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Volume-3 Issue-3: Published on January 25, 2015
22
Volume-3 Issue-3: Published on January 25, 2015
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S. No

Volume-3 Issue-3, January 2015, ISSN:  2319–6378 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

P. Murali, P. Nagasekhar Reddy

Paper Title:

Sensorless Control of Induction Motor Drive Using Direct Synthesis for Low Speed

Abstract:   This proposed paper proposes the controlling of Induction motor drives. The induction motor dynamics can be compared to that of a DC motor with fast transient response if the flux producing and torque producing components of the stator current can be controlled independently which means it is possible to control the amplitude and phase angle independently.  For high performance, variable speed applications, the Induction Motors are used widely due to its low cost, low maintenance, requirement, robustness and reliability, thus replacing the DC motor drives. For wide range of speed applications and fast torque response, IMs perform satisfactory with the vector control strategy. Because of low maintenance and robustness, induction motors have many applications in industries. Speed control of induction motor is more important to achieve maximum torque and efficiency. Various control techniques such as scalar control, vector control, Sensor-less control are used. These Schemes suffers from parameter sensitivity and limited performance at low speed of operation. To make the system sensorless, we go for rotor speed estimation using direct synthesis of state equation, as the closed loop control requires the speed sensor. By using speed sensor, the IM becomes more costly and less reliable and increased maintenance cost. The different simulation results are observed and studied and the analysis of the different simulated results are presented.

Keywords:
 sensorless, direct synthesis, drive, vectorcontrol.


References:

1.       Paul  C.  Krause,  ‘Method  of  Multiple  Reference  Frames  Applied  to  the Analysis of Symmetrical Induction Machinery’, IEEE Trans. Power App. System Vol.PAS-87, pp.218-228, Jan-1968
2.       T.A.Lipo and P.C.Krause, ‘Analysis and Simplified Representations pf a Rectifier-Inverter Induction Motor Drive’ IEEE Trans.Power App.Syst.vol. PAS-88,pp 588-596, May 1969.

3.       Edward P.Carnell and T.A.Lipo, ‘Modeling and Design of Controlled Current Induction Motor Drive systems’ IEEE Trans.Indu.Appl.IA-13, pp.321-330, July/August 1977.

4.       Joachim Holtz, ‘Sensor less Control of Induction motor Drives’ Proceedings of the IEEE, Vol.90, no.8.August 2002.

5.       Miran Rodic, ‘Speed Sensor less Sliding Mode Torque Control of Induction Motor’ IEEE Trans. On Indu. Elect. Frebravary 25, 2002.

6.       Young-Real  Kim,  ‘Speed  Sensor  less  Vector  Control  of  Induction  Motor Using  Extended  Kalman  Filter’,  IEEE  Trans.on  Indu.Appl.  Vol.30  no.5 September/October 1994.

7.       Tsugutoshi Othani, Noriyuki Takada and Koji Tanaka, “Vector control of induction motor without shaft encoder,” IEEE Trans. Ind. Applicat., Vol. 28, No. 1, Jan/Feb 1992, pp.157-164.

8.       Casadei, G. Serra, and A. Tani , “Sensitivity investigation of a speed sensorless induction motor drive based on stator flux vector control,” International Conf.   Rec. PESC’ 97 , St. Louis, MI, June 22-27,1997, pp. 1055-1060.

9.       N. Nash, “Direct torque control, induction motor vector control without an encoder,” IEEE Trans. Ind. Applicat., Vol.33,  pp. 333-341, Mar/Apr 1997.

10.    M. Vélez-Reyes, K Minami, G. C. Verghese, “Recursive speed and parameter estimation for induction machines,” IEEE Ind. Applicat. Society Meeting, San Diego, 1989.

11.    F.Z.Peng, T.Fukao “Robust Speed Identification for Speed Sensorless Vector Control of Induction Motors” IEEE Trans. IA vol. 30, no. 5, pp.1234-1239, Oct.1994.

12.    C.Schauder “Adaptive Speed Identification for Vector Control of Induction Motor without Rotational Transducers” IEEE Tran. on Ind. Appl., vol. 28, no. 5, pp. 1054 1061,Oct.1992.

