2013
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TCFLSFCL Provision for Improvement of Distribution System Reliability by TOPSIS based NSGAII Method
2
2
An approach for assignment of the optimal location and tap changer adjustment related to fluxlock type superconducting fault current limiters with tap changer (TCFLSFCL) is used in this paper by debating the reduction of fault current flowing from each device and enhancement of reliability varying with customer type in a distribution network connected with distribution generation (DG). TCFLSFCL is a flexible SFCL that it has some preference than previous SFCLs. In this type of SFCL the current limiting characteristics are improved and the fault current limiting level during a fault period can be adjusted by controlling the current in third winding, which also made the magnetic field apply to the highTc superconducting (HTSC) element. Three objective functions based on reliability index, reduction of fault current and number of installed TCFLSFCL is systematized and nondominated sorting genetic algorithmII (NSGAII) style is then formed in searching for best location and tuning of tap changer of TCFLSFCL to meet the fitness requirements. A decisionmaking procedure based on technique for order preference by similarity to ideal solution (TOPSIS) is used for finding best compromise solution from the set of Paretosolutions obtained through NSGAII. In a distribution network as Bus 4 of Roy Billinton test system (RBTS), comparative analysis of the results obtained from application of the resistive SFCL (RSFCL) and TCFLSFCL is presented. The results show that optimal placement of TCFLSFCL than RSFCL can improve reliability index and fault current reduction index with less number
1

65
76


Yashar
Hashemi
Electrical Engineering Department, University of Mohaghegh Ardabili , Ardabil, Iran
Electrical Engineering Department, University
Iran
yashar_hshm@yahoo.com


Khalil
Valipour
Electrical Engineering Department, University of Mohaghegh Ardabili , Ardabil, Iran
Electrical Engineering Department, University
Iran
kh_valipour@uma.ac.ir
NSGAII
TCFLSFCL
Reliability assessment
distribution system
TOPSIS
[S. Kalsi, Applications of high temperature superconductors to electric power equipment, WileyIEEE Press, 2010. ##E. Leung, B. Burley, N. Chitwood, H. Gurol, G. Miyata, D. Morris, et al., “Design and development of a 15 kV, 20 kA HTS fault current limiter”, IEEE Transactions on Applied Superconductivity, Vol.10, No.1, pp.832835, 2000. ##W. Paul, M. Chen, M. Lakner, J. Rhyner, D. Braun, and W. Lanz, “Fault current limiter based on high temperature superconductors–different concepts, test results, simulations, applications”, Physica C: Superconductivity, Vol.354, No.1, pp.2733, 2001. ##S. Elschner, F. Breuer, M. Noe, T. Rettelbach, H. Walter, and J. Bock, “Manufacturing and testing of MCP 2212 bifilar coils for a 10 MVA fault current limiter”, IEEE Transactions on Applied Superconductivity, Vol.13, No.2, pp.19801983, 2003. ##H.S. Choi, H.M. Park, Y.S. Cho, S.H. Lim, and B.S. Han, “Quench characteristics of current limiting elements in a fluxlock type superconducting fault current limiter”, IEEE Transactions on Applied Superconductivity, Vol.16, No.2, pp.670673, 2006. ##S. H. Lim, H. S. Choi, and B. S. Han, “Operational characteristics of a fluxlocktype highTc superconducting fault current limiter with a tap changer”, IEEE Transactions on Applied Superconductivity, Vol.14, No.1, pp.8286, 2004. ##K. Hongesombut, Y. Mitani, and K. Tsuji, “Optimal location assignment and design of superconducting fault current limiters applied to loop power systems”, IEEE Transactions on Applied Superconductivity, Vol.13, No.2, pp.18281831, 2003. ##B. C. Sung, D. K. Park, J.W. Park, and T. K. Ko, “Study on optimal location of a resistive SFCL applied to an electric power grid”, IEEE Transactions on Applied Superconductivity, Vol.19, No.3, pp.20482052, 2009. ##U. A. Khan, J. Seong, S. Lee, S. Lim, and B. Lee, “Feasibility Analysis of the Positioning of Superconducting Fault Current Limiters for the Smart Grid Application Using Simulink and SimPowerSystem”, IEEE Transactions on Applied Superconductivity, Vol.21, No.3, pp.21652169, 2011. ##J.H. Teng and C.N. Lu, “Optimum fault current limiter placement”, in Intelligent Systems Applications to Power Systems, ISAP 2007, 2007, pp. 16. ##J. Kumara, A. Atputharajah, J. Ekanayake, and F. Mumford, “Over current protection coordination of distribution networks with fault current limiters”, in Power Engineering Society General Meeting, 2006. ##W. ElKhattam and T. S. Sidhu, “Restoration of directional overcurrent relay coordination in distributed generation systems utilizing fault current limiter”, IEEE Transactions on Power Delivery, Vol. 23, No. 2, pp. 576585, 2008. ##G. Didier, J. Leveque, and A. Rezzoug, “A novel approach to determine the optimal location of SFCL in electric power grid to improve power system stability”, IEEE Transactions on Power Systems, Vol.28, No.99, pp.978984, 2012. ##M. FotuhiFiruzabad, F. Aminifar, and I. Rahmati, “Reliability study of HV substations equipped with the fault current limiter”, IEEE Transactions on Power Delivery, Vol.27, No.2, pp.610617, 2012. ##S.Y. Kim, W.W. Kim, and J.O. Kim, “Determining the location of superconducting fault current limiter considering distribution reliability”, Generation, Transmission & Distribution, IET, Vol.6, No.3, pp.240246, 2012. ##K. Deb, Multiobjective optimization using evolutionary algorithms. Singapore: John Wiley and Sons, 2001. ##K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGAII”, Evolutionary Computation, IEEE Transactions on, Vol.6, No.2, pp.182197, 2002. ##P. Schubert and W. Dettling, “Extended Web Assessment Method (EWAM)evaluation of ecommerce applications from the customer's viewpoint”, in System Sciences, 2002. HICSS. Proceedings of the 35th Annual Hawaii International Conference on, 2002. ##J. Wu, J. Sun, Y. Zha, and L. Liang, “Ranking approach of crossefficiency based on improved TOPSIS technique”, Journal of Systems Engineering and Electronics, Vol.22, No.4, pp.604608, 2011. ##R. Billinton, R. N. Allan, and R. N. Allan, Reliability evaluation of power systems vol. 2: Plenum Press New York, 1984. ##R. N. Allan, R. Billinton, I. Sjarief, L. Goel, and K. So, “A reliability test system for educational purposesbasic distribution system data and results”, IEEE Transactions on Power Systems, Vol.6, No.2, pp.813820, 1991. ##A. Chowdhury and D. O. Koval, “Application of customer interruption costs in transmission network reliability planning”, IEEE Transactions on Industry Applications, Vol.37, No.6, pp.15901596, 2001.##]
Image Stitching of the Computed Radiology images Using a PixelBased Approach
2
2
In this paper, a method for automatic stitching of radiology images based on pixel features has been presented. In this method, according to the smooth texture of radiological images and in order to increase the number of the extracted features after quality enhancement of initial radiology images, 45 degree isotropic mask is applied to each radiology image to observe the image details. After this process, we used statistical and heuristic image noise extraction method (SHINE) to acceptably reduce the noise resulting from radiation of alternating Xrays on detector. Pixel point’s features are obtained by selecting maximum or minimum value of the brightness of pixels in certain neighborhood of the resulting radiology images. This algorithm transmutes point’s features to 128 dimensional vector features. In order to identify the segments overlapping in basic radiology images, we specify equivalent vector features of each radiology image using the mathematical properties of the vectors and find the fit geometry transform between pairs features matched by the random sample consensus (RANSAC) algorithm. Finally, resulted motion model is applied to the initial radiology images and we stitch them together in a common surface
1

