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Volume-4 Issue-10, April 2017, ISSN: 2319–6386 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.

1.

Authors:

Purvi Kapoor, Manish Kr. Singh, Shashikant

Paper Title:

A Review on Distributed Generation Definitions and DG Impacts on Distribution System

Abstract: Rapidly growing the power consumption and decrease in generating and transmission capacities have set the trend towards the Distributed Generation (DG) sources. Still there is not a univer sal definition of DG. This paper discusses the different definitions proposed in the literature. For DG system to become a major part of the current power scenario it needs to be connected with the existing grid system. This integration will cause some technical, operational and economic impacts on distribution systems. This paper also summarizes these different impacts of DG on distribution system.

Keywords:  Distributed Generation, Impacts of DG, Islanding, Economic Impacts of DG, Power Quality, Voltage Regulation, Islanding, Dispatched Operation

References:

1.          The US Department of Energy, Office of Distributed Energy Resources, online publications available at: http://www.eere.energy.gov/der/, 2003.
2.          Distributed Generation in Liberalised Electricity Markets. International Energy Agency, 2002.

3.          T. Ackerman, G. Anderson, and L. Soder, “Distributed generation: a definition,” Electric Power System Research, vol. 57, pp. 195–204, 2001.

4.          Gas Research Institute, Distributed Power Generation: A Strategy for a Competitive Energy Industry, Gas Research Institute, Chicago, USA 1998

5.          D. Sharma, R. Bartels, Distributed electricity generation in competitive energy markets: a case study in Australia, in: The Energy Journal Special issue: Distributed Resources: Toward a New Paradigm of the Electricity Business, The International Association for Energy Economics, Clevland, Ohio, USA, 1998, pp. 17–40

6.          J. Cardell, R. Tabors, Operation and control in a competitive market: distributed generation in a restructured industry, in: The Energy Journal Special Issue: Distributed Resources: Toward a New Paradigm of the Electricity Business, The International Association for Energy Economics, Clevland, Ohio, USA, 1998, pp. 111–135.

7.          The Electric Power Research Institute, online publications available at: http://www.epri.com/, 2002.

8.          B. M. Balmat and A. M. Dicaprio, “Electricity market regulations and their impact on distributed generation,” in Proc. Conf. on Electric Utility Deregulation and Restructuring and Power Technologies (DRPT 2000), London, pp. 608–613.

9.          “Impact of increasing contribution of dispersed generation on the power system,” CIGRE study Committee no 37, Final Report, Tech. Rep., 2003.

10.       IEEE, Institute of Electrical and Electronics Engineers, http://www.ieee.org

11.       International Energy Agency (IEA). Distributed Generation in Liberalized Electricity Markets. OECD/IEA, Paris, France, 2002.

12. American Gas Association, “What is Distributed Generation?” Full article at: http://www.aga.org/Content/ContentGroups/Newsrom/Issue_Focus/Distributed_Generation.htm

13.       California Energy Commission. “Distributed Energy Resources: Guide”. http://www.energy.ca.gov/distgen/index.html

14.       Dondi P., Bayoumi, D., Haederli, C., Julian, D., Suter, M.,“ Network integration of distributed power generation” Journal of Power Sources, pp. 1–9, 2002.

15.       Chambers, A., “Distributed generation: a nontechnical guide.” Penn Well, Tulsa, OK, p. 283, 2001.

16.       Pepermans, J. Driesen, D. Haeseldonckx, R. Belmans, W. D‟haeseleer, "Distributed generation: definition, benefits and issues", Energy Policy Vol. 33, p.p. 787–798, 2005.

