ORIGINAL_ARTICLE
Corona Ring Optimization for Different Cases of Polymer Insulators Based on its Size and Distance
This paper describes the impact of the distance and the size of the corona ring, on the magnitude anddistribution of electrical potential across the polymer insulators. The procedure is based on finiteelement method numerical analysis and stochastic optimization algorithm of differential evolution(DE). The optimal selection of corona ring has a significant impact on the control of potential and,consequently, on dielectric strengths, both inside and outside the insulator. This method is used foroptimized determining of the size and the distance of corona ring from polymer insulators in existingstructures.
http://jaiee.iau-ahar.ac.ir/article_513218_595a863426ba979ec78337adb9c260e4.pdf
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1
7
Corona
electrical field
FEM
Optimization
polymer
Size
B.Marungsri, H.Shinokubo, R.Matsuoka and
1
S.Kumagai, "Effect of Specimen Configuration
2
on Deterioration of Silicone Rubber for
3
Polymer Insulators in Salt Fog Ageing Test",
4
IEEE Transactions on Dielectrics and
5
Electrical Insulation, vol. 13, no. 1, February
6
[2] A. J. Phillips, J. Burnham, W. Chisholm, A.
7
Gillespie and T. Saha, "Electric Fields on AC
8
Composite Transmission Line Insulators",
9
IEEE Transactions on Power Delivery, vol. 23,
10
no. 2, April 2008.
11
[3] K. Kato, X. Han and H. Okubo, "Insulation
12
Optimization by Electrode Contour
13
Modification Based on Breakdown
14
Area/Volume Effects", IEEE Transactions on
15
Dielectrics and Electrical Insulation, vol. 8, no.
16
2, April 2001.
17
[4] H. Mei, G. Peng, H. Dai, L. Wang, Z. Guan,
18
and L. Cao "Installing Insulation Jacket to
19
Improve Outdoor Insulation Performance of
20
Composite Insulator", IEEE Transactions on
21
Dielectrics and Electrical Insulation, vol. 18,
22
no. 6, December 2011.
23
[5] P. Kitak, J. Pihler, I. Ti˘car, A. Stermecki, O.
24
Bíró, and K. Preis, "Potential Control Inside
25
Switch Device Using FEM and Stochastic
26
Optimization Algorithm", IEEE Transactions
27
ORIGINAL_ARTICLE
Multi-objective Based Optimization Using Tap Setting Transformer, DG and Capacitor Placement in Distribution Networks
In this article, a multi-objective function for placement of Distributed Generation (DG) and capacitors with thetap setting of Under Load Tap Changer (ULTC) Transformer is introduced. Most of the recent articles have paidless attention to DG, capacitor placement and ULTC effects in the distribution network simultaneously. Insimulations, a comparison between different modes was carried out with, and without tap setting of ULTC.Simultaneous DG, capacitor placement, and ULTC transformer tap setting improve the voltage profile of loadbuses globally. In addition, they can also reduce loss and increase Available Transfer Capability (ATC). TheIEEE 41-bus radial distribution network is used to illustrate the effectiveness and feasibility of the proposedapproach.
http://jaiee.iau-ahar.ac.ir/article_513219_69320ae8d5f6f433e87621008aef29a8.pdf
2012-09-01T11:23:20
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DG placement
Capacitor Placement
ULTC Transformer
Loss reduction
Voltage profile
available transfer capability
multi-objective function
[1] M. Ladjavardi and M. A. S. Masoum
1
“Genetically Optimized Fuzzy Placement and
2
Sizing of Capacitor Banks in Distorted
3
Distribution Networks,” IEEE Transactions on
4
Power Delivery, Vol. 23, No. 1, Jan 2008.
5
[2] Ch. Chang “Reconfiguration and Capacitor
6
Placement for Loss Reduction of Distribution
7
Systems by Ant Colony Search Algorithm,”
8
IEEE Transactions on Power Systems, Vol. 23,
9
No. 4, Nov 2008.
10
[3] I. C. Silva, S. Carneiro, E. J. Oliveira, J. S.
