ORIGINAL_ARTICLE
Optimal Controller for Single Phase Island Photovoltaic Systems
Increasing of word demand load caused a new Distributed Generation (DG) to enter to powersystem. One of the most renewable energy is the Photovoltaic System. It is beneficial to use thissystem in both separately as well as connected to power system using power electronics interface.In this paper an optimal PID controller for Photovoltaic System systems has been developed. Theoptimization technique is applied to PID optimal controller in order to control the voltage ofPhotovoltaic System against load variation, is presents. Nonlinear characteristics of loadvariations as plant input, Photovoltaic System operational behavior demand for high qualityoptimal controller to ensure both stability and safe performance. Thus, Honey Bee MatingOptimization (HBMO) is used for optimal tuning of PID coefficients in order to enhance closedloop system performance. In order to use this algorithm, at first, problem is written as anoptimization problem which includes the objective function and constraints, and then to achievethe most desirable controller, HBMO algorithm is applied to solve the problem. In this study, theproposed controller is applied to the closed loop photovoltaic system behavior. Simulation resultsare done for various loads in time domain, and the results show the efficiency of the proposedcontroller in contrast to the previous controllers.
http://jaiee.iau-ahar.ac.ir/article_514340_8d72b23aec83b98009e1859b7ae4f465.pdf
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1
11
Photovoltaic System- HBMO- Optimal Controller- PID Controller
Mohammad Esmaeil
akbar
m-akbari@iau-ahar.ac.ir
true
1
AUTHOR
Noradin
Ghadimi
noradin.1364@gmail.com
true
2
AUTHOR
[1] Jiayi H, Chuanwen J, Rong X. A review
1
on distributed energy resources and
2
MicroGrid. Renewable and Sustainable
3
Energy Reviews 2008; 12: 2472–2483
4
[2] Nikkhajoei H, Iravani R.Steady-State
5
Model and Power Flow Analysis of
6
Electronically-Coupled Distributed
7
Resource Units. IEEE Trans. Power Del
8
2007; 22(1):721-728.
9
[3] Sao CK, Lehn PW.Control and Power
10
Management of Converter Fed
11
Microgrids. IEEE Trans. Power Systems
12
2008;23(3):1088-1098.
13
[4] Chen CL et al. Design of Parallel
14
Inverters for Smooth Mode Transfer
15
Microgrid Applications. IEEE Trans.
16
Power Electron 2010;25(1):6-15.
17
[5] Karimi H, Nikkhajoei H, Iravani R.
18
Control of an Electronically-Coupled
19
Distributed Resource Unit Subsequent
20
to an Islanding Event. IEEE Trans.
21
Power Del 2008;23(1):493-501.
22
[6] Katiraei F, Iravani MR, Lehn PW.
23
Micro-Grid Autonomous Operation
24
During and Subsequent to Islanding
25
Process. IEEE Trans. Power Del
26
2005;20(1):248-257.
27
[7] Katiraei F, Iravani MR. Power
28
Management Strategies for a Microgrid
29
with Multiple Distributed Generation
30
Units. IEEE Trans. Power Systems
31
2006; 21(4):1821-1831.
32
[8] Katiraei F, Iravani MR and Lehn PW.
33
Small-signal dynamic model of a microgrid
34
including conventional and
35
electronically interfaced distributed
36
resources. IET Gener. Transm. Distrib
37
2007; 1(3): 369–378.
38
[9] Cheng PT, Chen CA, Lee TL and Kuo
39
SY. A Cooperative Imbalance
40
Compensation Method for Distributed-
41
Generation Interface Converters. IEEE
42
Trans. Ind. Appl 2009;45(2):805-815
43
[10] Yannis M, Magdalene M, Georgios D
44
(2011). “Honey bees mating
45
optimization algorithm for the Euclidean
46
traveling salesman problem,” Infor. Sci.
47
181(20):4684–4698
48
[11] Niknam T (2011). An efficient multiobjective
49
HBMO algorithm for
50
distribution feeder reconfiguration.
