ORIGINAL_ARTICLE
Load Model Effect Assessment on Optimal Distributed Generation Sizing and Allocation Using Improved Harmony Search Algorithm
The operation of a distribution system in the presence of distributed generation systems has someadvantages and challenges. Optimal sizing and siting of DG systems has economic, technical, andenvironmental benefits in distribution systems. Improper selection of DG systems can reduce theseadvantages or even result in deterioration in the normal operation of the distribution system. DGallocation and capacity determination is a nonlinear optimization problem. The objective function ofthis problem is the minimization of the total loss of the distribution system. In this paper, the ImprovedHarmony Search (IHS) algorithm has been applied to the optimization problem. This algorithm has asuitable performance for this type of optimization problem. Active and reactive power demands of thedistribution system loads are dependent on bus voltage. This paper verifies the effect of voltagedependent loads on system power characteristics. The load model has an inevitable impact on DGsizing and placement. The proposed algorithm implemented and tested on 69-bus distribution systemsand the impact of voltage dependent load models are demonstrated. The obtained results show that theproposed algorithm has an acceptable performance.
http://jaiee.iau-ahar.ac.ir/article_513009_2c167c26342ab3b61e1442d27b2ee0a1.pdf
2012-05-21T11:23:20
2018-06-22T11:23:20
1
17
Distributed Generation
improved harmony search
DG sizing and sitting
load model
Hossein
Nasiraghdam
h.nasiraghdam@srbiau.ac.ir
true
1
AUTHOR
Morteza
Nasiraghdam
m-nasiraghdam@iau-ahar.ac.ir
true
2
AUTHOR
[1] Zhu, D., Broadwater, R.P., Tam, KS., Seguin,
1
R. and Asgeirsson, H. “Impact of DG
2
Placement on Reliability and Efficiency with
3
Time-Varying Loads,” IEEE Transaction on
4
Power Systems, vol. 21(1), pp.419-27, 2006.
5
[2] Celli, G., Ghiani, E., Loddo, M. and Pilo, F.
6
“Voltage Profile Optimization with Distributed
7
Generation,” IEEE Russia Power Tech, 2005.
8
[3] Zangiabadi, M., Feuillet, R., Lesani, H., Hadj-
9
Said, N. and Kvaløy, J. “Assessing the
10
performance and benefits of customer
11
distributed generation developers under
12
uncertainties,” Energy, vol. 36, pp.1703-12,
13
[4] Porkar, S., Poure, P., Abbaspour-Tehrani-fard,
14
A. and Saadate, S. “A novel optimal
15
distribution system planning framework
16
implementing distributed generation in a
17
deregulated electricity market,” Electr. Power
18
Syst. Res., vol. 80, pp.828–37, 2010.
19
[5] Zangeneh, A., Jadid, S. and Rahimi-Kian, A.
20
“A fuzzy environmental-technical-economic
21
model for distributed generation planning,”
22
Energy, vol.36, pp.3437-45, 2011.
23
[6] Bayod-Ru´ jula, A.A. “Future development of
24
the electricity systems with distributed
25
generation,” Energy, vol. 34, pp.377-83, 2009.
26
[7] Harrison, G.P. and Wallace, A.R. “Maximizing
27
distributed generation capacity in deregulated
28
markets,” Proceedings of the IEEE
29
Transmission and Distribution Conference and
30
Exposition, vol. 2, pp. 527–530, September,
31
[8] Harrison, G. P., Piccolo, A., Siano, P.and
32
Wallace, A. R. “Hybrid GA and OPF
33
evaluation of network capacity for distributed
34
generation connections,” Electr. Power Syst.
35
Res., vol. 78, pp. 392–98, 2008.
36
[9] Khalesi, N., Rezaei, N. and Haghifam, M. R.
37
“DG allocation with application of dynamic
38
programming for loss reduction and reliability
39
improvement,” Int. J. Elect. Power Energy
40
Syst., vol. 33(2), pp. 288–95, 2011.
41
[10] Wang, C. and Nehrir, MH. “Analytical
42
approaches for optimal placement of
43
distributed generation sources in power
44
systems,” IEEE Transaction on Power
45
Systems, vol. 19(4), pp. 2068–76, 2004.
46
[11] Acharya, N., Mahat, P., and Mithulananthan, N.
