ORIGINAL_ARTICLE
Solving Security Constrained Unit Commitment by Particle Swarm Optimization
The issue of unit commitment is one of the most important economic plans in power system. In modern and traditional power systems, in addition to being economical of the planning, the issue of security in unit operation is also of great importance. Hence power system operation confronts units’ participation and input considering network security constrains. The issue of units’ participation is defined as an optimization problem aimed at determining units' on or off condition and optimized level of units’ production
http://jaiee.iau-ahar.ac.ir/article_520440_d1ac847b1b3c51c2824a0a79f957b4f3.pdf
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1
8
Particle swarm optimization
unit commitment
security-constrained unit
commitment
SCUC
Shiva
Alipour Ghorbani
shiva_alp@yahoo.com
true
1
AUTHOR
Hossein
Nasiraghdam
h.nasiraghdam@iau-ahar.ac.ir
true
2
AUTHOR
[1] Wood, A.J and Wollenberg, B.F. (1966). Power generation, operation and control, 2nd edi., Chapter 5, New York: Wiley., 131–170.
1
[2] Ouyang, Z., and Shahidehpour, S.M . (1991). An intelligent dynamic programming for unit commitment application, IEEE Trans. Power Syst., Vol. 6(3), 1203–1209.
2
[3] R. Baldik, Feb. (1995). " The generalized unit commitment problem," IEEE Trans. Power Syst., VOL. 10,pp. 465-475.
3
[4] Selvakumar, A. I. and Thanushkodi, k. (2007). A New Particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems, IEEE Transaction on Power System, VOL. 22, No. 1, pp. 42-51.
4
[5] Bai, X., Wei, H. (2007). Semi-definite Programming-based method for securityconstrained unit commitment with operational and optimal power flow constraints, Coll. of Electr. Eng., Guangxi Univ. Vol. 3, pp. 1751- 8687.
5
[6] Zhao., Wang., J., Watson., p. J. and Guan., Y. (2013). Multi-stage robust unit commitment considering wind and demand response uncertainties. IEEE Transaction on Power Systems; 28: 52-63., 2013.
6
[7] Chandrasekaran., K. and Simon. S. P. (2013). Optimal deviation based firefly algorithm tuned fuzzy design for multi-objective UCP”. IEEE Transaction on Power Systems; 28: 460-471.
7
ORIGINAL_ARTICLE
Design and Analyses of a PWM based Seven Level CascadeCombination Inverter
Multilevel Inverters has big placement advantages in Power Electronics and its application can be mentioned to FACTS tools such as UPFC – D-STATCOM. Therefore, nowadays Multilevel Inverters have created vast field’s research and researchers are trying to present appropriate structures with fewer keys and greater advantages. The main advantages of Multilevel Inverters mentioned are low-key voltage stress and low harmonic content of the output voltage. Multilevel Inverters, having many advantages, can be referred to have low volume, low EMI, low losses and high efficiency. In this study, all equations dominating the circuit have been investigated and main and effective parameters have been identified in suggested Invertors and this method has been extended for use in n levels Invertors. Then it has investigated the changes possibility in the Invertors’ structure aiming to reduce dc voltage and to increase the number of output voltage levels. The results of simulations have emphasized the suggested structures.
http://jaiee.iau-ahar.ac.ir/article_520441_6bf10c854103be77209bbafc75d8149c.pdf
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18
: Inverter
Cascade hybrid
Pulse with Modulation
Seven LevelsB. Wu
(2006). High-Power Converters and AC
Drives. New York: IEEE Press/Wiley Interscience.
[2] H. Abu-Rub
J.Holtz
J.Rodriguez and G.
Baoming (2010). Medium-Voltage Multilevel
Converters- State of the Art
Challenges and
Requirements in Industrial Application. IEEE
Transactions on Industrial Electronics
Vol.57
No.8
pp.2581-2596
Mortaza
Sefidi
eng.sefidi93@gmail.com
true
1
AUTHOR
Mehdi
Jarahi
jarahi.mehdi@gmail.com
true
2
AUTHOR
[1]B. Wu, (2006). High-Power Converters and AC Drives. New York: IEEE Press/Wiley Interscience.
