Electrical and Electronic Engineering, Control and Optimization, Multidisciplinary
22
Scopus Publications
Scopus Publications
Grid-Integrated Photovoltaic System for Electric Vehicle Charging Station with Dynamic Energy Management R. Tharwin Kumar, C. Christober Asir Rajan 2025 IEEE Electrical Power and Energy Conference Epec 2025 Conference Proceedings, 2025 The rapid growth of Electric Vehicles (EVs), the increasing demand for sustainable charging infrastructure, and the pressing need to reduce dependence on fossil-fuel-based electricity are essential. Hence, this paper discusses a grid-tied Photovoltaic (PV) system for EV charging stations, focusing on dynamic energy management to maximize solar generation benefits and minimize grid strain. The system uses a high-gain Modified Luo converter to boost the PV system’s DC voltage output. Incremental Conductance (INC)-based Maximum power Point Tracking (MPPT) technique is a control method used in PV systems to ensure operation at the maximum power point under varying environmental conditions. Further, it responds quickly to dynamic weather conditions, making it particularly suitable for grid-connected and off-grid PV applications where efficiency and stability are critical. A single-phase Voltage Source Inverter (VSI), which links the PV system to the grid, receives the enhanced output of the Modified Luo converter. Additionally, an LC filter is used to reduce unwanted harmonics, smooth voltage and current waveforms, and improve power quality in power electronic systems. The proposed approach uses MATLAB/SIMULINK to model the proposed approach. The efficiency is noted to be $94.3 \%$ with a Total Harmonic Distortion (THD) of $\mathbf{2. 1 2 \%}$.
Optimized Hybrid Renewable Energy System for Sustainable Electric Vehicle Charging: Integration of Photovoltaic and Wind Power With Advanced Control Strategies R. Tharwin Kumar, C. Christober Asir Rajan IEEE Access, 2024 In an era where environmental and economic priorities increasingly intersect, advancing technologies are transforming Electric Vehicles (EVs) into more sustainable options. This research presents a novel Hybrid Energy System (HES) that integrates Photovoltaic (PV) and wind power systems into the grid, providing a continuous, reliable power supply specifically for EV charging. The core innovation lies in the design and implementation of a High Gain Bidirectional Modified Zeta (HGBMZ) converter, which significantly enhances PV system efficiency by boosting voltage levels. To optimize power extraction, the system employs an advanced Social Spider Optimization Algorithm (SSOA)-assisted Fuzzy controller for precise Maximum Power Point Tracking (MPPT), ensuring superior converter performance and maximal energy harvesting. A Doubly Fed Induction Generator (DFIG) driven by a Proportional Integral (PI) controller stabilizes the wind component, maintaining voltage levels and system stability across variable conditions. A key feature of this HES is its ability to feed surplus energy back into the grid, facilitated by a PI controller for effective control of voltage and synchronization during peak demand cycles. Validated through MATLAB simulations, the proposed system achieves an impressive converter efficiency of 93.04% and a low Total Harmonic Distortion (THD) of 3.21%. Laboratory validation further confirms a THD of 4.32%, establishing the system as a sustainable and optimized solution for EV charging.
Enhancing Grid Stability In Hybrid Renewable Energy Systems Through RNN-Based Grid Voltage Synchronization R. Tharwin Kumar, C. Christober Asir Rajan IEEE Power and Energy Society General Meeting, 2024 An innovative methodology to enhance power quality in grid-tied hybrid power systems integrating Photovoltaic (PV) and Wind Energy Conversion Systems (WECS) is presented in this research work. The distinctive innovation of the proposed work lies in employing Recurrent Neural Networks (RNNs) for grid voltage synchronization, a departure from traditional Synchronous Reference Frame (SRF) theory. This RNN-based controller eliminates the need for complex mathematical operations and costly hardware, offering a more efficient and adaptive solution. The PV and WECS contribute power to a common DC bus through dedicated converters, forming a resilient hybrid configuration. MATLAB simulations are conducted to evaluate the proposed RNN-based approach’s efficacy in comparison to conventional methods. The results highlight the superior performance of the RNN-based grid voltage synchronization, showcasing its adaptability to dynamic grid conditions. This research not only advances intelligent control strategies for grid-connected microgrids but also underscores the transformative potential of RNNs in achieving efficient grid synchronization for integrated renewable energy systems.