77
85


Mahan
Sedehzadeh
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, Central
Iran
author.lastname@x.ac.ir


Farokhi
Fardad
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, Central
Iran
f_farokhi@iauctb.ac.ir
radiology images automatic stitching
45 ° isotropic mask
statistical and heuristic image noise extraction (SHINE)
random sample consensus (RANSAC
[W.C.Chia, L.Ang, and K.P.Seng. “Performance Evaluation of Feature Detection in using Subsampled Images for Image Stitching”. In Proceeding Of 3th IEEE International Conference on Computer Science and Information Technology (ICCSIT), pp.6064, 2010. ##R.Szeliski, ”Image Alignment And Stitching: A Tutorial”. Foundations And Trends In Computer Graphics And Vision, Vol.2, No1, pp.1104, 2006. ##A.Gooben, T.Pralow, and R.R.Grigat. “Automatic Stitching Of Digital Radiographies Using Image Interpretation”. Germany, 2010. Unpublished. ##A.Gooben, M.Schluter, and M.Hensel, T.Pralow, R.R. Grigat. “RulerBased Automatic Stitching Of Spatially Overlapping Radiographs”. Germany, 2010. Unpublished. ##Y.Tang , H.Jiang. “Highly Efficient Image Stitching Based on Energy Map”. In Proceeding Of 2nd International congress on Image and Signal Processing (CISP’9), pp.15, 2009. ##Z. Xiuying , W.Hongyu , and W.Yongxue . “Medical Image Seamlessly Stitching By SIFT And GIST“ . In Proceeding Of International Conference on EProduct EService and EEntertainment (ICEEE), pp.14, 2010. ##Z.Hua, Y.Li, and J.Li. “Image Stitching Algorithm Based On SIFT And MVSC”. In Proceeding Of 7th International Conference On Fuzzy Systems And Knowledge Discovery (FSKD), pp.26282632, 2010. ##[8] Y.Lan, H.Ren, C.Li, Xuefeng Zhao, and Z.Min. “Feature Based Sequence Image Stitching Method”. In Proceeding Of International Conference on Computational Intelligence and Software Engineering (CISE), pp.14, 2010. ##[9] E. Sajjadi, R.fadaie. “Applied Learning advanced topics of Electrical engineering in MATLAB”. 2nd ed., Naghos ##International Journal of Smart Electrical Engineering, Vol.2, No.2, Spring 2013 ISSN: 22519246 ##Publications, Tehran, 2010. ##D.G.Lowe . “Distinctive Image Features From ScaleInvariant Keypoints”. International Journal on Computer Vision, pp.91110, 2004. ##Capek M , Wegenkittl R , and Felkel P . “A Fully Automatic Stitching of 2D Medical Data Sets “.Austria, 2001. Unpublished. ##Yu Wang, Mingquan Wang, “Research On Stitching Technique of Medical Infrared Images”. In Proceeding Of International Conference Computer Application and System Modeling (ICCASM), pp.490493, 2010. ##F.Estrada, A.Jepson, and D.Fleet. “Local Features Tutorial”. Private Communication, Nova, 2004. ##R.C.Gonzalez and R.E.Woods. “digital image processing” , in bibliographical, 2nded. New Jersey: Prentice Hall, pp.6680, 88112, 568585, 2002. ##A.Levin, A.Zomet , S.Peleg , and Y.Weiss. “Seamless Image Stitching In The Gradient Domain”. In Proceedings of 9th the European Conference on Computer Vision, 2006. ##A.Levin, A.Zomet , S.Peleg , Y.Weiss. “Seamless Image Stitching by Minimizing False Edges”. IEEE Transactions On Image Processing, Vol.15, pp.969977, April 2006. ##McAndrew, Alasdair. “An introduction to digital image processing with MATLAB”. 1sted. Cengage learning India, pp.141, 49107,127148, 2013. ##P .Hannequin, J.Mas. “Statistical And Heuristic Image Noise Extraction (SHINE): A New method For Processing Poisson Noise In scintigraphic images”. Institute Of Physics Publishing, pp.43294334, 2002. ##T.Linderberg. “ScaleSpace”. In Encyclopedia of Computer Science and Engineering 1nd ed., B.Wah, ed., Hoboken, New Jersey: John Wiley and Sons, Vol.4, pp.24952504, 2009. . ##G.VacaCastano. “Matlab Tutrial ,SIFT”. Private Communication, Nova, 2011. ##RANSAC. (Online) in Wikipedia Foundation Inc. Available: http://en.wikipedia.org/wiki/Ransac. last modified , April , 2012.##]
Reliability Assessment of Power Generation Systems in Presence of Wind Farms Using Fuzzy Logic Method
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2
A wind farm is a collection of wind turbines built in an area to provide electricity. Wind power is a renewable energy resource and an alternative to nonrenewable fossil fuels. In this paper impact of wind farms in power system reliability is investigate and a new procedure for reliability assessment of wind farms in HL1 level is proposed. In proposed procedure, application of Fuzzy – Markov for wind speed modelling and calculating reliability indices by probabilities of generation units by calculating state transition matrix is proposed. Fuzzy logic for this method will be possible to calculate the reliability in accordance with the existing uncertainties.
FuzzyMarkov approach is appropriate for wind farms that have insufficient data of wind speed. In this theory, by state transition probability matrix solution can be obtained probabilities of states of fuzzy Markov model, by solving state space differential equation. Finally, the authenticity of approach is shown with some simulation
1