1
[2] H. Abu-Rub, J.Holtz, J.Rodriguez and G. Baoming (2010). Medium-Voltage Multilevel Converters- State of the Art, Challenges and Requirements in Industrial Application. IEEE Transactions on Industrial Electronics, Vol.57, No.8, pp.2581-2596.
2
[3] T. PodlesakD.Katsis, P.Wheeler, J.Clare, L.Empringham and M. Bland (2005). A 150- kVA vector controlled matrix converter induction motor drive. IEEE Transactions on Industrial Electronics, Vol.41, No.3, pp. 841- 847.
3
[4] A. Nabae, I.Takahashi, Hageki, (1981). A new Neutral-Point-Clamped PWM, Inverter. IEEE Transactions on Industrial Electronics, Vol.IA- 17,No.5,pp.518-523,Septeber / October 1981.
4
[5] M. Veenstra, and A. Rufer (2005). NonEquilibrium State Capacitor-Voltage Stabilization in a Hybrid Asymmetric NineLevel Inverter: Nonlinear Model-Predictive Control”, European Power Electronics and Drives Association (EPE) Jornal, Vol.15, No.1, pp.28-35.
5
[6] A. Rufer, M.Veenstra, and K. Gopakuumar 1999). Resolution Voltage Phasor Generation. in Proceedings of the European Power electronics and Applications Conference (EPE 1999).
6
[7] M. Manjekar and T. Lipo (1998). A Hybrid Multilevel Inverter Topology for Drive Applications. in Proceedings of the IEEE Applied Power electronics Conference (APEC) , Vol.2 , pp.523-529.
7
[8] M. Manjekar, P. Steimer and T. Lipo (2000). Hybrid Multilevel Power Conversion System: A Competitive Solution for High-Power Applications. IEEE Transactions on Industrial Applications, Vol.36, No.3, pp.834-841.
8
[9] J. Lai and F. Peng (1996). Multilevel Converters a New Breed of Power Converter. IEEE Transactions on Industrial Applications, Vol. 32, No.3, pp.509-517.
9
[10] P. Hammand (1997). A New Approach to Enhance Power Quality for Medium Voltage AC-Drives. IEEE Transactions on Industrial Applications, Vol.33, No.1, pp.202-208.
10
[11] P. Hammand (1997). Medium Voltage PWM Drive and Method. U.S. Patent 5, 625,545.
11
[12] R. Teodorescu, F. blaabjerg, J. K.Pedersen , E. Cengelci, S. U. Sulistijo, B. O. Woo and P. Enjeti (1999). Multilevel Converters-A Survey. European Conference on Power Electronics and Applications.
12
[13] R. Menzies, P. Steimer, and J. Steinke (1994). Five-level GTO Inverters for Large Induction Motor Drives. IEEE Transactions on Industry Applications, Vol.30, No.4, pp.938-944.
13
[14] M. G. Hosseini Agdam, S. H. Fathi and G. B. Gharehpetion (2008). Analysis of Multi-Carrier PWM Methods for Asymmetric Multi-Level Inverter. The third IEEE Conference on Industry Electronics and Applications (ICIEA 2008), Vol.3, pp.2057-2062, 3-5, Holiday Inn, Singapore.
14
[15] J. Rodriguez, J. s. Lai and F. Z. Peng (2002). Multilevel Inverters: A Survey of Topologies, Control, and Applications. IEEE Transactions on Industry Electronics, Vol.49, No.4, pp.724- 738.
15
[16] M. Manjrekar, P. Steimer, and T. Lipo (2000). Hybrid Multilevel Power Conversion System: A Competitive Solution for High-Power Applications. IEEE Transactions on Industry Applications, Vol.36, No.3, pp.834-841.
16
[17] M.E.Akbari, et al. "Nonlinear H∞ controller for flexible joint robots with using feedback linearization." International Journal on Computer Science and Engineering (IJCSE), ISSN (2011): 0975-3397.