11
Costa, J. L. Pereira and P. A. Garcia “A
12
Heuristic Constructive Algorithm for Capacitor
13
Placement on Distribution Systems,” IEEE
14
Transactions on Power Systems, Vol. 23, No.
15
4, Nov 2008.
16
[4] H. Hedayati, S. A. Nabaviniaki and A.
17
Akbarimajd “A Method for Placement of DG
18
Units in Distribution Networks,” IEEE
19
Transactions on Power Delivery, Vol. 23, No.
20
3, Jul 2008.
21
[5] M. Moeini-Aghtaie, P. Dehghanian and S. H.
22
Hosseini “Optimal Distributed Generation
23
Placement in a Restructured Environment via a
24
Multi-Objective Optimization Approach,” 16th
25
Conf. on Electrical Power Distribution
26
Networks (EPDC), Bandar abbas, Iran, 19-20
27
April 2011.
28
[6] O. Aliman1, I. Musirin, M. M. Othman and M.
29
H. Sulaiman,” 5th International Power
30
Engineering and Optimization Conference
31
(PEOCO), Shah Alam, Selangor, Malaysia, 6-7
32
[7] A. K. Singh and S. K. Parida “Selection of
33
Load Buses for DG placement Based on Loss
34
Reduction and Voltage Improvement
35
Sensitivity,” IEEE International Conf. on
36
Power Eng., Energy and Electric Drives, p.p.
37
1-6 May 2011.
38
[8] C. Wang and M. H. Nehrir “Analytical
39
Approaches for Optimal Placement of
40
Distributed Generation Sources in Power
41
Systems” IEEE Transactions on Power
42
[9] M. Kalantari and A. Kazemi “Placement of
43
Distributed Generation unit and Capacitor
44
Allocation in Distribution Systems using
45
Genetic Algorithm,” 10th International Conf.
46
on Environment and Electrical Eng., EEEIC
47
p.p. 1-5, 2011.
48
[10] M. Wang, and J. Zhong “A Novel Method for
49
Distributed Generation and Capacitor Optimal
50
Placement considering Voltage Profiles,” IEEE
51
power and Energy Society General Meeting,
52
p.p. 1-6 Jul 2011.
53
[11] M. Tarafdarhagh, A. Sadighmanesh, and M. R.
54
Hesamzadeh “Improvement of Load Bus
55
Voltages Considering the Optimal Dispatch of
56
Active and Reactive Powers,” 43rd
57
International Universities Power Engineering
58
Conf. (UPEC), Padova, Italy, (on CD) 1-4 Sep
59
ORIGINAL_ARTICLE
A Novel Reference Current Calculation Method for Shunt Active Power Filters using a Recursive Algebraic Approach
This paper presents a novel method to calculate the reference source current and the referencecompensating current for shunt active power filters (SAPFs). This method first calculates theamplitude and phase of the fundamental load current from a recursive algebraic approach blockbefore calculating the displacement power factor. Next, the amplitude of the reference mains currentis computed with the corresponding phase voltage. Finally, the difference between the actual loadcurrent and the reference source current is considered the reference compensating current to bedelivered by the SAPF. The proposed method is presented and applied to the control system of thevoltage source converter of SAPFs. The performance of the proposed method in reducing harmonicsand improving the power factor is examined with a SAPF simulation model. The results are comparedwith the instantaneous active and reactive p-q power theory as other reference generation method.
http://jaiee.iau-ahar.ac.ir/article_513220_1143c3828bc944ee775de918db55fdcf.pdf
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Power Quality
p-q theory
recursive algebraic approach
reference source current
shunt active power filter
[1] A. Bhattacharya, C. Chakraborty, “A shunt
1
active power filter with enhanced performance
2
using ANN-based predictive and adaptive
3
controllers,” IEEE Trans. Ind. Electron., vol.
4
58, no. 2, pp. 421–428,Feb. 2011.
5
[2] S. Rahmani, N. Mendalek, and K. Al-Haddad,
6
“Experimental design of a nonlinear control
7
technique for three-phase shunt active power
8
filter,”IEEE Trans. Ind. Electron., vol. 57, no.
