ORIGINAL_ARTICLE
A Review of Application of Signal Processing Techniques for Fault Diagnosis of Induction Motors – Part I
Abstract - Use of efficient signal processing tools (SPTs) to extract proper indices for fault detection in induction motors (IMs) is the essential part of any fault recognition procedure. The Part1 of the two parts paper focuses on Fourier-based techniques including fast Fourier transform and short time Fourier transform. In this paper, all utilized SPTs which have been employed for fault fetection in IMs are analyzed in details. Then, their competency and their drawbacks for extracting indices in transient and steady state modes are criticized from different aspects. The considerable experimental results are used to certificate demonstrated discussion. Different kinds of faults, including eccentricity, broken bar and bearing faults as major internal faults, in IMs are investigated. The use of efficient signal processing tools (SPTs) to extract proper indices for faultdetection in induction motors (IMs) is the essential part of any fault recognition procedure. In thefirst part of the present paper, we focus on Fourier-based techniques, including fast Fourier transformand short time Fourier transform. In this paper, all utilized SPTs which have been employed forfault detection in IMs are analyzed in detail. Then, their competency and their drawbacks to extractindices in transient and steady state modes are criticized from different aspects. Different kinds offaults, namely, eccentricity, broken bar, and bearing faults as the major internal faults in IMs, areinvestigated.
http://eej.aut.ac.ir/article_1970_041ac2f10d8a5a556c322789094e12d7.pdf
2017-12-01T11:23:20
2018-03-18T11:23:20
109
122
10.22060/eej.2017.13219.5142
Fault diagnosis
induction motors
Signal Processing
Fourier transform
eccentricity fault
broken bars fault
bearing fault
J.
Faiz
jfaiz@ut.ac.ir
true
1
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran
LEAD_AUTHOR
A. M.
Takbash
true
2
Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada
Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada
Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada
AUTHOR
E.
Mazaheri-Tehrani
true
3
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran
AUTHOR
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60
ORIGINAL_ARTICLE
Data Hiding Method Based on Graph Coloring and Pixel Block‘s Correlation in Color Image
An optimized method for data hiding into a digital color image in spatial domainis provided. The graph coloring theory with different color numbers is applied. To enhance thesecurity of this method, block correlations method in an image is used. Experimental results showthat with the same PSNR, the capacity is improved by %8, and also security has increased in themethod compared with other methods. In the correlation block-based image method, data hidingcapacity of the host image varies according to image type and defined threshold level. In theproposed algorithm, during graph explanation, independent pixels placed side by side were colored.Then, based on “pixel block correlation data hiding” process is done. This method grows thesecurity and capacity of hiding process. Besides, this increases the effects of image format andcorrelation threshold on security and capacity.
http://eej.aut.ac.ir/article_1048_d3778c9d8ab8f947429ee2e19f01a6fe.pdf
2017-12-01T11:23:20
2018-03-18T11:23:20
123
130
10.22060/eej.2017.10676.4868
Data hiding
Graph coloring
correlation
Threshold
Security
Color number
G.
Ghadimi
g_ghadimi@yahoo.com
true
1
Dept. of Electrical Engineering, Emam Ali University, Tehran, Iran
Dept. of Electrical Engineering, Emam Ali University, Tehran, Iran
Dept. of Electrical Engineering, Emam Ali University, Tehran, Iran
LEAD_AUTHOR
M.
Nejati Jahromi
nejati@aut.ac.ir
true
2
Dept. of Electrical Engineering, Shahid Sattary Aeronautical University of Science and Technology, Tehran, Iran
Dept. of Electrical Engineering, Shahid Sattary Aeronautical University of Science and Technology, Tehran, Iran
Dept. of Electrical Engineering, Shahid Sattary Aeronautical University of Science and Technology, Tehran, Iran
AUTHOR
E. Ghaemi
Ghaemi
ghaemi_e78@yahoo.com
true
3
Dept. of Electrical Engineering, Ahar University, Ahar, Iran
Dept. of Electrical Engineering, Ahar University, Ahar, Iran
Dept. of Electrical Engineering, Ahar University, Ahar, Iran
AUTHOR
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13
ORIGINAL_ARTICLE
Performance Analysis Of Mono-bit Digital Instantaneous Frequency Measurement (Difm) Device
Instantaneous Frequency Measurement (IFM) devices are the essential parts of anyESM, ELINT, and RWR receiver. Analog IFMs have been used for several decades. However, thesedevices are bulky, complex and expensive. Nowadays, there is a great interest in developing a wideband, high dynamic range, and accurate Digital IFMs. One Digital IFM that has suitably reached allthese requirements is mono-bit zero-crossing IFM, made by some different producers at present. Inthis paper, the performance of mono-bit digital Instantaneous Frequency Measurement (IFM) deviceis analyzed. This analysis includes quantization error, thermal noise, clock jitter, comparator bias andalso “Pulse-on-Pulse” occurrence. The error limits due to all these factors are computed and analyzed,and a unified approach to the system design is presentedIn this paper, the performance of mono-bit digital Instantaneous frequency measurement (IFM) device is analyzed. This analysis includes quantization error, additive (thermal) noise, clock jitter, comparator bias and also “Pulse-on-Pulse” occurrence. The error limits due to all these factors are computed and analyzed, and a unified approach to the system design is presented
http://eej.aut.ac.ir/article_1985_ba25fee5986f35673c87c8b5e2ba10a2.pdf
2017-12-01T11:23:20
2018-03-18T11:23:20
131
140
10.22060/eej.2017.12155.5050
Digital Instantaneous Frequency
Measurement (DIFM)
Mono-bit Receiver
Zero-crossing
Y.
Norouzi
y.norouzi@aut.ac.ir
true
1
Dept. of Elrctrical Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Elrctrical Engineering, Amirkabir University of Technology, Tehran, Iran
Dept. of Elrctrical Engineering, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
H.
Shahbazi
true
2
Dept. of Science and Research, Azad University, Tehran, Iran
Dept. of Science and Research, Azad University, Tehran, Iran
Dept. of Science and Research, Azad University, Tehran, Iran
AUTHOR
S.
Mirzaei
true
3
Dept. of Elrctrical Engineering, Amirkabir University of Technology, Tehran,Iran
Dept. of Elrctrical Engineering, Amirkabir University of Technology, Tehran,Iran
Dept. of Elrctrical Engineering, Amirkabir University of Technology, Tehran,Iran
AUTHOR
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ORIGINAL_ARTICLE
Cascaded Multilevel Inverters with Reduced Structures Based on a Recently Proposed Basic Units: Implementing a 147-level Inverter
A multilevel inverter is capable of generating high-quality stepwise pseudo-sinusoidalvoltage with low THD , applicable to high-power and high-voltage systems. These types of topologiesmay require a large number of switches and power supplies. This leads to much cost, large size, andcomplicated control algorithms. Thus, newer topologies are being proposed to decrease the numberof power electronic devices for a large number of levels in output voltage. Recently, a new multilevelinverter has been reported in the literature to reduce component count. Its structure requires a lowernumber of active switches as compared to the existing ones. The available literature presents ageneralization of the topology with an especial asymmetrical sources ratio, but no investigations aremade for other symmetrical or asymmetrical sources ratio with cascaded configurations. This studypresents a comprehensive analysis of cascaded topologies with the proposed basic units. The topologyis analysed for both symmetric and asymmetric DC source configurations. Also, two algorithms forasymmetric source configuration suitable for cascaded structures are proposed. Moreover, the designand simulation of a 147-level inverter are presented under an optimal number of DC sources and powerswitches. Furthermore, experimental validation is performed by implementing a laboratory prototype.
http://eej.aut.ac.ir/article_964_e9d67543741c53cc60863075e0d9ba96.pdf
2017-12-01T11:23:20
2018-03-18T11:23:20
141
150
10.22060/eej.2017.11432.4967
Asymmetrical DC Sources
Multilevel Inverter
Packed U cell
Reduced Structures
M. J.
Mojibian
mojibian@ee.kntu.ac.ir
true
1
Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
LEAD_AUTHOR
M.