47
“An analytical approach for DG allocation in
48
primary distribution network,” Int. J. Electr.
49
Power Energy Syst., vol. 28, pp. 669–78, 2006.
50
[12] Gozel, T., and HakanHoucaoglu, M. “An
51
analytical method for sizing and siting of
52
distributed generators in radial systems,”
53
Electr. Power Syst. Res., vol. 79, pp. 912–8,
54
[13] Elnashar, M. M., ElShatshat, R. and Salama,
55
M. M. A. “Optimum siting and sizing of a large
56
distributed generator in a mesh connected
57
system,” Electr. Power Syst. Res., vol. 80, pp.
58
690–97, 2010.
59
[14] Parizad, A., Khazali, A. and Kalantar, M.
60
“Optimal Placement of Distributed Generation
61
with Sensitivity Factors considering Voltage
62
Stability and Losses Indices,” Proc. Iranian
63
Conference on Electrical Engineering (ICEE),
64
pp.848-5, 2010.
65
[15] Moradi, M. H. and Abedini, M. A.
66
“combination of genetic algorithm and particle
67
swarm optimization for optimal DG location
68
and sizing in distribution systems,” Int. J.
69
Elect.r Power Energy Syst., vol. 34, pp.66–74,
70
[16] AlRashid,i M.R. and AlHajri, M.F. “Optimal
71
planning of multiple distributed generation
72
sources in distribution networks: A new
73
approach,” Energy Conversion and
74
Management, vol. 55, pp.3301–8, 2011.
75
[17] Abu-Mouti, F. S.andEl-Hawary, M. E.
76
“Optimal Distributed Generation Allocation
77
and Sizing in Distribution Systems via
78
Artificial Bee Colony Algorithm,” IEEE
79
Transaction on Power Delivery, vol. 26(4),
80
pp.2090-101, 2011.
81
[18] Rao, R. S., Narasimham, S. V. L., Raju, M. R.
82
and Rao, A. S. “Optimal Network
83
Reconfiguration of Large-Scale Distribution
84
System Using Harmony Search Algorithm,”
85
IEEE Transaction on Power System, vol. 26(3),
86
pp.1080-88, 2011.
87
[19] Khazali, A. H. and Kalantar, M. “Optimal
88
reactive power dispatch based on harmony
89
search algorithm,” Int. J. Electr. Power Energy
90
Syst., vol.33, pp.684–92, 2011.
91
[20] Vasebi, A., Fesanghary, M. and Bathaee, S. M.
92
T. “Combined heat and power economic
93
dispatch by harmony search algorithm,” Electr.
94
Power Syst. Res., vol. 29, pp.713–19, 2007.
95
[21] Coelho, L. S. and Mariani, V. C. “An improved
96
harmony search algorithm for power economic
97
load dispatch,” Energy Conversion and
98
Management, vol. 50, pp.2522–6, 2009.
99
[22] Khorram, E. and Jaberipour, M. “Harmony
100
search algorithm for solving combined heat
101
and power economic dispatch problems,”
102
Energy Conversion and Management, vol.52,
103
pp.1550–4, 2011.
104
[23] Fesanghary, M. and Ardehali, M. M. “A novel
105
meta-heuristic optimization methodology for
106
solving various types of economic dispatch
107
problem,” Energy, vol. 34, pp.757-66, 2009.
108
[24] Singh, D. and Misra, R. K. “Multi-objective
109
feeder reconfiguration in different tariff
110
structures,” IET Gener. Transm.Distrib.,
111
2010;vol. 4(8), pp.974–988, 2010.
112
[25] Singh, D., Misra, R. K. and Singh, D. “Effect
113
of load models on assessment of energy losses
114
in distributed generation Planning,” IEEE
115
Transaction on Power Systems, vol.22(4), pp.
116
2204-12, 2007.
117
[26] Singh, D., Singh, D. and Verma, K.S.
118
“Multiobjective optimization for DG planning
119
with load models,” IEEE Transaction on Power
120
Systems, vol. 24(1), pp. 427-36, 2009.
121
[27] Eminoglu, U. and Hocaoglu, M. H. “A new
122
power flow method for radial distribution
123
systems including voltage dependent load
124
models,” Electr. Power Syst. Res., vol.76, pp.
125
106–114, 2005.
126
[28] Geem, Z. W., Kim, J. H. and Loganathan, G. V.