9
10, pp. 3364–3375, Oct. 2010.
10
[3] S.H. Fathi, M. Pishvaei, and G.B.
11
Gharehpetian, “A frequency domain method
12
for instantaneous determination of reference
13
current in shunt active filter,” TENCON, IEEE
14
Region 10 Conference,1-4, 2006.
15
[4] Z. Salam, P. C. Tan, and A. Jusoh,
16
“Harmonics mitigation using active power
17
filter: A technological review,” Elektrika
18
Journal of Electrical Engineering, 8: 17-26,
19
[5] T. Komrska, J. Zák, and Z. Peroutka,“Control
20
strategy of active power filter with adaptive
21
FIR filter-based and DFT-based reference
22
estimation,” Power Electronics Electrical
23
Drives Automation and Motion (SPEEDAM),
24
2010 International Symposium on, Page(s):
25
1524 – 1529, 2010.
26
[6] G. Chen, Y. Jiang, and H. Zhou, “Practical
27
Issues of Recursive DFT in Active Power Filter
28
Based on CPC Power Theory, “Power and
29
Energy Engineering Conference, APPEEC
30
2009. Asia-Pacific, Page(s): 1 – 5, 2009.
31
[7] H. Akagi, Yoshihira Kanazawa, and Akira
32
Nabae,“Instantaneous Reactive Power
33
Compensators Comprising Switching Devices
34
Without Energy Storage Components, ” IEEE
35
Transactions On Industry Applications, Vol.
36
IA20, No.3, May/June1998.
37
[8] M.A Kabir, U. Mahbub,“ Synchronous
38
Detection and Digital control of Shunt Active
39
Power Filter in Power Quality Improvement,”
40
IEEE Power and Energy Conference at Illinois
41
(IEEE PECI), University of Illinois at Urbana-
42
Champaign, USA, 2011.
43
[9] A. Khoshkbar Sadigh, M. Farasat, S.M.
44
Barakati,“Active power filter with new
45
compensation principle based on synchronous
46
reference frame,” North America Power
47
Symposium (NAPS),DOI:
48
10.1109/NAPS.2009.5484077,2009.
49
[10] B.S. Kumar, K.R. Reddy, V. Lalitha,“PI, fuzzy
50
logic controlled shunt active power filter for
51
three-phase four-wire systems with balanced,
52
unbalanced and variable loads,” Journal of
53
Theoretical and Applied Information
54
Technology 23 (2), pp. 122-130 0 ,2011.
55
[11] A. Peiravi, R. Ildarabadi ,“Recursive algebraic
56
method of computing power system
57
harmonics,” IEEJ Transactions on Electrical
58
and Electronic Engineering Volume 6, Issue
59
4, pages 338–344, July 2011.
60
[12] B. Berbaoui , C.Benachaiba,“Power Quality
61
Enhancement using Shunt Active Power Filter
62
Based on Particle Swarm
63
Optimization,” Journal of Applied Sciences,
64
11: 3725-3731, 2011.
65
[13] H.Akagi, Y.Kanazawa and
66
N.Nabae,”Generalized theory of the
67
instantaneous reactive power in three-phase
68
circuits”, in Proc. Int. Power El. Conf., pp
69
1375-1386, Tokyo, Japan, 1983
70
[14] G. Bhuvaneswari, M.G. Nair, “Design,
71
Simulation, and Analog Circuit
72
Implementation of a Three-Phase Shunt Active
73
Filter Using the I cos Algorithm,” Power
74
Delivery, IEEE Transactions on, Volume23,
75
Issue: 2, Page(s): 1222 – 1235, 2008.
76
[15] P. Karuppanan , K. K. Mahapatra , “PLL with
77
PI, PID and Fuzzy Logic Controllers based
78
Shunt Active Power Line Conditioners,”
79
IEEE International Conference on Power
80
Electronics, Drives and Energy Systems-Dec
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21 o 23, 2010.