Tavakoli Bina
tavakoli@ieee.org
true
2
Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
AUTHOR
B.
Eskandari
b.eskandary@gmail.com
true
3
Department of Electrical Engineering, Malayer University, Malayer, Iran
Department of Electrical Engineering, Malayer University, Malayer, Iran
Department of Electrical Engineering, Malayer University, Malayer, Iran
AUTHOR
[1] J. Rodríguez, S. Bernet, B. Wu, J.O. Pontt, S. Kouro, Multilevel voltage-source-converter topologies for industrial medium-voltage drives, IEEE Transactions on industrial electronics, 54(6) (2007) 2930-2945.
1
[2] L.G. Franquelo, J. Rodriguez, J.I. Leon, S. Kouro, R. Portillo, M.A. Prats, The age of multilevel converters arrives, IEEE industrial electronics magazine, 2(2) (2008).
2
[3] S. Kouro, M. Malinowski, K. Gopakumar, J. Pou, L.G. Franquelo, B. Wu, J. Rodriguez, M.A. Pérez, J.I. Leon, Recent advances and industrial applications of multilevel converters, IEEE Transactions on industrial electronics, 57(8) (2010) 2553-2580.
3
[4] X. Zha, L. Xiong, J. Gong, F. Liu, Cascaded multilevel converter for medium-voltage motor drive capable of regenerating with part of cells, IET Power Electronics, 7(5) (2014) 1313-1320.
4
[5] R. Teichmann, M. Malinowski, S. Bernet, Evaluation of three-level rectifiers for low-voltage utility applications, IEEE Transactions on Industrial Electronics, 52(2) (2005) 471-481.
5
[6] S. Daher, J. Schmid, F.L. Antunes, Multilevel inverter topologies for stand-alone PV systems, IEEE Transactions on Industrial Electronics, 55(7) (2008) 2703-2712.
6
[7] M. Malinowski, K. Gopakumar, J. Rodriguez, M.A. Perez, A survey on cascaded multilevel inverters, IEEE Transactions on industrial electronics, 57(7) (2010) 2197-2206.
7
[8] M.J. Mojibian, M.T. Bina, Classification of multilevel converters with a modular reduced structure: implementing a prominent 31-level 5 kVA class B converter, IET Power Electronics, 8(1) (2014) 20-32.
8
[9] J. Dixon, L. Moran, High-level multistep inverter optimization using a minimum number of power transistors, IEEE Transactions on Power Electronics, 21(2) (2006) 330-337.
9
[10] Y. Ounejjar, K. Al-Haddad, L.-A. Grégoire, Packed U cells multilevel converter topology: theoretical study and experimental validation, IEEE Transactions on Industrial Electronics, 58(4) (2011) 1294-1306.
10
[11] G. Konstantinou, M. Ciobotaru, V. Agelidis, Selective harmonic elimination pulse-width modulation of modular multilevel converters, IET Power Electronics, 6(1) (2013) 96-107.
11
[12] I. Colak, E. Kabalci, R. Bayindir, Review of multilevel voltage source inverter topologies and control schemes, Energy Conversion and Management, 52(2) (2011) 1114- 1128.
12
[13] A.M. AS, A. Gopinath, M. Baiju, A simple space vector PWM generation scheme for any general $ n $-level inverter, IEEE Transactions on Industrial Electronics, 56(5) (2009) 1649-1656.
13
[14] Z. Du, L.M. Tolbert, B. Ozpineci, J.N. Chiasson, Fundamental frequency switching strategies of a seven-level hybrid cascaded H-bridge multilevel inverter, IEEE Transactions on Power Electronics, 24(1) (2009) 25-33.
14
ORIGINAL_ARTICLE
Investigating Direct Torque Control of Six-Phase Induction Machines Under Open Phase Fault Conditions
This paper presents analysis and evaluation of classical direct torque control(DTC), for controlling a symmetrical six phase induction motor (SPIM) under open phasefault conditions. The machine has two three-phase windings spatially shifted by 60 electricaldegrees. The strategy of the proposed method consists of choosing the switching modesaccording to the configuration of living phases in such a way that it generates vectors thathave higher amplitude in α-β plane while their projections on z axis give zero or near zeroamplitude vectors. The goal is reducing parasitic currents and torque ripples of SPIM underfaulty mode. Based on the theoretical analysis, it will be shown that in the open phase faultconditions, the only non-pulsating operation is obtained by opening the fault three-phasewinding. Experimental test results are provided o support theoretical analysis in open phasefault conditions for SPIM.
http://eej.aut.ac.ir/article_1974_d089edf0b2460c10b04eb1be568c6025.pdf
2017-12-01T11:23:20
2018-03-18T11:23:20
151
160
10.22060/eej.2017.11535.4974
six phase induction machine
direct torque control
open phase fault
R.
Kianinezhad
reza.kiani@scu.ac.ir
true
1
Department of Electrical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Department of Electrical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Department of Electrical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
AUTHOR
A.
Hajary
alihajary@gmail.com
true
2
Department of Electrical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Department of Electrical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Department of Electrical Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
LEAD_AUTHOR
[1] J.-R. Fu, T.A. Lipo, Disturbance-free operation of a multiphase current-regulated motor drive with an opened phase, IEEE Transactions on Industry Applications, 30(5) (1994) 1267-1274.
1
[2] Y. Zhao, T.A. Lipo, Space vector PWM control of dual three-phase induction machine using vector space decomposition, IEEE Transactions on industry applications, 31(5) (1995) 1100-1109.
2
[3] R. Kianinezhad, B. Nahid-Mobarakeh, L. Baghli, F. Betin, G.-A. Capolino, Modeling and control of six-phase symmetrical induction machine under fault condition due to open phases, IEEE Transactions on Industrial Electronics, 55(5) (2008) 1966-1977.
3
[4] A. Tani, M. Mengoni, L. Zarri, G. Serra, D. Casadei, Control of multiphase induction motors with an odd number of phases under open-circuit phase faults, IEEE Transactions on Power Electronics, 27(2) (2012) 565-577.
4
[5] H.S. Che, M.J. Duran, E. Levi, M. Jones, W.-P. Hew, N.A. Rahim, Postfault operation of an asymmetrical six-phase induction machine with single and two isolated neutral points, IEEE Transactions on Power Electronics, 29(10) (2014) 5406-5416.
5
[6] H.-M. Ryu, J.-W. Kim, S.-K. Sul, Synchronous frame current control of multi-phase synchronous motor-part II asymmetric fault condition due to open phases, in: Industry Applications Conference, 2004. 39th IAS Annual Meeting. Conference Record of the 2004 IEEE, IEEE, 2004.
6
[7] Y. Zhao, T.A. Lipo, Modeling and control of a multi-phase induction machine with structural unbalance, IEEE Transactions on energy conversion, 11(3) (1996) 570-577.
7
[8] Y. Zhao,T. A. Lipo, Modeling and control of a multi-phase induction machine with structural unbalance Part II. Field-oriented control andexperimental verification, , IEEE Transactions on energy conversion, 11(3) (1996) 578-584.
8
[9] J.-P. Martin, F. Meibody-Tabar, B. Davat, Multiple-phase permanent magnet synchronous machine supplied by VSIs, working under fault conditions, in: Industry Applications Conference, 2000. Conference Record of the 2000 IEEE, IEEE, 2000, pp. 1710-1717.
