ORIGINAL_ARTICLE
Secure Collaborative Spectrum Sensing in the Presence of Primary User Emulation Attack in Cognitive Radio Networks
Collaborative Spectrum Sensing (CSS) is an effective approach to improve the detection performance in Cognitive Radio (CR) networks. Inherent characteristics of the CR have imposed some additional security threats to the networks. One of the common threats is Primary User Emulation Attack (PUEA). In PUEA, some malicious users try to imitate primary signal characteristics and defraud the CR users to prevent them from accessing the idle frequency bands. The present study investigates a new CSS scheme in the presence of a smart PUEA, which is aware of idle frequency channels and transmits its fake signal in a way that CR users are not easily able to discriminate between the received signal from the PU and PUEA. The idea is based on the Bayes risk criterion. More precisely, the sensing results of the CR users are summed up in the Fusion Center (FC) and compared with the optimum threshold that minimizes the Bayes risk. We also discuss practical limitation issue that need to be considered when applying the proposed method. Simulation results are provided to indicate the superiority of the proposed method against PUEA compared with conventional method.
https://eej.aut.ac.ir/article_577_b632ed6073deed0772accccbcb31885c.pdf
2015-11-22
1
8
10.22060/eej.2015.577
Cognitive Radio
Cooperative Spectrum Sensing
Primary User Emulation Attack
Optimum Threshold
Bayes Risk
A.A.
Sharifi
1
PhD. Student, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
LEAD_AUTHOR
M.
Sharifi
2
MSc. Student, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
AUTHOR
M.J.
Musevi Niya
3
Associate Professor, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
AUTHOR
[1]Mitola J, Maguire GQ. Cognitive radio: making software radios more personal. IEEE Personal Communication 1999; 6(4): 13-18.
1
[2]Akyildiz IF, Lee WY, Vuran MC, Mohanty S. NeXt generation/dynamic spectrum access cognitive radio wireless networks: A survey. Computer Networks 2006; 50(13): 2127-2159.
2
[3]Mishra SM, Sahai A, Brodersen RW. Cooperative sensing among cognitive radios. In Proceedings of the IEEE International Conference on Communications 2006; 1658-1663.
3
[4]R. Chen, J. Park, Y. Hou, and J. Reed, “Toward secure distributed spectrum sensing in cognitive radio networks,” IEEE Commun. Mag., vol. 46, no. 4, pp. 50–55, Apr. 2008
4
[5]Anand S, Jin Z, Subbalakshmi K. An analytical model for primary user emulation attacks in cognitive radio networks. In Proceeding IEEE International Dynamic Spectrum Access Networks 2008; 1-6.
5
[6]Jin Z, Subbalakshmi k. Detecting Primary User Emulation Attacks in Dynamic Spectrum Access Networks. IEEE International Conference on Communications 2009; 1–5.
6
[7]Chen C, Cheng H, Yao Y-D. Cooperative spectrum sensing in cognitive radio networks in the presence of the primary user emulation attack. IEEE Transactions on Wireless Communications 2011; 10(7): 2135-2141.
7
[8]Haghighat M, Sadough SMS. Cooperative spectrum sensing for cognitive radio networks in the presence of smart malicious users. International Journal of Electronics and Communications (AUE) 2014; 68(6): 520-527.
8
[9]Haghighat M, Sadough SMS. Smart primary user emulation in cognitive radio networks: defense strategies against radio-aware attacks and robust spectrum sensing. Transactions on Emerging Telecommunications Technologies 2014.
9
[10]Saber MJ, Sadough SMS. Optimisation of cooperative spectrum sensing for cognitive radio networks in the presence of smart primary user emulation attack. Transactions on Emerging Telecommunications Technologies 2014.
10
[11]Digham F, Alouini M, Simon M. On the energy detection of unknown signals over fading channels. In Proceedings of IEEE International Conference on Communications 2003; 5: 3575–
11
[12]Ma J, Zhao G, Li Y. Soft combination and
12
detection for cooperative spectrum sensing in cognitive radio networks. IEEE Transactions on Wireless Communications 2008; 7(11): 4502-4507.
13
[13]Varshney PK. Distributed detection and data fusion. Springer-Verlag 1997.
