Qualification: 
M.Tech, B-Tech
k_sireesha@blr.amrita.edu

K. Sireesha currently serves as Assistant Professor at the department of Electrical & Electronics, Amrita School of Engineering, Bangalore campus. She has more than 15 years of experience in teaching. She is currently pursuing Ph.D. Her Areas of interest in research includes Electric Vehicles, Battery management systems, smart systems, Intelligent Computing & Control Systems.

Education

Year Degree/Program Institution
2013 M.Tech (VLSI Design) Amrita Vishwa Vidyapeetham, India
2003 B.Tech(EEE) JNTUK University, India

Publications

Publication Type: Conference Paper

Year of Publication Title

2020

S. K. Sooraj, Sundaravel, E., Shreesh, B., and K. Sireesha, “IoT Smart Home Assistant for Physically Challenged and Elderly People”, in 2020 International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 2020.[Abstract]


Internet of Things (IoT) conceptualizes the objective of remotely connecting and keeping track of the real-world objects though the cyberspace. When it comes to home, this technology can be aptly used to forge it smarter, safer and automated. In this paper, an IoT based smart home assistant has been presented to assist the elderly and physically challenged people to be safe and active at home. Wireless communication technology and Artificial intelligence (AI) based voice recognition is used to enable them to remotely monitor, access and manage appliances, systems and surveillance of the home easily. Hypothesis is designed and tested by considering two use cases and implemented using Arduino, Raspberry-Pi.The results are analyzed and presented.

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2020

N. O. Teja, M. Ramakrishna, S., Bhavana, G. B., and K. Sireesha, “Fault Detection and Classification in Power Transmission Lines using Back Propagation Neural Networks”, in 2020 International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 2020.[Abstract]


Generally, overhead power transmission system is a set of conductors used to transfer power from generation station to consumer side. Since the conductors are left uncovered, they become more vulnerable to faults. These faults lead to discontinuity of supply and result in power losses, which will be negatively impacting the transmission system efficiency. An efficient and reliable power transmission system must be capable enough to detect and correct such faults. The proposed research work has developed an approach for transmission line fault classification and detection using back propagation neural networks (BPNN). A comparative analysis on various algorithms used in back propagation neural networks, by taking performance metrics as MSE, amount of time taken for training and no. of epochs is included. Simulations are performed using the MATLAB/Simulink® platform.

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2020

K. Poornesh, Nivya, K. Pannickott, and K. Sireesha, “A Comparative study on Electric Vehicle and Internal Combustion Engine Vehicles”, in 2020 International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 2020.[Abstract]


Electrification of the vehicles gains a significant research importance due to the increasing amount of greenhouse gas (GHG) emission by using conventional internal combustion engine vehicles (ICEV). The major benefits of electric vehicles (EVs) are a reduced amount of carbon monoxide (CO) emissions, higher efficiency, performance, and lower maintenance costs. It also allows energy diversification by switching to renewable resources. This paper analyzes the efficiency of an EV with ICEV by considering various parameters like required torque, speed and distance traveled. The simulation is developed in MATLAB/Simulink and analysis is presented for various performance metrics as a measure.

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2020

B. S. T. Reddy, Reddy, K. S., Dr. K. Deepa, and K. Sireesha, “A FLC based Automated CC-CV Charging through SEPIC for EV using Fuel Cell”, in 2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), 2020.[Abstract]


Energy extraction from renewable sources and its utilization in Electric Vehicles (EVs) have paved the way for greener transportation options. Battery charging is the present-day challenge in EVs. Constant Current (CC) and Constant Voltage (CV) charging methods have many advantages over other EV battery charging techniques. A fuel cell, being one of the ways of producing DC energy, can be directly used to charge batteries in EVs. With the fuel cell being the DC source, the on-board fast charging can be easily achieved by various power converters, but the Single-Ended Primary Inductor Converter (SEPIC) would serve most efficiently for the aforementioned scenario. In CC method, battery may overcharged if battery reaches full charge mode as current is constant and similarly in CV method in initial stages current drawn by battery is high which may cause temperature to rise to undesired values. To overcome these disadvantages the CC-CV charging can be switched according to the requirements using automated Fuzzy Logic Controller (FLC). The CC-CV charging of the EV battery using SEPIC with the help of a fuel cell through FLC has been proposed and simulated using MATLAB/Simulink software, and the results have been discussed.

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2019

S. Reddy, Neppalli, Y., and K. Sireesha, “Load Optimization and Forecasting for Microgrids”, in Proceedings of the 2nd International Conference on Intelligent Computing and Control Systems, ICICCS 2018, 2019, pp. 1106-1112.[Abstract]


The demand to supply proportion of power is highly misinterpreted and misconceived in many developed countries and its importance becomes vital when there is a heavy negative drift from the supply end. This phenomenon becomes adverse during prime hours and can have a severe impact on microgrid. The rhythmic growth in peak demand has increased the probability of power failures, blackouts and marginal cost of supply. This immeasurable fluctuation in the energy consumption rate has led to a considerable increase in the operational cost of grid. The after affects might be even more phenomenal in case of microgrids. The load optimization and forecasting helps grid to operate in a optimal way. This paper proposes an algorithm to reduce the complications faced by such unpredictable situation in microgrids using short term load forecasting and load optimization method, having consumers data (accepted)as input. © 2018 IEEE.

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2016

K. Kiranvishnu, Dr. J. Ramprabhakar, and K. Sireesha, “Comparative study of wind speed forecasting techniques”, in 2016 - Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy, PESTSE 2016, 2016.[Abstract]


Wind is one of the prominent renewable source, as it is clean energy and abundantly available, however there are immense issues owing to varying nature of wind flow. The said issues has attracted many researchers to work pertinent to wind forecasting models and hence it is possible for wind power forecast based on the capacity of wind turbine based energy conversion system. With the devised forecasting techniques, it is also possible to schedule required demand to match the generation. Forecasting is an important aid in wind speed prediction. The wind speed forecasting can effectively reduce or avoid the adverse effect of wind farm on power grid and it will also ensure the safe and economic operations of the power system. This work is aimed to design an appropriate model for wind forecasting with less mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square error (MSE) in comparison to back propagation neural network (BPNN) and linear regression technique. The simulation of the mentioned and proposed models are done and the results are presented to validate the effectiveness of the proposed technique

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Publication Type: Journal Article

Year of Publication Title

2015

C. Rajesh, Kranthi, K., Kishore, P., and K. Sireesha, “Intelligent Vehicle Security and SOS Messaging System with Embedded GSM Module”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 4, no. 6, 2015.