Srividya Prathiba

An enthused, ardent Associate Professor determined to inspire students to excel both in academics and extra-curricular activities. Has more than 2 decades of enriched experience in the field of education with a specialization in Organizational Behaviour. . In her capacity as Head, Department of Accounting and Finance has organized various Conferences, Workshops & FDP’s. As a Multifaceted and dynamic person, she has donned various positions in the college such as Staff Council Secretary, Autonomous Examination Valuation Camp and has served Internal Assessment Committee. Her primary area of research interest is Talent Management with a particular focus on employee engagement and leadership competencies. Has good track record of research publications on talent retention and employee engagement in reputed journals. She is a recognized Ph.D. guide in University of Madras. She is a certified parent counselor and has conducted many parent education programs in various schools and colleges.

EDUCATION

M.Com, M.Phil, MBA, Ph.D

RESEARCH INTERESTS

Employee Engagement & Talent Retention

Scopus Publications

Machine learning-based direct estimation of remaining useful life (RUL) of IGBT using multi-precursor prognostics
RV. Prathiba, M. Saravanan, Prince Winston David, and Hariharasudhan Thangaraj
Springer Science and Business Media LLC



Parametric Fault diagnosis of buck converter based on Machine Learning
Rv. Prathiba and M. Saravanan
IEEE
The role of power converters covers a wide range of applications from switched mode power supplies to power supplies used in large aircrafts and electric vehicles. Inorder to improve the reliability and ensure sustained operation, fault diagnosis of power converters needs to be done. The fault is sensed by the degradation of parameters in electronic components used in the power converter and the same is predicted by Machine learning (ML) algorithm. Various fault scenarios are simulated in a buck converter in MATLAB Simulink environment and the results are used for training the ML model. Single component fault in a Buck converter is considered for study. Feature Extraction along with the performance of two machine learning classifier algorithms in terms of accuracy are discussed in this paper.


Machine Learning based Parametric Fault diagnosis of Cuk converter
RV. Prathiba and M. Saravanan
IEEE
With the advent of renewable energy systems, Electric Vehicles and smart grid, there is a growing need for high performance DC-DC converters in various applications. Inorder to improve reliability and ensure sustained operation, fault diagnosis of power converters needs to be done. Parametric Fault diagnosis mainly deals with monitoring the deviation of components’ parameters from its nominal operating range present in a converter circuit. This work focusses on predicting the parametric faults in a Cuk converter using Machine Learning. Different Fault scenarios are simulated in MATLAB Simulink and the results are used in training the machine learning model. The trained ML model can sense single component fault of Cuk converter. Performance of Machine learning classifiers in terms of accuracy, along with feature extraction is discussed in this paper.


Performance Evaluation of Fuzzy Logic Controller Enabled Wind Energy Conversion System
S. Prathiba, A. Bhuvanesh, A. Sheela, S. Suresh, M. Petchithai, and Amirtharaj G
IEEE
In the present research, boost converter employing renewable energy as a source of input is designed and simulated. Currently, unlike with other forms of energy, wind energy remains the sustainable and clean method of renewable energy. In India, the wind typically blows at a variable speed, creating a variable input voltage as an energy source. The output voltage can be raised to a level appropriate for residential use using a DC-DC boost converter. The current research used MATLAB SIMULINK to develop and simulate a boost converter circuit based on a topological circuit of wind energy conversion systems. Fuzzy Logic Controller (FLC) is employed to tune the duty ratio or pulse width of the converter so as to optimize the output voltage transient response. The result revealed that the boost DC-DC converter with FLC performed better than the buck converter. The projected approach is relatively better than alternative controls in effectiveness, robustness, and flexibility.