14
ORIGINAL_ARTICLE
FPGA Implementation of a Hammerstein Based Digital Predistorter for Linearizing RF Power Amplifiers with Memory Effects
Power amplifiers (PAs) are inherently nonlinear elements and digital predistortion is a highly cost-effective approach to linearize them. Although most existing architectures assume that the PA has a memoryless nonlinearity, memory effects of the PAs in many applications ,such as wideband code-division multiple access (WCDMA) or orthogonal frequency-division multiplexing (OFDM), can no longer be ignored and memoryless predistortion has limited effectiveness. In this paper, a novel digital predistorter based on the Hammerstein structure has been proposed for linearization of radio frequency power amplifiers with memory effect. Designing the Hammerstein model based digital predistorter has been done using an accurate Wiener model of the power amplifier. The proposed digital predistorter has many advantages such as low computational complexity, low memory space and simple implementation. The elimination of nonlinear effects and constructing accurate behavioral model, which is the exact inverse of a power amplifier characteristic, have been demonstrated by simulating 64 QAM constellation diagram in Matlab. In order to validate the proposed predistorter, it is implemented in Kintex FPGA using Vivado HLS and acceptable results have been obtained.
https://eej.aut.ac.ir/article_578_85ac994a26b03e3f5ebe24429ffa820f.pdf
2015-11-22
9
17
10.22060/eej.2015.578
Power Amplifiers (PAs)
Wiener and Hammerstein model
Digital Predistorter (DPD)
Linearization
Field Programmable Gate Array (FPGA)
A.
Rahati Belabad
1
PhD. Student, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
E.
Iranpour
2
MSc. Student, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
S.
Sharifian
3
Assistant Professor, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
[1]S. Afsardoost, T. Eriksson, and C. Fager, “Digital predistortion using a vector-switched model,” IEEE Trans. Microw. Theory Tech., vol. 60, no. 4, pp. 1166–1174, Apr. 2012.
1
[2]Behzad Razavi, RF Microelectronics, 2nd Edition, September 2011.
2
Chowdhury D, Hull CD, Degani OB, Wang Y, Niknejad AM. “A fully integrated dual-mode highly linear 2.4 GHz CMOS power amplifier for 4G WiMAX applications:. IEEE J Solid-State Circuits 2009; 44 (December (12)): 3393–402.
3
Nazim Ceylan, inearization of power amplifiers by means of digital predistortion, Phd Dissertation, University of Nurnberg, Germany, 2005.
4
H. Qian, H. Huang, and S. Yao, “A general adaptive digital predistortion architecture for stand-alone RF power amplifiers,” IEEE Trans. Broadcasting, vol. 59, no. 3, pp. 528–538, Sep. 2013.
5
Rahul Gupta, Saad Ahmad, Reinhold Ludwig and John Mcneil, “Adaptive Digital Baseband Predistortion for RF Power Amplifier Linearization,” High Frequency Electronics, September 2006, pp. 16-25.
6
Chao Yu, Michel Allegue-Martínez, Yan Guo and Anding Zhu, “Output-Controllable Partial Inverse Digital Predistortion for RF Power Amplifiers,” IEEE Transactions on Microwave Theory and Techniques, Vol. 62, no. 11, November 2014.
7
J. Kimand K. Konstantinou, “Digital predistortion of wideband signals based on power amplifier model with memory,” Electron. Lett., vol. 37,no. 23, pp. 1417–1418, Nov. 2001.
8
T.Liu, S.Boumaiza,and F.M.Ghannouchi, “Augmented Hammerstein predistorter for linearization of broadband wireless transmitters,”IEEE Trans. Microw. Theory Techn., vol. 54, no. 4, pp. 1340–1349, Jun. 2006.
9
F. M. Ghannouchi and O. Hammi, “Behavioral modeling and predistortion,” IEEE Microw. Mag., vol. 10, no. 7, pp. 52–64, Dec. 2009.
10
L. Ding, G. T. Zhou, D. R.Morgan, Z.Ma, J. S. Kenney, J. Kim, and C. R. Giardina, “A robust digital baseband predistorter constructed using memory polynomials,” IEEE Trans. Communications, vol. 52, no. 1,pp. 159–165, Jan. 2004.
11
M. Younes and F. M. Ghannouchi, “An accurate predistorter based on feedforward Hammerstein structure,” IEEE Trans. Broadcast., vol. 58, no. 3, pp. 454–461, Sep. 2012.
12
L.Xu,X.Wu,M. Zhang,G.Kang, and P. Zhang, “A stable recursive algorithm for memory polynomial predistorter,” in Proc.MILCOM2007,Orlando, Florida, pp. 29-31, Oct.2007.
13
H. Jiang, X. Yu, and P. A. Wilford, “Digital
14
predistortion using stochastic conjugate gradient method,” IEEE Trans. Broadcast., vol. 58,no. 1, pp. 114–124, Mar. 2012.