17.       A.A. Bayod Rújula, J. Mur Amada, J.L. Bernal Agustín, J.M. Yusta Loyo, J.A, Domínguez Navarro1, “ Definitions for Distributed Generation: a review” ICREPQ-05, Zaragoza 16,17,18 of March, 2005

18.       Distributed Generation: Understanding the Economics, Arthur D. Little, Inc. Full white paper at: www.eren.doe.gov/distributedpower/pdfs/library/adlgecon.pdf

19.       Gonzalez-Longatt, C. Fortoul, “Review of the Distributed Generation Concept: Attempt of Unification” paper no 275, ICREPQ'05
20.       F. Ochoa, A. Padilha-Feltrin, and G. P. Harrison, “Evaluating distributed generation impacts with a multiobjective index,” IEEE Trans. Power Delivery, vol. 21, no. 3, pp. 1452-1458, July 2006.
21.       Gil and G. Joos, “Models for quantifying the economic benefits of distributed generation,” IEEE Trans. Power Systems, vol. 23, no. 2, pp. 327-335, May 2008.

22.       Philip P. Barker, R. W., “Determining the Impact of Distributed Generation on Power Systems: Part 1 - Radial Distribution Systems” from IEEE,12 IEEE, Feb 2011.

23.       Yiming and, K.N. Miu, “Switch placement to improve system reliability for radial distribution systems with distributed generation,” IEEE Trans. Power Systems, vol. 18, no. 4, pp. 1346-1352, Nov. 2003.

24.       B. Delfino, “Modeling of the integration of distributed generation into the electrical system,” in Proc. IEEE Conf. Power Engineering Society Summer Meeting, USA, 2002, pp. 170-175

25.       Gaudenz Koeppel, “Distributed Generation: Literature Review and Outline of the Swiss Situation” eeh power system laboratory internal report 2003.  26. Pehnt, M., Schneider, L., “Embedding Micro Cogeneration in the Energy Supply System”, in Pehnt, M., Cames, M., Fischer, C., Praetorius, B., Shneider, L., Schumacher, K., Voss, J.P.
Micro cogeneration towards decentralized energy systems, Berlin: Springer, pp. 197-218, 2006. 

26.       Angel Fernández Sarabia, “Impact of distributed generation on distribution system” A Dissertation Submitted to the Department of Energy and Technology, Faculty of Engineering, Science and Medicine, Aalborg University, June 2011 

27.       Jeremi Martin, “Distributed vs. centralized electricity generation: are we witnessing a change of paradigm?” May 2009

28.       Khan, U. N. ,“Impact of Distributed Generation on Distributed Network”, Wroclav, University of Technology, Poland, 2008

29.       IEEE Standards Association, “IEEE Standard for Interconnecting  Distributed Resources With Electric Power Systems,” IEEE Std. 1547-2003 (Issued 2003, Reaffirmed 2008), doi:10.1109/ IEEESTD.2003.94285.

30.       P. P. Barker and R. W. De Mello, “Determining the Impact of Distributed Generation on Power Systems,” presented at IEEE Power Engineering Society Summer Meeting, Seattle, WA, July 16–20, 2000.

31.       Subcontractor Report on DG Power Quality, Protection and Reliability Case Studies Report, NREL Colorado, 2003. Available electronically at http://www.osti.gov/bridge.

 

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2.

Authors:

Purwono Hendrad, Harry Budi Santoso, Zainal A Hasibuan

Paper Title:

Use Clustering  Data of Student  High School for Placement in Personalization E-Learning  on Higher Education

Abstract: Personalize the e-learning begins after students interact with the system by utilizing the functions and features to collect data and process it so that the resulting information from students who used to organize further activities. In another study, the educational background of the student (and types of SMA) also affects the success in education at the university. In this study developed a personalized e-learning design of the early, which is when the new students will interact with the system. The system will be a kind of student placement test. The case studies used subjects Program Building which is one of the core subjects in the study program Engineering Informatics. As the methods used Knowledge Data Discovery (KDD) using background data combined with a high school student math scores on the National Exam as an ingredient on the stage of Data Mining. This study will measure the extent of the student's educational background above can be used as a system of placement of students in personalized e-learning.

Keywords: high school background, data mining, placement, personalized e-learning.

References:

1.       Yayah Karyanah, "Hubungan Asal Jurusan dengan Prestasi Belajar Mahasiswa Program Sstudi Ilmu Keperawatan Universitas Esa Unggul," Forum Ilmiah, vol. 12, no. 2, pp. 156-163, May 2015.
2.       C., Romero, C., & Ventura Marquez-Vera, "Predicting School Failure Using Data Mining," in Proceedings of the 4th international conference on educational data mining, 2011, pp. 271– 275.