9
[10] D. Hadiouche, H. Razik, A. Rezzoug, Modelling of a double star induction motor for space vector pwm control, in: International conference on electrical machines, 2000, pp. 392-396.
10
[11] R. Kianinezhad, B. Nahid-Mobarakeh, L. Baghli, F. Betin, G.-A. Capolino, Torque ripples suppression for six-phase induction motors under open phase faults, in: IEEE Industrial Electronics, IECON 2006-32nd Annual Conference on, IEEE, 2006, pp. 1363-1368.
11
[12] V. Talaeizadeh, R. Kianinezhad, S. Seyfossadat, H. Shayanfar, Direct torque control of six-phase induction motors using three-phase matrix converter, Energy Conversion and Management, 51(12) (2010) 2482-2491.
12
[13] R. Alcharea, R. Kianinezhad, B. Nahid-Mobarakeh, F. Betin, G.A. Capolino;PWM Direct Torque Control of Symmetrical Six-Phase Induction Machines, Conference of the IEEE Industrial Electronics Society, IECON, 2008.
13
ORIGINAL_ARTICLE
Optimization of Mixed-Integer Non-Linear Electricity Generation Expansion Planning Problem Based on Newly Improved Gravitational Search Algorithm
Electricity demand is forecasted to double in 2035, and it is vital to address the economicsof electrical energy generation for planning purposes. This study aims to examine the applicability ofGravitational Search Algorithm (GSA) and the newly improved GSA (IGSA) for optimization of themixed-integer non-linear electricity generation expansion planning (GEP) problem. The performanceindex of GEP problem is defined as the total cost (TC) based on the sum of costs for investment andmaintenance, unserved load, and salvage. In IGSA, the search space is sub-divided for escaping fromlocal minima and decreasing the computation time. Four different GEP case studies are considered toevaluate the performances of GSA and IGSA, and the results are compared with those from implementingparticle swarm optimization algorithm. It is found that IGSA results in lower TC by 7.01%, 4.08%,11.00%, and 6.40%, in comparison with GSA, for the four case studies. Moreover, as compared withGSA, the simulation results show that IGSA requires less computation time, in all cases.
http://eej.aut.ac.ir/article_1959_2a71b0a0f931434f4c10a5974c3ff6e9.pdf
2017-12-01T11:23:20
2018-03-18T11:23:20
161
172
10.22060/eej.2017.12123.5041
Generation expansion planning
Improved gravitational search
algorithm
Optimization
Power system planning
F .J.
Ardakani
ardehali@aut.ac.ir
true
1
Energy Research Center, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Energy Research Center, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Energy Research Center, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
M. M.
Ardehali
true
2
Energy Research Center, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Energy Research Center, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Energy Research Center, Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
[1] F. Ardakani, M. Ardehali, Novel effects of demand side management data on accuracy of electrical energy consumption modeling and long-term forecasting, Energy Conversion and Management, 78 (2014) 745-752.
1
[2] EIA, World Energy Outlook (2013).
2
[3] EIA, World Energy Outlook (2009).
3
[4] I. Statistics, Energy balances of non-OECD countries in 2011, Paris: International Energy Agency, (2011).
4
[5] B. Alizadeh, S. Jadid, Reliability constrained coordination of generation and transmission expansion planning in power systems using mixed integer programming, IET generation, transmission & distribution, 5(9) (2011) 948-960.
5
[6] J.L.C. Meza, M.B. Yildirim, A.S. Masud, A multiobjective evolutionary programming algorithm and its applications to power generation expansion planning, IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 39(5) (2009) 1086-1096.
6
[7] G. Liu, H. Sasaki, N. Yorino, Application of network topology to long range composite expansion planning of generation and transmission lines, Electric Power Systems Research, 57(3) (2001) 157-162.
7
[8] L. Wenyuan, R. Billinton, A minimum cost assessment method for composite generation and transmission system expansion planning, IEEE Transactions on Power Systems, 8(2) (1993) 628-635.
8
[9] C.H. Antunes, A.G. Martins, I.S. Brito, A multiple objective mixed integer linear programming model for power generation expansion planning, Energy, 29(4) (2004) 613-627.
9
[10] S. Majumdar, D. Chattopadhyay, A model for integrated analysis of generation capacity expansion and financial planning, IEEE transactions on power systems, 14(2) (1999) 466-471.
10
[11] H. Khodr, J. Gomez, L. Barnique, J. Vivas, P. Paiva, J. Yusta, A. Urdaneta, A linear programming methodology for the optimization of electric power-generation schemes, IEEE Transactions on Power systems, 17(3) (2002) 864- 869.
11
[12] H. Tekiner, D.W. Coit, F.A. Felder, Multi-period multi-objective electricity generation expansion planning problem with Monte-Carlo simulation, Electric Power Systems Research, 80(12) (2010) 1394-1405.
12
[13] C. Unsihuay-Vila, J.W. Marangon-Lima, A.Z. De Souza, I. Perez-Arriaga, Multistage expansion planning of generation and interconnections with sustainable energy development criteria: A multiobjective model, International Journal of Electrical Power & Energy Systems, 33(2) (2011) 258-270.
13
[14] A. Ramos, I.J. Perez-Arriaga, J. Bogas, A nonlinear programming approach to optimal static generation expansion planning, IEEE Transactions on Power Systems, 4(3) (1989) 1140-1146.
14
[15] J.-B. Park, Y.-M. Park, J.-R. Won, K.Y. Lee, An improved genetic algorithm for generation expansion planning, IEEE Transactions on Power Systems, 15(3) (2000) 916-922.
15
[16] J. Sirikum, A. Techanitisawad, Power generation expansion planning with emission control: a nonlinear model and a GA.based heuristic approach, International Journal of Energy Research, 30(2) (2006) 81-99.
16
[17] B. Alizadeh, S. Jadid, A dynamic model for coordination of generation and transmission expansion planning in power systems, International Journal of Electrical Power & Energy Systems, 65 (2015) 408-418.
17
[18] Z. Hejrati, E. Hejrati, A. Taheri, Optimization generation expansion planning by HBMO, Optimization, 37(7) (2012) 99-108.
18
[19] S.-L. Chen, T.-S. Zhan, M.-T. Tsay, Generation expansion planning of the utility with refined immune algorithm, Electric Power Systems Research, 76(4) (2006) 251-258.
19
[20] B. HEDAYATFAR, A. BARJANEH, Least-Cost Generation Expansion Planning Using an Imperialist Competitive Algorithm, Life Science Journal, 10(8s) (2013).
20
[21] R.P. Kothari, D.P. Kroese, Optimal generation expansion planning via the cross-entropy method, in: Winter Simulation Conference, Winter Simulation Conference, 2009, pp. 1482-1491.
21
[22] S. Kannan, S.M.R. Slochanal, P. Subbaraj, N.P. Padhy, Application of particle swarm optimization technique and its variants to generation expansion planning problem, Electric Power Systems Research, 70(3) (2004) 203-210.
22
[23] M. Jadidoleslam, E. Bijami, N. Amiri, A. Ebrahimi, J. Askari, Application of shuffled frog leaping algorithm to long term generation expansion planning, International Journal of Computer and Electrical Engineering, 4(2) (2012) 115.
23
[24] M. Jadidoleslam, A. Ebrahimi, Reliability constrained generation expansion planning by a modified shuffled frog leaping algorithm, International Journal of Electrical Power & Energy Systems, 64 (2015) 743-751.
24
[25] E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, GSA: a gravitational search algorithm, Information sciences, 179(13) (2009) 2232-2248.