85
90


Shohreh
Monshizadeh
Electrical Engineering Department, Islamic Azad UniversitySouth Tehran Branch, Tehran, Iran,
Electrical Engineering Department, Islamic
Iran
bargh257@yahoo.com


Mahmoud Reza
Haghifam
Electrical Engineering Department, Islamic Azad University South Tehran Branch, Tehran, Iran,
Electrical Engineering Department, Islamic
Iran


Ali
Akhavein
Electrical Engineering Department, Islamic Azad University South Tehran Branch, Tehran, Iran,
Electrical Engineering Department, Islamic
Iran
aakhavein@azad.ac.ir
Wind farm
Fuzzy logic
Markov model
Reliability indices
[R.Billinton, S.Kumar, N.Chowdhury and K.Chu, “A Reliability Test System For Educational PurposesBasic Data”, IEEE Transaction on Power System, , Vol.4, No.3, pp.1238–1244, 1989. ##R.Billinton and R.Allan,“Reliability Evalution of Power Systems”, 2nded, New York; Plenum, 1996. ##B.EUAApprorn, A.Karunanoon,“Reliability Evaluation in Electrical Power Generation With Uncertainty Modeling by Fuzzy Number”, IEEE Conference on Power Engineering Society Summer Meeting, Vol.4, pp.2051–2056, 2000. ##M.Fotuhi, A.Ghafouri, “Uncertainty Consideration in Power System Reliability Indices Assessment Using Fuzzy Logic Method ”, IEEE Conference on Large Engineering System, pp.305309, 2007. ##R.Billinton, M.S.Grover,“ Reliability Assessment of Trans and Distribution Schemes”, IEEE Trans on Power Apparatus and Systems, Vol.94, Iss.3, pp.724732, 1975. ##WookWon.Kim, JinO.Kim, “Reliability Cost of Battery With Wind Farm”, 10th IEEE Conference on TENCON, Bali, pp.981–985, 2011. ##C.Nemes, F.Munteanu,“ Reliability Consideration on Wind Farms Energy Production”, 13th International Conference on OPTM, Brasov, pp.183–187, 2012. ##[8] J.Nikukar, “ Modeling of Wind Farms in Raliability Study by Means of Mont Carlo Simulation”, 12th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering, pp.140144, 2010. ##[9] M.Tanrioven, Q.H.Wu, D.R.Turner, C.Kocatepe and J.Wang, “A New Approach to Real Time Reliability Analysis of Transmition System Using Fuzzy Marvov Model”,International Journal of Electrical Power & Energy Systems, ELSEVIER, Vol.26, No.10, pp.821–832, 2004. ##H.J. Zimermann,“ Fuzzy Set Theory and it's Applications”,4th ed Springer, pp.514, 2001. ##[11] A.Ghaderi, M.R.Haghifam,“Wind power Modelling Using FuzzyMarkov Approach in Power System Reliability”, Scientific Information Database (SID), pp.101106,##]
Decreasing Starting Current for Separate Excited DC Motor using ANFIS Controller
2
2
Today, DC motors is still being used globally due to their easy speed controllability. In this article, an Adaptive NeuroFuzzy Inference System (ANFIS) controller is designed for DC motors. The main purpose of performing such task is to reduce the DC motor starting current and deleting the ripple current during starting time in considering control parameters such as: rise time, settling time, maximum overshoot and system steady state error. The results have been simulated in MATLAB and a comparison is made between ANFIS controller and PID controller
1

91
94


Saber
Ghadri
MSc Student, Electrical Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran
MSc Student, Electrical Engineering Department,
Iran
saberghadri@yahoo.com


Shahram
Javadi
Assistant Professor, Electrical Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran
Assistant Professor, Electrical Engineering
Iran
sh.javadi@iauctb.ac.ir