3.       Swarnalatha P, D. Ganesh Gopal Ramanathan.L, "Mining Educational Data for Students' Placement Prediction using Sum of Difference Method," International Journal of Computer Applications, vol. 99, no. 18, pp. 36-39, August 2014.

4.       Romero C. AND Ventura, "Educational Data mining: A Review of the State of the Art.," IEEE Transactions on Systems. Man, and Cybernetics., vol. 40, no. 6, pp. 601-618, 2010.

5.       Bertan Y. Badur Osman N. Darcan, "Student Profiling on Academic on Academic Performance Using Cluster Analysis," Journal of e-Learning & Higher Education, vol. 2012, p. 8, 2012.

6.       Narwati, "Pengelompokan Siswa Menggunakan Algoritma K-Means," Dinamika Informatika, pp. 12-16, 2010.

7.       Zainal A. Hasibuan, Harry Budi Santoso Mira Suryani, "Personalisasi Konten Pembelajaran Berdasarkan Pendekatan Tipe Belajar Triple-Factor Dalam Student Centered E-Learning Environment," in KNSI , Makasar, 2014.

8.       Zainal A Hasibuan Sfenrianto, "Triple Characteristic Model (TCM) in E-Learning System," Proceedings of 4th International Conference on Computer Science and Information Technology. IEEE, Chengdu, 2011.

9.       Zainal A Hasibuan, Heru Suhartanto Sfenrianto, "An Automatic Approach for Identifying Triple-Factor in e-Learning Process," International Journal of Computer Theory and Engineering, vol. 5, no. 2, pp. 371-376, April 2013.

10.    Zainal. A. Hasibuan and H. B. Santoso., "The Use of E-Learning towards New Learning Paradigm: Case Study Student Centered E-Learning Environment at Faculty of Computer Science - University of Indonesia," in Proc. IEEE International Conference on Advanced Learning Technologies (ICALT 05), Kaohsiung, Taiwan, 2005, pp. 1026-1030.

11.    Rajan Vohra Praveen Rani, "Generating Placement Intelligence in Higher Education Using Data Mining," (IJCSIT) International Journal of Computer Science and Information Technologies, vol. Vol. 6, no. 3, pp. 2298-2302, May 2015.

12.    Howard Hamilton. (2012, June) Howard J. Hamilton. [Online]. http://www2.cs.uregina.ca/~dbd/cs831/notes/kdd/1_kdd.html

13.    Daniel T Larose, Data Mining Methods and Models. Hoboken, New Jersey: Jhon Wiley & Sons, Inc, 2006.

14.    Daniel T Larose, Discovering Knowledge in Data: An Introduction to Data Mining: John Willey & Sons. Inc, 2005.

15.    T. Kanungo and D. M. Mount, "An Efficient K-means Clustering Algorithm: Analysis and Implementation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 35-39, 2002.

 

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3.

Authors:

Jishnu M Thampan, Femitha Mohammed, Tijin M S, Pratibha S Prabhu, Rince K. M.

Paper Title:

Eye Based Tracking and Control System

Abstract:  An eye based tracking and control system is primarily used to track the human eye movements and in turn control appliances as well as a replacement for mouse. Eye movements could be tracked by tracking the position of the pupil. Real time video processing is carried out with the help of a camera which samples the images constantly and a processor. The images taken by the camera are sent to a single board computer / PC where image processing is done to identify the location of the pupil. The necessary calibration is then carried out by which cursor tracking and appliance control could be made possible. For appliance control a separate unit is fed with the control signals which select the appliance to be controlled. In order to achieve cursor, control the control signals are fed to a computer and proper calibration would help to achieve the desired output results.

Keywords: proper calibration, computer / PC , image, possible,identify, tracking

References:

1.    K.M.Lam, H.Yan,, “ Locating and extracting eye in human face images”, Pattern Recognition, 29, [5], 771-779, [1996].
2.    K.-N. Kim and R. S. Ramakrishna, “Vision-based eye gaze tracking for human computer interface”, Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2, 324-329, [1999].