127
“A new heuristic optimization algorithm:
128
harmony search,” Simulation, vol.76(2),
129
pp.60–8, 2001.
130
[29] Lee, K. S. and Geem, Z. W. “A new metaheuristic
131
algorithm for continuous engineering
132
optimization: harmony search theory and
133
practice,” Appl. Mech. Eng., vol.194, pp.3902–
134
[30] Mahdavi, M., Fesanghary, M. and Damangir, E.
135
“An improved harmony searchalgorithm for
136
solving optimization problems,” Appl. Math.
137
Comput.,vol. 188(2), pp.1567–79, 2007.
138
[31] Baran, M. E. and Wu, F. F. “Optimum sizing of
139
capacitor placed on radial distribution
140
systems,” IEEE Transaction on Power
141
Delivery, vol. 4, pp.735-43, 1989.
142
[32] IEEE Standard for Interconnecting Distributed
143
Resources with Electric Power systems, IEEE
144
Std. 1547-2003, 2003, 1–16.
145
ORIGINAL_ARTICLE
A PSO-Based Static Synchronous Compensator Controller for Power System Stability Enhancement
In this paper Power system stability enhancement through static synchronous compensator (STATCOM)based controller is investigated. The potential of the STATCOM supplementary controllers to enhance thedynamic stability is evaluated. The design problem of STATCOM based damping controller is formulatedas an optimization problem according to the eigenvalue based objective function that is solved by a particleswarm optimization (PSO) algorithm. The controllers are tuned to simultaneously shift the lightly dampedand un-damped electro-mechanical modes of machine to a prescribed zone in the s-plane. The resultsanalysis reveals that the designed PSO based STATCOM damping controller has an excellent capability indamping the power system low frequency oscillations and enhance greatly the dynamic stability of thepower system.
http://jaiee.iau-ahar.ac.ir/article_513205_a26da9ea6cbb8e7ba9c97109cc260969.pdf
2012-05-21T11:23:20
2018-06-22T11:23:20
18
25
STATCOM
Particle swarm optimization
Damping Controller
Dynamic stability
Meisam
Mahdavi
me.mahdavi@ut.ac.ir
true
1
AUTHOR
Ali
Nazari
true
2
AUTHOR
Vahid
Hosseinnezhad
true
3
AUTHOR
Amin
Safari
true
4
AUTHOR
[1] J. Machowski and J. W. Bialek, “State variable
1
control of shunt FACTS devices using phasor
2
measurements,” Electric Power Systems
3
Research, Vol. 78, pp. 39-48, 2008.
4
[2] N.G. Hingorani and L. Gyugyi, Understanding
5
FACTS: concepts and technology of flexible AC
6
transmission systems, Wiley-IEEE Press, 1999.
7
[3] M.A. Abido, “Analysis and assessment of
8
STATCOM based damping stabilizers for power
9
system stability enhancement,” Electric Power
10
Systems Research, Vol. 73, no. 3, pp. 177-185,
11
[4] H. F. Wang, “Phillips-Heffron model of power
12
systems installed with STATCOM and
13
applications,” IEE Proc. Generation Transmission
14
and Distribution, Vol. 146, pp. 521-527, 1999.
15
[5] S. Morris, P. K. Dash and K. P. Basu, “A fuzzy
16
variable structure controller for STATCOM,”
17
Electric Power Systems Research, Vol. 65, pp. 23-
18
[6] A. H. M. A. Rahim and M. F. Kandlawala,
19
“Robust STATCOM voltage controller design
20
using loop shaping technique,” Electric Power
21
Systems Research, Vol. 68, pp. 61-74, 2004.
22
[7] J. Kennedy, “The particle swarm: social
23
adaptation of knowledge,” Proc. the International
24
Conf. Evolutionary and Computation,
25
Indianapolis, pp. 303-308, 1997.
26
[8] H. Shayeghi, A. Safari and H. Shayanfar,
27
“Multimachine power system stabilizers design
28
using PSO algorithm,” International Journal of
29
Elect Power and Energy System Engineering, Vol.
30
1, pp. 226-233, 2008.
31
[9] J. Kennedy, R. Eberhart and Y. Shi, Swarm
32
intelligence, Morgan Kaufmann Publishers, San
33
Francisco. 2001.
