Multi-objective optimization of a renewable-powered H2 microgrid using fractional-order control Rajesh G, Sebasthirani K, Maruthupandi P, Remya Sree R Journal of Renewable and Sustainable Energy, 2026 Renewable-powered hydrogen microgrids require coordinated control of electrolyzer operation, battery state-of-charge (SoC), and power balance under intermittent solar/wind generation and time-varying demand. This paper presents a reproducible multi-objective optimization framework that integrates fractional-order proportional–integral–derivative (FO-PID) control with genetic algorithms (GAs) and reinforcement learning (RL) to improve hydrogen production efficiency while reducing operational losses and preserving battery-friendly SoC behavior. The proposed approach is evaluated on a renewable-powered H2 microgrid model using five strategies: rule-based control, conventional order proportional–integral–derivative (PID), FO-PID, FO-PID tuned by GA (FO-PID + GA), and FO-PID with RL-based decision support (FO-PID + RL). Performance is assessed using hydrogen utilization, power-loss ratio, and a SoC stability index (SSI), where deviations outside the recommended 20%–90% operating window are penalized to discourage deep cycling and boundary stress. Results demonstrate that FO-PID + RL achieves the best overall trade-off among the objectives. Compared with the proportional–integral–derivative (PID) baseline, FO-PID + RL increases hydrogen utilization from 2.8 to 4.5 kg/h (≈60.7%) and reduces the power-loss ratio from 12.4% to 5.9% (≈52.4%), while improving SSI from 0.72 to 0.91, indicating smoother SoC trajectories and reduced stress. These outcomes confirm that combining fractional-order control with Pareto-based optimization and learning-based scheduling can enhance both efficiency and operational reliability in renewable-powered hydrogen microgrids.
Modeling and Control of Permanent Magnet Synchronous Motor Based Electric Vehicle Rajesh G., K. Sebasthirani Advances in Mechanical Engineering, 2025 This study provides a comprehensive examination of the modeling and control of Permanent Magnet Synchronous Motors (PMSM) utilized in electric vehicle applications. The research focuses on the design and optimization of Fractional Order PID (FOPID) controllers, leveraging Genetic Algorithm (GA) and Hybrid Reinforcement Genetic Algorithm-Recursive Backpropagation Learning (GA-RBL) techniques to enhance tuning performance. The PMSM, known for its high efficiency and reliability, is mathematically modeled, and its control dynamics are analyzed under various operating conditions. A novel approach to FOPID controller tuning is introduced, utilizing the robustness of hybrid algorithms. Our proposed Hybrid GA-RBL optimized FOPID controller achieved a peak overshoot of 4.0 mm at 900 rpm, settling time of 1.65 s, and steady-state error of 0.6%. Additionally, error metrics were significantly improved, with Integral of Squared Error (ISE) of 0.003 mm 2 s, Integral of Absolute Error (IAE) of 0.045 mm s, and Integral of Time-weighted Absolute Error (ITAE) of 0.021 mm s 2 . Compared to conventional controllers such as Ziegler-Nichols and Cohen-Coon PID tuning methods, the proposed model demonstrated superior performance in terms of faster response time, enhanced stability, and improved energy efficiency. These findings contribute to advancing control strategies for electric vehicles, setting a benchmark for future research and development in PMSM control optimization.
Impact of fast charging on battery performance and SOC variations across temperature conditions Rajesh G, K Sebasthirani Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering, 2025 The growing demand for electric vehicles has brought fast charging into focus as a practical solution to reduce downtime. However, little is understood about how such rapid charging methods affect battery performance under different temperature conditions, especially in real-world environments. This study examines how ambient temperature, alongside the type of charger used Level 1, Level 2, or DC fast charging affects key battery performance indicators such as state of charge (SOC) gain, energy usage, and long-term capacity behavior. Using data collected from 1320 real-world EV charging sessions, the analysis revealed a significant drop in SOC gain by as much as 27% in sub-zero conditions and 19% when temperatures exceed 40°C. Similarly, energy consumption increased by 20%–25% at these extremes, pointing to the additional power drawn by thermal management systems and reduced charging efficiency. Among the charging types, DC fast chargers were the most sensitive to temperature fluctuations, particularly at high ambient temperatures, where internal resistance effects were more pronounced. A key contribution of this work is the incorporation of electrochemical resistance parameters namely charge transfer resistance (Rct) and electrolyte resistance (Re) into a suite of machine learning models for performance prediction. Of these, the XGBoost model offered the most accurate results, with an R 2 of 0.95 and RMSE of 1.63. Interpretability analysis using SHAP confirmed that Rct and Re were stronger predictors of battery performance than temperature alone. By leveraging real-world data and explainable machine learning, this research offers a more realistic understanding of how EV batteries behave under varying conditions and proposes a path toward more intelligent, temperature-aware battery management systems.