3.    Shafi. M, Chung. P. W. H, “A Hybrid Method for Eyes Detection in Facial Images”, International Journal of Electrical, Computer, and Systems Engineering, 231-236,[2009].

4.    Ito, Nara, “Eye movement measurement by picture taking in and processing via a video capture card, an Institute of Electronics”, Information and Communication Engineers Technical Report, 102, 128, 31-36, [2002]

5.    Bradski, G. and Kaehler, A. „‟Learning OpenCV’’. Cambridge, MA: O‟Reilly Media, Inc., 2008.

6.    Principi E ; Dept. of Inf. Eng., Univ. Politec. delle Marche, Ancona, Italy, Colagiacom V. ; Squartini, S. ; Piazza, F.,” Low Power High Performance computing on the BeagleBoard Platfotm”, Education and Research Conference, 5° European DSP, 35 - 39 ,[2012]

7.    Huang Ying, W. Z.and Xuyan, T. A real-time compensation strategy for non-contact gaze tracking under natural head movement. Chinese Journal of Electroncis (July 2010).

8.    Wilson, P. I., and Fernandez, J. Facial feature detection using haar classifiers. J. Comput. Sci. Coll. 21, 4 (Apr. 2006), 127-133.

 

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4.

Authors:

Aashish Jaiswal, Garima Sikka

Paper Title:

Future Scope and Potential of Solar Energy in India An Overview

Abstract: After the oil crisis in 1973, the world has to think about the alternative resource of energy apart from conventional energy resources (coal, gas and petroleum etc.). Solar energy is the most important alternative resource of the world and has a large potential of green energy. India has a huge potential for generating green electricity from the renewable energy sources. To promote the green energy, government of India launching many schemes for the renewable energy resources. The Jawaharlal Nehru National Solar Mission was launched on the 11th January, 2010 by the Prime Minister. The Mission has set the ambitious target of deploying 20,000 MW of grid connected solar power by 2022 is aimed at reducing the cost of solar power generation in the country through (i) long term policy; (ii) large scale deployment goals; (iii) aggressive R&D; and (iv) domestic production of critical raw materials, components and products, as a result to achieve grid tariff parity by 2022. Mission will create an enabling policy background to achieve this objective and make India a global leader in solar energy. This paper provides an overview on solar energy in India. It reviews the current status of solar energy in terms of existing capacity, along with historical trends of solar energy and future potential of different form of solar energy in India.

Keywords:  Solar Energy, Solar policy and Renewable policy in India, policy; management.

References:

1.       Kapoor K, Pandey KK, Jain AK and Nandan A, “Evolution of solar energy in India: A review” Renewable and Sustainable Energy Reviews, 40(2014)475–487.
2.       Veeraboina P and Ratnam GY, “Analysis of the opportunities and challenges of solar water heating system (SWHS) in India: Estimates from the energy audit surveys & review” Renewable and Sustainable Energy Reviews, 16 (2012) 668– 676.

3.       Load Generation and Balance Report, Central Electricity Authority, Ministry of Power, Government of India, 2015–16.

4.       Renewable Energy in India: Growth and Targets, Ministry of New and Renewable Energy (MNRE), Government of India, May 2015.

5.       Power Sector at a Glance all India, Ministry of Power, Government of India, on 8 Oct. 2015 [Online]. Available http://powermin.nic.in/power-sector-glance-all-india

6.       Savita Lolla and Somnath Baidya Roy, “Wind and Solar Resources in India”, Energy Procedia, vol. 70, pp 187-192, 2015.

7.       Ashok Upadhyay and Arnab Chowdhury, “Solar Energy Fundamentals and Challenges in Indian restructured power sector”, International Journal of Scientific and Research Publications, vol. 4, issue 10, pp 1-13, Oct. 2014.

8.       Pandey S, Singh VS, Gnagwar NP and Vijayvergia MM,“Determinants of success for promoting solar energy in Rajasthan, India” Renewable and Sustainable Energy Reviews 16 (2012) 3593– 3598.

