Dr.Raja.R

@mec.edu.in

Associate Professor and Electrical and Electronics Engineering
Muthayammal Engineering College (Autonomous)

Dr.Raja.R


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https://researchid.co/dr.raja.r

RESEARCH INTERESTS

Power Electronics, Renewable Energy Systems and AC/DC Machines
22

Scopus Publications

Scopus Publications

  • Waterwheel Plant Algorithm and Capsule Attention Convolutional Neural Networks for Optimal Sizing Framework for Photovoltaic-Battery EV Charging Microgrids
    R. Raja, R. Geetha, Vemana U. P. Lavanya, G. Indumathi
    Energy Storage, 2025
    The increasing use of electric vehicles (EVs) highlights the importance of energy management (EM) and particularly photovoltaic (PV)‐battery microgrids (MGs). However, the conventional optimization methodologies are not always capable of striking an optimal balance between cost, energy, and size of the system, considering uncertainties such as the variability of solar resource and the fluctuating demand of charging of EVs. This paper proposes a hybrid method for the optimal sizing framework for PV‐battery EV charging MGs. The proposed method is the combined execution of the waterwheel plant algorithm (WWPA) and capsule attention convolutional neural networks (CACNN). Thus, the proposed method is referred to as the WWPA‐CACNN approach. The goal of this work is to achieve optimal sizing of PV‐battery systems, enhancing energy utilization, cost‐efficiency, and grid independence. The WWPA is used to optimize the sizing of PV panels and battery storage to minimize costs and maximize energy utilization in EV charging MGs. The CACNN is used to predict energy generation, storage, and demand, ensuring accurate forecasting and system adaptability. By then, the proposed method is simulated on the MATLAB platform and compared with various existing methods like particle swarm optimization (PSO), artificial neural network (ANN), non‐dominated sorting genetic algorithm‐II (NSGA‐II), modified snake optimization (MSO), and dung beetle optimizer (DBO). The proposed WWPA‐CACNN method also has the lowest total lifetime cost of $12 730 and high efficiency of 96%, which underlines its better overall performance to effectively manage PV‐battery EV charging MGs at optimal cost.
  • A Hybrid Approach for Smart Energy Management in Microgrids With Electric Vehicle Charging Using Snow Ablation Optimization and Cascade Chaotic Neural Network
    R. Raja, K. Sureshkumar, Kurra Venkateswara Rao, N. Jayashree
    Energy Storage, 2025
    Integration of Renewable Energy Sources (RES) with Electric Vehicles (EVs) elucidates a crucial area in Energy Management (EM) for Microgrids (MGs). Probably the most difficult job is stochastic behavior from RES together with unpredictable EV charging demands, aspires towards grid stability, and destabilizes prompt frequency control. This article introduces a hybrid methodology designed for intelligent EM in MGs with EV charging. Proposed method integrates Snow Ablation Optimization (SAO) and Cascade Chaotic Neural Network (CCNN); therefore, it is called the SAO‐CCNN technique. The aim is to improve economic performance of the MG integrated by EV charging by minimize the Operating Cost. SAO optimizes the utilization of RES and EVs, improving overall energy management. The CCNN is employed to predict the participation probability of EVs in grid support activities, thereby aiding in the accurate forecasting of energy demand. The suggested SAO‐CCNN technique is implemented on MATLAB platform and evaluated against existing optimization methods, including Firefly Optimization Algorithm (FOA), Particle Swarm Optimization (PSO), Robust Optimization Algorithm (ROA), Multi Objective Optimization (MOO), and Whale Optimization Algorithm (WOA). The operating cost achieved using the proposed method is $17 184.1, demonstrating improved cost‐efficiency compared to optimization methods.
  • Optimal power utilization in hybrid microgrid systems with IoT-based battery-sustained energy management using RSA-PFGAN approach
    R. Raja, K. Suresh Kumar, T. Marimuthu, Papana Venkata Prasad
    Journal of Energy Storage, 2025
  • Variable Speed Drive Applications Performing Effectively with ANN Technique
    A. Sathish Kumar, R. Saravanan, S. Vasanthi, R. Raja
    Wireless Personal Communications, 2024
  • Design and development of photovoltaic solar system based single phase seven level inverter
    Vijayakumar Govindaraj, Sujith Mayakrishnan, Shanmugasundaram Venkatarajan, Raja Raman, Ramesh Sundar
    Bulletin of Electrical Engineering and Informatics, 2024
    For solar photovoltaic (PV) systems, an upgraded triple gain seven-level inverter that works both independently and while connected to the grid is proposed. The two-stage configuration of the system is boost cascaded. The first stage has a one switch improved gain converter (OSIGC) to increase and normalize the input direct current (DC) voltage, and the second stage includes a unique seven level alternating current (AC) is produced via a multilevel inverter (MLI) design with triple voltage gain. The proposed OSIGC is appropriate for a broad range of conversions. The voltage gain in MLI was achieved using switched capacitor techniques. The DC-DC converter can achieve a maximum voltage gain of twelve and the MLI can achieve a maximum voltage gain of three, resulting in a DC-DC-AC voltage that can reach 36. Maximum power point tracking (MPPT) technique based on modified perturb and observe (PO) is used in OSIGC to maximise PV module power utilisation, and MLI control utilises sinusoidal pulse width modulation (SPWM) realistically. For the purpose of analysing the suggested system, a 200 Watt prototype statel is created. With a total harmonic distortion (THD) of 0.181%, up to 92.12% of the converter system’s overall efficiency is possible.