13.    C.C. Chan and H. Q. Wang, “New scheme of sliding mode control for high performance induction motor drives,” IEEE proc. On Electric Power Applications, vol. 143, no. 3, May 1996, pp. 177-185.

14.    D. A. Bradley, C. D. Clarke, R M. Davis, and D.A. Jones, “Adjustable frequency inverters and their application to variable speed drives," IEE Proc., Vol. 111, No. 11, Nov. 1964.


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2.

Authors:

Raghava Reddy. R, P. Ram Kishore Kumar Reddy

Paper Title:

Advanced Control of Direct Torque Control of Induction Motor Drive Using Pi Based Fuzzy Logic Controller

Abstract: A Variable-Frequency Drive is a type of adjustable-speed drive used in  Electro-Mechanical drive systems to control the AC motor speed and torque by varying motor input frequency and voltage. Variable-Frequency Drives are used in applications ranging from small appliances to the largest of mine mill drives and compressors. Over the last four decades, Power Electronics technology has reduced Variable-Frequency Drive cost and size and has improved performance through advances in semiconductor switching devices, drive topologies, simulation and control techniques, and control hardware and software. The speed control of the Variable-Frequency Drive  is of two types; Scalar and Vector. Scalar Control is based on the relationships valid in the steady state conditions, only magnitude and frequency of voltage, current and flux linkage are controlled. Vector Control is based on relationships valid for dynamic states, not only magnitude but also instantaneous positions of voltage , currents and flux. Direct Torque Control is one of the Vector Control method to control the Variable Frequency Drives. The main drawback of the DTC of IMD using conventional PI controller based SR is high torque, stator flux ripples and speed of IMD is decreasing under transient and steady state operating conditions. The work of this project is to study, evaluate and compare the technique of the conventional DTC and DTC-FLC applied to the induction machines through MATLAB/simulink.

Keywords:
  Induction Motor Drive, Direct Torque Control (DTC), Fuzzy Logic Controllers (FLC).


References:

1.       Abdesselam Chikhi1, Mohamed Djarallah “Comparative Study Of Field-Oriented control And       Direct-Torque Control Of Induction Motors using An Adaptive Flux observer” in Serbian journal of Electrical Engineering vol.7, no.1, may 2010.
2.       Ahmet Gani , Mustafa Sekkeli , “Speed Control of Direct Torque Controlled Induction Motor By   using PI, and Fuzzy Logic Controller” in Intelligent Systems and Applications in Engineering(IJISAE).

3.       Gaddam Mallesham, Member, IEEE, K.B. Venkata Ramana, “Improvement in Dynamic Response of Electrical Machines with PID and Fuzzy Logic Based Controllers” in Proceedings of the World Congress on Engineering and Computer Science 2007 oct 24-26 2007

4.       Implementation of a Direct Torque Control Algorithm for Induction Motor Based on  Discrete Space Vector Modulation,” IEEE Trans On Power Electronics,15 (4) 769- 776 July 2006

5.       K. B. Mohanty, " A direct torque controlled induction motor with variable hysteresis band"  in the Conf. on Compo Modeling and Simulation, UK Sim 2009

6.       Nik Rumzi Nik Idris, , IEEE, and Abdul Halim Mohamed Yatim, IEEE   “Direct Torque Control of Induction Machines With Constant Switching Frequency and Reduced Torque Ripple”, in IEEE Transactions On Industrial Electronics, Vol. 51, No. 4, August 2004 10)  N. Mohan, Advanced Electric Drives. Minneapolis, MN: MNPERE, 2001

7.       P. Grabowski "Direct Flux and Torque Neuro-Fuzzy Control of Inverter  Fed Induction Motor Drives",Warsaw University of Technology, 1999.       

8.       Turki Y. Abdalla, Haroution Antranik Hairik, Adel M. Dakhil, “Minimization of Torque Ripple in  DTC of Induction Motor Using Fuzzy Mode Duty Cycle Controller,” 2010 1st International Conference on Energy, Power and Control, November 2010, 237-244.