51
Expert Syst. Appl. 38(3):2878–2887
52
[12] Noradin Ghadimi. PI Controller Design
53
for Photovoltaic Systems in Islanding
54
Mode Operation. World Applied
55
Sciences Journal 15 (3): 326-330, 2011
56
ORIGINAL_ARTICLE
Digital Watermarking Technology in Different Domains
Due to high speed computer networks, the use of digitally formatted data has increased many folds.The digital data can be duplicated and edited with great ease which has led to a need for effectivecopyright protection tools. Digital Watermarking is a technology of embedding watermark withintellectual property rights into images, videos, audios and other multimedia data by a certainalgorithm .Digital watermarking is a well-known technique used for copy rights protection ofmultimedia data. A number of watermarking techniques have been proposed in literature. DigitalWatermarking is the process that embeds data called a watermark into a multimedia object such thatwatermark can be detected or extracted later to make an assertion about the object. A variety oftechniques in different domains have been suggested by different authors to achieve above mentionedconflicting requirements. All the watermarking techniques are different from each other and are usedfor differing applications.
http://jaiee.iau-ahar.ac.ir/article_514341_ead84c24d767dbd9249c5fe0459ee263.pdf
2014-06-01T11:23:20
2018-06-22T11:23:20
12
17
Watermarking
domain
DCT
DWT
Maryam
Hamrahi
true
1
AUTHOR
[1]W. Zhu, et al, ”Multi-resolution
1
Watermarking for Images and Video”,
2
IEEE Tran. on Circuits & Systems for
3
Video Technology, Vol.9, No.4, June 1999,
4
pp.545-550.
5
[2] Bassia P., Pitas I., and Nikolaidis 2001,
6
“Robust Audio Watermarking in Time
7
Domain”, IEEE Trans. On Multimedia,
8
Vol. 3, pp. 232-241.
9
[3] Bender W., Gruhl D., Morimoto N. and Lu
10
A. 1996, “Techniques for Data Hiding”,
11
IBM Systems Journal, Vol. 35, No. 3&4,
12
pp. 313- 335.
13
[4]J. T. Brassil, et al., ”Electronic Marking and
14
Identification Techniques to Discourage
15
Document Copying”, IEEE Journal on
16
Selected Areas in Communications, Vol.13,
17
No.8, Oct 1995, pp.1495-1504.
18
[5]C. Cachin, “An Information-Theoretic
19
Model for Steganography”, Proceedings of
20
Workshop on Information Hiding, MIT
21
Laboratory for Computer Science, May
22
[6]David Kahn, ”Codebreakers : Story of
23
Secret Writting”, Macmillan 1967.
24
[7]David Kahn, ”The History of
25
Steganography”, Proc. of First Int.
26
Workshop on Information Hiding,
27
Cambridge, UK, May30-June1 1996,
28
Lecture notes in Computer Science,
29
Vol.1174, Ross Anderson(Ed.), pp.1-7.
30
[8]F.A.P.Petitcolas, et al., ”Information Hiding
31
- A Survey”, Proceedings of the IEEE,
32
Vol.87, No.7, July 1999, pp.1062-1078
33
[9]R. B. Wolfgang and E. J. Delp, "A
34
watermark for digital images," Proceedings
35
of the IEEE International Conference on
36
Image Processing, Lausanne, Switzerland,
37
Sept. 16-19, 1996, vol. 3, pp. 219-222.
38
[10]Neha Singh and Arnab Nandi , Digital
39
Watermarking: mark this technology,
40
http://www.electronicsforu.com/efylinux/ef
41
yhome/cover/watermar.pdf.
42
[11]Navas. K A, Sreevidya S, Sasikumar M “A
43
benchmark for medical image
44
watermarking”, 14th International workshop
45
on systems, signals & image processing and
46
6th EURASIP Conference focused on
47
speech & image Processing, Multimedia
48
Communication and services IWSSIP-2007
49
& EC-SIPMCS-2007, Maribor, Slovenia,
50
27-30 June 2007, pp 249-252.
51
[12]W.Zhu, et al, ”Multi-resolution
52
Watermarking for Images and Video”,
53
IEEE Tran. on Circuits & Systems for
54
Video Technology, Vol.9, No.4, June 1999,
55
pp.545-550.
56
[13]C. Shoemaker, Hidden Bits: A Survey of
57
Techniques for Digital Watermarking,
58
http://www.vu.union.edu/~shoemakc/water
59
marking/watermarking.html#watermarkobject,
60
Virtual Union, 2002
61
[14]N.F. Johnson, S.C. Katezenbeisser, S.C.
62
Katzenbeisser et al., Eds. Northwood, “A
63
Survey of Steganographic Techniques” in
64
Information Techniques for Steganography
65
and Digital Watermarking, MA: Artec
66
House, Dec. 1999, pp 43-75.