17
[18] M.E.Akbari, , M. A. Badamchizadeh, and M. A. Poor. "Implementation of a fuzzy TSK controller for a flexible joint robot." Discrete Dynamics in Nature and Society 2012 (2012).
18
ORIGINAL_ARTICLE
Mathematical Modeling of Cancer Cells and Chemotherapy Protocol Dealing Optimization Using Fuzzy Differential Equations And Lypunov Stability Criterion
Mathematical models can simulate the growth and proliferation of cells in the interaction with healthy cells, the immune system and measure the toxicity of drug and its effects on healthy tissue pay. One of the main goals of modeling the structure and growth of cancer cells is to find a control model suitable for administration among patients. In this study, a new mathematical model is designed to describe the changes in different phases of the cycle T cell proliferation, the population of immune cells, the proposed concentration of drug toxicity and treatment using differential equation and fuzzy Lyapunov stability, an optimal treatment protocol. One feature to consider is the rate of clearance of the drug in the body.
http://jaiee.iau-ahar.ac.ir/article_520442_b5baaca1eeb0ea092a92271dc3e4f595.pdf
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28
The optimal treatment protocol
Mathematical modeling of cancer
fuzzy differential
equations
Lyapunov stability criteria
Hadi
Abbasnejad
abbasnejad.hadi@yahoo.com
true
1
AUTHOR
[1] Eisen, M.M,(1979). Mathematical Models in Cell Biology and Cancer Chemotherapy. Volume 30 of Lecture Notes in Biomathematics, Springer-Verlag, New York.
1
[2] Knolle, H. (1988). Cell Kinetic Modeling and the Chemotherapy of Cancer”, Volume 75 of Lecture Notes in Biomathematics, SpringerVerlag, New York.
2
[3] Swierniak A., Kimmel M., Smieja J. (2009). Mathematical modeling as a tool for planning anticancer therapy. European Journal of Pharmacology 625, 108–121.
3
[4] Kimmel, M. and Swierniak, A.(2006).Using control theory to make cancer chemotherapy beneficial from phase dependence and resistant to drug resistance”, J. Math. Biosci.
4
[5] Ghaffari A., Karimi M. (2009).Optimal Design of Chemotherapy Drug Protocol for Cancer Treatment Based on a New Mathematical Model” Int. J. Modeling, Identification and Control.
5
[6] Ghaffari A., Nasserifar N. (2009). Mathematical Modeling and Lyapunov based Drug Administration in Cancer Chemotherapy. Iranian Journal of Electronical and Electrical Engineering.
6
[7] Webb, G.F.(1992). A cell population model of periodic chemotherapy Treatment. In Biomedical Modeling and Simulation, Elsevier Science, .92-83
7
[8] Kheifetz, Y., Kogan, Y., Agur, Z.,“Long-range predictability in models of cell populations subjected to phase-specific drugs: Growth rate approximation using properties of positive compact operators,” Mathematical Models & Methods in the Applied Sciences. In Press.
8
[9] Birkhead, B.G., Rakin, E.M., Gallivan, S., Dones, L. and Rubens R.D. (1987). A mathematical model of the development of drug resistance to cancer chemotherapy”, J. Cancer.Clin.Oncol.23(9), 1421-1427.
9
[10] Swan, G.W. (1987). Tumor growth models and cancer chemotherapy”, In Cancer Modeling, Volume 83, Chapter 3, (Edited by J.R. Thompson and B. Brown), Marcel Dekker, New York, 91-179.
10
[11] Kirschner, D., Panetta, J. (1998). Modeling immunotherapy of the tumor immune interaction. J. Math. Biol. 37, 235-252.
11
[12] Villasana, M. (2001). A delay differential equation model for tumor Growth. PhD thesis, Mathematical Department, Claremont University,CA, USA.
12
[13] Kozusko, F. et al. (2001). A mathematical model of invitro cancer cell growth and treatment with the antimitoic agent curacin A,” Math.Biosci.170, 1-16.
13
[14] T. Burden, J. Ernstberger and K. Renee Fister (2004). Optimal control applied to immunotherapy”, Discrete and continuous dynamical systems-series B Vol 4.