Fardad
Farokhi
Assistant Professor, Electrical Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran
Assistant Professor, Electrical Engineering
Iran
f_farokhi@iauctb.ac.ir
ANFIS
PWM
Separate Excited DC Motor
Speed Control
[[1] Sanju Saini and Arvind Kumar, “ Speed Control of Separately ##Excited D.C Motor using Self Tuned Techniques” ##International Journal of Computer Science And Technology, ##Vol.3, Issue.1, Jan. March 2012. ##[2] Basma A.Omar and Amira Y.Haikal, Fayz F.Areed, “Fuzzy ##Speed Controller for a Separately excited DC Motor”, ##International Journal of Computer Applications (0975 – 8887), ##Vol.39, No.9, February 2012. ##[3] Boumediene Allaoua and Abdellah Laoufi and Brahim ##Gasbaoui, and Abdessalam Abderahmani, “NeuroFuzzy DC ##Motor Speed Control Using Particle Swarm Optimization” ##Leonardo Electronic Journal of Practices and Technologies, ##Issue 15, JulyDecember 2009. ##[4] Dan Mihai, “A Diso NeuroFuzzy Control For DC Servo ##drives”, Annals of the University of Craiova Electrical ##Engineering series, No.35, ISSN 18424805, 2011. ##[5] Mouloud A. Denai, Frank Palis and Abdelhafid Zeghbib, ##“Anfis Based Modeling and Control of Nonlinear Systems”, ##IEEE international conference on systems, Man and ##Cybernetics, Vol.4, pp.34333438, 2004. ##[6] Chapman StephenJ, “Electric machinery fundamentals” fourth ##edition ,McGrawHill, ISBN 007 2465239, 2005. ##[7] Yu H Zhang X and Hu Q, “Application of NNPID Control in ##Linear Elevator”, IEEE Computer Society Proceedings of Fifth ##International Conference on Nat##]
Optimization of the Microgrid Scheduling with Considering Contingencies in an Uncertainty Environment
2
2
In this paper, a stochastic twostage model is offered for optimization of the dayahead scheduling of the microgrid. System uncertainties including dispatchable distributed generation and energy storage contingencies are considered in the stochastic model. For handling uncertainties, Monte Carlo simulation is employed for generation several scenarios and then a reduction method is used to decrease the number of scenarios. The scenarios are used in second stage of the stochastic model to check the system security. The amount of spinning reserve and energy are optimized in the first stage by minimizing the total cost of operation. A sample microgrid is used to compare the offered stochastic model with the deterministic one
1

95
101


Saber
Talari
Electrical Engineering Department of Islamic Azad UniversitySouth Tehran Branch, Tehran, Iran
Electrical Engineering Department of Islamic
Iran
saber.talari@gmail.com


Mahmoud Reza
Haghifam
Electrical and Computer Engineering Islamic Azad UniversitySouth Tehran Branch, Tehran, Iran.
Electrical and Computer Engineering Islamic
Iran


Ali
Akhavein
Electrical Engineering Department of Islamic Azad UniversitySouth Tehran Branch, Tehran, Iran.
Electrical Engineering Department of Islamic
Iran
a_akhavein@azad.ac.ir
DER uncertainty
Energy Storage
Microgrid
Stochastic security constrained unit commitment
[A. Vaccaro, M. Popov, D. Villacci, V. Terzija, "An Integrated Framework for Smart MicrogridsModeling, Monitoring, Control, Communication, Proceedings of the IEEE,Vol.99, No.1, pp.119  132, 2011. ##P.M. Costa, M. Matos, "Assessing the contribution of microgrids to the reliability of distribution networks", Electrical Power & Energy Systems, Vol.79, No.2, pp.382–389, 2009. ##F. Katiraei, , et al., "Microgrid Management", IEEE Power and Energy Magazine, Vol.6, No.3, pp.5465, 2008. ##Logenthiran, T., Srinivasan, D., Khambadkone, M., "Multiagent system for energy resource scheduling of integrated ##microgrids in a distributed system", Electric Power Systems Research, Vol.81, No.1, pp. 138148, 2011. ##Tsikalakis, A.G., Hatziargyriou, N.D., "Operation of multi agent system for microgrid control". IEEE Trans. Power Syst., Vol. 20, No. 3, pp. 1447  55, 2005. ##Tsikalakis, A.G., Hatziargyriou, N.D., "Centralized control for optimizing microgrids operation", IEEE Trans. Energy Convers., Vol.23, No.1, pp. 241  248, 2008. ##Ross, M., Hid Energy storage system scheduling for an isolated microgrid", IET Renew. Power Gener., Vol.5, No.2, pp. 117  123, 2011. ##Mohamed, F., A, Koivo, H.N., "System modeling and online optimal management of MicroGrid using Mesh Adaptive Direct Search", Electrical Power & Energy Systems, Vol.32, No.5, pp. 398–407, 2010. ##Bouffard, F., Galiana, F.D., "An electricity market with a probabilistic spinning reserve criterion", IEEE Trans. Power Syst., Vol.19, No.1, pp. 300 307, 2004. ##Bouffard, F., Galiana, F.D., Conejo, A.J., "Marketclearing with stochastic securitypart I: formulation", IEEE Trans. Power Syst., Vol.20, No.4, pp. 1818 1826, 2005. ##Aminifar, F., FotuhiFiruzabad, M., Shahidehpour, M., "Unit commitment with probabilistic spinning reserve and interruptible load considerations", IEEE Trans. Power Syst., Vol.24, No.1, pp. 388 397, 2009. ##Yazdaninejad, M., Haghifam, M.R., "Evaluation of responsive load participation in optimal satisfying system security constraints", IEEE Power and Energy Society General Meeting, San Diego, CA., pp. 18, 2012. ##Valenzuela, J., Mazumdar, M., "Monte Carlo computation of power generation production costs under operating constraints", IEEE Trans. Power Syst., Vol.16, No.4, pp. 671 677, 2001. ##Lei, W., Shahidehpour, M., Tao, L., "Stochastic securityconstrained unit commitment", IEEE Trans. Power Syst., Vol.22, No.2, pp. 800 811, 2007. ##Khodayar, M.E., Barati, M., Shahidehpour, M., "Integration of high reliability distribution system in microgrid operation", IEEE Trans. Smart Grid, Vol.3, No.4, pp. 19972006, 2012. ##Parvania, M., FotuhiFiruzabad, M., "Demand response scheduling by stochastic SCUC", IEEE Trans. Smart Grid, Vol.1, No.1, pp. 8998, 2010. ##Li, Y., Zio, E., "Uncertainty analysis of the adequacy assessment model of a distributed generation system", Renewable Energy, Vol.41, No.1, pp. 235–244, 2012. ##Jun, Z., Junfeng, L., Jie, W., Ngan, H.W., "A multiagent solution to energy management in hybrid renewable energy generation system". Renewable Energy, Vol.36, No.1, pp. 135263, 2011. ##Niknama, T., Golestaneh, F., Malekpourb, A., "Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and selfadaptive gravitational search algorithm", Energy, No.43, Vol.1, pp. 427–437, 2012. ##N. Amjady, J. Aghaei, H. A. Shayanfar, "Stochastic Multiobjective Market Clearing of Joint Energy and Reserves Auctions Ensuring Power System Security", IEEE Trans. Power Syst., Vol. 24, No. 4, pp. 18411854 ,2009 ##Heitsch, H., Roemisch, W., "Scenario tree reduction for multistage stochastic programs", Comput. Manag. Sci., 6, (2), pp.117–133, 2009. ##Scenred2/Gams documentation [Online] available from: www.gams.com/dd/docs/solvers/scenred2.pdf accessed April 2013.##]
A Novel Comprehensive Taxonomy of IntelligentBased Routing Protocols in Wireless Sensor Networks
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2
Routing in adhoc networks, specifically intelligentbased ones, is a highly interested research topic in recent years. Most of them are simulationbased study. Large percentages have not even mentioned some of the fundamental parameters. This strictly reduces their validity and reliability. On the other hand, there is not a comprehensive framework to classify routing algorithms in wireless sensor networks yet. In this paper, we present a novel comprehensive taxonomy for routing algorithms along with a complete experimental evaluation framework. It makes the ability to put each routing algorithm in its place. It also provides a complete view of the algorithm behavior. At the end, a proper framework is introduced to express essential simulation parameters too. This can lead to improve the quality of scientific practices in the simulation studies.
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103
109