67
[15]Peter Meerwald and Andreas and Jakob-
68
Haringer-Str. , Uhl,A survey of Waveletdomain
69
Watermarking Algorithms,
70
Department of Scientific Computing
71
,University of Salzburg, Jakob-Haringer-
72
Str. A-5020 Salzburg, Austria
73
ORIGINAL_ARTICLE
Load Frequency Control in Power Systems Using Improved Particle Swarm Optimization Algorithm
The purpose of load frequency control is to reduce transient oscillation frequencies than its nominal valueand achieve zero steady-state error for it.A common technique used in real applications is to use theproportional integral controller (PI). But this controller has a longer settling time and a lot of Extramutation in output response of system so it required that the parameters be adjusted as appropriate . In thispaper, we aim to design a system based on PI controllers using improved particle swarm optimizationalgorithm for load frequency control .Multi-population approach and local search to improve theoptimization algorithms is used and displayed. That this approach will lead to accelerating the achievementof results, preventing entrapment in a local minimum, and get better system output compared with similarmethods.
http://jaiee.iau-ahar.ac.ir/article_514342_155614c2d05b4e7670a6b41e428bc2af.pdf
2014-06-01T11:23:20
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18
26
Load Frequency Control
proportional integral control
improved particle swarm optimization algorithm
Milad
Babakhani Qazijahan
babakhani.milad@yahoo.com
true
1
AUTHOR
ORIGINAL_ARTICLE
FPGA Can be Implemented Using Advanced Encryption Standard Algorithm
This paper mainly focused on implementation of AES encryption and decryption standard AES-128. All the transformations of both Encryption and Decryption are simulated using an iterativedesign approach in order to minimize the hardware consumption. This method can make it avery low-complex architecture, especially in saving the hardware resource in implementing theAES InverseSub Bytes module and Inverse Mix columns module. As the S -box is implemented bylook-up-table in this design, the chip area and power can still be optimized. The new MixColumn transformation improves the performance of the inverse cipher and also reduces thecomplexity of the system that supports the inverse cipher. As a result this transformation hasrelatively low relevant diffusion power .This allows for scaling of the architecture towardsvulnerable portable and cost-sensitive communications devices in consumer and militaryapplications.
http://jaiee.iau-ahar.ac.ir/article_514343_c37ddc0109fe0c64241e17e8ee528118.pdf
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27
33
AES
Encryption
decryption
FPGA
Shahin
Shafei
shahin_shafei@yahoo.com
true
1
AUTHOR
1]Daemen J., and Rijmen V, "The Design of
1
Rijndael: AES-the Advanced Encryption
2
Standard", Springer-Verlag , 2002
3
[2]FIPS 197, “Advanced Encryption Standard
4
(AES)”, November 26, 2001.
5
[3]Tessier, R., and Burleson, W.,
6
“Reconfigurable computing for digital signal
7
processing: a survey”, J.VLSI Signal Process,
8
2001, 28, (1-2), pp.7-27.
9
[4]Ahmad, N.; Hasan, R.; Jubadi, W.M;
10
“Design of AES S-Box using combinational
11
logic optimization”, IEEE Symposium on
12
Industrial Electronics & Applications
13
(ISIEA), pp. 696-699, 2010.
14
ORIGINAL_ARTICLE
Sliding-Mode Control of the DC-DC Ćuk Converter in Discontinuous Conduction Mode
In this paper, a novel approach for two-loop control of the DC-DC Ćuk converter in discontinuousconduction mode is presented using a sliding mode controller. The proposed controller can regulatethe output of the converter in a wide range of input voltage and load resistance. Controllerparameters are selected using PSO algorithms. In order to verify the accuracy and efficiency of thedeveloped sliding mode controller, the proposed method is simulated in MATLAB/Simulink. It isshown that the developed controller has, the faster dynamic response compared with standardintegrated circuit (MIC38C42-5) based regulators.
http://jaiee.iau-ahar.ac.ir/article_514344_73411511f50e0977183ad1f325a404bc.pdf
2014-06-01T11:23:20
2018-06-22T11:23:20
34
45
sliding mode controller
steady-state error
a double-loop controller
inductor current
sampling
Particle Swarm Optimization (PSO) and discontinuous conduction mode
[1] C. S. and M. R.D., "A new optimum
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topology switching DC-to-DC converter,"
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presented at the IEEE Power Electronics
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Specialists Conference, California, 1977.
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[2] H. El Fadil and F. Giri, "Robust nonlinear
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adaptive control of multiphase
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synchronous buck power converters,"
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Control Engineering Practice, vol. 17, pp.