14
[15] K.R. Fister and J.H. Donnelly (2005). Immunotherapy: an optimal control theory approach. Mathematical Bioscience and engineering Vol 2.
15
[16] Liu, W., Hillen, T., Freedman, H., I. (2007). A Mathematical Model for M-PHASE Specific Chemotherapy Including the G0-PHASE and Immune response, J. of MATHEMATICAL BIOSCIENCES AND ENGINEERING Volume 4, Number 2. [17] Mackey, M.C. (2001). Cell kinetic status of hematopoietic stem cells. Cell Prolif., 34, 71-83. [18] Kuznetsov, A., et al. (1994). Nonlinear dynamics of immunogenic tumors: Parameter estimation and global bifurcation analysis”, B. Math. Biol,. .321-295 ,56 [19] Jankovic, M. (1999). Control LyapunovRazumikhin functions for time delay systems. Proceedings of CDC, Phoenix, AZ. [20] Jankovic, M. (2000). Extention of control lyapunov functions to time delay systems. Proceedings of the 39’ IEEE Conference on Decision and Control, Sydney, Australia. [21] Slotine, J.J.E., Li, W.(1991). Applied Nonlinear Control, Prentice Hall International, Englewood Cliffs pp. 58-65. [22] Nikdel, Parisa, et al. "Improved Takagi–Sugeno fuzzy model-based control of flexible joint robot via Hybrid-Taguchi genetic algorithm." Engineering Applications of Artificial Intelligence 33 (2014): 12-20. [23] Pourmahmood, Mohammad, Mohammd Esmaeel Akbari, and Amin Mohammadpour. "An efficient modified shuffled frog leaping optimization algorithm." Int. J. Comput. Appl 32.1 (2011): 0975-8887.
16
ORIGINAL_ARTICLE
Raising Power Quality and Improving Reliability by Distribution Network Reconfiguration in the Presence of Renewable Energy Sources
In this paper, reconfiguration problem of distribution network has been investigated toimprove reliability and reduce power loss by placement of renewable energy sources; i.e. solarcell and wind turbine. For this, four reliability indices are considered in objective function;which are as follows: System Average Interruption Frequency Index (SAIFI), System AverageInterruption Duration Index (SAIDI), Cost of Energy Not Supplied (CENS), and MomentaryAverage Interruption Frequency Index (MAIFI). By using a novel technique, the target functionwas normalized. Simulation has been performed on IEEE 69-bus test system. A genetic algorithmcould solve this nonlinear problem.
http://jaiee.iau-ahar.ac.ir/article_520479_a636a9061d2b1aa270c8e347fb521615.pdf
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38
Reconfiguration
solar cell
Wind Turbine
Genetic Algorithm
power loss reduction
Reliability Improvement
Mohamad Taghi
Babajani BaghmisheZad
babajani.mohamad@yahoo.com
true
1
AUTHOR
Hosein
NasirAghdam
true
2
AUTHOR
[1]T.Niknam and E.Azad Farsani (2010). A hybrid
1
self-adaptive particle swarm optimization and
2
modified shuffled frog leaping algorithm for
3
distribution feeder reconfiguration, Engin Applic
4
Artific Intellgn, vol. 23, no. 8, pp.1340-1349.
5
[2] J.Olamaei, T.Niknam, S.Badali, and A. Arefi
6
(2012). Distribution feeder reconfiguration for
7
loss minimization based on modified honey bee
8
mating optimization algorithm, Energ Policy,
9
vol. 14, pp.304-311.
10
[3] L. W.De Oliveira, E.J. De Oliveira, F.V. Gomes,
11
S I.C.ilva, A. L.M.Marcato, and P.V.C. Resende,
12
(2014). Artificial immune systems applied to the
13
reconfiguration of electrical power distribution
14
networks for energy loss minimization, Int J Elec
15
Power, vol. 56, pp.64-74.
16
[4] J E.osé de Oliveira, G.José Rosseti, L.Willer de
17
Oliveira, F.Vanderson Gomes, and W.Peres
18
(2014). New algorithm for reconfiguration and
19
operating procedures in electric distribution
20
systems, Int J Elec Power, vol. 57, pp.129-134.