Seyed Hassan
Mosakazemi Mohammadi
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran
Electrical Engineering Department, Central
Iran
hmosakazemi@gmail.ir


Reza
sabbaghiNadooshan
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, Central
Iran
r_sabbaghi@iauctb.ac.ir
Taxonomy
Classify
Fundamental Parameters
Evaluation Frameworks
[J. N. AlKaraki and A. E. Kamal, “On the correlated data gathering problem in wireless sensor networks,” in Proceedings. ISCC 2004. Ninth International Symposium on Computers And Communications, Vol.1, pp.226–231, 2004,. ##K. Akkaya and M. F. Younis, “A Survey on Routing Protocols for Wireless Sensor Networks,” Ad Hoc Networks, Vol.3, No.3, pp.325–349, May 2005. ##W. R. W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energyefficient communication protocol for wireless microsensor networks,” in Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, Vol.1, p.10, 2000. ##S. Kurkowski, T. Camp, and M. Colagrosso, “MANET simulation studies: The incredibles,” ACM SIGMOBILE Mob. Comput. Commun. Rev., Vol.9, No.4, p.50, Oct. 2005. ##M. Saleem, “A BeeInspired Power Aware Routing Protocol for Wireless Ad Hoc Sensor Networks A BeeInspired Power Aware Routing Protocol for Wireless Ad Hoc Sensor Networks,” Engineering & Technology Taxila Pakistan, 2010. ##S. Tilak, N. B. Abughazaleh, W. R. Heinzelman, and C. System, “A taxonomy of wireless microsensor network models,” Mob. Comput. Commun. Rev., Vol.6, No.2, pp.28–36, 2002. ##A. Boukerche, M. Z. Ahmad, B. Turgut, and D. Turgut, “A Taxonomy of Routing Protocols in Sensor Networks,” Algorithms Protoc. Wirel. Sens. Networks, pp.129–160, 2008. ##J. Zheng, A. Jamalipour, and W. Sensor, Wireless Sensor Networks: A Networking Perspective, Illustrate. Wiley, p.500, 2009. ##M. Farooq, BeeInspired Protocol Engineering: From Nature to Networks, Illustrate. Springer, p.326, 2008. ##M. Farooq, G. Di Caro, and G. Di Caro, “Routing Protocols for NextGeneration Networks Inspired by Collective Behaviors of Insect Societies: An Overview,” in in Swarm Intelligence SE  4, C. Blum and D. Merkle, Eds. Springer Berlin Heidelberg, pp.101–160, 2008. ##H. F. Wedde and M. Farooq, “A comprehensive review of nature inspired routing algorithms for fixed telecommunication networks,” J. Syst. Archit., Vol.52, No.8–9, pp.461–484, Aug. 2006. ##M. Saleem, G. a. Di Caro, M. Farooq, and G. A. Di Caro, “Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions,” Inf. Sci. (Ny)., Vol.181, No.20, pp.4597–4624, Oct. 2011. ##T. He, B. Krogh, S. Krishnamurthy, J. a. Stankovic, T. Abdelzaher, L. Luo, R. Stoleru, T. Yan, L. Gu, and J. Hui, “Energyefficient surveillance system using wireless sensor networks,” in Proceedings of the 2nd international conference on Mobile systems, applications, and services  MobiSYS ’04, p.270, 2004. ##A. Woo, T. Tong, and D. Culler, “Taming the underlying challenges of reliable multihop routing in sensor networks,” in Proceedings of the first international conference on Embedded networked sensor systems  SenSys ’03, p.14, 2003. ##Y. Choi, M. G. Gouda, H. Zhang, and A. Arora, “Routing on a Logical Grid in Sensor Networks”, pp.1–28, 2004. ##Y. Zhang, M. Fromherz, and L. Kuhn, “Smart routing with learningbased qosaware metastrategies”, Qual. Serv. Emerg. …, pp. 298–307, 2004. ##C. E. Perkins and E. M. Royer, “Adhoc ondemand distance vector routing,” in Proceedings WMCSA’99. Second IEEE Workshop on Mobile Computing Systems and Applications, pp.90–100, 1999. ##D. B. Johnson and D. A. Maltz, “Dynamic Source Routing in ##Ad Hoc Wireless Networks,” in in Mobile Computing, T. Imielinski and H. Korth, Eds. The Kluwer International Series in Engineering and Computer Science, pp.153–181, 1999. ##M. Maróti and M. Maroti, “Directed Floodrouting Framework for Wireless Sensor Networks,” in Proceedings of the 5th ACM/IFIP/USENIX International Conference on Middleware, pp.99–114, 2004. ##R. D. Poor, “Gradient Routing in Ad Hoc Networks,” Massachusetts Institute of Technology, 2000. ##S. Kumar and R. Miikkulainen, “ConfidenceBased QRouting: An OnLine Adaptive Network Routing Algorithm,” in Artificial Neural Networks in Engineering, No.1, 1998. ##Y. Yu, R. Govindan, and D. Estrin, “Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks”, 2001. ##G. Di Caro and M. Dorigo, “AntNet: Distributed Stigmergetic Control for Communications Networks,” J. Artif. Intell. Res., Vol.9, pp.317–365, 1998. ##G. Di Caro, F. Ducatelle, L. M. Gambardella, and M. Dorigo, “AntHocNet: an adaptive natureinspired algorithm for routing in mobile ad hoc networks,” Eur. Trans. Telecommun., Vol.16, No.5, pp.443–455, 2005. ##E. M. E. M. Royer, S. Barbara, and C.K. Toh, “A Review of Current Routing Protocols for Ad Hoc Mobile Wireless Networks,” IEEE Pers. Commun., Vol.6, No.2, pp.46–55, Apr. 1999. ##A. A. Abbasi and M. F. Younis, “A survey on clustering algorithms for wireless sensor networks,” Comput. Commun., Vol.30, No.14–15, pp.2826–2841, Oct. 2007. ##W. B. Heinzelman, “Application Specific Protocol Architectures for Wireless Networks,” MIT, 2000. ##L. Angeles, C. Intanagonwiwat, R. Govindan, D. Estrin, J. S. Heidemann, and F. Silva, “Directed diffusion for wireless sensor networking,” IEEE/ACM Trans. Netw., Vol.11, No.1, pp.2–16, 2003. ##P. T. H. Eugster, P. A. Felber, A. Kermarrec, and