Performance Analysis of Interleaved Sepic Converter for Renewable Energy Applications 16th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2025, 2025
Multi-Modal Wildfire Detection Using Swin Transformer and EfficientNet With RGB and Infrared Imagery Vishwa R, Abinaya R, Judith Vishal Kanth S R, Manimegalai M, Sebasthirani K Proceedings of 2025 International Conference on Signal Processing Computation Electronics Power and Telecommunication Iconscept 2025, 2025 Forest fires result in catastrophic damage to ecosystems, wildlife, property, and human populations across the globe. Traditional fire detection approaches relying exclusively on RGB cameras and conventional deep learning architectures frequently produce numerous false alarms and demonstrate reduced performance in adverse scenarios including dense smoke coverage, fluctuating lighting conditions, and visual barriers. To go beyond such constraints, this paper proposes a new deep architecture that incorporates both the infrared (IR) and optical (RGB) modalities for strong wildfire detection. Our proposed method adopts state-of-the-art models like the Swin Transformer and EfficientNet-B0 to support four-channel (RGB+IR) inputs, thus enabling enhanced feature extraction from multi-spectral inputs. An improved dataset creation and preprocessing pipeline also supports robust model training. Experimental results for benchmark wildfire datasets demonstrate that the proposed multi-modal system is superior to state-of-the-art single-modal and conventional methods in terms of detection accuracy and reliability, offering a promising alternative for improved wildfire monitoring and management.
Water Leakage Detection and Recognition System Selvaprakash J, Prem Arasu S. K, Vetri Xavier. S, Orlin Rudina Maria. R, Manimegalai. M, Sebasthirani. K 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things Idciot 2025, 2025 An IoT-enabled water leak detection system is proposed. The proposed system uses real-time data collection and advanced machine learning techniques for continuous monitoring. It consists of two leak sensors integrated with Arduino nodes. It also uses NodeMCU to enable IoT communication and alerts. The sensors continuously monitor water flow and pressure to detect any anomalies that may indicate a leak. The system collects real-time datasets, which are then processed using a modified Long Short-Term Memory (LSTM) model for accurate leak prediction. The modified LSTM uses a new attention mechanism to increase the model's ability to focus on significant data patterns. This integration is used to increase the prediction accuracy. The loT-enabled system sends immediate alerts to users upon detecting a leak. This ensures swift response and minimizing water loss. This approach increases leak detection efficiency, resource management, and reduces manual intervention. Experimental results show that the proposed system achieves higher accuracy and best suitable for both residential and industrial water distribution networks.
IoT Enhanced Greeting System for Smart Environment Divya Shree S, Muthu Bharathi M, Shreelakshmi C, Manimegalai M, K. Sebasthirani 3rd International Conference on Automation Computing and Renewable Systems Icacrs 2024 Proceedings, 2024 This research explores the development and implementation of a robust facial recognition system for enhanced user engagement and operational efficiency. By strategically placing cameras, the system detects individuals entering a designated area and activates a facial recognition algorithm to identify them. Upon successful identification, the system provides personalized greetings and displays the individual's name on screen. The proposed system incorporates advanced techniques such as Convolutional Neural Networks (CNNs) to ensure accurate face detection and recognition under varying lighting conditions and angles. By optimizing processing efficiency and incorporating robust error detection mechanisms, the system ensures reliable and accurate identification. Beyond personalized greetings, the system can be effectively integrated into educational settings for automated attendance tracking. By identifying students and automatically logging their attendance, the system streamlines administrative tasks and enhances student accountability. This research demonstrates the potential of facial recognition technology to enhance user experiences, improve operational efficiency, and contribute to a more streamlined and engaging environment across various applications.