25
[26] P.K. Roy, Solution of unit commitment problem using gravitational search algorithm, International Journal of Electrical Power & Energy Systems, 53 (2013) 85-94.
26
[27] P. Roy, B. Mandal, K. Bhattacharya, Gravitational search algorithm based optimal reactive power dispatch for voltage stability enhancement, Electric Power Components and Systems, 40(9) (2012) 956-976.
27
[28] A. Bhattacharya, P. Roy, Solution of multi-objective optimal power flow using gravitational search algorithm, IET generation, transmission & distribution, 6(8) (2012) 751-763.
28
[29] IAEA (International Atomic Energy Agency), Wien automatic system planning (WASP) package a computer code for power generating system expansion planning in, Vienna, 2001.
29
[30] G.M. Cole, Surveyor reference manual, fifth ed., Professional publications Inc. (PPI), 2009.
30
[31] M. Clerc, J. Kennedy, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE transactions on Evolutionary Computation, 6(1) (2002) 58-73.
31
[32] E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, BGSA: binary gravitational search algorithm, Natural Computing, 9(3) (2010) 727-745.
32
[33] C. Li, J. Zhou, Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm, Energy Conversion and Management, 52(1) (2011) 374-381.
33
[34] T.-H. Huynh, A modified shuffled frog leaping algorithm for optimal tuning of multivariable PID controllers, in: Industrial Technology, 2008. ICIT 2008. IEEE International Conference on, IEEE, 2008, pp. 1-6.
34
[35] A. David, Z. Rongda, An expert system with fuzzy sets for optimal planning (of power system expansion), IEEE Transactions on Power Systems, 6(1) (1991) 59-65.
35
ORIGINAL_ARTICLE
Increasing Voltage Gain by New Structure of Inductive Switching DC-DC Converter
In a photovoltaic system, sun light energy is converted to electricity. The generatedelectricity has a low DC voltage. In order to increase voltage generated by photovoltaic cells (PV),an additive DC-DC converter is required to raise the low voltage to a good level which provides theconditions for connection to DC-DC converters. Low wastes, low costs, and high efficiency are someother specifications of such converters. This paper presents a new structure for an additive DC-DCconverter with inductive and capacitor switching for increasing high voltage gain to be used in PVsystem. It is based on the inductive and non-insulated switching which increases voltage in a duty cycleup to 10 times of input voltage. In addition, using a switch, low elements, and also low voltage stresson the switch is the advantage of this new setup. The easy increasing of levels to reach the highervoltages is another benefit of this structure. The paper continues with the analysis of circuit functionand PWM (Pulse Width Modulation) adjustments. PSCAD/EMTDC software is used for confirming theauthenticity of the performance of the suggested model. The results are presented.
http://eej.aut.ac.ir/article_1047_6b99877c4800b8b9d799fd1bce29dc40.pdf
2017-12-01T11:23:20
2018-03-18T11:23:20
173
178
10.22060/eej.2017.11555.4978
PV
DC-DC Converter
High Voltage Gain
PWM
PSCAD Software
S.
Nabati
salman.nabati@yahoo.com
true
1
Department of Electrical Eng., Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Electrical Eng., Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Electrical Eng., Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
A.
Siadatan
true
2
Dept. of Electrical Eng., Faculty of Technical & Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran
Dept. of Electrical Eng., Faculty of Technical & Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran
Dept. of Electrical Eng., Faculty of Technical & Engineering, West Tehran Branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
S. B.
Mozafari
true
3
Department of Electrical Eng., Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Electrical Eng., Science and Research Branch, Islamic Azad University, Tehran, Iran
Department of Electrical Eng., Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
[1] W.-Y. Choi, J.-S. Yoo, J.-Y. Choi, High efficiency dc-dc converter with high step-up gain for low PV voltage sources, in: Power Electronics and ECCE Asia (ICPE & ECCE), 2011 IEEE 8th International Conference on, IEEE, 2011, pp. 1161-1163.
1
[2] Q. Zhao, F.C. Lee, High-efficiency, high step-up DC-DC converters, IEEE Transactions on Power Electronics, 18(1) (2003) 65-73.
2
[3] B. Wu, S. Li, S. Keyue, A new hybrid boosting converter, in: Energy Conversion Congress and Exposition (ECCE), 2014 IEEE, IEEE, 2014, pp. 3349-3354.
3
[4] M. Prudente, L.L. Pfitscher, G. Emmendoerfer, E.F. Romaneli, R. Gules, Voltage multiplier cells applied to non-isolated DC–DC converters, IEEE Transactions on Power Electronics, 23(2) (2008) 871-887.
4
[5] S. Lee, P. Kim, S. Choi, High step-up soft-switched converters using voltage multiplier cells, IEEE Transactions on Power Electronics, 28(7) (2013) 3379-3387.
5
[6] J.C. Rosas-Caro, J.M. Ramirez, F.Z. Peng, A. Valderrabano, A DC–DC multilevel boost converter, IET Power Electronics, 3(1) (2010) 129-137.
6
[7] F.L. Luo, H. Ye, Positive output multiple-lift push-pull switched-capacitor Luo-converters, IEEE transactions on industrial electronics, 51(3) (2004) 594-602.
7
[8] J.A. Starzyk, Y.-W. Jan, F. Qiu, A DC-DC charge pump design based on voltage doublers, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 48(3) (2001) 350-359.
8
[9] F.L. Luo, H. Ye, Positive output super-lift converters, IEEE Transactions on Power Electronics, 18(1) (2003) 105-113.
9
[10] N. Vazquez, L. Estrada, C. Hernandez, E. Rodriguez, The tapped-inductor boost converter, in: Industrial Electronics, 2007. ISIE 2007. IEEE International Symposium on, IEEE, 2007, pp. 538-543.
10
[11] T.-F. Wu, Y.-S. Lai, J.-C. Hung, Y.-M. Chen, Boost converter with coupled inductors and buck–boost type of active clamp, IEEE Transactions on Industrial Electronics, 55(1) (2008) 154-162.
11
[12] L.-S. Yang, T.-J. Liang, J.-F. Chen, Transformerless DC–DC converters with high step-up voltage gain, IEEE Transactions on Industrial Electronics, 56(8) (2009) 3144-3152.
12
ORIGINAL_ARTICLE
Measurement and Computational Modeling of Radio-Frequency Electromagnetic Power Density Around GSM Base Transceiver Station Antennas
Evaluating the power densities emitted by GSM1800 and GSM900 BTS antennas isconducted via two methods. Measurements are carried out in half a square meter grids around twoantennas. CST Microwave STUDIO software is employed to estimate the power densities in order fordetailed antenna and tower modeling and simulation of power density. Finally, measurements obtainedfrom computational and experimental methods were compared through the contour lines using thestatistical Surfer software. After measuring and simulating all values, it turns out that power density isgenerally lower than the permissible exposure limits although exceeds the limits in some sample points. According to the measurements, simulation error in stations GSM900 and GSM1800 are 10% and 8%,respectively. Findings from contour-line-maps illustrates that direct measurement method follows thesame emission pattern as the computational method does. It validates the computational approach andthe models attained for BTS power density estimation.
http://eej.aut.ac.ir/article_1969_490c4f2ca8f97d93b92cabc1091d926e.pdf
2017-12-01T11:23:20
2018-03-18T11:23:20
179
186
10.22060/eej.2017.12018.5026
BTS antenna
Simulation
power density
permissible exposure limits
P.
Nassiri
nassiri@sina.tums.ac.ir
true
1
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
M.
Saviz
msaviz@aut.ac.ir
true
2
Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
M.