R. Guerraoui, “The many faces of publish/subscribe,” ACM Comput. Surv., Vol.35, No.2, pp.114–131, 2003. ##D. Chen and P. K. Varshney, “QoS Support in Wireless Sensor Networks: A Survey,” in International Conference on Wireless Networks, Vol.13244, pp.227–233, 2004. ##N. Sadagopan, B. Krishnamachari, and A. Helmy, “Active query forwarding in sensor networks,” Ad Hoc Networks, Vol.3, No.1, pp.91–113, 2005. ##C. Ramachandran, S. Misra, and M. S. Obaidat, “A probabilistic zonal approach for swarminspired wildfire detection using sensor networks,” Int. J. Commun. Syst., Vol.21, No.10, pp.1047–1073, Oct. 2008. ##B. Krishnamachari, D. Estrin, and S. Wicker, “Modelling DataCentric Routing in Wireless Sensor Networks,” in IEEE INFOCOM, pp.1–11, 2002. ##L. Subramanian and R. H. Katz, “An architecture for building selfconfigurable systems,” in Mobile Ad Hoc Networking and Computing, pp.63–73, 2000. ##R. Rajagopalan and P. K. Varshney, “Dataaggregation techniques in sensor networks: A survey,” IEEE Commun. Surv. Tutorials, Vol.8, No.1–4, pp.48–63, 2006. ##F. Hu and N. K. Sharma, “Security considerations in ad hoc sensor networks,” Ad Hoc Networks, vol. 3, no. 1, pp. 69–89, Jan. 2005. ##C. Karlof and D. Wagner, “Secure routing in wireless sensor networks: Attacks and countermeasures,” Ad Hoc Networks, Vol.1, No.2–3, pp.293–315, Sep. 2003. ##X. Chen, K. Makki, K. Yen, and N. Pissinou, “Sensor network security: a survey,” IEEE Commun. Surv. Tutorials, Vol.11, ##International Journal of Smart Electrical Engineering, Vol.2, No.2, Spring 2013 ISSN: 22519246 ##No.2, pp.52–73, 2009. ##K. Akkaya and M. F. Younis, “An EnergyAware QoS Routing Protocol for Wireless Sensor Networks,” in International Conference on Distributed Computing Systems, pp.710–715, 2003. ##M. Abolhasani, M. Meybodi, and M. Esna'ashari, “LABER: An EnergyAware Routing Protocol Based Learning Automata for Wireless Sensor Networks,” in International Conference on Information and Knowledge Technology (IKT), 1386 (in persian). ##M. Meybodi, and M. Ahmadinia, “Clustering in Wireless Sensor Networks by Using Learning Automata,” AMIRKABIR University of Technology, 1388 (in persian). ##A. Forster and A. L. Murphy, “FROMS: Feedback Routing for Optimizing Multiple Sinks in WSN with Reinforcement Learning,” in 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, No.5005, pp.371–376, 2007. ##A. Forster, A. L. Murphy, and F. Anna, “CLIQUE: RoleFree Clustering with QLearning for Wireless Sensor Networks,” in 2009 29th IEEE International Conference on Distributed Computing Systems, pp.441–449, 2009. ##T. Camilo, C. Carreto, J. S. Silva, and F. Boavida, “An EnergyEfficient AntBased Routing Algorithm for Wireless ##Sensor Networks,” in ANTS  Ant Colony Optimization and Swarm Intelligence, pp.49–59, 2006. ##S. Hussain, A. W. Matin, and O. Islam, “Genetic Algorithm for Energy Efficient Clusters in Wireless Sensor Networks,” Fourth Int. Conf. Inf. Technol., Vol.2, No.5, pp.147–154, Apr. 2007. ##I. M. ALMomani and M. K. Saadeh, “FEAR: FuzzyBased Energy Aware Routing Protocol for Wireless Sensor Networks,” Int’l J. Commun. Netw. Syst. Sci., Vol.04, No.06, pp.403–415, 2011. ##J.M. Kim, S.H. Park, Y.J. Han, and T.M. Chung, “CHEF: Cluster Head Election mechanism using Fuzzy logic in Wireless Sensor Networks,” in 2008 10th International Conference on Advanced Communication Technology, Vol.1, pp.654–659, 2008. ##M. Cordina and C. J. Debono, “Increasing wireless sensor network lifetime through the application of SOM neural networks”, 3rd International Symposium on Communications, Control and Signal Processing, March, pp. 467–471, 2008. ##J. Barbancho, C. Leon, J. Molina, and A. Barbancho, “Giving neurons to sensors. QoS management in wireless sensors networks”, IEEE Conference on Emerging Technologies and Factory Automation, pp.594–597, 2006.##]
Sensorless Vector Control of Linear Induction Motor on Primary and Secondary Flux Oriented based on Fuzzy PI Controller
2
2
This paper presents a sensorless system drive on primary flux oriented control (PFOC) and secondary flux oriented control (SFOC) for the linear induction motor (LIM) with taking into account end effect. Extended kalman filter (EKF) is applied to estimate LIM speed by measuring motor voltages and currents. In order to achieve desirable dynamic and robustness motor performance instead of traditional PI controller, a fuzzy PI controller is used for speed regulation in LIM vector control. The accuracy and validity of fuzzy PI controller operation are investigated and evaluated and its results are compared with traditional PI controller. Transient and steady state responses of proposed controller under load thrust variations and speed command are studied. Also characteristics and performances of primary flux oriented control (PFOC) and secondary flux oriented control (SFOC) for the linear induction motor are compared with each other. In order to evaluate the proposed method, simulations are performed in MATLAB/SIMULINK. Results show that the fuzzy PI controller has more excellent performance than the traditional PI controller and also PFOC has better performance than SFOC, because SFOC depend on rotor resistance. EKF properly estimate motor speed by measuring motor voltages and currents and therefore speed sensor can be eliminated
1

