S. Senthilkumar
@egspec.org
Assistant Professor / Electronics and Communication Engineering
E.G.S. Pillay Engineering College
Dr. S. Senthilkumar is working as an Assistant Professor in E.G.S. Pillay Engineering College, Nagapattinam in the department of Electronics and Communication Engineering. He has completed his Ph.D (Information and Communication Engineering) at Anna University, Chennai in 2023. He has completed his postgraduate (Nanoelectronics) at SASTRA University, Thanjavur and undergraduate (Electronics and Communication Engineering) at Anajalai Ammal Mahalingam Engineering College, Kovilvenni. He has around 10 years of experience in teaching. He is a recogonized research supervisor in Anna University, Chennai. He has contributed to more than 25 research publications in international journals and conferences, 3 text books 1 Indian patent. He has been well trained person in the fields of accreditation processes like NBA, NAAC and NIRF in India.
EDUCATION
M.Tech., Ph.D.,
RESEARCH, TEACHING, or OTHER INTERESTS
Electrical and Electronic Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
K. Krishnaram, T. Suresh Padmanabhan, Faisal Alsaif, and S. Senthilkumar
Springer Science and Business Media LLC
AbstractThe conventional MPPT method has drawbacks, such as that under partial shading conditions, several peaks occur and identifying the global peak is difficult. It may converge to a local peak and lead to poor conversion efficiency and tracking efficiency. Implementation of a hybrid algorithm by integrating P&O and metaheuristic algorithms can perform better under partial shading conditions. But the tracking speed is low and the response time is longer. To mitigate the issues mentioned above, a new hybrid algorithm has been suggested that integrates GWO and a modified fast terminal sliding mode controller (MFTSMC). The suggested method with three phase ILBC is incorporated into the PV system. The MATLAB tool is employed to experiment with this study. The performance of GWO-MFTSMC is analysed through MATLAB/ SIMULINK and compared with the performance of ANN-FTSMC and PSO-FTSMC algorithm based MPPT techniques. A hardware prototype is developed and tested for 5 × 200 W solar PV modules with the GWO-MFTSMC algorithm. The proposed method conversion efficiency is 99.72% and 96.15% under simulation and hardware realisation, respectively, which is higher than the ANN-FTSMC and PSO-FTSMC methods.
K. Nandakumar, V. Mohan, Faisal Alsaif, and S. Senthilkumar
Springer Science and Business Media LLC
AbstractConsidering different applications that require varied power and voltage conversion levels between AC grids and AC loads, AC–AC power conversion between AC grids has become an inevitable technology of energy management systems. An isolated converter for performing AC-to-AC transmission is proposed with minimal components for reduced losses and enhanced system efficiency. Single-phase direct buck-boost AC to AC converter with minimum components constituted with two dual IGBT control units (IGBT 1–IGBT 4), inductor (Lf), and capacitor (Cf) is proposed in this work. The MATLAB/Simulink platform is used to provide in-depth analysis of the circuit and components along with the design guidelines, and simulation outcomes of this proposed model. The voltage gains of G = 2.13, power factor of 0.97, and overall efficiency of 98% are achieved in the proposed system with minimum components of 4 switches, 2 conductors, and 1 capacitor and inductor respectively. The obtained results are compared with existing technology to evaluate the proposed system.