Helmi-kohnehShahri
m.hk680925@yahoo.com
true
3
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
LEAD_AUTHOR
M.
Pourhosein
mehr5632@yahoo.com
true
4
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
R.
Divani
m.hk680925@gmail.com
true
5
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
[1] P. Ushie, V.U. Nwankwo, A. Bolaji, O. Osahun, Measurement and Analysis of Radio-frequency Radiation Exposure Level from Different Mobile Base Transceiver Stations in Ajaokuta and Environs, Nigeria, arXiv preprint arXiv:1306.1475, (2013).
1
[2] P. Baltrėnas, R. Buckus, Indoor measurements of the power density close to mobile station antenna, (2011).
2
[3] Q.Q. He, W.C. Yang, Y.X. Hu, Accurate method to estimate EM radiation from a GSM base station, Progress In Electromagnetics Research M, 34 (2014) 19-27.
3
[4] M.A. Keow, S. Radiman, Assessment of radiofrequency/ microwave radiation emitted by the antennas of rooftop-mounted mobile phone base stations, Radiation protection dosimetry, 121(2) (2005) 122-127.
4
[5] S. Miclaus, P. Bechet, Estimated and measured values of the radiofrequency radiation power density around cellular base stations, Romanian Journal of Physics, 52(3/4) (2007) 429.
5
[6] W. Suwansin, P. Phasukkit, C. Pintavirooj, A. Sanpanich, Analysis of heat transfer and specific absorption rate of electromagnetic field in human body at 915 MHz and 2.45 GHz with 3D finite element method, in: Biomedical Engineering International Conference (BMEiCON), 2012, IEEE, 2012, pp. 1-4.
6
[7] S. Banik, S. Bandyopadhyay, S. Ganguly, Bioeffects of microwave––a brief review, Bioresource technology, 87(2) (2003) 155-159.
7
[8] A. Khavanin, Nonthermal Effects of Radar Exposure on Human: A Review Article, Iranian Journal of Health, Safety and Environment, 1(1) (2014) 43-52.
8
[9] A. Vander Vorst, A. Rosen, Y. Kotsuka, RF/microwave interaction with biological tissues, John Wiley & Sons, 2006.
9
[10] T. Alanko, M. Hietanen, P. Von Nandelstadh, Occupational exposure to RF fields from base station antennas on rooftops, annals of telecommunications-annales des télécommunications, 63(1-2) (2008) 125-132.
10
[11] R. Kitchen, RF and microwave radiation safety handbook, Newnes, 2001.
11
[12] I.C.o.N.-I.R. Protection, Guidelines for limiting exposure to time-varying electric and magnetic fields (1 Hz to 100 kHz), Health physics, 99(6) (2010) 818-836.
12
[13] R.G. Sargent, Verification and validation of simulation models, in: Proceedings of the 37th conference on Winter simulation, winter simulation conference, 2005, pp. 130-143.
13
[14] Y. Alfadhl, Numerical evaluations on the interaction of electromagnetic fields with animals and with biological tissues, University of London, 2006.
14
[15] Roof Top guide 122-A, n.kouhestani cartographer2015.
15
[16] Roof Top guide 195-A n.kouhestani, cartographer2015
16
[17] P. Gajšek, D. šimunic, Occupational exposure to base stations—compliance with EU Directive 2004/40/ EC, International Journal of Occupational Safety and Ergonomics, 12(2) (2006) 187-194.
17
ORIGINAL_ARTICLE
Combination of Transformed-means Clustering and Neural Networks for Short-Term Solar Radiation Forecasting
In order to provide an efficient conversion and utilization of solar power, solar radiation datashould be measured continuously and accurately over the long-term period. However, the measurement ofsolar radiation is not available to all countries in the world due to some technical and fiscal limitations. Hence,several studies were proposed in the literature to find mathematical and physical models to estimate andforecast the amount of solar radiation such as stochastic prediction models based on time series methods. Thispaper proposes a hybridization framework, considering clustering, pre-processing, and training steps for shorttermsolar radiation forecasting. The proposed method is a combination of a novel data clustering method,time-series analysis, and multilayer perceptron neural network (MLPNN). The proposed Transformed-Means clustering method is based on inverse data transformation and K-means algorithm that presents moreaccurate clustering results when compared to the K-Means algorithm; its improved version and also otherpopular clustering algorithms. The performance of the proposed Transformed-Means is evaluated usingseveral types of datasets and compared with different variants of K-means algorithm. The proposed methodclusters the input solar radiation time-series data into an appropriate number of sub-datasets which are thenpreprocessed by the time-series analysis. The preprocessed time-series data provide the input for the trainingstage where MLPNN is used to forecast the solar radiation. Solar time-series data with different solar radiationcharacteristics are also used to determine the accuracy and the processing speed of the developed forecastingmethod with the proposed Transformed-Means and other clustering techniques.
http://eej.aut.ac.ir/article_942_8c27e9fa2507f7e0a9a8490a7f9a4497.pdf
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187
194
10.22060/eej.2017.12487.5077
Data Mining
Time Series Analysis
Forecasting
Solar
K-Means
M.
Ghayekhloo
true
1
Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran
LEAD_AUTHOR
M. B.
Menhaj
mbmenhaj@yahoo.com
true
2
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
[1] A. Mellit, M. Benghanem, A.H. Arab, A. Guessoum, Modelling of sizing the photovoltaic system parameters using artificial neural network, in: Proc. of IEEE, CCA, 2003, pp. 353-357.
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[2] M. Nehrir, C. Wang, K. Strunz, H. Aki, R. Ramakumar, J. Bing, Z. Miao, Z. Salameh, A review of hybrid renewable/alternative energy systems for electric power generation: Configurations, control, and applications, IEEE Transactions on Sustainable Energy, 2(4) (2011) 392-403.
2
[3] E. Lorenz, D. Heinemann, Prediction of solar irradiance and photovoltaic power, (2012).
3
[4] A. Mellit, Artificial Intelligence technique for modelling and forecasting of solar radiation data: a review, International Journal of Artificial intelligence and soft computing, 1(1) (2008) 52-76.
4
[5] K. Tanaka, K. Uchida, K. Ogimi, T. Goya, A. Yona, T. Senjyu, T. Funabashi, C.-H. Kim, Optimal operation by controllable loads based on smart grid topology considering insolation forecasted error, IEEE transactions on smart grid, 2(3) (2011) 438-444.
5
[6] P. Zhang, Generation Scheduling for Supply and Demand Balancing in Power Systems with Renewable Power Generation, Kyushu University, 2013.
6
[7] A. Yona, T. Senjyu, T. Funabshi, H. Sekine, Application of neural network to 24-hours-ahead generating power forecasting for PV system, IEEJ Transactions on Power and Energy, 128 (2008) 33-39.
7
[8] S. Cao, W. Weng, J. Chen, W. Liu, G. Yu, J. Cao, Forecast of solar irradiance using chaos optimization neural networks, in: Power and Energy Engineering Conference, 2009. APPEEC 2009. Asia-Pacific, IEEE, 2009, pp. 1-4.
8
[9] G. Capizzi, C. Napoli, F. Bonanno, Innovative second-generation wavelets construction with recurrent neural networks for solar radiation forecasting, IEEE Transactions on neural networks and learning systems, 23(11) (2012) 1805-1815.
9
[10] J. Shi, W.-J. Lee, Y. Liu, Y. Yang, P. Wang, Forecasting power output of photovoltaic systems based on weather classification and support vector machines, IEEE Transactions on Industry Applications, 48(3) (2012) 1064-1069.
