J Jayakumar
    International Journal of Modelling, Identification and Control 23 (2), 131-139 2015
    Citations: 13

  • A 0.13-m implementation of 5 Gb/s and 3-mW folded parallel architecture for AES algorithm
    K Rahimunnisa, P Karthigaikumar, J Kirubavathy, J Jayakumar, SS Kumar
    International Journal of Electronics 101 (2), 182-193 2014
    Citations: 13

  • Distribution transformer monitoring using GPRS
    J Jayakumar, JHJ Queen, T James, G Hemalatha, N Lonappan
    International Journal of Scientific & Engineering Research 4 (6), 1199-1204 2013
    Citations: 12

  • A review on the feasibility of deployment of renewable energy sources for electric vehicles under smart grid environment
    PH Femy, J Jayakumar
    IJEER 9 (3), 57-65 2021
    Citations: 11

  • Network performance analysis of cloud based multimedia streaming service
    S Kesavan, J Jayakumar
    International Journal of New Computer Architectures and their Applications 4 2014
    Citations: 11

  • Intelligent Wearable Device for Coal Miners
    P Prabhu, JJ Umang, CP Kumar
    Int. J. Eng. Technol 7 (3.12), 677 2018
    Citations: 10

  • Controlled multimedia cloud architecture and advantages
    S Kesavan, J Anand, J Jayakumar
    Advanced Computing 3 (2), 29 2012
    Citations: 10

  • Effective client-driven three-level rate adaptation (TLRA) approach for adaptive HTTP streaming
    S Kesavan, J Jayakumar
    Multimedia Tools and Applications 77, 8081-8114 2018
    Citations: 9

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

Patent Number: 202041011809
Intelligent Integrated Control System for Electric Vehicle Battery