82
[16] L. A. Zadeh, “The concept of a linguistic
83
variable and its application to approximate
84
reasoning-1,” Inf. Sci., vol. 8, pp. 199-249,
85
[17] J.M. Mendel, Uncertain Rule-Based Fuzzy
86
Logic: Introduction and new directions,
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Prentice Hall, USA, (2000).
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[18] J.M. Mendel, R.I. John and F. Liu, “ Interval
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type-2 fuzzy logic systems made simple”,
90
IEEE Trans. Fuzzy Syst., 14: 808-821. 2006.
91
[19] P . Karuppanan , K. K. Mahapatra , “PI and
92
fuzzy logic controllers for shunt active power
93
filter — A report,” ISA Transactions vol. 51
94
ORIGINAL_ARTICLE
Improved Binary Particle Swarm Optimization Based TNEP Considering Network Losses, Voltage Level, and Uncertainty in Demand
Transmission network expansion planning (TNEP) is an important component of power system planning. Itdetermines the characteristics and performance of the future electric power network and influences the powersystem operation directly. Different methods have been proposed for the solution of the static transmissionnetwork expansion planning (STNEP) problem till now. But in all of them, STNEP problem considering thenetwork losses, voltage level and uncertainty in demand has not been solved by improved binary particle swarmoptimization (IBPSO) algorithm. Binary particle swarm optimization (BPSO) is a new population-basedintelligence algorithm and exhibits good performance on the solution of the large-scale and nonlinearoptimization problems. However, it has been observed that standard BPSO algorithm has prematureconvergence when solving a complex optimization problem like STNEP. To resolve this problem, in this study,an IBPSO approach is proposed for the solution of the STNEP problem considering network losses, voltagelevel, and uncertainty in demand. The proposed algorithm has been tested on a real transmission network of theAzerbaijan regional electric company and compared with BPSO. The simulation results show that consideringthe losses even for transmission expansion planning of a network with low load growth is caused thatoperational costs decreases considerably and the network satisfies the requirement of delivering electric powermore reliable to load centers. In addition, regarding the convergence curves of the two methods, it can be seenthat precision of the proposed algorithm for the solution of the STNEP problem is more than BPSO.
http://jaiee.iau-ahar.ac.ir/article_513221_6d3ec2cb0fe88322c83b67f33cd3a6f9.pdf
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42
STNEP
network losses
voltage level
uncertainty in demand
IBPSO
[1] AR Abdelaziz, “Genetic algorithm-based
1
power transmission expansion planning,” 7th
2
IEEE Int Conf Electron Circuits and Syst,
3
Lebanon, vol. 78, pp. 642-645, 2000.
4
[2] VA Levi and MS Calovic, “Linearprogramming-
5
based decomposition method for
6
optimal planning of transmission network
7
investments,” IEE Proc Gener Transm Distrib,
8
vol. 140, pp. 516-522, 1993.
9
[3] J Choi, TR Mount, “Thomas Transmission
10
system expansion plans in view point of
11
deterministic, probabilistic and security
12
reliability criteria,” The 39th Hawaii Int Conf
13
Syst Sci, vol. 10, pp.1-10, 2006.
14
[4] IDJ Silva, MJ Rider, R Romero, CA Murari
15
“Transmission network expansion planning
16
considering uncertainness in demand,” IEEE
17
Power Eng Soc Gen Meet, vol. 2, pp. 1424-
18
1429, 2005.
19
[5] S Binato, MVF Periera, S Granville, “A new
20
Benders decomposition approach to solve
21
power transmission network design Problems,”
22
IEEE Trans Power Syst, vol. 16, pp. 235-240,
23
[6] LL Garver, “Transmission network estimation
24
using linear programming,” IEEE Trans Power
25
Appar Syst, vol. PAS-89, pp.1688-1696, 1970.
26
[7] IDJ Silva, MJ Rider, R Romero, CA Murari,
27
“Transmission network expansion planning
28
considering uncertainness in demand,” IEEE
29
Power Eng Soc Gen Meet, vol. 2, pp. 1424-
30
1429, 2005.