10
[11] T.-C. Yu, H.-T. Chang, The forecast of the electrical energy generated by photovoltaic systems using neural network method, in: Electric Information and Control Engineering (ICEICE), 2011 International Conference on, IEEE, 2011, pp. 2758-2761.
11
[12] S. Wang, N. Zhang, Y. Zhao, J. Zhan, Photovoltaic system power forecasting based on combined grey model and BP neural network, in: Electrical and Control Engineering (ICECE), 2011 International Conference on, IEEE, 2011, pp. 4623-4626.
12
[13] T. Kohonen, The self-organizing map, Proceedings of the IEEE, 78(9) (1990) 1464-1480.
13
[14] C. Cortes, V. Vapnik, Support-vector networks, Machine learning, 20(3) (1995) 273-297.
14
[15] A.K. Yadav, S. Chandel, Solar radiation prediction
15
using Artificial Neural Network techniques: A review, Renewable and Sustainable Energy Reviews, 33 (2014) 772-781.
16
[16] S.A. Kalogirou, Artificial neural networks in renewable energy systems applications: a review, Renewable and sustainable energy reviews, 5(4) (2001) 373-401.
17
[17] H. Esen, M. Inalli, A. Sengur, M. Esen, Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system, Energy and Buildings, 40(6) (2008) 1074-1083.
18
[18] C. Paoli, C. Voyant, M. Muselli, M.-L. Nivet, Forecasting of preprocessed daily solar radiation time series using neural networks, Solar Energy, 84(12) (2010) 2146-2160.
19
[19] M.S. Bobi, Use, operation and maintenance of renewable energy systems: Experiences and future approaches, Springer, 2014.
20
[20] N. Sengupta, S. Aloka, B. Narayanaswamy, H. Ismail, S. Mathew, Time series data mining for demand side decision support, in: Innovative Smart Grid Technologies-Asia (ISGT Asia), 2013 IEEE, IEEE, 2013, pp. 1-6.
21
[21] Y. Yang, L. Dong, Short-term PV generation system direct power prediction model on wavelet neural network and weather type clustering, in: Intelligent Human- Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on, IEEE, 2013, pp. 207-211.
22
[22] R. Li, H. Wang, Y. Cui, X. Huang, Solar flare forecasting using learning vector quantity and unsupervised clustering techniques, SCIENCE CHINA Physics, Mechanics & Astronomy, 54(8) (2011) 1546-1552.
23
[23] K. Benmouiza, A. Cheknane, Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models, Energy Conversion and Management, 75 (2013) 561-569.
24
[24] M.I. Malinen, R. Mariescu-Istodor, P. Fränti, K-means⁎: Clustering by gradual data transformation, Pattern Recognition, 47(10) (2014) 3376-3386.
25
[25] http://cs.uef.fi/sipu/clustering/animator/.
26
[26] D.J. Ketchen Jr, C.L. Shook, The application of cluster analysis in strategic management research: an analysis and critique, Strategic management journal, (1996) 441-458.
27
[27] http://cs.uef.fi/sipu/datasets.
28
[28] https://archive.ics.uci.edu/ml/datasets.
29
[29] http://mesonet.agron.iastate.edu
30
[30] D. Arthur, S. Vassilvitskii, k-means++: The advantages of careful seeding, in: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, 2007, pp. 1027-1035.
31
[31] J. Herbert, J. Yao, A game-theoretic approach to competitive learning in self-organizing maps, Advances in Natural Computation, (2005) 418-418.
32
ORIGINAL_ARTICLE
Implementation of a Low- Cost Multi- IMU by Using Information Form of a Steady State Kalman Filter
In this paper, a homogenous multi-sensor fusion method is used to estimate the trueangular rate and acceleration with a combination of four low cost (< 10$) MEMS Inertial MeasurementUnits (IMU). An information form of steady state Kalman filter is designed to fuse the output of four lowaccuracy sensors to reduce the noise effect by the square root of the number of sensors. A hardware isimplemented to test the method with three types of experiments: static test, constant rate, and oscillatingtest. Results of static test for z-axis show that ARW coefficient reduces to 0.0022°/√s and VRW error isdecreased by %50. Also, dynamic test results show the reduction of the standard deviation of combinedrate signal up to six times compared with a single sensor. A comparison between the proposed filter andthe simple averaging method is made in which the results indicate that the Kalman filter is more accuratecompared to the averaging method.
http://eej.aut.ac.ir/article_1972_38ceb31639cc65cc871f53c941bb3f05.pdf
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195
204
10.22060/eej.2017.12045.5028
Multi-sensor fusion
IMU
information form of steady-state
Kalman Filter
A. M.
Shahri
shahri@iust.ac.ir
true
1
Department of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Tehran, Iran
Department of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Tehran, Iran
Department of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
R.
Rasoulzadeh
r.rasoulzadeh@qiau.ac.ir
true
2
Department of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Tehran, Iran
Department of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Tehran, Iran
Department of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Tehran, Iran
AUTHOR
[1] D.S. Bayard, S.R. Ploen, High accuracy inertial sensors from inexpensive components, in, Google Patents, 2005.
1
[2] H. Chang, L. Xue, W. Qin, G. Yuan, W. Yuan, An integrated MEMS gyroscope array with higher accuracy output, Sensors, 8(4) (2008) 2886-2899.
2
[3] H. Chang, L. Xue, C. Jiang, M. Kraft, W. Yuan, Combining numerous uncorrelated MEMS gyroscopes for accuracy improvement based on an optimal Kalman filter, IEEE Transactions on Instrumentation and Measurement, 61(11) (2012) 3084-3093.
3
[4] C. Jiang, L. Xue, H. Chang, G. Yuan, W. Yuan, Signal processing of MEMS gyroscope arrays to improve accuracy using a 1st order markov for rate signal modeling, Sensors, 12(2) (2012) 1720-1737.
4
[5] L. Xue, L. Wang, T. Xiong, C. Jiang, W. Yuan, Analysis of dynamic performance of a Kalman filter for combining multiple MEMS gyroscopes, micromachines, 5(4) (2014) 1034-1050.
5
[6] H. Martin, P. Groves, M. Newman, R. Faragher, A new approach to better low-cost MEMS IMU performance using sensor arrays, in, The Institute of Navigation, 2013.
6
[7] M. Tanenhaus, D. Carhoun, T. Geis, E. Wan, A. Holland, Miniature IMU/INS with optimally fused low drift MEMS gyro and accelerometers for applications in GPS-denied environments, in: Position Location and Navigation Symposium (PLANS), 2012 IEEE/ION, IEEE, 2012, pp. 259-264.
7
[8] I. Skog, J.-O. Nilsson, P. Handel, An open-source multi inertial measurement unit (MIMU) platform, in: Inertial Sensors and Systems (ISISS), 2014 International Symposium on, IEEE, 2014, pp. 1-4.
8
[9] R. Rasoulzadeh, A.M. Shahri, Implementation of A low-cost multi-IMU hardware by using a homogenous multi-sensor fusion, in: Control, Instrumentation, and Automation (ICCIA), 2016 4th International Conference on, IEEE, 2016, pp. 451-456.
9
[10] G. Yuan, W. Yuan, L. Xue, J. Xie, H. Chang, Dynamic performance comparison of two Kalman filters for rate signal direct modeling and differencing modeling for combining a MEMS gyroscope array to improve accuracy, Sensors, 15(11) (2015) 27590-27610.
10
[11] I. Skog, J.-O. Nilsson, P. Händel, A. Nehorai, Inertial Sensor Arrays, Maximum Likelihood, and Cramér–Rao Bound, IEEE Transactions on Signal Processing, 64(16) (2016) 4218-4227.
11
[12] A. Unknown, IEEE Standard Specification Format Guide and Test Procedure for Coriolis Vibratory Gyros, IEEE Standards, 1431 1-79.