9.       Sharma NK, Tiwari PK and Sood YR, “Solar energy in India: Strategies, policies, perspectives and future potential” Renewable and Sustainable Energy Reviews 16 (2012) 933–941.

10.    Ministry of New and Renewable Energy source(MNRE),http://www.mnre.gov.in/achievements.htm; 2015.

11.    http://www.solarindiaonline.com/solar-india.html#present.

12.    Akshay Urja. Newsletter of the Ministry of New and Renewable Energy, Government of India 2010;4(November–December (2–3)).

13.    Jawaharlal Nehru National Solar Mission (MNRE) Website of Ministry of New & Renewable Energy, Government of India, http://mnre.gov.in/; 2015.

14.    http://www.eai.in/ref/ae/sol/sol.html

15.    http://en.wikipedia.org/wiki/Solar_power_India.

 

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5.

Authors:

Ankita Singh, Nar Singh

Paper Title:

Analysis of Wireless Mac 802.11 and 802.11Ext in NS-2

Abstract:  The major issues with increasing of wireless networks are throughput, packet delivery ratio, average delay and MAC specifications. IEEE 802.11 standard is a set of media access control (MAC) and physical layer (PHY) for implementing wireless MAC. New modeling of IEEE 802.11 have been developed in NS-2, which introduces two new modules: Mac802_11Ext and Wireles Phy Ext for aiming at a significantly higher level of simulation accuracy.  In this paper, we analysis the throughput, packet delivery ratio and average delay for Mac802_11 and 802_11Ext. Simulation results are evaluated by NS-2 using different no. of nodes for both Mac 802_11 and 802_11Ext based networks. After analysis of results from NS-2 the Mac 802_11Ext is better perform to compare Mac802_11 of IEEE 802.11in wireless network.

Keywords:
IEEE802.11, Mac802_11, Mac802_11Ext, NS2.


References:

1.       Manjusha Methew, Mary John, “Performance Analysis of IEEE 802.11 Modified Distributed Coordination Function for Wireless LANs based on data rate”, IOSR Journal of Computer Engineering (IOSR-JCE), Vol 16, Issue 6 (Nov-Dec 2014). PP: 08-13, e-ISSN: 2278-0661, p-ISSN: 2278-8727.
2.       Jin-Uk Jung, Kyo-Hong Jin, “Modification of Extended Version of IEEE 802.11 in ns-2 and Performance Analysis with Error Rate Using Computer Simulation”, Changwon National University Electronics, 2009.

3.       IEEE Std. 802.11TM-2012 IEEE Standard for Information Technology—Telecommunications and Information Exchange Between Systems—Local and Metropolitan Area Networks— Specific Requirements. Part 11: WirelessLAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE, New York, 2012.

4.       Qi Chen, Felix Schmidt-Eisenlohr, Daniel Jiang, “Overhaul of IEEE 802.11 Modeling and Simulation in NS-2 (802.11Ext)”, University of Karlsruhe (TH), 2008.

5.       UC Berkeley, LBL, USC/ISI, and Xerox PARC, “The ns Manual (formerly ns Notes and Documentation)1”, www.isi.edu/nsnam/ns/doc/ns_doc.pdf .

6.       Tritva Jyothi K P and Kavitha Athota, “Performance Analysis of IEEE 802..11e over WMNs”, 2012 World Congress on Information and Communication Technologies, IEEE 2012.

7.       http://www.isi.edu/nsnam/ns/index.html

8.       “ns-allinone-2.34.tar.gz-OSDN”, en.osdn.jp/projects/.../allinone/ns-allinone-2.34/ns-allinone-2.34.tar.gz/

9.       Heidemann, Tom Handerson, “nsnam”, http://sourceforge.net/project/showfiles.php?group id=149743&package id=169689&release id=588643

10.    Cali F, Conti M, Gregori E, “IEEE 802.11 protocol: design and performance evaluation of an adaptive backoff mechanism”, IEEE Journal on Selected Areas in Communications, 1774-1786, 2000.