15
R. Raich, H. Qian, and G. T. Zhou, “Orthogonal polynomials for power amplifier modeling and predistorter design,” IEEE Trans. Veh. Tech.,vol. 53, no. 5, pp. 1468–1479, Sep. 2004.
16
Gilabert Pinal, Multi look-up table digital predistortion for RF power amplifier linearization. Phd Dissertation, Control Monitoring and Communications Group Department of Signal Theory and Communications, Universitat Politecnica de Catalunya, 2007.
17
L. Ding and G. T. Zhou, “Effects of even-order nonlinear terms on power amplifiermodeling and predistortion linearization,” IEEE Trans.Veh. Te chl., vol. 53, no. 1, pp. 156–162, Jan. 2004.
18
J. C. Pedro and S. Maas, “A comparative overview of microwave and wireless power-amplifier behavioral modeling approaches,” IEEE Trans.Microw. Theory Tech., vol. 53, no. 4, pp. 1150–1163, Apr. 2005.
19
C. J. Clark, G. Chrisikos, M. S. Muha, A. A. Moulthrop, and C. P.Silva, “Time-domain envelope measurement technique with application to wideband power amplifier modeling,” IEEE Trans. MicrowaveTheory and Techniques, vol. 46, no. 12, pp. 2531–2540, Dec. 1998.
20
L. Guan, and A. Zhu, “Low-cost FPGA implementation of Volterra series-based digital predistorter for RF power amplifiers,” IEEE Trans. Microw. Theory Tech, vol. 58, no. 4, pp. 866 - 872, Apr. 2010.
21
A. A. M. Saleh, “Frequency-independent and frequency-dependent nonlinear models of TWT amplifiers,” IEEE Trans. Communications,vol. COM-29, no. 11, pp. 1715–1720, Nov. 1981.
22
I. Teikari, “Digital Predistortion Linearization Methods for RF Power Amplifiers” PHD Thesis, Helsinki University of Technology, 2008.
23
F. Taringou, O. Hammi, B. Srinivasan, R. Malhame, and F.M.Ghannouchi, “Behavior modeling of wideband RF transmitters using Hammerstein–Wiener models,” IET Circuits, Devices Syst., vol. 4, no. 4,pp. 282–290, Jul. 2010.
24
Xilinx, Vivado Design Suite User Guide: High-Level Synthesis (UG902 (v2015.2)), PDF File, Xilinx, San Jose, California, June 2015.
25
ORIGINAL_ARTICLE
A New Fairness Index and Novel Approach for QoS-Aware Resource Allocation in LTE Networks Based on Utility Functions
Resource allocation techniques have recently appeared as a widely recognized feature in LTE networks. Most of existing approaches in resource allocation focus on maximizing network’s utility functions. The great potential of utility function in improving resource allocation and enhancing fairness and mean opinion score (MOS) indexes has attracted large efforts over the last few years. In this paper, a new fairness index is proposed to measure resource allocation performance for real-time/delay-tolerant applications. This index can suggest a new approach for resource allocation. There are several methods in resource allocation of cellular networks which employ fairness index for performance evaluation. Here, we focus on utility-function-based resources allocation and related algorithms. According to the suggested method, the base station (BS) allocates resources based on different services requirements. Appropriate utility function for each application is defined, and the requested quality-of-services (QoS) are satisfied through solving the corresponding optimization problem. The new well-defined fairness index shows that the proposed method has a good performance for different real-time/delay-tolerant applications. Additionally, numerical results show that this approach is able to improve other important indicators such as throughput and MOS as well.
https://eej.aut.ac.ir/article_579_2b0a6852b8445a6bc645313bf9a274b9.pdf
2015-11-22
19
25
10.22060/eej.2015.579
Resource allocation
Fairness Index
MOS
Throughput
Utility Function
M. J.
Rezaei
1
MSc. Student, Electrical Engineering Department, University of Isfahan, Isfahan, Iran.
AUTHOR
M. F.
Sabahi
2
Assistant Professor, Electrical Engineering Department, University of Isfahan, Isfahan, Iran.
LEAD_AUTHOR
K.
Shahtalebi
3
Assistant Professor, Electrical Engineering Department, University of Isfahan, Isfahan, Iran.
AUTHOR
R.
Mahin Zaeem
4
MSc. Student, Electrical Engineering Department, University of Isfahan, Isfahan, Iran
AUTHOR
R.