9.       R. Rajendran  and Dr. N. Devarajan presented a paper on “A Comparative Performance Analysis   of Torque Control Schemes for Induction Motor Drives” in International Journal of Power Electronics and Drive System (IJPEDS) Vol.2, No.2, June 2012, pp. 177~191.

10.    Srinivas Rao and Avinash, “SVPWM Based Speed Control of Induction Motor Drive Using V/F Control Based 3- Level Inverter.

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3.

Authors:

D. Thejasvi, P. Ram Kishore Kumar Reddy

Paper Title:

Dual Mode Control of Motor Drive with Integrated Inverter/Converter Circuit for EV/HEV Application

Abstract:  The system configuration including green power generator, energy storage element, dc appliance and equipment, and energy management system (EMS) with fuzzy logic will be introduced. The proposed integrated circuit allows the machine to operate in motor mode or acts as boost inductors of the boost converter, and thereby boosting the output torque coupled to the same transmission system or dc-link voltage of the inverter connected to the output of the integrated circuit. In motor mode, the proposed integrated circuit acts as an inverter and it becomes a boost-type boost converter, while using the motor windings as the boost inductors to boost the converter output voltage. Enhancement of a renewable power management system with intelligence control techniques (Fuzzy) for a micro grid system. Modeling, analysis, and control of distributed power sources and energy storage devices with MATLAB/ Simulink are proposed, and the integrated monitoring EMS is implemented. To improve the life cycle of the battery, intelligence control techniques manage the desired state of charge. The controller is to optimize energy distribution and to set up battery state of charge SOC parameters. In the development of the green energy systems, a control method is required to optimize energy distribution of a micro grid system. The design concept of this study was to increase the useful life of lithium batteries and to include charge and over discharge protection mechanisms. The power generator includes PV panels, wind turbines, and fuel cells. The fuel cells provide base power for the emergency loads when the system is operated during a power failure. Maximum power point trackers are associated with PV panels and wind turbines to draw maximum power, which is fed into the dc grid. The loads are connected to the grid and supplied from the grid directly. If there is power shortage, the bidirectional inverter will take power from the ac grid and it is operated in rectification mode with power factor correction to regulate the dc grid voltage within a range of 380 ± 20 V.

Keywords:
Energy management system (EMS), Fuzzy Logic, State of charge (SOC), Micro grid, MATLAB/SIMULINK


References:

[1]           H. Rongxian, L. Zhiwen, C. Yaoming, W. Fu, and R. Guoguang, “DC micro-grid simulation test platform,” in Proc. 9thTaiwan Power Electron. Conf., 2010, pp. 1361–1366.
[2]           S. Morozumi, “Micro-grid demonstration projects in Japan,” in Proc. IEEE Power Convers. Conf., Apr. 2007, pp. 635–642.

[3]           Y. Uno, G. Fujita, R. Yokoyama, M. Matubara, T. Toyoshima, and T. Tsukui, “Evaluation of micro-grid supply and demand stability for different interconnections,” in Proc. Power Energy Conf., 2006, pp. 611–616.

[4]           M. HabibUllah, T. S. Gunawan, M. R. Sharif, and R. Muhida, “Design of environmental friendly hybrid electric vehicle,” in Proc. IEEE Conf.Comput. Commun. Eng., Jul. 2012, pp. 544–548.

[5]           Experience in Developing and Promoting 400 V DC Datacenter Power, T. V. Aldridge, Director, Energy Systems Research Lab, Intel Corporate Technology Group, Green Building Power Forum, Jun. 2009.

[6]           Maximizing Overall Energy Efficiency in Data Centres, S. Lidstrom, CTO, Netpower Labs AB, Green Building Power Forum, Jun. 2009.
[7]           Renewable Energy & Data Centers, J. Pouchet, Director Energy Initiatives, Emerson Network Power., Green Building Power Forum, Jun. 2009.
[8]           Development of Higher Voltage Direct Current Power Feeding System in Data Centers, K. Asakura, NTT Energy/Environment, Green Building Power Forum, Dec. 2010.