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1245-1254, 11// 2009.
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[3] M. Salimi, J. Soltani, G. A. Markadeh, and
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N. R. Abjadi, "Adaptive nonlinear control
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of the DC-DC buck converters operating in
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CCM and DCM," International
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Transactions on Electrical Energy
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Systems, vol. 23, pp. 1536–1547, Nov
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2013 2013.
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[4] R. Leyva, A. Cid-Pastor, C. Alonso, I.
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Queinnec, S. Tarbouriech, and L.
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Martinez-Salamero, "Passivity-based
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integral control of a boost converter for
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large-signal stability," Control Theory and
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Applications, IEE Proceedings, vol. 153,
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pp. 139-146, 2006.
23
[5] J. Liu, W. Ming, and F. Gao, "A new
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control strategy for improving performance
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of boost DC/DC converter based on inputoutput
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feedback linearization," in
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Intelligent Control and Automation
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(WCICA), 2010 8th World Congress on,
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2010, pp. 2439-2444.
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[6] M. J. Jafarian and J. Nazarzadeh, "Timeoptimal
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sliding-mode control for multiquadrant
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buck converters," Power
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Electronics, IET, vol. 4, pp. 143-150,
34
[7] S. C. Tan, Y. M. Lai, M. K. H. Cheung,
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and C. K. Tse, "On the practical design of
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a sliding mode voltage controlled buck
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converter," Power Electronics, IEEE
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Transactions on, vol. 20, pp. 425-437, Mar
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2005 2005.
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of Power Converters: California Institute
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of Technology., 1986.
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[9] L. Martinez-Salamero, A. Cid-Pastor, R.
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Giral, J. Calvente, and V. Utkin, "Why is
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sliding mode control methodology needed
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for power converters?," in Power
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Electronics and Motion Control
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Conference (EPE/PEMC), 2010 14th
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International, 2010, pp. S9-25-S9-31.
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[10] Z. Chen, "Double loop control of buckboost
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converters for wide range of load
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resistance and reference voltage," Control
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Theory & Applications, IET, vol. 6, pp.
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900-910, 2012.
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[11] T. Siew-Chong, Y. M. Lai, C. K. Tse, and
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M. K. H. Cheung, "Adaptive feedforward
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and feedback control schemes for sliding
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mode controlled power converters," Power
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Electronics, IEEE Transactions on, vol. 21,
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pp. 182-192, 2006.
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[12] H. J. Sira-Ramirez and M. Ilic, "A
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geometric approach to the feedback control
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of switch mode DC-to-DC power
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supplies," Circuits and Systems, IEEE
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Transactions on, vol. 35, pp. 1291-1298,
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[13] T. Siew-Chong, Y. M. Lai, and C. K. Tse,
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"Indirect Sliding Mode Control of Power
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Converters Via Double Integral Sliding
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Surface," Power Electronics, IEEE
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Transactions on, vol. 23, pp. 600-611,
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[14] S. C. Tan, Y. M. Lai, C. K. Tse, L.
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Martinez-Salamero, and W. Chi-Kin, "A
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Fast-Response Sliding-Mode Controller
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for Boost-Type Converters With a Wide
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Range of Operating Conditions," Industrial
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Electronics, IEEE Transactions on, vol. 54,
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pp. 3276-3286, Dec 2007 2007.
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[15] S. C. Tan, Y. M. Lai, C. K. Tse, and L.
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Martinez-Salamero, "Special family of
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PWM-based sliding-mode voltage
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controllers for basic DC-DC converters in
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discontinuous conduction mode," Electric
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Power Applications, IET, vol. 1, pp. 64-74,
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Jan 2007 2007.
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reduced-state sliding mode
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current controller for Cuk converters,"
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Power Electronics, IET, vol. 1, pp. 466-
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477, 2008.
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of a Cuk Converter," Power Electronics,
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[18] M. Veerachary, "Two-loop voltage-mode
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control of coupled inductor step-down
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pp. 1516-1524, 2005.