11.    Cali F, Conti M, Gregori E, “IEEE 802.11 wireless LAN: capacity analysis and protocol enhancement”, In Proceedings of IEEE INFOCOM’ 1998, March 1998.

12.    Hongqiang Zhai, Younggoo Kwon, Yuguang Fang, “ Performance analysis of IEEE 802.11 MAC protocols in wireless LANs”, wireless communications and mobile computing, PP: 917-931, 2004.

 

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6.

Authors:

M. Vidhya, G. Zayaraz

Paper Title:

Object Oriented Design Refactoring for Enhancing the Technical Debt

Abstract: Code Refactoring is a process of changing the internal behavior without changing its external behavior or functionality. Manual refactoring is hard to modify changes, if we automate refactoring there are much more benefits possible. Software refactoring is a valuable process in software development and is often aimed at repaying technical debt. The automated refactoring techniques, software metrics and Metaheuristic Search and automated refactoring tool are combined to improve the quality of software without affecting its functionality. The four different refactoring approaches are compared using automated refactoring tool. The more number of metrics are added to improve the quality and reduce the complexity. Metrics are combined to measure Abstraction, coupling, inheritance and technical debt. This will improve the quality of software and also reduces technical debt by maintenance cost and time.

Keywords:
Search based techniques; refactoring; Software metrics; software quality; design level metric; Technical debt.


References:

1.       Mel Ó Cinnéide, Laurence Tratt , Mark Harman, Steve Counsell , Iman Hemati Moghadam. Experimental Assessment of Software Metrics Using Automated Refactoring. ESEM’12, September 19–20, 2012, Lund, Sweden Copyright 2012 ACM 978-1-4503-1056-7/12/09.
2.       Michael Mohan , Des Greer , Paul McMullan. Technical debt reduction using a search based automated refactoring, The Journal of Systems and Software 0 0 0 (2016) 1–12.

3.       Gomathi. S and Edith Linda. P. An overview of Object Oriented Metrics A complete Survey. International Journal of Computer Science & Engineering Technology (IJCSET). Vol. 4 No. 09 Sep 2013. ISSN : 2229-3345.

4.       Muktamyee Sarker. An overview of Object Oriented Design Metrics. Master Thesis Department of Computer Science, Umeå University, Sweden June 23, 2005.

5.       Sonia Chawla. Review of MOOD and QMOOD metric sets. International Journal of Advanced Research in Computer Science and Software Engineering. Volume 3, Issue 3, March 2013 ISSN: 2277 128X .

6.       Safwat M. Ibrahim, Sameh A. Salem, Manal A. Ismail, and Mohamed Eladawy. Identification of Nominated Classes for Software Refactoring Using Object-Oriented Cohesion Metrics. IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 2, March 2012. ISSN (Online): 1694-0814. www.IJCSI.org

7.       Amandeep Kaur, Satwinder Singh, Dr. K. S. Kahlon and Dr. Parvinder S. Sandhu. Empirical Analysis of CK & MOOD Metric Suit. International Journal of Innovation, Management and Technology, Vol. 1, No. 5, December 2010. ISSN: 2010-0248.

8.       Gauri Khurana and Sonika Jindal. A model to compare the degree of Refactoring opportunities of three Projects using a machine algorithm . Advanced Computing: An International Journal ( ACIJ ), Vol.4, No.3, May 2013. DOI : 10.5121/acij.2013.4302 17.

9.       Ramanath Subramanyam and M.S. Krishnan. Empirical Analysis of CK Metrics for Object-Oriented Design Complexity:Implications for Software Defects. IEEE Transactions On Software Engineering, VOL. 29, NO. 4, APRIL 2003.

10.    D.I. George Amalarethinam and P.H. Maitheen Shahul Hameed. Analysis of Object Oriented Metrics on a Java Application. International Journal of Computer Applications (0975 – 8887) Volume 123 – No.1, August 2015.

11.    Elvira Maria Arvanitou, Apostolos Ampatzoglou, Alexander Chatzigeorgiou, Paris Avgeriou.,2015. Software metrics fluctuation: a property for assisting the metric selection process, Information and Software Technology 72 (2016) 110–124.

 

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