Dr. P. AROKIYA PRASAD
@stjoseph.ac.in
Assistant Professor in Department of Electrical and Electronics Engineering
ST.JOSEPH COLLEGE OF ENGINEERING
Assistant Professor in EEE
EDUCATION
B.E., M.E., Ph.D.
RESEARCH, TEACHING, or OTHER INTERESTS
Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment, Automotive Engineering, Computer Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
P. Sivakumar, P.Arokiya Prasad, M. Kaleeswari, D. Anitha, A. Aswini, and P. Tamilvani
IEEE
With the increase in global warming, the diminishing supply of fossil fuels, and the high density of carbon dioxide emissions, the world has become more worried about energy generation using alternate sources of energy such as solar, wind, biomass, and tidal energy. When non-linear loads are connected to the grid it produces the issues like voltage instability, frequency fluctuations, and overloading of system components and it affects the overall reliability of system components. Hence frequency regulation is important for the operation of any grid or Renewable Energy Source (RES) that is connected to the load. When the total power supply is lesser than or more than the required load demand, then it produces frequency fluctuations. So, the Machine Learning-based Load Frequency Control (LFC) method is implemented for counteracting demand fluctuations. When consumer loads are integrated with Distributed Generation (DG) which is comprised of both Solar and wind, the energy demand of the loads will be satisfied by the Renewable Energy Sources during peak hours. In this proposed system, the four areas are considered along with the grid, PV, and wind power plant for Load Frequency Control using Machine Learning (ML). The proposed model is controlled by Proportional Integral and derivative controller (PID) for the betterment of less oscillatory response. The core focus of the proposed research is to implement modeling of a Machine Learning (ML) based modified LFC for a four-area model which is penetrated with a green source such as gust energy and photovoltaic energy for minimizing rapid load fluctuations.
P. Siva Kumar, A. Aswini, M. Kaleeswari, P.Arockia Prasad, R. Manickavasagam, and P.Rathi Devi
IEEE
Induction heating technique is now the preferred heating method in various engineering, household, and therapeutic applications owing to its profits in terms of efficiency, rapid heating, safety, spotlessness, and accurate control. Solar energy is the greatest plentiful energy source for producing heat in induction heating system. In this project, an induction heating system integrating Internet of Things has been suggested for use in healthcare. The main aim of this research is to use internet of things technology and induction heating to assist treat cancer (IoT). According to a previously established notion, cancer cells cannot live in environments 5 to 6 degrees Celsius warmer than normal body temperature. Hyperthermia. involves heating to 39-45°C to promote susceptible to chemotherapy and radiotherapy, are routinely used in the clinic. In order to slaughter cancer cells without damaging healthy cells, a ferromagnetic implant will be positioned close to the cancerous cells in the body. By applying induction heating, the temperature of the implant can be elevated between 41 and 43 degrees Celsius. Small stand-alone photovoltaic (PV) systems can now be remotely supervised with the help of the Internet of Things (IoT). Additionally, IoT will monitor the patient’s behaviors and record everything in real-time to the cloud, where it can be accessed from any location with an internet connection at any time. They also constantly keep an eye on the temperature of the ferromagnetic implant to ensure that the therapy is comfortable.
P. Rathi Devi, P. Sivakumar, and P. Arokiya Prasad
Hindawi Limited
Summary. The dynamically rising demand for electricity must be satiated by both conventional and unconventional power sources. PV electricity generation is essential. The installed PV sources are not used to their full potential due to low irradiance and poor weather. The goal of many research publications was to maximize PV power using different MPPT approaches. In order to extract the most power under the aforementioned circumstances, this study introduces a novel notion of using a voltage boosting PV panels. Additional PV panels are used in the suggested way to provide enough voltage and current. Using a voltage-boosting PV panel, a 20 kW panel is examined for low irradiance and unfavorable weather conditions. In a proposed method, results are compared with the previous INC MPPT method using a 20-kW panel. Through the power analysis, the proposed method extracted 1277.5 kW of power per year that is more than the INC MPPT method, and Rs. 10,220 per year was benefited during low irradiation alone. The power extraction and cost benefits are similar in cloudy and misty conditions. With power, cost, and efficiency analyses, the new method is contrasted with the traditional incremental conductance method and perturb and observe methods. Simulations were run using the MATLAB/Simulink programme, and experimental results were assessed using the appropriate hardware setup.
Rajalakshmi Alavanthan, P. Sivakumar, and P. Arokiya Prasad
Springer Nature Singapore
P. Arokiya Prasad and P. Sivakumar
Hindawi Limited
Growing electrical demand is to be met dynamically through conventional and nonconventional power sources. PV power generation plays a vital role. Because of low irradiation and overcast weather condition, the installed PV sources are not fully utilized. Many research papers were presented to extract maximum power from PV using various MPPT techniques. This paper presents a new idea of implementing a biased transformer to extract maximum power during the above condition. The proposed method uses three winding transformers to give sufficient biased voltage and current. The biased current and voltage were obtained from the grid that is fed to the primary winding—two of the three winding transformers through the controller. A 10 kW panel is checked for low irradiation and overcast weather condition using the biased MPPT technique. The proposed method is compared with the conventional incremental conductance method with power, cost, and efficiency analysis. Simulation was carried out in MATLAB/Simulink software and experimentally evaluated through a suitable hardware setup.