34
[10] H. Shayeghi, H. A. Shayanfar, S. Jalilzadeh and
35
A. Safari, “Design of output feedback UPFC
36
controller for damping electromechanical
37
oscillations using PSO,” Energy Conversion and
38
Management, Vol. 50, pp. 2554-2561, 2009.
39
[11] A. T. Al-Awami, Y. L. Abdel-Magid and M. A.
40
Abido, “A particle-swarm-based approach of
41
power system stability enhancement with unified
42
power flow controller,” Elect. Power and Energy
43
Systems, Vol. 29, pp. 251-259, 2007.
44
ORIGINAL_ARTICLE
Using Neural Network to Control STATCOM for ImprovingTransient Stability
FACTS technology has considerable applications in power systems, such as; improving the steady stateperformance, damping the power system oscillations, controlling the power flow, and etc. STATCOM is oneof the most important FACTS devices used in the parallel compensation, enhancing transient stability andetc. Since three phase fault is widespread in power systems, in this paper STATCOM is used to improve thetransient stability of power system when three phase fault occurred. Neural Network has been used foradjusting the gain of the supplementary controller of STATCOM. The simulation performed in MATLAB /Simulink software. Simulation results showed when STATCOM combines with proposed Neural Networkbased supplementary controller; the transient stability of power system improves.
http://jaiee.iau-ahar.ac.ir/article_513206_914032aa545a6d465e7a382f128ea08c.pdf
2012-05-21T11:23:20
2018-06-22T11:23:20
26
31
FACTS
STATCOM
Artificial neural network (ANN)
Power Oscillation Damping
Mozhgan
Balavar
m-balavar@iau-ahar.ac.ir
true
1
AUTHOR
[1] M. Karrari, “Dynamic and Control Of Power
1
System,” First Publishing, Tehran, Amirkabir
2
University Publishing Center, Winter 1382.
3
[2] M. Najari, A. A. Ghareveysi, M. A. Sadrniya, E.
4
Ebadi, “Khorasan Network Optimization By Facts
5
Tools,” 2005.
6
[3] Raviraj Vsc And Sen Pc, “Comparative Study Of
7
Proportional-Integral, Sliding Mode, And Fuzzy
8
Logic Controllers For Power Converters,” IEEE
9
Transaction On Industry Applications, Pp.18-24,
10
[4] J. Lu, M. H. Nehrir, D. A. Pierre, “A Fuzzy Logic
11
Based Adaptive Damping Controller For Static
12
VAR Compensator,” Electric Power Systems
13
Research 68 (2004), 113-118.
14
[5] N. G. Hyngurany, L. Gayogi, “Introduction With
15
A Flexible Transmission Network Productivity
16
Concepts And Technologies, Facts,” First
17
Publishing, Advisor Engineers Of Qods, Spring
18
[6] S.M. Bamasak,”Facts-Based Stabilizers For Power
19
System Stability Enhancement,” PhD Thesis, King
20
Fahad University Of Petroleum, 2005.
21
[7] N. Jamshidi, R.Rasoli, A. Abavi Mehrizi,
22
“Applied Learning Advanced Topics In Electrical
23
Engineering With Matlab,” Second Publishing,
24
Tehran, 1386.
25
ORIGINAL_ARTICLE
Voltage Flicker Parameters Estimation Using Shuffled Frog Leaping Algorithm and Imperialistic Competitive Algorithm
Measurement of magnitude and frequency of the voltage flicker is very important for monitoring andcontrolling voltage flicker efficiently to improve the network power quality. This paper presents twonew methods for measurement of flicker signal parameters using Shuffled Frog Leaping Algorithm(SFLA) and Imperialist Competitive Algorithm (ICA). This paper estimates fundamental voltage andflicker magnitudes and frequencies with proposed methods. The goal is to minimize the error of theestimated magnitudes and frequencies via a designed fitness function. At first, we introduce voltageflicker and its measuring techniques. Then voltage flicker model is analyzed. At the next part, a reviewof SFLA and ICA is presented. These methods will be applied to a test voltage signal and the resultsare be analyzed.