31
[8] P Maghouli, SH Hosseini, MO Buygi, M
32
Shahidehpour, “A scenario-based multiobjective
33
model for multi-stage transmission
34
expansion planning,” IEEE Trans Power Syst,
35
vol. 26, pp. 470-478, 2011.
36
[9] AML Silva, LS Rezende, LAF Manso, LC
37
Resende, “Reliability worth applied to
38
transmission expansion planning based on ant
39
colony system,” Int J Electr Power and Energy
40
Syst, vol. 32, pp. 1077-10841, 2010 .
41
[10] NH Sohtaoglu, “The effect of economic
42
parameters on power transmission planning,”
43
9th Mediterr Electrotech Conf, vol. 2, pp. 941-
44
945, 1998.
45
[11] B Graeber, “Generation and transmission
46
expansion planning in southern Africa,” 1999
47
IEEE Africon, vol. 14, pp. 983-988, 1999.
48
[12] MS Kandil, SM El-Debeiky, NE Hasanien,
49
“Rule-based system for determining unit
50
locations of a developed generation expansion
51
plan for transmission planning,” IEE Proc
52
Gener Transm Distrib, vol. 147, pp. 62-68,
53
[13] RS Chanda, PK Bhattacharjee, “A reliability
54
approach to transmission expansion planning
55
using minimal cut theory,” Electr Power Syst
56
Res, vol. 33, pp. 111-117, 1995.
57
[14] RS Chanda, PK Bhattacharjee, “A reliability
58
approach to transmission expansion planning
59
using fuzzy fault-tree model,” Electr Power
60
Syst Res, vol. 45, pp. 101-108, 1998.
61
[15] S Granville, MVF Pereira, GB Dantzig, B Avi-
62
Itzhak, M Avriel, A Monticelli, LMVG Pinto,
63
“Mathematical decomposition techniques for
64
power system expansion planning-analysis of
65
the linearized power flow model using the
66
Benders decomposition technique,” EPRI,
67
Technical Report, RP, pp. 2473-6, 1988.
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[16] R Romero, A Monticelli, “A hierarchical
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decomposition approach for transmission
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network expansion planning,” IEEE Trans
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Power Syst, vol. 9, pp. 373-380, 1994.
72
[17] S Binato, GC de Oliveira, Araujo JL, “A
73
greedy randomized adaptive search procedure
74
for transmission expansion planning,” IEEE
75
Trans Power Syst, vol. 16, pp. 247-253, 2001.
76
[18] STY Lee, KL Hocks, H Hnyilicza,
77
“Transmission expansion by branch and bound
78
integer programming with optimal cost
79
capacity curves,” IEEE Trans Power Appar
80
Syst, vol. PAS-93, pp. 1390-1400, 1974.
81
[19] MVF Periera, LMVG Pinto, “Application of
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sensitivity analysis of load supplying capability
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to interactive transmission expansion
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PAS-104, pp. 381 -389, 1985.
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[20] R Romero, RA Gallego, A Monticelli,
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“Transmission system expansion planning by
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simulated annealing,” IEEE Trans Power Syst,
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vol. 11, pp. 364-369, 1996.
90
[21] RA Gallego, AB Alves, A Monticelli, R
91
Romero, “Parallel simulated annealing applied
92
to long term transmission network expansion
93
planning,” IEEE Trans Power Syst, vol. 12, pp.
94
181-188, 1997.
95
[22] T Al-Saba, I El-Amin, “The application of
96
artificial intelligent tools to the transmission
97
expansion problem,” Electr Power Syst Res,
98
vol. 62, pp. 117-126, 2002.
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[23] J Contreras, FF Wu, “A kernel-oriented
100
algorithm for transmission expansion
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planning,” IEEE Trans Power Syst, vol. 15, pp.
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1434-1440, 2000.
103
[24] ASD Braga, JT Saraiva, “A multiyear dynamic
104
approach for transmission expansion planning
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and long-term marginal costs computation,”
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IEEE Trans Power Syst, vol. 20, pp. 1631-
107
1639, 2005.