21
[5] C.H.Nogueira de Resende Barbosa, M.H. Soares
22
Mendes, and J.Antônio de Vasconcelos (2014).
23
Robust feeder reconfiguration in radial
24
distribution networks, Int J Elec Power, vol. 54,
25
pp.619-630.
26
[6] Ch.T.Su, Ch.F.Chang, and J.P.Chiou (2005).
27
Distribution network reconfiguration for loss
28
reduction by ant colony search algorithm, Int J
29
Elec Power, vol. 75, pp.190-199.
30
[7] A.Kavousi-Fard, and M.R.Akbari-Zadeh (2013).
31
Reliability enhancement using optimal
32
distribution feeder reconfiguration,
33
Neurocomput, vol. 106, pp.1-11.
34
[8] J.P.Chiou, Ch.F.Chang, and Ch.-T. Su (2005).
35
Variable scaling hybrid differential evolution for
36
solving network reconfiguration of distribution
37
systems, IEEE T Power Syst, vol. 20, no. 2,
38
pp.668-674.
39
[9] J.Mendoza, R.López, D.Morales, E.López,
40
P.Dessante, and R.Moraga (2006). Minimal loss
41
reconfiguration using genetic algorithms with
42
restricted population and addressed operators:
43
real application, IEEE T Power Syst, vol. 21, no.
44
2, pp.948-954.
45
[10] S.H.Mirhoseini, S.M. Hosseini, M.Ghanbari, and
46
M.Ahmadi (2014). A new improved adaptive
47
imperialist competitive algorithm to solve the
48
reconfiguration problem of distribution systems
49
for loss reduction and voltage profile
50
improvement, Int J Elec Power, vol. 55, pp.128-
51
[11] A.E. Milani, and M.R.Haghifam (2013). A new
52
probabilistic approach for distribution network
53
reconfiguration: Applicability to real networks,
54
Math Compt Model, vol. 57, pp.169-179.
55
[12] A.E.Milani, and M.R.Haghifam (2013). An
56
evolutionary approach for optimal time interval
57
determination in distribution network
58
reconfiguration under variable load, Math Compt
59
Model, vol. 57, pp.68-77.
60
[13] B.Tomoiaga, M.Chindris, A.Sumper,
61
R.Villafafila-Robles, and A.Sudria-Andreu
62
(2013). Distribution system reconfiguration using
63
genetic algorithm based on connected graphs, Int
64
J Elec Power, vol. 104, pp.216-225.
65
[14] A.Kavousi-Fard, and T.Niknam (2014). Multiobjective
66
stochastic Distribution Feeder
67
Reconfiguration from the reliability point of
68
ORIGINAL_ARTICLE
Novel Circularly Polarized Substrate Integrated Waveguide SLOT Antanna
Circularly-polarized antenna is built based on a new SIW (substrate integrated waveguide)structure which contains cavity-backed resonator and a conventional polarized ring with twosquare slits in inner and outer ring that differ 90° at position and is proposed for right-handedcircular polarization (RHCP) and fabricated in two separate layers. A broadband impedancebandwidth of 13.9% and a RHCP axial ratio of 0.5GHz have been obtained under the conditionof less than VSWR ≤2 and axial ratio≤ 3 dB, respectively
http://jaiee.iau-ahar.ac.ir/article_520480_c5e6275834c43a9cab90817533b98ab8.pdf
2015-09-01T11:23:20
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39
43
PCB
SIW
HMSIW
QMSIW
HFSS
RHCP
Omid
Khodadad
khodadad.o@gmail.com
true
1
AUTHOR
Pejman
Mohammadi
p.mohammadi@iaurmia.ac.ir
true
2
AUTHOR
[1] H. Uchimura, T. Takenoshita, and M. Fujii (1998). Development of a ‘laminated waveguide. IEEE Trans.Microw. Theory Tech., vol. 46, no. 12, pp. 2438–2443.
1
[2] J. Hirokawa and M. Ando (2000). Efficiency of 76-GHz post-wall waveguide- fed parallel-plate slot arrays. IEEE Trans. Antennas Propag., vol. 48, no. 11, pp. 1742–1745.