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[19] M. Seker and E. Zergeroglu, "A new
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sliding mode controller for the DC to DC
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and Engineering (CASE), 2011 IEEE
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swarm optimization," 1995, pp. 1942-
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[21] H. Erdem and O. T. Altinoz,
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frequency sliding mode controller for buck
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Systems and Applications (INISTA), 2011
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117
ORIGINAL_ARTICLE
Efficient Short-Term Electricity Load Forecasting Using Recurrent Neural Networks
Short term load forecasting (STLF) plays an important role in the economic and reliable operation ofpower systems. Electric load demand has a complex profile with many multivariable and nonlineardependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. Theproposed model is capable of forecasting next 24-hour load profile. The main feature in this networkis internal feedback to highlight the effect of past load data for efficient load forecasting results.Testing results on the three year demand profile shows higher performance with respect to commonfeed forward back propagation architecture.
http://jaiee.iau-ahar.ac.ir/article_514345_2bccca8b82b63e8aeb22051dd5ecd26f.pdf
2014-06-01T11:23:20
2018-06-22T11:23:20
46
53
Short term load forecasting (STLF)
Recurrent neural network (RNN)
hourly load
forecast
Load Data normalization
[1] Eugene A. Feinberg and Dora Genethliou,
1
"Chapter 12 Load Forecasting" Weather
2
(2006), Issue: August, Publisher:
3
Springer, pp. 269-285
4
[2] Milos Bozic, Milos Stojanovic and Zoran
5
Stajic, "Short-term electric load
6
forecasting using least square support
7
vector machines" Facta Universitatis,
8
Series: Automatic Control and Robotics
9
Vol. 9, No 1, pp. 141-150,2010
10
[3] R. C. Garcia, et al., “GARCH Forecasting
11
Model to Pre-dict Day-ahead Electricity
12
Prices,” IEEE Transactions on Power
13
Systems, Vol. 20, No. 2, May 2005, pp.
14
doi:10.1109/TPWRS.2005.846044
15
[4] M. Stevenson, “Filtering and Forecasting
16
Spot Electricity Prices in the Increasingly
17
Deregulated Australian Elec-tricity
18
Market,” Quantitative Finance Research
19
Centre, University of Technology,
20
Sydney, 2001.
21
[5] N. Hubele, et al., “Identification of
22
Seasonal Short-term Load Forecasting
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Models Using Statistical Decision
24
Functions,” IEEE Transactions on Power
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Systems, Vol. 5, No. 1, 1990, pp. 40-5.
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doi:10.1109/59.49084
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[6] M. El-Hawary, et al, “Short-Term Power
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System Load Forecasting Using the
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Iteratively Reweighted Least Squares
30
Algorithm,” Electrical Power Systems
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Research, Vol. 19, 1990, pp. 11-22.
32
doi:10.1016/0378-7796(90)900 03-L
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[7] V. S. Kodogiannis and E. M.
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Anagnostakis, “A Study of Advanced
35
Learning Algorithms for Short-term Load
36
Fo-recasting,” Engineering Applications
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of Artificial Intelli-gence , Vol. 12, 1999,
38
pp. 159-173. doi:10.1016/S0952-
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1976(98)00064-5
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[8] G.-C. Liao and T.-P. Tsao, “Application
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of Fuzzy Neural Networks and Artificial
42
Intelligence for Short-term load
43
Forecasting,” Electrical Power Systems
44
Research, Vol. 70, 2004, pp. 237-244.
45
doi:10.1016/j.epsr. 2003.12.012
46
[9] H. Yamin, M. Shahidehpour and Z. Li,
47
“Adaptive short-term Price Forecasting
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using artificial Neural Net-works in the
49
Restructured Power Markets,” Electrical
50
Power and Energy Systems, Vol. 26,
51
2004, pp. 571-581.
52
doi:10.1016/j.ijepes.2004.04.005
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[10] A. K. Topalli, I. Erkmen and I. Topalli,
54
“Intelligent Short-term Load Forecasting
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in Turkey,” Electrical Pow-er and Energy
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Systems, Vol. 28, 2006, pp. 437-447. doi:
57
10.1016/j.ijepes.2006.02.004
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[11] R.C.Bansal, “Overview and Literature
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Survey of Artificial Neural Networks
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Applications to Power Systems (1992-
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2004)”,IE Journal, Vol86, March, 2006.
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[12] M.Tarafdar Haque, A.M.Kashtiban,
63
“Application of Neural Networks in
64
Power systems: A Review”, Proceedings
65
of world Academy of Science and
66
Technology, Vol 6, June 2006
67
[13] Zbigniew Gontarand Nikos
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Hatziargyriou, “Short Term load
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forecasting using Radial basis neural
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network”, SM, IEEE2001 IEEE Porto
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Power Tech Conference, September,
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Porto, Portugal.
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networks and back propagation", Report
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from NUTEK-supported project AIS-8:
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