Sadeghi
5
Assistant Professor, Department of Electrical Engineering, Dolatabad Branch, Islamic Azad University, Isfahan, Iran
AUTHOR
[1] Araniti, Giuseppe, et al. “Evaluating the Performanceof Multicast Resource Allocation Policies over LTE
1
Systems.” arXiv preprint arXiv, pp. 1-6, June 2015.
2
[2] Y.L Lee, et al. “Recent advances in radio resourcemanagement for heterogeneous LTE/LTE-A
3
networks.” Communications Surveys & Tutorials,vol. 16, no 4 ,pp. 2142-2180, June 2014.
4
[3] E.B. Rodrigues. “Adaptive radio resourcemanagement for OFDMA-based macro-andfemtocell networks.” Diss. Universitat Politècnica deCatalunya, 2011.
5
[4] G. Gómez, et al. “Towards a qoe-driven resourcecontrol in lte and lte-a networks.” Journal of
6
Computer Networks and Communications, 2013.
7
[5] P. Xue, et al. “Radio resource management withproportional rate constraint in the heterogeneous
8
networks.” Wireless Communications, IEEE Trans.,vol. 11, no. 3, pp. 1066-1075, Mar. 2012.
9
[6] C. Huang, et al. “Radio resource management ofheterogeneous services in mobile WiMAX systems
10
[Radio Resource Management and ProtocolEngineering for IEEE 802.16].” WirelessCommunications, IEEE Trans., vol. 14, no. 1, pp. 20-26, Feb. 2007.
11
[7] P. Ameigeiras, et al. “QoE oriented cross-layerdesign of a resource allocation algorithm in beyond
12
3G systems.” Computer Communications, vol. 33, no.5, pp. 571-582, 2010.
13
[8] X. Pei, et al. “Radio-resource management andaccess-control mechanism based on a novel
14
economic model in heterogeneous wirelessnetworks.” vehicular technology, IEEE Trans., vol.59, no. 6, pp. 3047-3056, Jul. 2010.
15
[9] M.J. Neely, M. Eytan, and L. Chih-Ping. “Fairnessand optimal stochastic control for heterogeneous
16
networks.” Networking, IEEE/ACM Trans., vol. 16,no. 2, pp. 396-409, Apr. 2008.
17
[10] J.Jin, W. Wei-Hua, and P. Marimuthu. “Utility max–min fair resource allocation for communication
18
networks with multipath routing.” ComputerCommunications, vol. 32, no. 17, pp. 1802-1809,
19
[11] M. Ghorbanzadeh, A. Abdelhadi, and Ch. Clancy.“A utility proportional fairness radio resource block
20
allocation in cellular networks.” Computing,Networking and Communications (ICNC), 2015International Conference on. IEEE, 2015, pp. 499-504.
21
[12] S. AlQahtani, and M. AlHassany. “Performancemodeling and evaluation of novel scheduling
22
algorithm for LTE networks.” Network computingand applications (NCA), 2013 12th IEEE
23
international symposium on. IEEE, 2013, pp. 101-105.
24
[13] P. Tang, et al. “QoE-based resource allocationalgorithm for multi-applications in downlink LTE
25
systems.” 2014 International Conference onComputer, Communications and Information
26
Technology (CCIT 2014). Atlantis Press, 2014, pp.1011-1016.
27
[14] M. Li, C.Zhenzhong, and T. Yap-Peng. “AMAXMIN resource allocation approach for scalable
28
video delivery over multiuser MIMO-OFDMsystems,” Circuits and Systems (ISCAS), 2011 IEEE
29
International Symposium on. IEEE, May 2011, pp.2645 - 2648.
30
[15] R.K. Jain, D.M.W. Chiu, W.R Hawe. “A quantitative measure of fairness and discrimination for resource allocation,” in shared computer systems. Public, TR-301, Digital Equipment Corp., 26, September 1984.
31
[16] M.J. Fischer, et al. “Distributed FIFO allocation of identical resources using small shared space.” ACM Transactions on Programming Languages and Systems (TOPLAS), vol. 11, no. 1, pp. 90-114, 1989.
32
[17] M. Shreedhar, and G.Varghese. “Efficient fair queuing using deficit round-robin.” Networking, IEEE/ACM Trans., vol. 4, no. 3, pp. 375-385, Jun. 1996.
33
[18] A. Pokhariyal, et al. “HARQ aware frequency domain packet scheduler with different degrees of fairness for the UTRAN long term evolution.” Vehicular Technology Conference, 2007. VTC2007-Spring. IEEE 65th. IEEE, Apr. 2007, pp. 2761-2765.