[9]           M. B. Camara, B. Dakyo, and H. Gualous, “Polynomial control method of DC/DC converters for DC/DC converters for DC-Bus voltage and currents management-battery and supercapacitors,” IEEE Trans. Power Electron., vol. 27, no. 3, pp. 1455–1467, Mar. 2012.

[10]         F.-J. Lin,M.-S. Huang, P.-Y.Yeh, H.-C. Tsai, and C.-H.Kuan, “DSP-based probabilistic fuzzy neural network control for li-ion battery charger,” IEEE Trans. Power Electron., vol. 27, no. 8, pp. 3782–3794, Aug. 2012.

[11]         M. F. Naguib and L. Lopes, “Harmonics reduction in current source converters using fuzzy logic,” IEEE Trans. Power Electron., vol. 25, no. 1, pp. 158–167, Jan. 2010.

[12]         W. Baosheng, “A controllable rectifier wind and solar hybrid power system based on digital signal processor developed,” M.S. thesis in electrical engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, 2009.

[13]         “Battery Energy Management System for DC Micro Grids with Fuzzy Controller”

[14]         I. Cvetkovic, D. Boroyevich, P.Mattavelli, F. C. Lee, and D. Dong, “Nonlinear, Hybrid Terminal Behavioral Modeling of a DC-based Nanogrid System,” in Proc. Appl. Power Electron. Conf., 2011, pp. 1251–1258.

[15]         “Design and Implementation of Energy Management System With Fuzzy Control for DC Microgrid Systems” by Yu-Kai Chen, Member, IEEE, Yung-Chun Wu, Chau-Chung Song, and Yu-Syun Chen.

[16]         R.-J. Wai and L.-C. Shih, “Adaptive fuzzy-neural-network design for voltage tracking control of a DC–DC boost converter,” IEEE Trans. Power Electron., vol. 27, no. 4, pp. 2104–2115, Apr. 2012.

[17]         L. Zhengmin and C. Mingzong, “Small stand-alone wind turbine device characteristics analysis,” M.S. thesis in electrical engineering, Southern Taiwan University of Science and Technology,Tainan, Taiwan, vol. 35, May 2010.

[18]         R. Bharanikumar and A. N. Kumar, “Analysis of wind turbine driven PM generator with power converter,” Int. J. Comput. Electr. Eng., vol. 2, no. 4, pp. 766–769, Aug. 2010.

[19]         Development of Socket-outlet Bar and Power Plug for 400 V Direct Current Feeding System, T. Yuba, R&D Manager, Fujitsu Components Ltd. Green Building Power Forum, Jan. 2010.

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4.

Authors:

Amit Saxena, Leeladhar Kumar Gavel, Madan Madhaw Shrivas

Paper Title:

Rough Sets: An Overview, Hybridization and Applications

Abstract:   Rough set theory has emerged as a useful mathematical tool to extract conclusions or decisions from real life data involving vagueness, uncertainty and impreciseness and is therefore applied successfully in the field of pattern recognition, machine learning and data mining. This paper presents basic concepts and terms of rough set theory. The paper also presents hybridization approach of rough sets with various other established techniques along with developments from time to time.

Keywords:
Pattern recognition, rough sets, hybridization of rough sets, neural networks, fuzzy sets.


References:

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4.          Daijin Kim, “Data classification based on tolerant rough set,” Pattern Recognition , vol. 34, 2001,pp. 1613-1624.

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7.          Rajen B. Bhatt, M. Gopal, “On fuzzy-rough sets approach to feature selection,”  Pattern Recognition Letters, vol. 26, 2005, pp. 965-975.

8.          Zuqiang Meng, Zhongzhi Shi, “Extended rough set-based attribute reduction in inconsistent incomplete decision systems,” Information Sciences, vol. 204, 2012, pp. 44-69.

9.          Pramod Kumar P, Prahlad Vadakkepat, Loh Ai Poh, “Fuzzy- rough discriminative feature selection and classification algorithm, with application to microarray and image datasets,” Applied Soft Computing, vol. 11, 2011, pp. 3429-3440.