http://jaiee.iau-ahar.ac.ir/article_513207_081dcf406676483a0864acd1220c9a26.pdf
2012-05-21T11:23:20
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32
39
Voltage flicker signal
Flicker magnitude and frequency measurement
Shuffled Frog
Leaping Algorithm (SFLA)
Imperialist competitive algorithm (ICA)
Saeid
Jalilzade
jalilzadeh@znu.ac.ir
true
1
AUTHOR
Mehdi
Mardani
m_mardani@znu.ac.ir
true
2
AUTHOR
ORIGINAL_ARTICLE
The Intelligent Modeling of Human Hand Motion Using Magnetic Based Techniques
With increasing use of robots instead of human in industrial, medicine and military applications etc.the importance of research on designing and building of robots is increasing. In this paper variousmethods of the human hand motion simulation has been investigated and we used one of most commonmethod named Data-gloves which extract data from hand and then we simulated hand motion duringseveral processing stages. At first step we designed and built circuits to digitize analog data receivedfrom sensors and we sent them to computer. Then we received extracted data in MATLAB andprocessed them to simulate bending of the wrist and fingers joints graphically. In this method wemapped data linearly to 0-90 and rotate points around relative Coordinate axis in the specificconditions. Results show that we can simulate hand motion in real time with low cost, lowest error andwithout complex and expensive equipments.
http://jaiee.iau-ahar.ac.ir/article_513208_820d2b7047a9f60d3bfd8d1ad57072b9.pdf
2012-05-21T11:23:20
2018-06-22T11:23:20
40
47
Hand motion simulation
Data-Glove
Data extraction circuit
simulation in
MATLAB
M
Asghari
m-asgari@iau-ahar.ac.ir
true
1
AUTHOR
M. A
Badamchizadeh
true
2
AUTHOR
M. E
Akbari
m-akbari@iau-ahar.ac.ir
true
3
AUTHOR
[1] Chin-Shyurng Fahn and Herman Sun,
1
“Development of a Sensory Data Glove,
2
“Using Neural-Network-Based
3
Calibration”, Taipei, Taiwan: ICAT 2000,
4
pages 1-8.
5
[2] Sturman, D.J.; Zeltzer, D. “A survey of
6
glove-based input”, IEEE Comput.
7
Graphics, 14, 30-39, 1994, pages 106-111.
8
[3] [Online], Available:
9
www.tekscan.com/flexiforce
10
[4] M. Bezdicek1, D. G.
11
aldwell2“PORTABLE ABSOLUTE
12
[13] Amir Hossein Omidvar, ”EMG Feature
13
Extraction to Control the Prosthetic Hand
14
”A thesis Presented to Sharif University of
15
Technology, International Campus, Kish
16
Island, Iran, 2010pages 151-158.
17
[14] Kostas N. Tarchanidis, Member, IEEE, and
18
John N. Lygouras , “Data Glove With a
19
Force Sensor ”IEEE TRANSACTIONS ON
20
INSTRUMENTATION AND
21
MEASUREMEN”, VOL. 52, NO. 3, JUNE
22
2003, pages 88-95.
23
[15] http://www.edaboard.com/thread47834.html
24
[16] www.mathworks.com/help/toolbox/instrume
25
nt/serialreceive.html.
26
[17] http://www.mathkb.com.
27
ORIGINAL_ARTICLE
FACTS Control Parameters Identification for Enhancement of Power System Stability
The aim of this paper is to investigate a novel approach for output feedback damping controller design ofSTATCOM in order to enhance the damping of power system low frequency oscillations (LFO). The design ofoutput feedback controller is considered as an optimization problem according with the time domain-basedobjective function which is solved by a honey bee mating optimization algorithm (HBMO) that has a strongability to find the most optimistic results. The effectiveness of the proposed controller are tested anddemonstrated through nonlinear time-domain simulation studies over a wide range of loading conditions. Thesimulation study shows that the designed controller by HBMO has a strong ability to damping of power systemlow frequency oscillations. Moreover, the system performance analysis under different operating conditionsshow that the φ based controller is superior to the C based controller.
http://jaiee.iau-ahar.ac.ir/article_513209_9119ee62b491787a49715632d655819f.pdf
2012-05-21T11:23:20
2018-06-22T11:23:20
48
55
FACTS
STATCOM
Honey Bee Mating Optimization
Damping Controller
Low frequency oscillations
Power System
Dynamic stability
Ali
Ahmadian
ali.ahmadian1367@gmail.com
true
1
AUTHOR
Masoud
Aliakbar Golkar
golkar@kntu.ac.ir
true
2
AUTHOR
[1] M. Anderson, and A.A. Fouad, “Power System
1
Control and Stability”, Ames, IA: Iowa State
2
Univ. Press, 1977.