34
[19] A. Demers, K. Srinivasan, and Sh. Scott. “Analysis and simulation of a fair queueing algorithm.” ACM SIGCOMM Computer Communication Review. vol. 19. no. 4. ACM, 1989.
35
[20] S. Boyd, and V. Lieven. “Convex optimization.” Cambridge university press, 2004.
36
[21] M.A. Freitag, A. Spence. “A Newton-based method for the calculation of the distance to instability”. Linear Algebra and its Applications. vol. 435, no. 12, pp. 3189-3205, 2011.
37
[22] F. Capozzi, G. Piro, L.A. Grieco, G. Boggia and P. Camarda, Downlink Packet Scheduling in LTE Cellular Networks: Key Design Issue and a Survey, IEEE Communications Surveys & Tutorials, Vol. 15, No.2, 2013, pp. 678-700.
38
[23] P. Kela, J. Puttonen, N. Kolehmainen, T. Ristaniemi, T. Henttonen, and M. Moisio, "Dynamic packet scheduling performance in UTRA Long Term Evolution downlink," in Proc. Of International Symposium on Wireless Pervasive Comput, Santorini, Greece, May 2008, pp. 308-313.
39
[24] A. S. Tanenbaum, Modern Operating Systems, 3rd ed. Upper Saddle River, NJ, USA: Prentice Hall Press, 2007.
40
[25] 3GPP, “TS 23.203 v11.7.0: Policy and charging control architecture,” 2012.
41
ORIGINAL_ARTICLE
Electromagnetic Field Due to Lightning Strikes to Mountainous Ground
The produced electric and magnetic fields due to lightning strikes to mountainous ground are determined in this paper. For the sake of simplicity a cone-shaped ground with finite conductivity is assumed to represent a natural nonflat ground. By this assumption, we deal with an axillary symmetrical structure so we use the cylindrical 2D-FDTD to save the simulation memory and time, dramatically. The return stroke channel is modeled using the antenna theory model with fixed inductive loading (ATIL-F) which is appropriately incorporated into the FDTD algorithm. We have derived the updating equations of 2D-FDTD for distributed resistance and inductance in ATIL-F model. Both the first and the subsequent return strokes are considered and their related radiated electromagnetic fields are determined and compared with each other. The fields are calculated at an intermediate horizontal distance from the cone-axis. The calculated results show that the presence of the cone-shape ground introduces an enhancement in electric and magnetic fields, for both first and subsequent return strokes. Since sharper lossy cone means larger current density in the ground, the increment in the amplitude of the fields is inversely proportional to the cone-angle. A hump is observed in electromagnetic field waveforms because of the current reflection form joint point of the cone-shape ground and the flat ground beneath it. It is more specific in the subsequent-stroke fields. simulation results for different cone-height are presented. It shows that for small values of height, the results approaches to those of the flat ground.
https://eej.aut.ac.ir/article_580_708b2b70c036445a6cfd6539488b1d42.pdf
2015-11-22
27
37
10.22060/eej.2015.580
Lightning
Mountainous ground
Return stroke
ATIL model
Cylindrical 2D-FDTD
R.
Khosravi
1
PhD. Student, Department of Electrical and Computer Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
S. H.
Sadeghi
2
Professor, Department of Electrical and Computer Engineering, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
R.
Moini
3
Professor, Department of Electrical and Computer Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
[1] V. A. Rakov, F. Rachidi, "Overview of recent progress in lightning research and lightning protection", IEEE Transaction of Electromagnetic Compatibility, vol. 51, no. 3, pp. 428-442, August 2009.
1
[2] F. Rachidi, et al, “Influence of a lossy ground on lightning-induced voltages on overhead lines,” IEEE Transaction on Electromagnetic Compatibility, vol. 38, no. 3, pp. 250-264, 1996.
2
[3] A. Shoory, R. Moini, S. H. H. Sadeghi, and V. A. Rakov, “Analysis of lightning-radiated electromagnetic fields in the vicinity of lossy grounds,” IEEE Transaction on Electromagnetic Compatibility, vol. 47, no. 1, pp.131-145, Fab. 2005.
3
[4] M. Khosravi-Farsani, R. Moini, S. H. H. Sadeghi, and F. Rachidi, “On the validity of approximate formulas for the evaluation of the lightning electromagnetic fields in the presence of a lossy ground,” IEEE Transaction on Electromagnetic Compatibility, vol. 55, no. 2, April 2013.