10.       Neil Mac Parthalain, Richard Jensen, “Unsupervised fuzzy-rough set-based dimensionality reduction,” Information Sciences, vol.  229, 2013, pp. 106-121.

11.       Asif Sikander Iquebal, Avishek Pal, Darek Ceglarek, Manoj Kumar Tiwari, “Enhancement of Mahalanobis – Taguchi System via rough sets based feature selection,” Expert Systems with Applications, In Press, 2014.

12.       Darshit Parmar, Teresa Wu, Jennifer Blackhurst, “MMR: An algorithm for clustering categorical data using Rough Set Theory,” Data & Knowledge Engineering, vol. 63, 2007, pp. 879-893.

13.       Hong Yu, Zhanguo Liu, Guoyin Wang, “An automatic method to determine the number of clusters using decision-theoretic rough set,” International Journal of Approximate Reasoning , vol. 55, 2014, pp. 101-115.

14.       In-Kyoo Park, Gyoo-Seok Choi, “Rough set approach for clustering categorical data using information-theoretic dependency measure, “Information Systems, In Press, 2014.

15.       Yee Leung, Wei-Zhi Wu, Wwn-Xiu Zhang, “Knowledge acquisition in incomplete information systems: A rough set approach,” European journal of Operation Research, vol. 168,  2006, pp. 164-180.

16.       Shoji Hirano, Shusaku Tsumoto, Rough representation of a region of interest in medical images , International Journal of Approximate Reasoning, vol. 40, 2005,  pp. 23-34.

17.       B. S. Ahn, S.S. Cho, C.Y. Kim, “The integrated methodology of rough set theory and artificial neural network for business failure prediction,” Expert System with Applications, vol. 18,2000, pp. 65-74.

18.       Y. Y.  Yao ,“Constructive and algebraic methods of the theory of rough sets,” Journal of Information Sciences,vol. 109, 1998, pp. 21-4.

19.       Mohamed Quafafou, “α-RST: a generalization of rough set,“Information Sciences, vol.124, 2000,  pp. 301-316.

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5.

Authors:

Anitha N, Anirban Basu

Paper Title:

Neural Network Based Resource Allocation using Run Time Instrumentation with Virtual Machine Migration in Cloud Computing

Abstract: The enterprise level and the market level both are seeing a huge growth in the cloud computing. The resource is accessed in a large with better way and also globally. An individual or organization can lease the computational or storage resources, in return reducing the cost of the infrastructure. The resources optimization is one of the major issue faced in the cloud computing for the cloud service providers. Most of the optimization of resources allocation is done after the calculation of the resources needed and on the go. In this paper, a mathematical system model for the resource allocation using neural network with run time instrumentation has been proposed. The proposed model shows the better resource utilization.

Keywords:
Cloud Computing, Deep Inspection, Instrumentation, Machine Learning, Neural Network,


References:

1.    Anitha N and Anirban Basu, “Dynamic Resource Allocation in Cloud using Runtime Instrumentation”, International conference on Communication and Computing ICC 2014 and Elsevier Science and Technology Publications June 2014, PP 482-490 (Self referenced paper)
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6.

Authors:

Murat Copcu, Hong-In Cheng

Paper Title:

The Quality of Contextual Experience of Multimedia on the Smartphone

Abstract: The smartphone is now an essential personal electronic device. Multimedia is prevalent and the preferred content on the smartphone and enormous amount of videos are shared on the phone. Diverse videos are downloaded and watched everywhere easily with the smartphone. QoE (Quality of Experience) is examined by measuring picture quality, continuity, and overall satisfaction in this study to assess users’ experiences with multimedia in stationary and walking usage contexts. Encoding factors such as frame rate and resolution directly affect the quality of videos. Proper settings of encoding factors were not, however, studied in the actual context. Smartphone owners watch videos while sitting, walking, and standing in various environments. Diverse settings of encoding elements for digital videos were compared in static and dynamic situations and efficient levels of these settings are suggested. Index

Keywords:
 Encoding, multimedia, QoE, smartphone, usage context.


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