K. Krishnaram, T. Suresh Padmanabhan, Faisal Alsaif, and S. Senthilkumar
Springer Science and Business Media LLC
AbstractSolar energy is the most promising among many renewable energy sources to meet the increasing demand. Photovoltaic (PV) based power generating solutions are expected to gain popularity as a power source for different applications, including independent and grid connected loads, due to their cleanliness, high performance, and high dependability. The efficacy of photovoltaic systems is impacted by several elements, including geographical location, positioning, shadowing effects, and local climate conditions. In order to fulfil the demands of loads, an interleaved boost converter is utilized, which has a reduced number of filters with less stress on the devices. Solar powered systems employ several maximum power point tracking (MPPT) methodologies. However, when there is partial shading, many power peaks arise, which complicates the identification of the overall peak. Although MPPT approaches are designed to measure and maintain the global maximum power point (GMPP), there are still significant oscillations observed around the GMPP with subpar settling time, tracking efficiency, and conversion efficiency. In this work, novel hybrid MPPT technique called artificial neural network supported adaptable stepped-scaled perturb and observe (ANN-ASSPO) method and water cycle optimization based perturb and observe (WCO-PO) have been proposed. Artificial neural network (ANN) has been used to determine the best scaling factor in ANN-ASSPO MPPT. Performance is enhanced in ANN-ASSPO MPPT by using the optimum scaling factor, particularly in situations when the irradiance is rapidly changing/partial shading conditions. Similarly, in WCO-PO MPPT water cycle optimization is used to determine the peak power when the PV panel is subjected to partial shading conditions. The performances of proposed hybrid MPPT ANN-ASSPO and WCO-PO techniques have been compared in terms of power generated, output voltage, average settling time and conversion efficiency. The MATLAB/Simulink tool is employed to carry out the experiment for this study.
S. Sivamani, S. P. Mangaiyarkarasi, R. Gandhi Raj, and S. Senthilkumar
Springer Science and Business Media LLC
AbstractThe concept of an isolated DC source cascaded multilevel inverter finds good solutions for generating quality output voltage for low-medium power applications. It shapes the output voltage from three levels into a number of steps closer to a sinusoidal shape using small DC sources or batteries. Several advantages have been sighted like lower voltage stress and bearing noise, and lesser THD. However, a common issue in the MLIs is the total components required which increase with the rise in voltage levels. This paper proposes a three-phase MLI design having several isolated quad voltage source modules including an H-Bridge inverter. The design suggested claims reduced switching components for current conduction paths showing improved output quality. The operational features of the suggested MLI have been analyzed using Matlab/Simulink software, furthermore, an experimental module is constructed for demonstrating the effectiveness of the simulated results.
L. Ramachandran, S. P. Mangaiyarkarasi, A. Subramanian, and S. Senthilkumar
Springer Science and Business Media LLC
R Manivannan, S Senthilkumar, K Kalaivani, and N Prathap
IOP Publishing
Abstract The management of the hardware as well as software resources entrusted to third-party service suppliers have shown to be extraordinarily remarkable and accessible with Cloud Computing (CC), a potential computing technology for the future. Yet, a major undertaking is characterized by security as well as the use of Virtual Machines (VMs) to perform a request. The main issue with using technology is security worries, which raises the need for a strong security system to safeguard data on the cloud. Thus, to address this problem, a method called Weighted Mean-based Radial Basis Function Network with Advanced Encryption Standard (WMRBFN-AES) is developed to improve cloud security along with the time of execution. Random keys are produced utilizing the Pseudo Random Number Generator (PRNG) technique, and the best keys are created employing the WMRBFN technique. In the WMRBFN technique, randomly generated keys are used as input, and the hidden layers yield an estimate associated with the secret key of the input. The output layer generates the most precise secret keys to encrypt the data. Encryption is carried out using the AES algorithm. The files about the encrypted data are kept in a cloud storage system. Reduced execution times for stakeholder demands (tasks) as well as optimal cloud resource utilization result from the selection of VMs, which significantly improves reliability. The Migration Algorithm (MA) is used in the cloud framework to optimize the selection of VMs to improve resource utilization and performance. The experimental findings of the suggested framework and the state-of-the-art frameworks are compared, and it was found that the new model performed better in terms of cost, resource utilization, service latency, throughput, and execution time.
N. Chitra Kiran, S. Senthilkumar, G. Satish, Narendra Soni, Amit Ganatra, Essam A. Al-Ammar, and Amjad Iqbal
Springer Science and Business Media LLC
L. Ramachandran, V. Mohan, S. Senthilkumar, and J. Ganesh
IOS Press
White Spot Syndrome Virus (WSSV) is a major virus found in shrimp that causes huge economic loss in shrimp farms. A selective diagnostic approach for WSSV is required for the early diagnosis and protection of farms. This work proposes a novel recognition method based on improved Convolutional Neural Network (CNN) namely Dense Inception Convolutional Neural Network (DICNN) for diagnoses of WSSV disease. Initially, the process of data acquisition and data augmentation is carried out. The Inception structure is then used to improve the performance of multi-dimensional feature extraction. As a result, the proposed work has the highest accuracy of 97.22% when compared to other traditional models. The proposed work is targeted to Litopenaeus Vannamei (LV), and Penaeus Monodon (PM) diversities for major threats detection of White Spot Syndrome (WSS). Performance metrics related to accuracy have been compared with other traditional models, which demonstrate that our model will efficiently recognize shrimp WSSV disease.