IoT-Based Second-Life Storage System Prototype for Lifepo4 Batteries K. Sebasthirani, G. Rajesh, Murapaka Dhanalakshmi Bhavani, Niharika Varshney, T. Sathish, K. Karthigaivel Proceedings of the 2nd IEEE International Conference on Advances in Computing Communication and Applied Informatics Accai 2023, 2023
A novel ann based harmonics mitigation and monitoring approach of shunt active power filter International Journal of Innovative Technology and Exploring Engineering, 2019
Performance analysis of photovoltaic system with subpanel MPPT converter International Journal of Applied Engineering Research, 2015
Performance enhancement of Shunt Active Power Filter with fuzzy and hysteresis controllers Journal of Theoretical and Applied Information Technology, 2014
RECENT SCHOLAR PUBLICATIONS
GA-optimized fractional-order PID control with data-driven thermal forecasts for PMSM drives in electric vehicles G Rajesh, K Sebasthirani, P Maruthupandi, R Remya Sree Discover Electronics 3 (1), 49 , 2026 2026
Predictive Modeling and Comparative Performance Analysis of PMSM Designs in EV and Industrial Drives G Rajesh, K Sebasthirani, P Maruthupandi, R Remyasree 2026
Temperature-Aware Fractional-Order PID Scheduling for PMSM Drives Using Data-Driven Thermal Forecasts G Rajesh, K Sebasthirani, P Maruthupandi, R Remyasree 2026
Multi-Modal Wildfire Detection Using Swin Transformer and EfficientNet With RGB and Infrared Imagery R Vishwa, R Abinaya, SR Judith Vishal Kanth, M Manimegalai, ... 2025 International Conference on Signal Processing, Computation, Electronics … , 2025 2025
Impact of fast charging on battery performance and SOC variations across temperature conditions G Rajesh, K Sebasthirani PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF … , 2025 2025
Securing Smart Grids: Decentralized Anomaly Detection Using Federated Learning and Recurrent Neural M Manimegalai, K Sebasthirani, P Maruthupandi, G Rajesh Proceedings of International Conference on Recent Trends in Computing: ICRTC … , 2025 2025
Modeling and control of Permanent Magnet synchronous Motor based electric Vehicles R Sebasthirani Advances in Mechanical Engineering 17 (4), 1-18 , 2025 2025 Citations: 4
IoT Enhanced Greeting System for Smart Environment M Sebasthirani 2024 3rd International Conference on Automation, Computing and Renewable … , 2024 2024
Securing Smart Grids: Decentralized Anomaly Detection Using Federated Learning and Recurrent Neural Networks M Manimegalai, K Sebasthirani, P Maruthupandi, G Rajesh International Conference on Recent Trends in Computing, 77-95 , 2024 2024 Citations: 1
Design and Simulation of Sinusoidal Pulse Width Modulation Technique Based Five Level Inverter KM K.Sebasti Rani, Deepakkumar.R , Hitesh Balaji.N International Conference on Reent Applications of Artificial Intelligence in … , 2024 2024
Performance Analysis of Solar Powered High Gain Quadratic Boost Converter For Agricultural Applications DKS Dr. P.Maruthupandi International Conference on RecReent Applications of Artificial Intelligence … , 2024 2024
Heart Attack Prompt and Identification System SK K. Sebasthirani Surya V. , Tirupathi Raja G. , Nithyananth M. , Sharal ... Journal of Advance Electrical Engineering and Devices , 2024 2024
HYDROGEN FUEL CELLED ELECTRIC VEHICLE RKN Dr.K.Sebasthirani,Surya.V, Sharal.V, Sumiya.K International Conference on Recent Challenges in Cardiology,Kottayam, India , 2024 2024
HYDROGEN FUEL CELLED ELECTRIC VEHICLE RKN Dr.K.Sebasthirani,Surya.V, Sharal.V, Sumiya.K International Conference on Recent Challenges in Cardiology,Kottayam, India , 2024 2024
"Heart Attack Prompt and Identification System (Using IOT)" SV Sebasthirani,Sumiya K,Surya V International Conference on Recent Challenges in Cardiology,Kottayam, India , 2024 2024
MODELLING AND TIME DOMAIN ANALYSIS OF SYNCHRONOUS MACHINE WITH FUZZY BASED AVR V Maruthupandi, Sebasthirani Periodico di Mineralogia 93 (5), 160-168 , 2024 2024
Review of Recent Research and Future Scope of Explainable Artificial Intelligence in Wireless Communication Networks Vijay, K Sebasthirani, J Jeyamani, M Gokul, S Arunkumar, AM John International Conference on Information and Communication Technology for … , 2023 2023
DESIGN AND mODELLING OF A LEVEL cONTROLSYSTEMUSING THE aRTIFICIAL NEURALNETWORK DKS Mr.Raja.S. Dr.P.Maruthupandi International Journal of Modern Engineering & Reserach technology. 10 (4), 47-53 , 2023 2023
Iot-based second-life storage system prototype for lifepo4 batteries K Sebasthirani, G Rajesh, MD Bhavani, N Varshney, T Sathish, ... 2023 International Conference on Advances in Computing, Communication and … , 2023 2023 Citations: 2