3
[2] A.T. Al-Awami, Y.L. Abdel-Magid and M.A.
4
Abido, “A particle-swarmbased approach of
5
power system stability enhancement with
6
unified power flow controller", International
7
Journal of Electrical Power and Energy
8
System, Vol. 29, , pp. 251-259, 2007.
9
[3] J. Machowski, and J. W. Bialek, "State
10
variable control of shunt FACTS devices using
11
phasor measurements", Electric Power Systems
12
Research, Vol. 78, pp. 39-48, 2008.
13
[4] N. Mithulanathan, C.A. Canizares, J. Reeve,
14
G.J. Rogres, “ comparison of PSS, SVC and
15
STATCOM controllers for damping power
16
systems oscillations”, IEEE Trans. On power
17
syst. Vol. 18(No.2), pp.786-792, 2003.
18
[5] S. Morris, P. K. Dash and K. P. Basu, "A fuzzy
19
variable structure controller for STATCOM",
20
Electric Power Systems Research, Vol. 65, pp.
21
23-34, 2003.
22
[6] N.G. Hingorani, and L. Gyugyi,
23
“Understanding FACTS: concepts and
24
technology of flexible AC transmission
25
systems“, Wiley-IEEE Press, 1999.
26
[7] H. F. Wang, "Phillips-Heffron model of power
27
systems installed with STATCOM and
28
applications", IEE Proc. on Generation
29
Transmission and Distribution, Vol. 146, No. 5,
30
pp. 521-527, 1999.
31
[8] M.A. Abido, "Analysis and assessment of
32
STATCOM based damping stabilizers for
33
power system stability enhancement", Electric
34
Power Systems Research, Vol. 73, pp. 177-185,
35
[9] A. H. M. A. Rahim, and M. F. Kandlawala,
36
"Robust STATCOM voltage controller design
37
using loop shaping technique", Electric Power
38
Systems Research, Vol. 68, pp. 61-74, , 2004.
39
[10] S. Lee, "Optimal Decentralized Design for
40
Output Feedback Power System Stabilizers",
41
IEE Proc. Gener. Transm. Distrib., Vol. 152,
42
No. 4, pp. 494-502, 2005.
43
[11] X. R. Chen, N. C. Pahalawaththa, U.D.
44
Annakkage and C.S. Cumble, "Design of
45
Decentralized Output Feedback TCSC
46
Damping Controllers by Using Simulated
47
Annealing", IEE Proc. Gen. Transm. Dist., Vol.
48
145, No. 5, pp. 553-558, 1998.
49
[12] F. Armansyah, N. Yorino and H. Sasaki,
50
"Robust Synchronous Voltage Sources
51
Designed Controller for Power System
52
Oscillation Damping", Electrical Power
53
Energy System, Vol. 24, pp. 41-49, 2002.
54
[13] H. Shayeghi, H.A. Shayanfar, S. Jalilzadeh and
55
A. Safari, “Simultaneous Coordinated
56
Designing of UPFC and PSS Output Feedback
57
Controllers using PSO”, Journal of Electrical
58
Engineering, Vol. 60, No. 4, pp.177- 184,
59
ORIGINAL_ARTICLE
A Novel Heuristic Optimization Methodology for Solving of Economic Dispatch Problems
This paper presents a biogeography-based optimization (BBO) algorithm to solve the economic loadDispatch (ELD) problem with generator constraints in thermal plants. The applied method can solvethe ELD problem with constraints like transmission losses, ramp rate limits, and prohibited operatingzones. Biogeography is the science of the geographical distribution of biological species. The modelsof biogeography explain how a organisms arises, immigrate from an environment to another and getseliminated. The BBO has some characteristics that are shared with other population basedoptimization procedures, similar to genetic algorithms (GAs) and particle swarm optimization (PSO).The BBO algorithm mainly based on two steps: migration and mutation. The BBO has some goodfeatures in reaching to the global minimum in comparison to other evolutionary algorithms. Thisalgorithm applied on two practical test systems that have six and fifteen thermal units, results of thispaper are used to see the comparison between performances of the BBO algorithm with other existingalgorithms. The result of this investigation proves the efficiency and good performance of applyingBBO algorithm on ELD problem and show that this method can be a good substitute for otheralgorithms.