ANFIS MPPT Assisted PV Fed DC-DC Switched Z Source Converter for Microgrid Applications V. P. Karthika Preethy, R. Tharwin Kumar, P. Radhika, Ananthan Nagarajan, A. A. Mohamed Faizal, M. Sasikumar Proceedings of International Conference on Circuit Power and Computing Technologies Iccpct 2024, 2024 Photovoltaic (PV) modules are crucial components of contemporary distribution networks, but they have historically been employed primarily to provide profitable, clean, and green energy. An Adaptive Neuro Fuzzy Inference System (ANFIS) is described in this study as a technique for MPP prediction using information on solar interaction and ambient temperature. The proposed technique has the benefits of a quick reaction, overall harmonic distortion discount, more effective utilization of PV units, and straightforward ANFIS algorithm training. An innovative type of large step up DC to DC converter is produced by combining the switched-capacitor cells by a conventional Z-source impedance network. The recommended converter delivers a great voltage gain lacking the limited accountability sequence and does not necessitate an enormous quantity of modules while decreasing a voltage heaviness on the diodes and the control switch to fewer than partial the productivity power. The performance and application of the recommended converter are established by the simulation and experimental findings. The comparison of the controllers shows the superior MPPT effectiveness of ANFIS as 92.3%.
Interleaved Luo Converter for PV Based BLDC Vehicle System R. Tharwin Kumar, G. Sireesha, K. Sakthidhasan, T. R. Permila, R. Ramya, P.T. Subasini 2024 2nd International Conference on Computer Communication and Control Ic4 2024, 2024 Economically used Brushless Direct Current (BLDC) motors have been dramatically replacing conventional DC drives in a variety of submissions, plus electrical vehicles and mechanical mechanization. The interleaved Luo converter is used in the Solar Photovoltaic (PV), which leads to greater capability improvement of this converter. Supremacy at evaluated DC power is delivered to the BLDC motor under normal trial conditions. Interleaved Luo converter topology that are appropriate for BLDC motors are proposed in this study. The spider monkey optimized PI (SMO-PI) controller is applied in this paper, a well-known Proportional Integral (PI) controller is adjusted and tested on a simulated solo PV system with local load using the spider monkey method also it is utilized to uphold the firm state power at the yield of the Interleaved Luo converter and the presentation of the recommended converter is experimentally tested utilizing the SMO-based PI controller. The Mat lab/Simulink environment was used to simulate both the PV system and the method that proposed. This work also includes a comparison analysis and monitors the Interleaved Luo converters’ efficiency at 91.2%.
High Speed Neural Network MPPT Algorithm for DFIG Based Wind Energy Conversion System R. Tharwin Kumar, V. Balaji, K. Sakthidhasan, S. Gomathi, R. Sreedhar, N. Janaki Proceedings of International Conference on Circuit Power and Computing Technologies Iccpct 2024, 2024 For the generation of electrical energy, breeze energy is a trustworthy and talented renewable vitality basis. An innovative proposal with many scientific and economic benefits is using wind source technology to charge electric vehicles. Worldwide, the market for Wind Energy Conversion System (WECS) has been expanding significantly. High-quality output power is commonly produced when wind energy demand grows. WECS has significant modelling, control, and grid integration issues as a result of the intermittent nature of wind energy. A proper controller mechanism has to be created in order to significantly improve WECS. An innovative method for maximizing the power of wind energy founded on Radial Basis Function Neural Networks (RBFNN) is proposed in this study to increase the power of Double Fed Induction Generators (DFIG). DFIG based wind turbine, and to provide outstanding dynamic performance in response to breeze rapidity fluctuations. MPPT is a strong algorithm that operates around an ideal rotating speed by utilizing RBFNN for mechanical speed control. We evaluated the performance of DFIG founded WECS with MATLAB/Simulink. The RBFNN efficiency is 91% when comparing MPPT controllers.
FPGA Interfaced IoT System for Smart Medical Robot Monitoring System R. Tharwin Kumar, S. P. Abinaya, D. Prakash, N. Janaki, S. Sivarajan, P. Mani 2024 2nd International Conference on Computer Communication and Control Ic4 2024, 2024 New and improved technology for illness prevention, early detection, and treatment are being encouraged by rising health consciousness and rising medical care costs. The creation of a low-cost initial verification platform layout depends on the FPGA-based system. It has been suggested in the past that sensor nodes built on the FPGA platform would be entry-level IoT devices. The goal of this paper is to create an IoT-based smart health tracking system. The system was created to use wearable technologies and FPGA to gather health data. This technology employs a robot's sensors as an intelligent sensor interface. A multi-sensor healthcare platform may be coupled with the FPGA-based IoT low-end programmable pulse sensor interface architecture to monitor a person's pulse vital sign. Three sensors the heartbeat sensor, temperature sensor, and blood pressure sensor are employed in this system to collect patient data. It has the ability to constantly track a patient's vital signs. Patients' conditions are communicated to loved ones and medical personnel via SMS and the Blynk website. In an Internet of Things context, the complexity of the code may be minimized by using reconfigurable smart sensor interface with a robot.
