Qualification: 
Ph.D
p_supriya@cb.amrita.edu

Dr. Supriya P. joined Amrita School of Engineering in the year 1996. She did her B. E. from Coimbatore Institute of Technology, Coimbatore and M. S. in Electronics & Control from Birla Institute of Technology, Pilani. She did her Ph. D. on Application of Blind Signal Processing Techniques to Power System Network from Amrita Vishwa Vidyapeetham. She has ten years of teaching experience in postgraduate programs and eighteen years in undergraduate programs.

Dr. Supriya was the co - investigator for internally funded project in 2006 for Remote Load Control using Wireless Network. She is also a coordinator of the DST - VINNOVA Smart Grid project with KTH, Sweden. She has published five international journal papers. Her research interest includes Embedded System Based Control Design for Smart Grid, Power System Harmonics and Signal Processing Applications to Smart Grid.

She has guided several undergraduate and postgraduate projects in inter disciplines like VLSI, Embedded Systems and Signal Processing applied to Power System Networks, Biomedical, Automobiles and Allied areas.

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2017

Conference Paper

P. Joshy and Supriya, P., “Implementation of robotic path planning using Ant Colony Optimization Algorithm”, in Proceedings of the International Conference on Inventive Computation Technologies, ICICT 2016, 2017, vol. 1.[Abstract]


Mobile robot path planning is critical in the present day of automation. Several situations may occur for humans, like the environment may be dirty, hazardous, might cause death, or injury as in case of mining, detecting leakage in pipe, cleaning of pipe etc. where robots can be successfully employed. The idea of this paper is to develop a mobile robot that finds the shortest route from source to destination by using Ant Colony Optimization Algorithm with a single robot. The hardware used is iRobot Create interfaced to NXP LPC1768 Cortex M3 controller. The same is simulated using MATLAB. The output of the hardware is also made visible in Teraterm. It is observed that the mobile robot is enhanced with considerable skills to trace a path of optimum distance from source to destination without any collision in most of the situations, barring a few.

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2016

Conference Paper

R. Subhashree, Preethi, C. S., and Supriya, P., “Fault distance identification in transmission line using STFT algorithm”, in 2016 International Conference on Computer Communication and Informatics, ICCCI 2016, 2016.[Abstract]


This paper primarily aims in analyzing the performance of STFT algorithm in locating faults and tries to analyse its accuracy for mainly three conventional faults namely LL, LG, LLG faults and study variation in error for different fault times and fault location distances using MATLAB software. Various algorithms have been devised for fault classification of transmission line in the research field. However, completely optimized and efficient solution for fault identification and location is yet to be materialized. The computation time taken to perform the STFT algorithm was also estimated. The novelty of the work lies in using STFT for fault location as STFT has already been used for fault classification in literature. The least error achieved was marginally low. The test was conducted for 300km transmission line at 120km of fault distance. More »»

2013

Conference Paper

B. Emayavaramban, Thiyagaraj, R., Vivek, B., Kishan, Y. S., and Supriya, P., “Amplitude computation of harmonic voltages based on Adaptive Comb filter in a smart grid system”, in 2013 IEEE Conference on Information and Communication Technologies, ICT 2013, Thuckalay, Tamil Nadu, 2013, pp. 42-46.[Abstract]


Harmonics are a major source of pollution in power system networks. These harmonic current and voltage are to be estimated with great accuracy. Several methods exist for the estimation of harmonic voltage magnitude like FFT, FIR filters etc. But these methods have a common drawback of requiring high sampling frequency and difficulty in making them adaptive. Adaptive Comb filter is a potential solution to these problems. It is a cascade filter requiring a comparatively low sampling frequency and gives a comparatively more accurate result. In this paper, simulation results for various sampling frequencies of Adaptive Comb filter are presented. The results are compared with other methods like FFT and FIR filter using figure of merit like maximum absolute error (MAE) and mean square error (MSE). The amplitude computation scheme is proposed to be implemented on a suitable embedded platform. © 2013 IEEE.

