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Exploring Innovation| ISSN:2319–6386(Online)| Reg. No.:68121/BPL/CE/12| Published by BEIESP| Impact Factor:3.86
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Volume-4 Issue-10: Published on April 15, 2017
01
Volume-4 Issue-10: Published on April 15, 2017

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S. No

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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