Integration of hybrid PV-wind system for electric vehicle charging: Towards a sustainable future R. Tharwin Kumar, C. Christober Asir Rajan E Prime Advances in Electrical Engineering Electronics and Energy, 2023 This research addresses the pressing need for sustainable energy solutions in the context of Electric Vehicle (EV) charging. It focuses on the integration of Hybrid Renewable Energy Sources (HRES) such as Photovoltaic (PV) and wind systems, coupled with grid connectivity to ensure uninterrupted power supply. The study's primary objective is to design an efficient HRES framework that optimally harnesses solar and wind energy for EV battery charging while maintaining grid compatibility. The research introduces a novel approach by incorporating a High Gain Zeta-SEPIC (HGZS) converter to enhance power output of PV system. A key innovation lies in the development of a Type 2 Fuzzy Maximum Power Point Tracking (MPPT) controller that ensures precise tracking of maximum power point (MPP), thus optimizing the operation of HGZS converter and maximizing the energy extraction from PV system. Additionally, a Doubly Fed Induction Generator (DFIG) based wind system, controlled by Proportional Integral (PI) controller is implemented, for maintaining controlled voltage levels, ensuring system stability, and enhancing overall performance under diverse operating conditions. Furthermore, the research focuses on grid interaction, allowing surplus energy generated by HRES to be efficiently supplied to grid, making it available for consumption during peak demand periods. A PI controller is implemented on the grid side to ensure effective voltage control and synchronization with the grid. The proposed HRES architecture is rigorously validated through extensive MATLAB simulations. The results demonstrate exceptional efficiency, with proposed converter achieving an efficiency rating of 99.6% and significantly enhanced voltage gain.
Implementation of PV-Wind based Microgrid System using Whale Optimization Algorithm Negesh R K, Karthikeyan S, Tharwin Kumar, Sivasubramanian M Ssrg International Journal of Electrical and Electronics Engineering, 2023 Recently, Micro Grids (MGs) have become extremely popular due to their advantages of effective power conversion and high transmission efficiency. The MG and Nonlinear Loads (NL) are being incorporated into the electricity network. MGs are connected by Voltage Source Converters (VSCs), and NL infuses harmonics into the utility grid using power devices. However, the emergence of stability problems in the MG is caused by the nonlinear characteristics of Renewable Energy Sources (RESs), the rising use of power electronic devices and unexpected variations in load. This paper aims to suggest a microgrid that employs RESs comprising wind and Photovoltaic (PV) systems. This method is established to distribute stable power to loads without any interruptions. A Doubly Fed Induction Generator (DFIG) is deployed as a wind system. To stabilize the PV input voltage, the Boost converter is implemented. Furthermore, intended for enhancing the microgrid's performance, a constant output without distortion is attained from the converter with the deployment of a Whale Optimized Proportional Integral (WO-PI) controller. The 3Φ inverter is utilized to sustain the DC link voltage, and it combines PV, wind, and battery output at a single point and feeds it to the grid. The results are implemented using the MATLAB platform, and simulation outcomes show that the suggested control technique is effective with a THD of 2.33% and reduced overshoot issues.
Modelling and Analysis of Hybrid Fuzzy Tuned PI Controller based PMBLDC Motor for Electric Vehicle Applications Kavin K S, Subha Karuvelam P, Abhinav Pathak, Premila T R, Hemalatha R, Tharwin Kumar Ssrg International Journal of Electrical and Electronics Engineering, 2023 Permanent magnet machines are very suitable for Electric vehicles and Motor pump applications because of their simple construction, ease of control and high efficiency. Among the different Permanent Magnet Brushless DC motors (PMBLDC) as well as permanent magnet motors are particularly very suitable for EV applications owing to their high-speed performance and constant speed-torque features. In this paper, the PMBLDC motor is modelled and analysed mathematically using its equivalent circuit. The PMBLDC motor speed is controlled using an intelligent fuzzy logic-tuned PI controller. This fuzzy logic-tuned PI controller and hysteresis current controller (HCC) perform steady state speed and closed loop speed control operation. This proposed hybrid speed controller reduces the second-order underdamped system drawbacks such as settling time, integral absolute error, steady-state error and maximum peak overshoot. A proposed controller achieves a settling time for motor speed at 0.1 s, which provides smooth control for the speed of the PMBLDC motor. The design and modelling of the entire work are done with Matlab's help to verify the proposed hybrid system's performance.