111
119


Mohammad
Sarvi
Faculty of Technical & Engineering, Imam Khomeini International University Qazvin, Iran.
Faculty of Technical & Engineering, Imam
Iran
sarvi@eng.ikiu.ac.ir


Hassan
Zamani
Faculty of Technical & Engineering, sImam Khomeini International University Qazvin, Iran.
Faculty of Technical & Engineering, sImam
Iran
zamani.4566@gmail.com
Linear Induction Motor
Vector Control
Fuzzy PI Controller
Extended Kalman Filter
Primary Flux Oriented
Secondary Flux Oriented
[Kang G, Nam K (2005) “FieldOriented Control Scheme for Linear Induction Motor with the End Effect”, IEE Proceedings Electric Power Application, Vol.152, Iss.6, pp.15651572, 2005. ##Jeng LF, Tao TL, Kai CC, “Adaptive Back Stepping Control for Linear Induction Motor Drive Using FPGA”, Proceeding of the 32nd IECON, Vol.2, Iss.6, pp.12691274, 2006. ##Silva EFD, Santos CCD, Nerys JWL, “Field Oriented Control of Linear Induction Motor Taking into Account EndEffect”, Proceeding of the 8th IEEEAMC, pp.689694, 2004. ##Silva EFD, Santos EBD, Machado PC, Oliveria MAAD, ”Vector Control for Linear Induction Motor”, Proceeding of the IEEEICIT, pp.518523, 2003. ##VaezZadeh S, Satvati MR, “Vector Control of Linear Induction Motors with End Effect Compensation”, Proceeding of the 8th ICEMS, pp.635638, 2005. ##Doncker RWD, Profumo F, Pastorelli M, Ferraris P “Comparison of Universal Field Oriented (ufo) Controllers in Different Reference Frames”, IEEE Trans. on Power Electronics Vol.10, pp.205213, 1995. ##Palma JP, Dente JG, Carvalhal FJ,”A Comparison of Simplified Field Oriented Control Methods for VoltageSource Induction Motor Drives”, Proceedings of the ISIE'03, pp.134139, 2003. ##Doncker RW, Prufumo F, ”The Universal Field Oriented Controller Applied to Tapped Stator Winding Induction Motors”, Proceeding of the 20th PESC’89, pp.10311036, 1989. ##Prufumo F, Tenconi, Doncker RW, “The Universal Field Oriented (ufo) Controller Applied to Wide Speed Range Induction Motor Drives”, Proceeding of the 22nd PESC’91, pp.681686, 1991. ##HaoBin Z, Bo L, BingGang C ,”Vector Control System of Induction Motor Based on Fuzzy Control Method”, Proceeding of the PEITS, pp.136139, 2008. ##Yunhai H, Yuhua W, ”Based on Neuron Adaptive Controller for Linear Motor Slip Frequency Vector Control system”, Proceeding of the IHMSC, Vol.2, pp.9498, 2009. ##Yang Z, Zhao J, Zheng TQ, ”High Performance Vector Control of Linear Induction Motors Using Single Neuron Controller”, Proceeding of the 4th ICNC '08, pp.534538, 2008. ##Madadi Kojabadi H, Ghribi M, “Sensorless Control of Permanent Magnet Synchronous Motora Survey”, Proceeding of the VPPC '08, pp.18, 2008. ##Darabi A, Salahshoor K, ”EKF and UKFBased Estimation of a SensorLess Axial Flux PM Machine Under an InternalModel Control Scheme Using a SVPWM Inverter” Proceeding of the 29th CCC, pp.56765681, 2010. ##Boussak M, ”Implementation and Experimental Investigation of Sensorless Speed Control with Initial Rotor Position Estimation for Interior Permanent Magnet Synchronous Motor Drive”, IEEE Trans. on Power Electron., Vol.20, pp.1413–1422, 2005. ##Wenqiang Y, Shuguang Li, ”Speed Sensorless Vector Control Induction Motor Based on Reduced Order Extended Kalman Filter”, Proceeding of the 5th PEDS, pp.111, 2003. ##Duncan J, ”Linear Induction MotorEquivalent Circuit Model”, IEE Electr. Power Appl., Vol.130, Iss.1, pp.5157, 1983. ##Bose BK,” Modern power electronics and AC drives”, PrenticeHall press, 2002. ##Darabi A, Salahshoor K, ”EKF and UKFbased Estimation of a SensorLess Axial Flux PM Machine Under an InternalModel Control Scheme Using a SVPWM Inverter”, Proceeding of the 29th CCC, pp.56765681, 2010.##]
An Efficient Cluster Head Selection Algorithm for Wireless Sensor Networks Using Fuzzy Inference Systems
2
2
An efficient cluster head selection algorithm in wireless sensor networks is proposed in this paper. The implementation of the proposed algorithm can improve energy which allows the structured representation of a network topology. According to the residual energy, number of the neighbors, and the centrality of each node, the algorithm uses Fuzzy Inference Systems to select cluster head. The algorithm not only balances the energy load of all nodes, but also provides a reliable selection of a new cluster head and optimality routing for the whole networks. Simulation results demonstrate that the proposed algorithm effectively increases the accuracy to select a cluster head and prolongs the network lifetime
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121
125