4
[5] F. H. Silveira, S. Visacro, R. Alipio, and A. De Conti, “Lightning-induced voltages over lossy ground: the effect of frequency dependence of electrical parameters of soil,” IEEE Transactions on Electromagnetic Compatibility, vol. 56, no. 5, pp. 1129-1136, October 2014.
5
[6] F. Delfino, R. Procopio, M. Rossi, A. Shoory, and F. Rachidi, “The effect of a horizontally stratified ground on lightning electromagnetic fields,” International Symposium on EMC, 2010.
6
[7] V.A. Rakov, “Transient response of a tall object to lightning,” IEEE Transactions on Electromagnetic Compatibility, vol. 43, no. 4, pp. 654-661, November 2001.
7
[8] F. Rachidi, V.A. Rakov, C.A. Nucci, and J.L. Bermudez, “Effect of vertically-extended strike object on the distribution of current along the lightning channel,” Journal of Geophysical Research, vol. 107, no. D23, 2002.
8
[9] S. Bonyadi-Ram, R. Moini, and S. H. H. Sadeghi, and V. Rakov, “On the representation of lightning return stroke as a lossy monopole antenna with inductive loading,” IEEE Transaction on Electromagnetic Compatibility, vol. 50, no. 1, pp. 118-127, February 2008.
9
[10] A. Taflove and S. C. Hagness, Computation Electrodynamics: The Finite Difference Time Domain Method, 3rd ed. Artech House, 2005.
10
[11] Y. Baba and V. A. Rakov, “Electromagnetic models of lightning return stroke,” Journal of Geophysics Research, vol. 112, no. D04102, 2007.
11
[12] Y. Baba and V. A. Rakov, “Application of Electromagnetic models of the lightning return stroke,” IEEE Transactions on Power Delivery, vol. 23, no. 2, pp. 800-811, April 2008.
12
[13] R. Moini, B. Kordi, G. Z. Rafi, and V. Rakov, “A new lightning return stroke model based on antenna theory,” Journal of Geophysics Research, vol. 105, no. D24, December 2000.
13
[14] V. A. Rakov, “Lightning return stroke speed: A review of experimental data,” in Proc. 27th Int. Conf. Lightning Protection, Soc. de l’Electr., et des Technol. de l’Inf. et de la Commun., Avignon, France, September 2004.
14
[15] F. Heidler, “Analytische blitzstromfunktion zur LEMP-Berchnung," Proc. 18th Int. Conf. Lightning Protection, Munich, Sept. 1985, paper 1.9, pp. 63-66.
15
[16] F. Rachidi, et al., “Current and electromagnetic field associated with lightning-return strokes to tall towers,” IEEE Transaction on Electromagnetic Compatibility, vol. 43, no. 3, pp. 356-367, August 2001.
16
[17] W. Yu and R. Mittra, “A conformal finite difference time domain technique for modeling curved dielectric surfaces,” IEEE Microwave and Wireless Components, vol. 11, no. 1, January 2001.
17
[18] G. Mur, “Absorbing boundary conditions for the finite-difference approximation of the time-domain electromagnetic field equations,” IEEE Transaction on Electromagnetic Compatibility, vol. 23, no. 4, pp. 377-382, November 1981.
18
[19] B. Kordi, et al., “Application of the antenna theory model to a tall tower struck by lightning,” Journal of Geophysical Research, vol. 108, no. D17, 2003.
19
[20] V. Cooray, “Horizontal electric field above-and underground produced by lightning flashes,”IEEE Transaction on Electromagnetic Compatibility, vol. 52, no. 4, pp.936-943, Nov. 2010.
20
ORIGINAL_ARTICLE
Resource Scheduling in a Smart Grid with Renewable Energy Resources and Plug-In Vehicles by MINLP Method
This paper presents a formulation of unit commitment for thermal units integrated with wind and solar energy systems and electrical vehicles with emphasizing on Mixed Integer Nonlinear Programming (MINLP). The renewable energy resources are included in this model due to their low electricity cost and positive effect on environment. As well as, coordinated charging strategy of electrical vehicles and reasonable usage of V2G power can reduce the generating cost. Electric vehicles and renewable energy resources are the most promising options for alternative sources in the near future. The proposed method is solved using MINLP solver in GAMS software. The problem is finding a solution which satisfies the constraints and minimizes the objective function. As a case study, results on IEEE ten-unit system are presented in this paper. The numerical tests and results showing that their inclusion with the conventional power generating sources reduces the operational cost and greenhouse gas emissions were presented in electric power industry.
https://eej.aut.ac.ir/article_581_f52c3dd89efdf6d3aeebeaff047978ea.pdf
2015-11-22
39
47
10.22060/eej.2015.581
Unit commitment
Generation scheduling
Renewable energy resources
Vehicle to grid
MINLP method
A.