12
[13] N. El-Sheimy, H. Hou, X. Niu, Analysis and modeling of inertial sensors using Allan variance, IEEE Transactions on instrumentation and measurement, 57(1) (2008) 140-149.
13
[14] R.J. Vaccaro, A.S. Zaki, Statistical modeling of rate gyros, IEEE Transactions on Instrumentation and Measurement, 61(3) (2012) 673-684.
14
[15] R.E. Kalman, R.S. Bucy, New results in linear filtering and prediction theory, Journal of basic engineering, 83(1) (1961) 95-108.
15
[16] D. Simon, Optimal state estimation: Kalman, H infinity, and nonlinear approaches, John Wiley & Sons, 2006.
16
[17] R.S. Bucy, P.D. Joseph, Filtering for stochastic processes with applications to guidance, American Mathematical Soc., 1987.
17
[18] M. Grewal, A. Andrews, Kalman theory, theory and practice using MATLAB, in, John Wiley & Sons, Inc, 2008.
18
[19] C. Chen, Linear System Theory and Design. New York: Holt, Rinehart and Winston, Decoupling with stability for linear periodic systems, 765 (1984).
19
[20] Q. Gan, C.J. Harris, Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion, IEEE Transactions on Aerospace and Electronic systems, 37(1) (2001) 273-279.
20
[21] N. Assimakis, M. Adam, A. Douladiris, Information filter and kalman filter comparison: Selection of the faster filter, International Journal of Information Engineering, 2(1) (2012) 1-5.
21
[22] D.W. Allan, Statistics of atomic frequency standards, Proceedings of the IEEE, 54(2) (1966) 221-230.
22
[23] InvenSens MPU9150 Motion Sensor Document number: PS-MPU9150A, Rev4.0
23
[24] NXP (Phillips),LPC17xx 32-bit ARM Cortex-M3 microcontroller, Rev. 5.3.
24
ORIGINAL_ARTICLE
Internal Fault Detection, Location, and Classification in Stator Winding of the Synchronous Generators Based on the Terminal Voltage Waveform
In this paper, a novel method is presented for detection and classification of the faultyphase/region in the stator winding of synchronous generators on the basis of the resulting harmoniccomponents that appear in the terminal voltage waveforms. Analytical results obtained through DecisionTree (DT) show that the internal faults are not only detectable but also they can be classified andthe related region can be estimated. Therefore, this scheme can be used to protect the synchronousgenerators against the various internal faults. Fuji technical documents and data sheets for an actualsalient pole synchronous generator (one unit of an Iran’s hydroelectric power plants) are used for themodeling. Simulations in Maxwell software environment are presented. All the related parameters, suchas B-H curve, unsymmetrical air gap and pole saliency, slot-teeth effect, and other actual parameters, areconsidered to obtain a comprehensive model to generate acceptable terminal voltage waveforms withoutany simplification.
http://eej.aut.ac.ir/article_1977_11740ac95bc98317614f241d4faf020e.pdf
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205
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10.22060/eej.2017.12131.5043
Synchronous Generator
Internal Faults
Turn-Turn Faults
Phase To Ground Faults
Detection
Classification
Location
Harmonic Components
Decision Tree
M.
Fayazi
m.fayazi69@yahoo.com
true
1
Dept. of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
Dept. of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
Dept. of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
AUTHOR
F.
Haghjoo
f_haghjoo@sbu.ac.ir
true
2
Dept. of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
Dept. of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
Dept. of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
LEAD_AUTHOR
[1] B. Ravindranath, M. Chander, Power system protection and switchgear, New Age International, 1977.
1
[2] M. Fayazi, F. Haghjoo, Turn to turn fault detection and classification in stator winding of synchronous generators based on terminal voltage waveform components, in: Power Systems Protection and Control Conference (PSPC), 2015 9th, IEEE, 2015, pp. 36-41.
2
[3] ABB Lecture, “Generator and Transformer Protection”, 3rd Edition, 1998.
3
[4] K. Louie, A new accurate phase-domain synchronous generator model for transient simulation, in: Electrical and Computer Engineering, 2006. CCECE’06. Canadian Conference on, IEEE, 2006, pp. 2245-2248.
4
[5] D.D.S. Muthumuni, E. Dirks, Internal fault simulation in synchronous machines, in: Electrical and Computer Engineering, 2000 Canadian Conference on, IEEE, 2000, pp. 1202-1206.
5
[6] A. Megahed, O. Malik, Simulation of internal faults in synchronous generators, IEEE Transactions on Energy Conversion, 14(4) (1999) 1306-1311.
6
[7] A. Megahed, O. Malik, Synchronous generator internal fault computation and experimental verification, IEE Proceedings-Generation, Transmission and Distribution, 145(5) (1998) 604-610.
7
[8] P. Subramaniam, O. Malik, Digital simulation of a synchronous generator in direct-phase quantities, in: Proceedings of the Institution of Electrical Engineers, IET, 1971, pp. 153-160.
8
[9] D. Muthumuni, P. McLaren, E. Dirks, V. Pathirana, A synchronous machine model to analyze internal faults, in: Industry Applications Conference, 2001. Thirty-Sixth IAS Annual Meeting. Conference Record of the 2001 IEEE, IEEE, 2001, pp. 1595-1600.
9
[10] X. Tu, L.-A. Dessaint, M. El Kahel, A.O. Barry, A new model of synchronous machine internal faults based on winding distribution, IEEE Transactions on Industrial electronics, 53(6) (2006) 1818-1828.
10
[11] S. Hemmati, S. Shokri, S. Saied, Modeling and simulation of internal short circuit faults in large hydro generators with wave windings, in: Power Engineering, Energy and Electrical Drives (POWERENG), 2011 International Conference on, IEEE, 2011, pp. 1-6.
11
[12] A. Dehkordi, A. Gole, T. Maguire, P. Neti, A real-time model for testing stator-ground fault protection schemes of synchronous machines, in: International Conference on Power Systems Transients (IPST2009), 2009.
12
[13] A. Sinha, D. Vishwakarma, R. Srivastava, Modeling and simulation of internal faults in salient-pole synchronous generators with wave windings, Electric Power Components and Systems, 38(1) (2009) 100-114.
13
[14] X. Tu, L.-A. Dessaint, M. El Kahel, A. Barry, Modeling and experimental validation of internal faults in salient pole synchronous machines including space harmonics, Mathematics and Computers in Simulation, 71(4) (2006) 425-439.
14
[15] A. Sinha, D. Vishwakarma, R. Srivastava, Modeling and Real-time Simulation of Internal Faults in Turbogenerators, Electric Power Components and Systems, 37(9) (2009) 957-969.
15
[16] X. Tu, L.-A. Dessaint, N. Fallati, B. De Kelper, Modeling and real-time simulation of internal faults in synchronous generators with parallel-connected windings, IEEE Transactions on Industrial Electronics, 54(3) (2007) 1400-1409.
16
[17] H. Jiang, R. Aggarwal, G. Weller, S. Ball, L. Denning, A new approach to synchronous generator internal fault simulation using combined winding function theory and direct phase quantities, (1999).
17
[18] X. Wang, Y. Sun, B. Ouyang, W. Wang, Z. Zhu, D. Howe, Transient behaviour of salient-pole synchronous machines with internal stator winding faults, IEE Proceedings- Electric Power Applications, 149(2) (2002) 143-151.
18
[19] P. Le-Huy, C. Larose, F. Giguère, Flexible Phase- Domain Synchronous-Machine Model with Internal Fault for Protection Relay Testing and related Real-Time Implementation Issues, in: International Conference on Power Systems Transients (IPST2011), 2011.