M. Kavitha, S. Senthilkumar, K. Kalaivani, E. Saranya, P. Arunkumar, and V. Malu
IEEE
Agriculture is deemed to be the most significant segment in India. The main objective of this research work is to monitor plant diseases and analyze the system using a smartphone. The remote monitoring framework, including temperature, stickiness and soil humidity sensors, a microcontroller, an ultrasonic sensor, a camera, and a GSM module is validated for effective environment observation. The technology could offer a fresh method for farming to obtain information related to agriculture. In this monitoring system of agricultural diseases, the main aim is to achieve disease monitoring information and collections of the results are detected by Android application through GSM. ARM processor is used for analog to digital converter (ADC) and sends the gathered information to portable through GSM. An IP web camera is occasionally empowered to catch the plant leaf picture for disease checking and an ultrasonic sensor is utilized with the end goal of plant development checking. It uses a soil battery to drive the temperature sensor to foster plant development checking framework and plant infection observation, likewise adaptive android application to screen such circumstances by utilizing this innovation to accomplish a green hardware framework.
S. Senthilkumar, E. Saranya, M. Kavitha, Anbarasi Selvakumar, S. Rajasri, and R. Ramiya
IEEE
A three-way lock system using face detection, an Radio Frequency Identification (RFID) reader, and a digital lock is presented in this article. A safe and solid locking framework can be used in homes, workplaces, schools, and any category of organization. Even unregistered users use the alternate method. They can unlatch the lock with a digital passcode. Only the enrolled clients can get to it utilizing face location. Assuming any unapproved people attempting to utilize face location, an email is conveyed to the administrator first. A Short Message Administration (SMS) will send an instant message to a proprietor by some unacceptable secret word being placed. Simultaneously, a remote camera will take a snap of that individual and store it in an SD card for the reference of the proprietor and use for observation. The framework includes the client screening the entryway by capturing pictures utilizing an exclusive remote camera so that the entryway shall not be opened to unapproved individuals. A facial recognition framework captures facial pictures of an individual, affirms his character to check participation, and gives an alert using the bell if the individual is unapproved. The subtleties are transferred to the concerned mail through IoT. The center of the framework is the Arduino UNO and Raspberry Pi, which is interfaced with a Pi camera, Keypad, a Liquid Crystal Display (LCD), EM-18 RFID reader including an electric door lock.
S. Senthilkumar, M. Kavitha, P. Arunkumar, G. Pushpa, R. Ramanan, and L. Anbazhagan
IEEE
The requirement for renewable energy especially solar is growing rapidly day by day. Solar Photovoltaic (PV) is a major type of solar power production in all over the world. A proper simulation studies are required for a solar PV system before implementing them into real time applications to avoid the unwanted errors and drop in system performance. Maximum Power Point Tracking (MPPT) is a technique to capture the maximum power from incident light under dissimilar environmental conditions. In this research work four different types of MPPT algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), BAT optimization, and Grey Wolf Optimization (GWO) are used to analyze the performance of Single Diode Model (SDM) solar PV under different solar irradiations with Matlab Simulink. 5 panels are considered and 3 different cases are framed with dissimilar irradiations to study system performance like maximum power and MPPT Power (MPPTP). From the findings of this study, it is confirmed that out of four mentioned algorithms, GWO based MPPT algorithms provides better performance in terms of maximum power and MPPTP.