2
[3] D. Deslandes and K. Wu (2006). Accurate modeling, wave mechanisms, and design considerations of a substrate integrated waveguide. IEEE Trans. Microw. Theory Tech., vol. 54, no. 6, pp. 2516–2526.
3
[4] G. Q. Luo, Z. F. Hu, L. X. Dong, and L. L. Sun (2008). Planar slot antenna backed by substrate integrated waveguide cavity. IEEE Antennas Wireless Propag. Lett.IEEE, Vol. 7, pp. 236–239.
4
[5] J. Liu, D. R. Jackson, and Y. Long (2012). Substrate integrated waveguide (SIW) leaky-wave antenna with transverse slots. IEEE Trans. Antennas Propag., vol. 60, no. 1, pp. 20–29.
5
[6] O. Kramer, T. Djerafi, and K. Wu (2011). Very small footprint 60 GHz stacked Yagi antenna array. IEEE Trans. Antennas Propag., vol. 59, no. 9, pp. 3204–3210.
6
[7] G.Q. Luo, Z. F.Hu, Y. Liang, L. Y.Yu, and L. L. Sun (2009). Development of low profile cavity backed crossed slot antennas for planar integration. IEEE Trans. Antennas Propag., vol. 57, no. 10, pp. 2972–2979.
7
[8] S. Razavi and M. Neshati (2013). Development of a low profile circularly polarized cavity backed antenna using HMSIW technique.
8
[9] C. Jin, R. Li, A. Alphones, and X. Bao (2013). Quarter-mode substrate integrated waveguide and its application to antennas design. IEEE Trans. Antennas Propag., vol. 61, no. 6, pp. 2921–2928.
9
[10] S. Sam and S. Lim (2013). Electrically small eighth-mode substrate-integrated waveguide antenna with different resonant frequencies depending on rotation of complementary split ring resonator. IEEE Trans. Antennas Propag., vol. 61, no. 10, pp. 4933–4939.
10
[11] J. Xu, W. Hong, H. Tang, Z. Kuai, and K. Wu (2008). Half-mode substrate integrated waveguide (HMSIW) leaky-wave antenna for millimeter wave applications. IEEE Antennas Wireless Propag. Lett., vol. 7, pp. 85–88.
11
[12] B. Liu, W. Hong, Y.-Q.Wang, Q.-H. Lai, and K.Wu (2005). Half mode substrate integrated waveguide (HMSIW) 3-dB coupler. IEEE Microw. Wireless Compon. Lett., vol. 17, no. 1, pp. 22–24.
12
[13] N. Grigoropoulos, B. Sanz-Izquierdo, and P. R. Young (2005). Substrate integratedfolded
13
waveguides (SIFW) and filters. IEEE Microw. Wireless Compon. Lett., vol. 15, no. 12, pp. 829–831.
14
[14] S. Zhang, T.-J. Bian, Y. Zhai,W. Liu, G. Yang, and F.-L. Liu (2012). Quarter substrate integrated waveguide resonator applied to fractal-shaped BPFs,” Microw. J., vol. 55, pp. 200–208.
15
[15] Q. Lai, C. Fumeaux, W. Hong, and R. Vahldieck (2009). Characterization of the propagation properties of the half-mode substrate integrated waveguide. IEEE Trans. Microw. Theory Tech., vol. 57, no. 8, pp. 1996–2004.
16
ORIGINAL_ARTICLE
Increasing the Efficiency of Photovoltaic Systems by Using Maximum Power Point Tracking (MPPT)
Using Photovoltaic systems is gradually expanded by increasing energy demand. Abundance and availability of this energy, has turned to one of the most important sources of renewable energy. Unfortunately, photovoltaic systems have two big problems: first, those have very low energy conversion efficiency (in act between 12 and 42 percent under certain circumstances). Second, the power produced by the solar cell depends on nonlinear conditions such as solar radiation, temperature and charge feature. According to this, received power maximum of photovoltaic cells depends on different non-linear variables, it is necessary to be continuously traced, as maximum received power of the cell (by controller). In this research, the increasing efficiency of photovoltaic systems has been investigated by using Maximum Power Point Tracking (MPPT) in two different modes contained connected to the Grid and disconnected from the grid with simulation by MATLAB software. The obtained results showed that the proposed technique is able to improve the current, voltage and power output of photovoltaic cells.