  • Anti-Theft Alert System for Home Security
    A. S. Anishvishwa Sathiyamoorthi, S. Rahul Senthil, Sudha Vittaldass, Raja Raman
    Proceedings of the 2024 3rd Edition of IEEE Delhi Section Flagship Conference Delcon 2024, 2024
    In this project, the ATAS, or Anti-Theft Alert System, will make home security critical toward ensuring residential safety and against unauthorized access. This study involves the design and implementation of an anti-theft alert system based on a combination of microcontrollers, sensors, and display components for the achievement of all-inclusive security. The system includes an Arduino UNO, NodeMCU ESP-12E, ESP32-Cam, relay module, LCD display, magnetic door sensor, buzzer, and good power supply to allow real-time monitoring capability and alert. The methodology used allows for the installation of a magnetic door sensor that constantly monitors whether entry points are opened or not. Once it detects unauthorized entry access, it triggers the relay module to activate the buzzer that gives an instantaneous audible alarm to alert homeowners. On the other hand, the LCD display shows other security-related information such as the status of the door or alerts concerning any unauthorized access. The real-time video surveillance is carried out using ESP32-Cam, capturing and streaming video in live feed so that the actual breach can be identified visually. The communication between sensors, the alarm system, and the display is taken care of by NodeMCU ESP-12E, which directs the response actions and ensures the smooth functioning of the whole system. Power management is assured by the stable power supply and ensures that continuous operation and reliability of the system components can be guaranteed.
  • IoT-Enabled Facial Recognition for Smart Hospitality for Contactless Guest Services and Identity Verification
    S. Srinivasan, R. Raja, C. Jehan, S. Murugan, C. Srinivasan, M. Muthulekshmi
    2024 11th International Conference on Reliability Infocom Technologies and Optimization Trends and Future Directions Icrito 2024, 2024
    The proposed system integrates face recognition and Internet of Things (IoT) technology to revolutionize the hospitality industry. It uses OpenCV, a widely recognized computer vision library, to deliver smooth, contactless guest services and rigorous identification verification. This paper proposes a novel method to improve visitor experiences by removing check-in and allowing personalized services. The IoT -enabled face recognition system presented in this paper streamlines the visitor experiences. Facial biometrics eliminates the need for identity papers, making the check-in process simple. The proposed system, OpenCV, is used for face detection, identification, and verification. The system's design includes strategically positioned cameras for visitor identification and tracking. Health and safety are crucial, so this system's contactless nature is a major benefit. The solution also increases visitor safety by lowering the danger of fraud and illegal access. The IoT-enabled face recognition technology improves operational efficiency, guest satisfaction, and security in the hospital industry.
  • RETRACTION:MAC-Leonets-A novel optimized hybrid convolutional neural networks for the segmentation and diagnosis of Edema diseases using retinal OCT images
    V. Sudha, Sathiya Priya Shanmugam, D. Anitha, R. Raja
    Journal of Intelligent and Fuzzy Systems, 2023
    An intelligent segmentation and identification of edemas diseases constitutes a most important crucial ophthalmological issues since they provide important information for the diagnosis process in accordance to the disease severity. But diagnosing the different edema diseases using the OCT-images are considered to be daunting challenge among the researchers. The implementation of computational intelligence techniques such as machine learning, deep learning, bio inspired algorithms and image processing techniques may help the doctors for some extent in improving the automatic extraction and diagnosis process consequently improving patients’ life quality. But, these are liable to more errors and less performance, which requires further improvisation in designing the intelligent systems for an effective classification of edema diseases. In this context, this paper proposes the hybrid intelligent framework for the identification, segmentation and classification of three types of edemas such as using the retinal optical coherence tomography (OCT) Images. In this process, Single Feed Forward Training networks (SLFTN) are integrated with Convolutional Layers whose hyperparameters are tuned by using Lion Optimization algorithm. An intensive experimentation is carried out using the Kaggle Retinal OCT Image datasets-2020 with Tensor flow and the proposed framework is trained with the different set of 84,494 images in which performance metrics such as accuracy, sensitivity, specificity, recall and f1score are calculated. Results shows the proposed system has provided satisfactory performance, reaching the average highest accuracy of 99.9% in identifying and classifying the respectively.