Senthilkumar Selvaraj and S. Selvaraj
ACCENTS
S. Senthilkumar, Moazzam Haidari, G. Devi, A. Sagai Francis Britto, Rajasekhar Gorthi, Hemavathi, and M. Sivaramkrishnan
IEEE
The focus of this study is to develop a bidirectional wireless charging system for E-Vehicles (EV). The bidirectional DC-DC converter employs the LCC resonant circuit designed separately for the source and the vehicle. With the use of bidirectional power electronic device, the converters act as inverters or rectifiers according to the requirement or the direction of power flow. The grid side DC link voltage is maintained at 700–800V for 3phase, on the other hand, the DC link voltage of the batten* side is 350–400V for the efficient power transfer. The proposed converter is designed for 10KW. The switching frequency 25kHz reduces the size and volume of energy storing elements. The proposed circuit is designed and simulated to verify various parameters such as power transfer capacity, load variation, efficiency, and sensitivity. The results indicate that the efficiency of DC-DC converter is 97.2% when the power flows from grid to vehicle, whereas the efficiency is found to be 97.4% for the vice versa.
V. Mohan and S. Senthilkumar
IOS Press
Due to the shortage of fossil fuel usage, the solar Photovoltaic (PV) energy has increased grownup over the last decade. Most conventional applications of renewable energy are being phased out in order to reduce costs and save the environment. PV plants undergo numerous failures in faults detection and ultimate power developments. These consequences demonstrate in the environmental field and internal components. Even when internal standards are followed, the faults are unavoidable and undetectable. Due to this, the performance of manufacturing plants are not predictable. As a result, a proper fault detection mechanism is required for a PV system to detect faults and avoid energy losses. To address these issues, this research work proposed Internet of Things (IoT) sensor-based fault identification in a solar PV system. The PV panel status is monitored using pressure, light intensity, voltage, and current sensors. These sensor data’s are stored in the cloud for further analysis using a web-based control server. To classify the sensor data, models of Support Vector Machine (SVM), and Extreme Learning Machine (ELM) are utilized. The experimental results indicate that ELM achieves a classification accuracy of 96.32%. Which is higher than SVM and other optimization control techniques. The proposed model uses the IoT cloud to provide real-time monitoring and fault detection in plant environmental and electrical parameters.
S. Senthilkumar, V. Mohan, S. P. Mangaiyarkarasi, and M. Karthikeyan
Hindawi Limited
The performance of photovoltaic (PV) systems must be predicted through accurate simulation designs before proceeding to a real-time application to avoid errors. However, predicting the cohesive relationship between current and voltage and estimating the parameters of a single diode model become a perplexing task due to insufficient data in the datasheet of PV panels. This research work presents single-diode solar PV system simulation analysis under different conditions, and the performance is improved by introducing an optimization-based maximum power point tracking (MPPT) strategy. Before simulation, a mathematical model for a single diode and optimization approaches are presented in this research work. Particle swarm optimization (PSO), genetic algorithm (GA), BAT optimization, and grey wolf optimization (GWO) model-based MPPT circuits are designed, and the performances are comparatively analyzed. The simulation results identify the nonlinear relationship between current and voltage and between power and voltage as characteristic curves for different temperature and irradiance values. For maximum power (Pmax), the maximum peak point tracking power and efficiency are analyzed to verify the optimization-based MPPT system. The simulation results demonstrate that the GWO model obtains a maximum tracking efficiency (TE) of 98%, which is much better than that of other optimization techniques.
A. Asuvaran and S. Senthilkumar
IEEE
For perfect communication during data transmission between transmitter and receiver the reliability is important factor, sometimes reliability is missed due to appearance of errors. ECC provides reliable data delivery over unreliable communication channel. Error correction codes (ECCs) are used to shield memories from soft errors and stuck-at defects. Single error correction (SEC) codes that can correct 1-bit error per word are a typical choice for memory protection. In some cases, SEC codes are extended to conjointly offer double error detection and are referred to as SEC-DED codes. Repair proficiencies are usually used for defects, whereas error correction codes are used for soft errors. Recently, some proposals are created to use error correction codes to handle with defects. In this paper tend to correct two single bit errors within the combination of one soft error and one stuck-at defect and to reduce delay of Error Correction Codes.