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2011

Conference Paper

P. Supriya and Nambiar, T. N. Padmanabha, “Estimation of harmonic voltages using independent component analysis”, in IET Conference Publications, Chennai, 2011, vol. 2011, pp. 218-222.[Abstract]


Non-linear loads like the electric arc furnace and PWM inverters generate voltage harmonics on a large scale. Arc Furnaces generate even order harmonics and certain odd order harmonics not commonly present in other loads. Under such a situation application of conventional Independent Component Analysis (ICA) methods is not possible. So, certain modifications have to be incorporated to the existing ICA methods. Modified FastICA and JADE are the two ICA algorithms considered in this paper. Efficient Variant ICA (EFICA) has better accuracy than FICA in the presence of finite samples, but it is not attempted in this paper as performance is given more importance than accuracy. A simple five bus system with the electric arc furnace and the PWM inverter is investigated by applying the modified JADE and Fast ICA algorithms. The simulation results of the work point a compromise between accuracy and performance. This arises as the FastICA (or EFICA) collapse when only one independent component exists. More »»

2011

Conference Paper

P. Supriya and Padmanabhannambiar, T. N., “Harmonic current estimation using blind signal processing techniques”, in 2011 - International Conference on Signal Processing, Communication, Computing and Networking Technologies, ICSCCN-2011, Thuckalay, 2011, pp. 116-120.[Abstract]


Blind Source Separation methods are used for feature extraction in biomedical and image processing applications. An increased use of non-linear loads results in generation of harmonics, which cannot be easily identified in electric power systems. Using blind source separation methods like fastICA and efficient fastICA the harmonic currents are estimated in an interconnected system. In this work, a performance evaluation between these two methods for harmonic state estimation is done in the form of recording and analyzing the miniscule error that exists between the actual and estimated harmonic currents. The graphical results of a simple five bus system for the two methods are also discussed. © 2011 IEEE. More »»

2011

Conference Paper

U. Nimitha and Supriya, P., “Independent component approach for the analysis of ECG signals”, in Proceedings of 2011 International Conference on Process Automation, Control and Computing, PACC 2011, Coimbatore, 2011.[Abstract]


Automated analysis of electrocardiogram (ECG) has got great attention for cardiac diagnosis in the recent years. This paper describes two different ECG analysis algorithms using Independent Component Analysis (ICA) algorithm. ICA refers to set of algorithms for blind source separation (BSS). The underlying principle is to separate N signals from a mix of different source contributions, into signals of independent. © 2011 IEEE. More »»

Publication Type: Journal Article

Year of Publication Publication Type Title

2016

Journal Article

R. Shanmughasundaram, Nambiar, T. N. Padmanabha, and Supriya, P., “Development of fuzzy logic based ignition control using microcontroller”, International Journal of Control Theory and Applications, vol. 9, pp. 6151-6156, 2016.[Abstract]


The application of soft computing techniques for spark ignition (SI) engines improves the performance of the system. This paper presents the development of fuzzy logic based ignition control system for a single cylinder SI engine. The fuzzy logic controller (FLC) was designed and developed in Matlab. The inputs to the FLC are engine speed and throttle position and the output is spark angle value. The spark angle value obtained from the Matlab simulation for different engine speeds and throttle position conditions is found to be satisfactory. To validate the simulation results, the FLC is implemented in a microcontroller with ARM7TDMI core. It has been observed that the hardware results match the simulation results. As the control system is programmable, it could be used to enhance the performance of different engines.

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2013

Journal Article

P. Supriya and Nambiar, P., “Integrated Kalman-independent component analysis method for harmonic current estimation on an interconnected four bus simulated and laboratory model”, International Journal of Emerging Electric Power Systems, vol. 13, 2013.[Abstract]


Wide use of non-linear loads results in harmonic propagation throughout the entire power system. The harmonics generated in the power system by the harmonic injection buses need to be properly measured and quantified using minimal information about the power system network. Independent Component Analysis (ICA) provides several algorithms for harmonic state estimation, some of which are more accurate at specific harmonic frequencies. In this paper, the best ICA algorithm for steady state performance (i.e. the algorithm with the least error) is chosen and the resulting mixing matrix is processed by a Kalman Filter which functions as an optimal estimator. The harmonic state estimation is implemented on a simulated four bus system and a laboratory four bus model is also wired and the results of the work are presented. © 2012 De Gruyter. All rights reserved.

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2012

Journal Article

P. Supriya and Nambiar, T. N. Padmanabha, “Blind signal separation based harmonic voltage/current estimation in an interconnected power system- a comparative study”, International Journal on Electrical Engineering and Informatics, vol. 4, pp. 426-434, 2012.[Abstract]


The extensive use of non linear loads results in proliferation of voltage and current harmonics in an interconnected power system. Harmonic estimation without the information on the topology can be done using Independent Component Analysis(ICA). In this work, the time structured ICA algorithms - Fast ICA (FICA), Joint Approximate Diagonalisation of Eigen Matrices (JADE) and Entropy Bound Minimisation (EBM) are employed for harmonic voltage and current estimation by applying the principle of superposition. These algorithms are implemented on a IEEE 14 bus system. The Spearman's coefficient and the concurrent deviation method of correlation coefficient are formulated for performance comparison. However, when FICA is integrated with weight adjusted second order blind identification technique(WASOBI) resulting in FIWAS algorithm superior performance for estimating the even harmonics and other conventional harmonic frequencies is observed.