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[25] EL Silva, HA Gil, JM Areiza, “Transmission
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network expansion planning under an
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improved genetic algorithm,” IEEE Trans
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Power Syst, vol. 15, pp. 1168-1174, 2000.
112
[26] EL Silva, JMA Oritz, GC Oleveria, S Binato,
113
“Transmission network expansion planning
114
under a Tabu search approach,” IEEE Trans
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Power Syst, vol. 16, pp. 62-68, 2001.
116
[27] S Jalilzadeh, A Kazemi, H Shayeghi, M
117
Mahdavi, “Technical and economic evaluation
118
of voltage level in transmission network
119
expansion planning using GA,” Energy
120
Convers Manag, vol. 49, pp. 1119-1125, 2008.
121
[28] H Shayeghi, S Jalilzadeh, M Mahdavi, H
122
Haddadian, “Studying influence of two
123
effective parameters on network losses in
124
transmission expansion planning using
125
DCGA,” Energy Convers Manag, vol. 49, pp.
126
3017-3024, 2008.
127
[29] H Shayeghi, M Mahdavi, “Studying the effect
128
of losses coefficient on transmission expansion
129
planning using decimal codification based
130
GA,” Int J Tech Phys Probl Eng, vol. 1, pp. 58-
131
[30] H Shayeghi, M Mahdavi, “Genetic algorithm
132
based studying of bundle lines effect on
133
network losses in transmission network
134
expansion planning,” J Electr Eng, vol. 60, pp.
135
237-245, 2009.
136
[31] JH Zhao, J Foster, ZY Dong, KP Wong,
137
“Flexible transmission network planning
138
considering distributed generation impacts,”
139
IEEE Trans Power Syst, vol. 26, pp. 1434-
140
1443, 2011.
141
[32] M Mahdavi, H Shayeghi, A Kazemi, “DCGA
142
based evaluating role of bundle lines in TNEP
143
considering expansion of substations from
144
voltage level point of view,” Energy Convers
145
Manag, vol. 50, pp. 2067-2073, 2009.
146
[33] H Shayeghi, M Mahdavi, A Kazemi, HA
147
Shayanfar, “Studying effect of bundle lines on
148
TNEP considering network losses using
149
decimal codification genetic algorithm,”
150
Energy Convers Manag, vol. 51, pp. 2685-
151
2691, 2010.
152
[34] H Shayeghi, M Mahdavi, HA Shayanfar, A
153
Bagheri, “Application of binary particle swarm
154
optimization for transmission expansion
155
planning considering lines loading,” In
156
proceedings of the 2009 Int Conf Artif Intell,
157
USA, pp. 653-659, 2009.
158
[35] H Shayeghi, A Jalili, HA Shayanfar, “Multistage
159
fuzzy load frequency control using PSO,”
160
Energy Convers Manag, vol. 49, pp. 2570-
161
2580, 2008.
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stability, and convergence in a
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multidimensional complex space,” IEEE Trans
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Evol Comput, vol. 6, pp. 58-73, 2002.
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[37] N Jin, YR Samii, “Advances in particle swarm
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optimization for antenna designs: real-number,
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binary, single-objective and multiobjective
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implementations,” IEEE Trans Antennas
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Propag, vol. 55, pp. 556-567, 2007.
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[38] AAA Esmin, GL Torres, ACZ de Souza, “A
172
hybrid particle swarm optimization applied to
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loss power minimization,” IEEE Trans Power
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175
ORIGINAL_ARTICLE
Optimal Location and Parameter Settings of UPFC Device in Transmission System based on Imperialistic Competitive Algorithm
In this paper, we present a new method to determine the optimal location and parameter settings of UnifiedPower Flow Controller (UPFC) for removing voltage violations and transmission lines overloading. UPFC isconsidered as the most powerful member of the FACTS devices, that it can control shunt and series power flow.This option gives to UPFC the power to control the voltage profile and transmission lines flow simultaneously.We used the Imperialistic Competitive Algorithm (ICA) to determine the optimal location and optimal parametersettings of UPFC to improve the performance of the power system specially removing voltage violations in thebuses and solving transmission lines overloading to increase loadability in the power networks. This procedureis proposed to be applied on IEEE 14 bus system to show the validity of the method.