http://jaiee.iau-ahar.ac.ir/article_513210_3633447b91abd5f291a74484b825f02d.pdf
2012-05-21T11:23:20
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55
65
biogeography-based optimization
economic load dispatch
prohibited operating zone
ramp rate limits
Ali
Nazari
true
1
AUTHOR
Amin
Safari
a-safari@iau-ahar.ac.ir
true
2
AUTHOR
Hossein
Shayeghi
true
3
AUTHOR
[1] A. J. Wood and B. F. Wollenberg, Power
1
Generation, Operation, and Control, 2nd ed.
2
New York: Wiley, 1996.
3
[2] A. A. El-Keib, H. Ma, and J. L. Hart,
4
“Environmentally constrained economic
5
dispatch using The Lagrangian relaxation
6
method,” IEEE Trans. Power Syst., vol. 9, no.
7
4, pp. 1723–1729, Nov. 1994.
8
[3] C.-T. Su and C.-T. Lin, “New approach with a
9
Hopfield modeling framework to economic
10
Dispatch,” IEEE Trans. Power Syst., vol.
11
15,no. 2, p. 541, May 2000.
12
[4] C.-T. Su and C.-T. Lin, “New approach with a
13
Hopfield modeling framework to economic
14
Dispatch,” IEEE Trans. Power Syst., vol. 15,
15
no. 2, p. 541, May 2000.
16
[5] P. H. Chen and H. C. Chang, “Large-scale
17
economic dispatch by genetic algorithm,” IEEE
18
Trans. Power Syst., vol. 10, no. 4, pp. 1919–
19
1926, Nov. 1995.
20
[6] Lin WM, Chen FS, Tsay MT. An improved
21
tabu search for economic dispatch with
22
multiple Minima. IEEE Trans Pow Syst
23
2002;17(1):108–12.
24
[7] Panigrahi B.K., Yadav S. R., Agrawal S. &
25
Tiwari M.K. (2007). A clonal algorithm to
26
solve Economic loadispatch. Electrical Power
27
System Research, 77: 1381-1389.
28
[8] K. P. Wong and C. C. Fung, “Simulated
29
annealing based economic dispatch
30
algorithm,” Proc. Inst. Elect. Eng. C, vol. 140,
31
no. 6, pp. 509–515, 1993.
32
[9] H. T. Yang, P. C. Yang, and C. L. Huang,
33
“Evolutionary programming based economic
34
dispatch for units with non-smooth fuel cost
35
functions,” IEEE Trans. Power Syst., vol. 11,
36
no. 1, pp. 112–118, Feb. 1996.
37
[10] D. Simon, “Biogeography-based optimization,”
38
IEEE Trans. Evol. Comput., vol. 12, no. 6, pp.
39
702–713, Dec. 2008.
40
[11] A. Bhattacharya and P. K. Chattopadhyay
41
“Biogeography-Based Optimization for
42
Different Economic Load Dispatch Problems”
43
IEEE Trans. Power Syst., VOL. 25, NO. 2 , pp
44
1064 - 1077 , MAY 2010.
45
[12] Z.-L. Gaing, “Particle swarm optimization to
46
solving the economic dispatch considering the
47
generator constraints,” IEEE Trans. Power
48
Syst., vol. 18, no. 3, pp. 1187–1195, Aug. 2003.
49
[13] I. Selvakumar and K. Thanushkodi, “A new
50
particle swarm optimization solution to
51
nonconvex economic dispatch problems,”
52
IEEE Trans. Power Syst., vol. 22, no. 1, pp.
53
42–51, Feb. 2007.
54
[14] K. T. Chaturvedi, M. Pandit, and L. Srivastava,
55
“Self-organizing hierarchical particle swarm
56
Optimization for nonconvex economic
57
dispatch,” IEEE Trans. Power Syst., vol. 23,
58
no. 3, p. 1079, Aug. 2008.
59
[15] Pereira-Neto A, Unsihuay C, Saavedra OR.
60
Efficient evolutionary strategy optimization
61
Procedure to solve the non convex economic
62
dispatch problem with generator constraints.
63
IEE Proc – GenerTransm Distrib 2005;
64
152(5):653–660.
65