Mohsen
Jahanshahi
Young Researchers and Elite club, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Young Researchers and Elite club, Central
Iran
mjahanshahi@iauctb.ac.ir


Shaban
Rahmani
Department of Computer Architecture and Network, Science & Research Branch, Islamic Azad University, Qazvin, Iran.
Department of Computer Architecture and Network,
Iran
sh.rahmani@qiau.ac.ir


Shaghayegh
Ghaderi
Faculty of Computer and Information Technology Engineering,Qazvin Branch, Islamic Azad University, Qazvin, Iran.
Faculty of Computer and Information Technology
Iran
sh.ghaderi@qiau.ac.ir
wireless sensor networks
Clustering
Energy
Fuzzy Inference Systems
[[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, “Wireless sensor networks: a survey”, Computer networks, Vol.38, No.4, pp.393422, 2002. ##[2] J. Yick, B. Mukherjee and D. Ghosal, “Wireless sensor networks Survay”, Computer networks, Vol.52, No.12, pp.22922330, 2008. ##[3] X. Fu and Z. Yu, “A reliable and efficient clustering algorithm for WSNs using Fuzzy Petri Nets”, IEEE Conference on 6th International In Wireless Communications Networking and Mobile Computing, pp.14, 2010. ##A. A. Abbasi and M. Younis, “A survey on clustering algorithms for wireless sensor networks”, Computer communications, Vol.30, No.14, pp.28262841, 2007. ##[5] W. B. Heinzelman, A. P. Chandrakasan and H. Balakrishnan, “An applicationspecific protocol architecture for wireless microsensor networks”, IEEE Transactions on Wireless Communications, Vol.1, No.4, pp.660670, 2002. ##[6] S. Lindsey and C. S. Raghavendra, “PEGASIS: Power efficient gathering in sensor information systems”, IEEE In Aerospace conference proceedings, Vol.3, pp.11251130, 2002. ##[7] O. Younis and S. Fahmy, “HEED: a hybrid, energy efficient, distributed clustering approach for adhoc sensor networks”, IEEE Transactions on Mobile Computing, Vol.3, No.4, pp.366379, 2004. ##[8] J. Yen, “Fuzzy logica modern perspective”, IEEE Transactions on Knowledge and Data Engineering, Vol.11, No.1, pp.153165, 1999. ##[9] S. N. Sivanandum, S. Sumathi and S. N. Deepa, “Introduction to fuzzy logic using MATLAB”, Springer, 2007. ##[10] I. Gupta, D. Riordan and S. Sampalli, “Clusterhead election using fuzzy logic for wireless sensor networks”, IEEE Conference In Communication Networks and Services Research, Proceedings of the 3rd Annual, pp.255260, 2005. ##[11] L. Lao and J. H. Cui, “Reducing multicast traffic load for cellular networks using ad hoc networks”, IEEE Transactions on Vehicular Technology, Vol.55, No.3, pp.822830, 2006. ##[12] T. C. Chiang, C. F. Tai and T. W. Hou, “A knowledgebased inference multicast protocol using adaptive fuzzy Petri nets”, Expert Systems with Applications, Vol.36, No.4, pp.81158123, 2009. ##[13] H. Karl and A. Willing, “Protocols and architectures for wireless sensor networks”, John Wiley & Sons, 2005. ##[14] Y. Qu, J. Fang and S. Zhang, “Identifying Neighbor and Connectivity of Wireless sensor networks with Poisson Point Process”, Wireless Personal Communications, Vol.64, No.4, pp.795809, 2012##]