  • Wind Energy System Based Bidirectional Converter with Energy Storage System
    R. Raja, Gundala Srinivasa Rao, P. Duraipandy, K. Mathan, L. Chitra
    Aip Conference Proceedings, 2023
    Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation R. Raja, Gundala Srinivasa Rao, P. Duraipandy, K. Mathan, L. Chitra; Wind energy system based bidirectional converter with energy storage system. AIP Conference Proceedings 30 January 2023; 2523 (1): 020062. https://doi.org/10.1063/5.0121762 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
  • Machine Learning-Based Early Chronic Kidney Disease Detection and Risk Analysis
    Sakthi C, Yogeshwaran G, Sudha V, Raja R
    Proceedings 1st International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems Itech Secom 2023, 2023
    Chronic kidney disease (CKD) poses an extensive public health task globally, necessitating accurate and early prediction for powerful intervention. In this investigation the predictive competencies of machine learning algorithms utilizing a dataset comprising 25 cautiously selected attributes. CKD's pervasive impact on worldwide health necessitates progressive answers, and this observation stands at the leading edge of pioneering efforts. Interpretability techniques are applied to enhance the transparency of the models, allowing for a deeper understanding of the functions influencing CKD prediction. Validation and evaluation metrics played an important role in guiding the refinement of the model. Precision, recall, and F1 scores are carefully balanced to avoid false positives and negatives. The ADAM (Adaptive moment Estimation) algorithm was deployed to optimize the version's parameters, ensuring fast convergence and advanced predictive overall performance. ADAM is known for being less sensitive to the initial parameter values compared to some other optimization techniques. This adaptability ensures that the algorithm performs optimally throughout a variety of attributes in your dataset.
  • Development and Deployment of an Internet of Things (IoT) Enabled Intelligent Medication Dispensing Solution
    Harshini D A, Amiritavarshini S, Sudha V, Raja R
    IEEE 9th International Conference on Smart Structures and Systems Icsss 2023, 2023
  • Power Quality Enhancement using Dynamic Voltage Restorer in Distributed Networks
    R. Raja, L. Jones Nirmal, P. K. Mani, M. Kavitha
    Proceedings of the 8th International Conference on Communication and Electronics Systems Icces 2023, 2023
  • Hybrid Energy Sources using Current Fed Inverter for High Gain in Single Phase to AC Loads
    Sekar K, Jayaprakash S, Hemalatha M, Raja R
    Proceedings of the 3rd International Conference on Artificial Intelligence and Smart Energy Icais 2023, 2023
  • Automated Brain Tumor Classification and Detection Using Modified Convolutional Neural Networks for Early Diagnosis
    Adithya D, Rahul R, Sudha V, Vikram N, Raja R
    Proceedings 1st International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems Itech Secom 2023, 2023
  • Reduced switch cascaded asymmetrical 27 level inverter-STATCOM with fuzzy logic controller
    Sundar Ramesh, Vijayakumar Govindaraj, Raja Raman, Shanmugasundaram Venkatarajan, Kamatchi Kannan Vijayarangan
    Indonesian Journal of Electrical Engineering and Computer Science, 2023
  • A Medical Image Enhancement to Denoise Poisson Noises Using Neural Network and Autoencoders
    V. Sudha, K. Kalyanasundaram, R. C. S. Abishek, R. Raja
    Lecture Notes in Networks and Systems, 2022
  • LCL -Τ filter based analysis of two stage single phase grid connected module with intelligent FANN controllers
    R. Raja, V. Sudha, Balachandra Pattanaik, P. Madhumathy
    Machine Learning and Iot for Intelligent Systems and Smart Applications, 2021
  • Comparison of detection and classification of hard exudates using artificial neural system vs. SVM radial basis function in diabetic retinopathy
    V. Sudha, T. R. Ganesh Babu, N. Vikram, R. Raja
    Mcb Molecular and Cellular Biomechanics, 2021
  • Frequency control of PV-connected micro grid system using fuzzy logic controller
    T. Rajesh, B. Gunapriya, M. Sabarimuthu, S. Karthikkumar, R. Raja, M. Karthik
    Materials Today Proceedings, 2021
  • Performance analysis of LCL-T filter based 2 stage single phase gird connected module with ANN controller using PV panel
    R. Raja, C. Nagarajan
    Current Signal Transduction Therapy, 2018
  • Performance of variable frequency ISPWM technique for a cascaded multilevel inverter
    C. Navabalachandru, B. Ashok, A. Jagadeesan, R. Raja
    2013 International Conference on Energy Efficient Technologies for Sustainability Iceets 2013, 2013
  • An efficient high-step-up interleaved with a common active clamp DC-DC converter for electric vehicle
    R. Raja, R. PrincelynJebaKiruba, A. Jagadeesan, C. Navabalachandru
    2013 International Conference on Energy Efficient Technologies for Sustainability Iceets 2013, 2013