http://jaiee.iau-ahar.ac.ir/article_513223_3f53abb15e78ca61e46cc373f3b7b402.pdf
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43
53
Imperialistic Competitive Algorithm (ICA)
Loadability
Optimal Location
Optimal settings
Unified Power Flow Controller (UPFC)
Voltage profile
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utilities: role of power electronics in future
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76 No. 4 Apr. 1988, pp. 481-482.
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[2] L. Gyugyi, "A Unified Power Flow Control concept
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for Flexible AC transmission system", IEEE Proc.,
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7
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power networks", IEEE Trans. Power System, Vol.
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11
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controller for enhancing power system loadability",
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International Conference on Future Power System,
14
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unified power flow controllers through the robust
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load flow calculation", IEE Proc. Gener. Transm.
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Distribution, 1999, 146, pp. 1-5.
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[6] Ramirez, J.M. Davalos, R.J. and Valenzuela
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Coronado, "FACTS based stabilizers coordination",
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22
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number and location of thyristor-controlled phase
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shifter using Genetic based algorithm", IEE Proc.
25
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26
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approach to optimal allocation of FACTS devices
28
for power system security", IEEE power
29
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31
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32
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ORIGINAL_ARTICLE
Neuro-Optimizer: A New Artificial Intelligent Optimization Tool and Its Application for Robot Optimal Controller Design
The main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrectionstrategy supported by a recurrent neural network for finding a near optimal solution of a givenobjective function. Recently there have been attempts for using artificial neural networks (ANNs) in optimizationproblems and some types of ANNs such as Hopfield network and Boltzmann machine have been applied incombinatorial optimization problems. However, ANNs cannot optimize continuous functions and discreteproblems should be mapped into the neural networks architecture. To overcome these shortages, we introduce anew procedure for stochastic optimization by a recurrent artificial neural network. The introduced neurooptimizer(NO) starts with an initial solution and adjusts its weights by a new heuristic and unsupervised rule tocompute the best solution. Therefore, in each iteration, NO generates a new solution to reach the optimal ornear optimal solutions. For comparison and detailed description, the introduced NO is compared to geneticalgorithm and particle swarm optimization methods. Then, the proposed method is used to design the optimalcontroller parameters for a five bar linkage manipulator robot. The important characteristics of NO are:convergence to optimal or near optimal solutions, escaping from local minima, less function evaluation, highconvergence rate and easy to implement.
http://jaiee.iau-ahar.ac.ir/article_513224_d89ea5be29b8a5cc172fb8d213024fb6.pdf
2012-09-01T11:23:20
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69
numerical optimization
Neural networks
Objective function
weight updating
five bar linkage
manipulator robot
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ORIGINAL_ARTICLE
Optimization of Conventional Stabilizers Parameter of Two Machine Power System Linked by SSSC Using CHSA Technique
This paper presents a method for damping of low frequency oscillations (LFO) in a power system. The powersystem contains static synchronous series compensators (SSSC) which using a chaotic harmony searchalgorithm (CHSA), optimizes the lead-lag damping stabilizer. In fact, the main target of this paper isoptimization of selected gains with the time domain-based objective function, which is solved by chaoticharmony search algorithm. The performance of the proposed two-machine power system equipped with SSSC isevaluated under various disturbances and operating conditions and compared to power system stabilizer (PSS).The effectiveness of the proposed SSSC controller to damp out of oscillations, over a wide range of operatingconditions and variation of system parameters is shown in simulation results and analysis.
http://jaiee.iau-ahar.ac.ir/article_513225_5ad2a47be9892bc8d1e18bee845640e2.pdf
2012-09-01T11:23:20
2017-11-21T11:23:20
70
77
power system stability
SSSC
Chaotic Harmony Search
Conventional Stabilizer
Two Machine System
LFO
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