19
[20] M. Rahnama, J. Nazarzadeh, Synchronous machine modeling and analysis for internal faults detection, in: IEEE International Conference on Electric Machines & Drives (IEMDC’07), 2007.
20
[21] N. Yadaiah, N. Ravi, Statistical method for fault detection in synchronous generators, in: Computer Communication and Informatics (ICCCI), 2012 International Conference on, IEEE, 2012, pp. 1-4.
21
[22] A. Dehkordi, D. Ouellette, P. Forsyth, Protection testing of a 100% stator ground fault using a phase domain synchronous machine model in real time, (2010).
22
ORIGINAL_ARTICLE
K-Complex Detection Based on Synchrosqueezing Transform
K-complex is an underlying pattern in the sleep EEG. Due to the role of sleep studies inneurophysiologic and cognitive disorders diagnosis, reliable methods for analysis and detection of this patternare of great importance. In our previous work, Synchrosqueezing Transform (SST) was proposed for analysisof this pattern. SST is an EMD-like tool, which benefits from wavelet transform and reallocation approaches.This method is able to decompose signals into their time-varying oscillatory ingredients. In addition, itprovides a time-frequency representation with less blurring compared to wavelet transform. In this paper,firstly, the ability of SST is investigated by applying the ANOVA test, which is approved by proper p-values.This paper proposes SST for K-complex detection. The proposed method is based on a so-called “detectionof K-complexes and sleep spindles” (DETOKS) framework. DETOKS is based on spares optimizationand decomposes signals into four components, namely transient, low frequency, oscillatory, and a residual.Applying the Teager-Kaiser energy operator and setting a threshold on the low-frequency component resultin K-complex detection. We modify DETOKS using SST. The proposed method is applied to DREAMSdataset. The dataset provides two visual scorings accompanied by an automatic one. As the visual labels wereextremely different, the automatic detection is considered as the third expert’s scoring and data is re-labeledby a voting approach among three experts. For DETOKS, DETOKS modified by CWT, and the proposedmethod, MCC measure is 0.62, 0.71, and 0.76, respectively. It shows superiority of the proposed method.
http://eej.aut.ac.ir/article_1973_d79c497bc933dd892d0446093bb36060.pdf
2017-12-01T11:23:20
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214
222
10.22060/eej.2017.12577.5096
K-complex
Sleep EEG
Synchrosqueezing Transform (SST)
Sparse Optimization
Teager-Kaiser Energy Operator
Z.
Ghanbari
zahraghanbari@yahoo.com
true
1
Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
AUTHOR
M. H.
Moradi
mhmoradi@aut.ac.ir
true
2
Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
LEAD_AUTHOR
[1] E. Hernández-Pereira, V. Bolón-Canedo, N. Sánchez- Maroño, D. Álvarez-Estévez, V. Moret-Bonillo, A. Alonso-Betanzos, A comparison of performance of K-complex classification methods using feature selection, Information Sciences, 328 (2016) 1-14.
1
[2] T. Lajnef, S. Chaibi, J.-B. Eichenlaub, P.M. Ruby, P.-E. Aguera, M. Samet, A. Kachouri, K. Jerbi, Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis, Frontiers in human neuroscience, 9 (2015).
2
[3] A.L. Pinto, I.S. Fernández, J.M. Peters, S. Manganaro, J.M. Singer, M. Vendrame, S.P. Prabhu, T. Loddenkemper, S.V. Kothare, Localization of sleep spindles, k-complexes, and vertex waves with subdural electrodes in children, Journal of Clinical Neurophysiology, 31(4) (2014) 367-374.
3
[4] V. Kokkinos, G.K. Kostopoulos, Human non.rapid eye movement stage II sleep spindles are blocked upon spontaneous K.complex coincidence and resume as higher frequency spindles afterwards, Journal of sleep research, 20(1pt1) (2011) 57-72.
4
[5] http://www.tcts.fpms.ac.be/~devuyst/Databases/ DatabaseKcomplexes/
5
[6] V. Kokkinos, A.M. Koupparis, G.K. Kostopoulos, An intra-K-complex oscillation with independent and labile frequency and topography in NREM sleep, Frontiers in human neuroscience, 7 (2013).
6
[7] W.O. Tatum IV, Handbook of EEG interpretation, Demos Medical Publishing, 2014.
7
[8] T.A. Camilleri, K.P. Camilleri, S.G. Fabri, Automatic detection of spindles and K-complexes in sleep EEG using switching multiple models, Biomedical Signal Processing and Control, 10 (2014) 117-127.
8
[9] T. Babaie, S. Chawla, R. Abeysuriya, Sleep analytics and online selective anomaly detection, in: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2014, pp. 362-371.
9
[10] Z.R. Zamir, N. Sukhorukova, H. Amiel, A. Ugon, C. Philippe, Convex optimisation-based methods for k-complex detection, Applied Mathematics and Computation, 268 (2015) 947-956.
10
[11] A. Parekh, I.W. Selesnick, D.M. Rapoport, I. Ayappa, Detection of K-complexes and sleep spindles (DETOKS) using sparse optimization, Journal of neuroscience methods, 251 (2015) 37-46.
11
[12] I. Daubechies, J. Lu, H.-T. Wu, Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool, Applied and computational harmonic analysis, 30(2) (2011) 243-261.
12
[13] C. Yücelbaş, Ş. Yücelbaş, S. Özşen, G. Tezel, S. Küççüktürk, Ş. Yosunkaya, Automatic detection of sleep spindles with the use of STFT, EMD and DWT methods, Neural Computing and Applications, (2016) 1-17.
13
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ORIGINAL_ARTICLE
Combination of Feature Selection and Learning Methods for IoT Data Fusion
In this paper, we propose five data fusion schemes for the Internet of Things (IoT) scenario,which are Relief and Perceptron (Re-P), Relief and Genetic Algorithm Particle Swarm Optimization (Re-GAPSO), Genetic Algorithm and Artificial Neural Network (GA-ANN), Rough and Perceptron (Ro-P)and Rough and GAPSO (Ro-GAPSO). All the schemes consist of four stages, including preprocessingthe data set based on curve fitting, reducing the data dimension and identifying the most effective featuresets according to data correlation, training classification algorithms, and finally predicting new databased on classification algorithms. The results derived from five compound schemes are investigated andcompared with each other with three metrics, namely, Quality of Train (QoT) Accuracy (Ac) and StorageCapacity (SC). While the Re-P scheme is only capable of separating classes that are linearly separable,Re-GAPSO one is a dynamic method, appropriate for constantly changing problems of the real life. Onthe other hand, GA-ANN is a Wrapper method and despite Relief can adapt itself to the machine learningalgorithm. Meanwhile, Ro-P scheme is useful for analyzing vague and imprecise information and, unlikeGA-ANN, has less calculative costs. Among these five schemes, Ro-GAPSO is a more precise one, whichhas less calculative cost and does not become stuck in local minima. Experimental results show that Re-Poutperforms other proposed and existing methods in terms of computational time complexity.
http://eej.aut.ac.ir/article_1960_5b7511e4f87d3b6a9eb1a6bc95cececc.pdf
2017-12-01T11:23:20
2018-03-18T11:23:20
223
232
10.22060/eej.2017.12151.5046
Internet of Things
Data Fusion
Rough Set Theory
Perceptron
GAPSO
V.
Sattari-Naeini
vsnaeini@uk.ac.ir
true
1
Dept. of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Dept. of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Dept. of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
LEAD_AUTHOR
Zahra
Parizi-Nejad
za_parizi87@yahoo.com
true
2
Dept. of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Dept. of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Dept. of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
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