Shahmoradi
1
MSc. Student, Department of Electrical Engineering, Iran University of science and technology (IUST), Tehran, Iran.
LEAD_AUTHOR
M.
Kalantar
2
Professor, Department of Electrical Engineering, Iran University of science and technology (IUST), Tehran, Iran.
AUTHOR
[1] Saber, Ahmed Yousuf, and Ganesh Kumar Venayagamoorthy. "Resource scheduling under uncertainty in a smart grid with renewables and plug-in vehicles." Systems Journal, IEEE 6.1 (2012): 103-109.
1
[2] Khodayar, Mohammad E., Lei Wu, and Mohammad Shahidehpour. "Hourly coordination of electric vehicle operation and volatile wind power generation in SCUC." Smart Grid, IEEE Transactions on 3.3 (2012): 1271-1279.
2
[3] Mantawy, A. H., Youssef L. Abdel-Magid, and Shokri Z. Selim. "Unit commitment by tabu search." IEE Proceedings-Generation, Transmission and Distribution 145.1 (1998): 56-64.
3
[4] Seki, Takeshi, Nobuo Yamashita, and Kaoru Kawamoto,"New local search methods for improving the Lagrangian-relaxation-based unit commitment solution," Power Systems, IEEE Transactions on 25.1 (2010): 272-283.
4
[5] Zhai, Qiaozhu, Xiaohong Guan, and Jian Cui, "Unit commitment with identical units successive subproblem solving method based on Lagrangian relaxation," Power Systems, IEEE Transactions on 17.4 (2002): 1250-1257.
5
[6] Rajan, C. Christober Asir, and M. R. Mohan, "An evolutionary programming-based tabu search method for solving the unit commitment problem," Power Systems, IEEE Transactions on 19.1 (2004): 577-585.
6
[7] Shobana, S., and R. Janani,"Optimization of Unit Commitment Problem and Constrained Emission Using Genetic Algorithm," International Journal of Emerging Technology and Advanced Engineering 3.5 (2013):367-371.
7
[8] Samuel, G. Giftson, and C. Christober Asir Rajan. "A Modified Shuffled Frog Leaping Algorithm for Long-Term Generation Maintenance Scheduling." Proceedings of the Third International Conference on Soft Computing for Problem Solving. Springer India, (2014): 11-24.
8
[9] Gaing, Zwe-Lee. "Particle swarm optimization to solving the economic dispatch considering the generator constraints." Power Systems, IEEE Transactions on 18.3 (2003): 1187-1195.
9
[10] Samadi Biniazy, M; Rajabi, H, "determine the optimum amount of spinning reserve capacity and its distribution between the units considering possible events in the power system", Twenty-Fourth International Power System Conference Tehran, tavanir company, Energy Research Institute, (2009).
10
[11] Jianxue, Wang, Wang Xifan, and Song Yonghua, "Study on reserve problem in power market," Power System Technology, 2002. Proceedings. PowerCon 2002. International Conference on. Vol. 4. IEEE, (2002): 2418 – 2422.
11
[12] National Renewable Energy Laboratory (NREL) Solar Radiation Research Laboratory (SRRL), Golden, CO. [Online]. Available: http://www.nrel.gov/midc/srrl_bms/.
12
[13] National Renewable Energy Laboratory (NREL) National Wind Technology Center (NWTC), Boulder, CO. [Online]. Available: http://www.nrel.gov/midc/nwtc_m2/.
13
[14] General Electric 1.85-87 Wind Turbine Datasheet. [Online].Available: https://renewables.gepower.com/content/dam/gepowerrenewables/global/en_US/documents/GEA30627A_Wind_1.85-87_Brochure_LR.pdf.
14
[15] Ting, T. O., M. V. C. Rao, and C. K. Loo, "A novel approach for unit commitment problem via an effective hybrid particle swarm optimization," Power Systems, IEEE Transactions on 21.1 (2006): 411-418.
15
[16] Saber, Ahmed Yousuf, and Ganesh Kumar Venayagamoorthy. "Plug-in vehicles and renewable energy sources for cost and emission reductions." Industrial Electronics, IEEE Transactions on 58.4 (2011): 1229-1238.