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2012

Journal Article

P. Supriya and Nambiar, T. N. Padmanabha, “Blind signal separation of harmonic voltages in non-linear loads”, Journal of Electrical Systems, vol. 8, pp. 433-441, 2012.[Abstract]


Extensive use of non linear loads like arc furnace and PWM Inverters generate considerable voltage harmonics. These harmonics need to be estimated and quantified using minimum knowledge about the topology of the system. Blind Signal Processing Techniques like Fast ICA (FICA), Joint Approximate Diagonalisation of Eigen Matrices (JADE) and Entropy Bound Minimisation (EBM) are applied for harmonic voltage estimation in a simple four bus system. Conventional ICA algorithms like FICA and EBM algorithm break down when only the arc furnace load contributes to the harmonics in the power system. An algorithm termed as FICOMB which is a combination of Fast ICA and COMBI is employed for harmonic voltage estimation. The graphical results and error indices of a simple four bus system indicate that FICOMB and JADE are suitable for harmonic frequencies like h = 3 and EBM is suitable for h = 5.The primary objective of the present work is to use the estimation further for either the design of mitigation equipment or for identifying the perpetrators of harmonics with accuracy being more significant for both the applications. This calls for an optimal estimator to choose a definite algorithm for a particular frequency.© JES 2012.

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2012

Journal Article

P. Supriya and Nambiar, T. N. Padmanabha, “Review of harmonic source identification techniques”, International Review of Electrical Engineering, vol. 7, pp. 4525-4531, 2012.[Abstract]


In a deregulating power scenario, harmonic source detection assumes significance in an interconnected power system. Literature proposes single point methods and multipoint methods as the two main techniques of harmonic source identification. This paper provides a detailed survey of these two methods along with their primary merits and demerits. With accuracy as the main criterion, it is concluded that the multipoint methods using harmonic state estimation are better suited for harmonic source determination and the harmonic estimation thereof. However, in a deregulating environment without sufficient information on the topology of the power system, correct harmonic source identification methods are essential and investigations in this regard assumes great significance. © 2012 Praise Worthy Prize S.r.l. - All rights reserved.

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2012

Journal Article

P. Supriya and Nambiar, T. N. Padmanabha, “Noise based Independent Component Analysis model for harmonic current estimation”, Advanced Materials Research, vol. 433-440, pp. 2551-2555, 2012.[Abstract]


In a deregulating environment, Independent Component Analysis (ICA) is used to estimate the harmonic currents of non linear loads as it does not require information about the topology of the network. However, analysis is done by ignoring the effect of various noises that creep into the measurement system. In the present work, the effect of environmental noise on a simple interconnected power system with five buses is taken up. The two algorithms namely Fast ICA (FICA) and Efficient Variant Fast ICA(EFICA) are used for the analysis. A fixed noise is added and it is eliminated using whitening technique The simulation results of both algorithms show that noise elimination by whitening technique is highly successful. However, EFICA gives better results than FICA when random fluctuations of load exist rather than when fixed variations exist. © (2012) Trans Tech Publications, Switzerland.

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Publication Type: Conference Proceedings

Year of Publication Publication Type Title

2012

Conference Proceedings

R. K. Chamarthi, Sriramachandiran, S., Balaji Hariharan, Sivaramakrishnan, H., and Supriya, P., “Harmonic current estimation using mutual information based independent component analysis”, 2012 IEEE 5th Power India Conference, PICONF 2012. Murthal, Haryana, pp. 1-4, 2012.[Abstract]


Harmonic analysis is an integral part of system planning, design and operation of power systems. Harmonic voltage and current measurements require synchronized measurements which are complicated and more expensive than ordinary measurements. Generally a large number of measurements are required to estimate the harmonic sources using Harmonic State Estimation techniques. In this work, the harmonic current profile in the power system is estimated using minimal information on the power system structure. A blind signal processing technique using Independent Component Analysis based on basic neural network is applied for the detection of harmonic components generated by nonlinear current loads. The harmonic current estimation is carried out on a five bus system and the results obtained in simulation are transported to the embedded platform.NET Micro framework board. © 2012 IEEE.

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