http://jaiee.iau-ahar.ac.ir/article_520481_4bf9cb57a7c2cdbe6f5d37edfa2d9692.pdf
2015-09-01T11:23:20
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54
photovoltaic cell
MPPT
Renewable Energy
production power
Alireza
Tofigh Rihani
alireza.reahani@gmail.com
true
1
AUTHOR
Majid
Ghandchi
majid.ghandchi@gmail.com
true
2
AUTHOR
[1] Mohamed M. Algazara & Hamdy Almonierb &
1
Hamdy Abd EL-halima & Mohamed Ezzat El
2
Kotb Salem (2012). Maximum power point
3
tracking using fuzzy logic control. Electrical
4
Power and Energy Systems, Vol. 39, pp. 21-28.
5
[2] I.H. Altasa & A.M. Sharaf (2008). A novel
6
maximum power fuzzy logic controller for
7
photovoltaic solar energy systems", Renewable
8
Energy, Vol. 33, pp. 388–399.
9
[3] Naseer Ahmad, Anwar K. Sheikh, P. Gandhidasan, Moustafa Elshafie (2015). Modeling, simulation and performance evaluation of a community scale PVRO water desalination system operated by fixed and tracking PV panels: A case study for Dhahran city, Saudi Arabia. Renewable Energy, Volume 75, Pages 433-447.
10
[4] Rakibuzzaman Shah, N. Mithulananthan, R.C.
11
Bansal, V.K. Ramachandaramurthy (2015). A
12
review of key power system stability challenges
13
for large-scale PV integration. Renewable and
14
Sustainable Energy Reviews, Volume 41, January
15
2015, Pages 1423-1436.
16
[5] K. Sundareswaran, V. Vigneshkumar, S. Palani
17
(2015). Application of a combined particle swarm
18
optimization and perturb and observe method for
19
MPPT in PV systems under partial shading
20
conditions. Renewable Energy, Volume 75, Pages
21
[6] Jorge Esteban Tobón, Juan M. Ramirez, Rosa E.
22
Correa Gutierrez (2015). Tracking the maximum
23
power transfer and loadability limit from
24
sensitivities-based impedance matching. Electric
25
Power Systems Research, Volume 119, Pages
26
[7] GO OKADA, Katsuya, Shigeyasu (2006).
27
DEVELOPMENT OF A HIGH – SPEED
28
SYSTEM MEASURING A MAXIMUM POWER
29
OF PV MODULES. IEEE photovoltaic Energy
30
conversion conference; 5:2262-2263.
31
[8] ChihchiangHua, jongrong Lin (2004). A modified tracking algorithm for maximum power tracking of solar array. .ELSEVIER Energy conversion and Management:911-925
32
[9] Chen-chi chu , chieh –Li chen (2009). Robust maximum power point tracking method for photovoltaic cell: A sliding control approach. ,ELSEVIER Solar Energy,83 2009:1370-1378
33
[10] Jancarle L. Santos , Fernando Antunes , AnisChehab , Cicere Cruz (2006). A maximum power point tracker for PV systems using a high performance boost converter. ELSEVIER Solar Energy:772-778
34
[11] V.Salas , E .Olias , A.Lazaro , A.Barrado (2005). New algorithm only one variable measurement applied to a maximum power point tracker. ELSEVIER Solar Energy Materials & solar cell:675-684
35
[12] B.J. Huang & W.L. Ding & Y.C. Huang (2011).
36
Long-term field test of solar PV power
37
generation using one-axis3-position sun tracker.
38
Solar Energy , Vol. 85, 1935–1944.
39
[13] L. Piegari& R. Rizzo (2010). Adaptive perturb
40
and observe algorithm for photovoltaic maximum
41
power point tracking" IET Renew.Power Gener,
42
Vol. 4, pp. 317–328.
43
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