Reparation of voltage disturbance using PR controller-based DVR in Modern power systems
D. Danalakshmi, S. Prathiba, M. Ettappan, and D. Mohan Krishna
Stowarzyszenie Menedzerow Jakosci i Produkcji
Abstract The Smart Grid environment gives more benefits for the consumers, whereas the power quality is one of the challenging factors in the smart grid environment. To protect the system equipment and increase the reliability, different filter technologies are used. Even though, consumers’ expectations towards the power quality are not fulfilled. To overcome these drawbacks and enhance the system reliability, a new Custom Power Devices (CPD) are introduced in the system. Among different CPDs, the Dynamic Voltage Restorer (DVR) is one of the voltage compensating devices that is used to improve the power quality during distortions. When the distortions such as voltage swell and sag occur in the distribution system, the control strategy in the DVR plays a significant role. In this article, the DVR performance using Proportional Integral (PI), Proportional Resonant (PR) controllers are analyzed. A robust optimization algorithm called Self Balanced Differential Evolution (SBDE) is used to find the optimal gain values of the controllers in order to reach the target of global minimum error and obtain fast response. Then, a comparative analysis is performed between different controllers and verified that the performance of PR controller is superior than the other controllers. It has been found that the proposed PR controller strategy reduces the Total Harmonic Distortion (THD) values for all types of faults. The proposed SBDE optimized DVR with PR controller reduces the THD value less than 4% under voltage distoration condition. The DVR topology is validated in MATLAB/SIMULINK in order to detect the disturbance and inject the voltage to compensate the load voltage.


Improvement in Voltage Stability by Optimal Location and Sizing of Hybrid Energy Sources by Genetic Algorithm
L Ramya Hyacinth, A. Sheela, S Prathiba, J Alwin Joseph, M Arul Victor, T Febin Joseph, and J Leo Allwin
IEEE
Like any human activity, all energy sources have an impact on our environment. Renewable and Non-Renewable energy is no exception to the rule, and each source has its own trade-offs. However it can’t replaced with a non-renewable energy after utilizing it. And when this non-renewable resource and its byproducts are used it also causes damage to the environment by emitting harmful gases like nitrous oxide, sulfur dioxide and greenhouse gases. So instead of depleting precious resources and polluting the environment we can go for renewable energy sources which are harmless and sustainable source of energy. So our project is mainly intended to optimize the power system in means of proper siting and sizing methodology using renewable energy resources. This is necessary to reduce the ever increasing load demand, reactive power demand and to safeguard the power system from unnecessary issues. This also benefits in reduction of cost in the consumer electric bills. Basically we have two parts in this project, First part of the project is proper sizing, where we locate the area (busbar) which has low voltage profile while comparing to all other sections of the system. In this process we use Newton Raphson method for power flow analysis of the system to locate the weak bus. Second part of our project is to optimize the system. That is, we need to integrate a proper generator to compensate the load demand so that the system becomes stable. For compensation we are using a “Hybrid Renewable Energy Source” which is a combination of solar and wind energy. Thus the system we considered would be stable so that it would be free from all the issues as we mentioned above. As we are using renewable energy, the system would be more efficient and sustainable than the conventional energy which we are dependent on.


Design and development of portable stand-alone solar power generator




Control strategies on speed of dc motor and power sensor based speed regulator using scilab




Multiple output radial basis function neural network with reduced input features for on-line estimation of available transfer capability




Multi-output on-line atc estimation in deregulated power system using ann
R. Prathiba, B. Balasingh Moses, Durairaj Devaraj, and M. Karuppasamypandiyan
Springer International Publishing



ANN and gradient based optimal power flow




MOGA with fuzzy decision making for ATC improvement using generator rescheduling




Hybrid ANN based optimal power flow and its validation for deregulated environment




Optimal selection and allocation of generator for static atc using differential evolution algorithm




Flower pollination algorithm applied for different economic load dispatch problems




Estimation of available transfer capability using soft computing techniques
R. Prathiba and C.C.A. Rajan
Institution of Engineering and Technology
In today's electric power industry, accurate and flexible information is needed to provide nondiscriminatory access to all market participants. This paper primarily calculates the available transfer capability (ATC) based on fuzzy set theory for Repeated Power flow(RPF),there by capturing uncertainty. Assuming that uncertainty involved are estimated, a fuzzy model is formulated in which uncertainty parameters affecting the ATC are regarded as fuzzy variables. The purpose of this paper is to calculate ATC using Continuation Power Flow (CPF) and then compare the result with the Repeated Power Flow (RPF).To investigate into the potentials of fuzzy logic and the way it can be applied for ATC determination. To identify the most suitable one from among the widely used fuzzy models for ATC determination. To develop a novel method that would adopt fuzzy logic for determining ATC involving minimum number of input variables in a large power system is fuzzy for repeated power flow (FRPF) algorithm that can handle uncertainties in load parameters and bus injections as well.. As such the solution will assist in providing additional information on the actual ability of the network to a system operator. The viability of the proposed method would be verified with the one obtained from the Repeated power flow (RPF) in the IEEE-24 RTS bus system.

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