Detection and Prevention of SQL Injection Attack using Naive Bayes and ADA BOOST based Deep Forest model in Distributed Cloud manimegalai sebasthirani International conference on recent technologies , engineering, science … , 2023 2023
MOST CITED SCHOLAR PUBLICATIONS
Multilevel shunt active filter based on sinusoidal subtraction methods under different load conditions KS Rani, K Porkumaran 2010 IEEE Region 8 International Conference on Computational Technologies in … , 2010 2010.0 Citations: 24
PERFORMANCE ENHANCEMENT OF SHUNT ACTIVE POWER FILTER WITH FUZZY AND HYSTERESIS CONTROLLERS. K Sebasthirani, K Porkumaran Journal of Theoretical & Applied Information Technology 60 (2) , 2014 2014.0 Citations: 14
Comparison of Multiple Carrier Disposition PWM Techniques Applied for Multi-Level Shunt Active Filter S Kathalingam, P Karantharaj Journal of Electrical Engineering 63 (4), 261 , 2012 2012.0 Citations: 11
Optimum control of total harmonic distortion in field programmable gate array-based cascaded multilevel inverter P Maruthupandi, N Devarajan, K Sebasthirani, JK Jose Journal of Vibration and Control 21 (10), 1999-2005 , 2015 2015.0 Citations: 9
An optimal model for power quality improvement in smart grid using gravitational search-based proportional integral controller and node microcontroller unit M Manimegalai, K Sebasthirani Electric Power Components and Systems 50 (16-17), 989-1005 , 2022 2022.0 Citations: 7
Efficient Routing in smart grid communication by secured ABC-ANN Algorithm DKS M.Manimegalai IEEE 7th international conference for convergence Technology(I2CT, doi.10 … , 2022 2022.0 Citations: 7
Design of shunt active power filter with fuzzy logic control for mitigating harmonics K Sebasthirani, G Mahalingam Bonfring International Journal of Industrial Engineering and Management … , 2018 2018.0 Citations: 6
Efficient control of shunt active power filter with self-adaptive filter using average power algorithm K Sebasthirani, K Porkumaran International Journal of Emerging Technology and Advanced Engineering 3 (5 … , 2013 2013.0 Citations: 5
Modeling and control of Permanent Magnet synchronous Motor based electric Vehicles R Sebasthirani Advances in Mechanical Engineering 17 (4), 1-18 , 2025 2025.0 Citations: 4
Performance analysis of smart meters for enabling a new era for power and utilities with securing data transmission and distribution using end-to-end encryption (E2EE) in smart … M Manimegalai, K Sebasthirani Intelligent Computing and Applications: Proceedings of ICICA 2019, 1-12 , 2020 2020.0 Citations: 3
Iot-based second-life storage system prototype for lifepo4 batteries K Sebasthirani, G Rajesh, MD Bhavani, N Varshney, T Sathish, ... 2023 International Conference on Advances in Computing, Communication and … , 2023 2023.0 Citations: 2
Devolpment of Novel Mobile Application for Smart Meter Monitoring in Smart Grid DPM Dr.K.Sebasthirani* GEDRAG & ORGANISATIE REVIEW - ISSN:0921-5077 33 (3), 155-161 , 2020 2020.0 Citations: 2
Recent Advances in Shunt Active Power Filter with Recursive Least Square algorithm and its applications manimegalai K.sebasthirani d International Conference on Trends in Electronics and Informatics (ICOEI … , 2018 2018.0 Citations: 2
Securing Smart Grids: Decentralized Anomaly Detection Using Federated Learning and Recurrent Neural Networks M Manimegalai, K Sebasthirani, P Maruthupandi, G Rajesh International Conference on Recent Trends in Computing, 77-95 , 2024 2024.0 Citations: 1
Performance Analysis of Recurrent Neural Network and Fuzzy Logic Algorithms in Cloud Information Retrieval System. K Sebasthirani International Journal of Early Childhood Special Education 14 (4) , 2022 2022.0 Citations: 1
Power Quality Enhancement using Shunt Active Power Filter with Vienna Rectifier M K Sebasthirani Seventh International Conference on” Information and Communication … , 0 Citations: 1
GA-optimized fractional-order PID control with data-driven thermal forecasts for PMSM drives in electric vehicles G Rajesh, K Sebasthirani, P Maruthupandi, R Remya Sree Discover Electronics 3 (1), 49 , 2026 2026.0
Predictive Modeling and Comparative Performance Analysis of PMSM Designs in EV and Industrial Drives G Rajesh, K Sebasthirani, P Maruthupandi, R Remyasree 2026.0
Temperature-Aware Fractional-Order PID Scheduling for PMSM Drives Using Data-Driven Thermal Forecasts G Rajesh, K Sebasthirani, P Maruthupandi, R Remyasree 2026.0
Multi-Modal Wildfire Detection Using Swin Transformer and EfficientNet With RGB and Infrared Imagery R Vishwa, R Abinaya, SR Judith Vishal Kanth, M Manimegalai, ... 2025 International Conference on Signal Processing, Computation, Electronics … , 2025 2025.0