16
[17] Kazarlis, Spyros A., A. G. Bakirtzis, and Vassilios Petridis, "A genetic algorithm solution to the unit commitment problem," Power Systems, IEEE Transactions on 11.1 (1996): 83-92.
17
[18] Damousis, Ioannis G., Anastasios G. Bakirtzis, and Petros S. Dokopoulos, "A solution to the unit-commitment problem using integer-coded genetic algorithm," Power Systems, IEEE Transactions on 19.2 (2004): 1165-1172.
18
[19] Saber, Ahmed Yousuf, and Ganesh Kumar Venayagamoorthy, "Intelligent unit commitment with vehicle-to-grid—A cost-emission optimization," Journal of Power Sources 195.3 (2010): 898-911.
19
[20] Cheng, Chuan-Ping, Chih-Wen Liu, and Chun-Chang Liu, "Unit commitment by Lagrangian relaxation and genetic algorithms," Power Systems, IEEE Transactions on 15.2 (2000): 707-714.
20
[21] Ting, T. O., M. V. C. Rao, and C. K. Loo, "A novel approach for unit commitment problem via an effective hybrid particle swarm optimization," Power Systems, IEEE Transactions on 21.1 (2006): 411-418.
21
[22] Seki, Takeshi, Nobuo Yamashita, and Kaoru Kawamoto, "New local search methods for improving the Lagrangian-relaxation-based unit commitment solution," Power Systems, IEEE Transactions on 25.1 (2010): 272-283.
22
[23] Ebrahimi, Javad, Seyed Hossein Hosseinian, and Gevorg B. Gharehpetian, "Unit commitment problem solution using shuffled frog leaping algorithm," Power Systems, IEEE Transactions on 26.2 (2011): 573-581.
23
[24] Juste, K. A., H. Kita, E. Tanaka, and J. Hasegawa, "An evolutionary programming solution to the unit commitment problem," Power Systems, IEEE Transactions on 14, no. 4 (1999): 1452-1459.
24
[25] Elbehairy, Hatem, Emad Elbeltagi, Tarek Hegazy, and Khaled Soudki, "Comparison of two evolutionary algorithms for optimization of bridge deck repairs," Computer aided Civil and Infrastructure Engineering 21,8 (2006): 561-572.
25
[26] Zwe-Lee Gaing. "Discrete particle swarm optimization algorithm for unit commitment," IEEE Power Engineering Society General Meeting, 1 (2003): 418-424.
26
[27] Balci, Huseyin Hakan, and Jorge F. Valenzuela. "Scheduling electric power generators using particle swarm optimization combined with the Lagrangian relaxation method." International Journal of Applied Mathematics and Computer Science 14.3 (2004): 411-422.
27
[28] Kumar, Dwivedi Sanjeet, Dipti Srinivasan, and Thomas Reindl. "Optimal power scheduling of distributed resources in Smart Grid." Innovative Smart Grid Technologies-Asia (ISGT Asia), 2013 IEEE. IEEE, (2013): 1-6.
28
ORIGINAL_ARTICLE
The Impact of Superconducting Fault Current Limiter Locations on Voltage Sag in Power Distribution System
In this paper, the impacts of installing superconducting fault current limiter (SFCL)in radial and loop power distribution system are evaluated to improve voltage sag in both cases of with and without distributed generations (DG). Among various SFCLs, the hybrid type with a superconducting element in parallel with a current limiting reactor (CLR) is selected. This is more effective than resistor-type SFCLs because it reduces the burden on the superconducting element, ac losses and cost in distribution system. According to SFCLs impedance and their locations in power system, voltage sag will be improved by reducing the fault current. In this paper, SFCLs with various arrangement and CLR magnitudes are installed in distribution system and improving the voltage sags on different buses are examined according to fault position. Area of severity (AOS) method and expected annual sag frequency (ESF) are used to analyze the voltage sag. The results show that installing SFCL can improve the voltage sags as well as fault current reduction in radial and loop distribution systems.
https://eej.aut.ac.ir/article_582_17e7b483b0438cb8668e70967937df46.pdf
2015-11-22
49
60
10.22060/eej.2015.582
Superconducting Fault Current Limiter (SFCL)
Voltage sag
Radial and Loop Distribution System
distributed generation
M.
Ehsanipour
1
AUTHOR
J.S.
Moghani
2
LEAD_AUTHOR
S.H.
Hosseinian
3
AUTHOR
M.
Saberi
4
AUTHOR