Volume 56
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No.5, October 2012
45 Table 9 gives the technical specifications of four
configurations of the General Electric GE 1.5-MW series wind turbine used in this study [34].
Table 9 . Characteristics of 1.5 MW Series Wind Turbine used in this study.
We calculate the capacity factor C
F
for each turbine-site, and the results are presented in tables 10, 11 and 12 at the height
of 10, 30 and 50m, respectively. The capacity factor of the system can be a useful indication
for the effective matching of wind turbine and regime. For turbines with the same rotor size, rated power and conversion
efficiency, the capacity factor is influenced by the wind generators cut-in speed, rated speed and cut-out speed and
the site characteristics shape and scale parameters.
Table 10. Capacity factors
10 F
C
calculated for 4 turbines configurations and for different sites at 10 m
height
Table 11. Capacity factors
30 F
C
calculated for 4 turbines configurations and for different sites at 30 m
height
Table 12. Capacity factors
50 F
C
calculated for 4 turbines configurations and for different sites at 50 m
height
We notice that: The capacity factor depends on five parameters; shape
and scale Weibull parameters, cut-in, cut-out and rated speeds.
The best capacity factor for each site is shown in boldface in tables 10, 11 and 12.
The best site for each turbine is N°3 Adrar: 10.34
10 F
C
25.18 at 10 m height, 18.00
30 F
C
37.21 at 30 m height and for 50 m height 23.21
50 F
C
44.04. The perfects turbines for all sites are respectively N°4
and N°1. The capacity factor increases as the hub height increases.
At 10 m height the capacity factors of the selected wind turbines are in the range 08
–25. Since the capacity factor at 50 m height is expected to be in the range 18
–44.
6. CONCLUSIONS
In this paper, the first part of the study presents an evolution of the wind resource of in four Algerian sites Algiers, Oran,
Adrar and Ghardaïa, the annual average wind velocity and power density at the standard height 10m above the ground
are calculated using the Weibull distribution function. The annual average wind speed for the considered sites ranges
from 3.81 to 6.38 ms and the mean wind power density vary between 70.88 and 283.12 wm
2
at standard height of 10m. In the second part we calculate the capacity factor and
the average power output for 04 commercial wind turbines used in this study. At 10 m height the capacity factors of the
selected wind turbines are in the range 08 –25. Since the
capacity factor at 50 m height is expected to be in the range 18
–44. Wind energy exploitation in Adrar is favourable for
applications of low power, as water pumping systems and production of electricity using small wind turbines, and even
for the installations of great power and wind farms.
7. REFERENCES
[1] Guidelines to renewable energies. Ministry of energy and
mines. Algeria .Edition 2007. [2]
Khelif , A.
Expérience, potentiel
et marché
photovoltaïque algérien.
www.worldenergy.orgdocumentscongresspapers94. pdf
[3] Ministère de l’aménagement du territoire et de
l’environnement ; Communication nationale initiale de l’Algérie à la conversion cadre des nations unies sur les
changements climatiques. Mars 2001. [4]
www.worldtravelguide.netAlgeriaweather-climate- geography.
[5] National Office of the Statistics-Algeria. www.ons.dz
[6] Hammouche, R. 1990 Atlas vent de l’AlgérieONM.
Algiers: Office des publications Universitaires OPU. [7]
M.N. Kasbadji, Wind energy potential of Algeria . Renewable energy 21. 2000. 553-562.
[8] Capderou, M. 1989 Atlas solaire de l’Algérie, Tome 2,
Aspect énergétique. [9]
Maouedj, R, Bouchouicha, K, and Benyoucef, B. 2011. Evaluation of wind energy potential in the Saharan sites
of Alegria , 10
ème
Conférence sur l’Environnement et le
Génie Electrique. Rome, Italie.
N° Manufacturer
P R
kW v
C ms
v R
ms v
F ms
01 02
03 04
1.5 se 1.5 sl
1.5 sle 1.5 xle
1.5 1.5
1.5 1.5
4 3.5
3.5 3.5
13 14
14 12.5
25 20
25 20
Site N° Turbin
N°
1 2
3 4
1 2
3 4
Algiers Oran
Adrar Ghardaia
08.36 11.04
22.12 15.85
08.10 11.13
20.01 15.22
08.10 11.13
20.01 15.22
10.34 13.02
25.18 18.41
Site N° Turbin
N°
1 2
3 4
1 2
3 4
Algiers Oran
Adrar Ghardaia
15.34 17.44
33.70 24.63
13.91 17.03
29.97 22.96
13.91 17.04
29.97 22.96
18.00 19.92
37.21 27.66
Site N° Turbin N°
1 2
3 4
1 2
3 4
Algiers Oran
Adrar Ghardaia
20.20 21.29
40.43 29.83
17.92 20.52
35.97 27.55
17.92 20.53
35.97 27.55
23.21 23.99
44.04 33.03
Volume 56
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No.5, October 2012
46 [10]
ASK. Darwish, AAM. Sayigh, Wind energy potential in Iraq. Solar and Wind Technology 1988; 5 3:215
–22. [11]
TJ. Chang, YT. Wu, HY. Hsu, CR. Chu, CM. Liao. Assessment of wind characteristics and wind turbine
characteristics in Taiwan. Renew energy 2003; 28:851 –
71. [12]
M. Elamouri, and F. Ben Amar, Wind energy potential in Tunisia. Renewable energy 33. 2008. 758-768.
[13] M. Jamil, S. Parsa, M. Majidi, Wind power statistics
and evaluation of wind energy density. Renew Energy 1995; 6: 623
–8. [14]
Johnson, G. L. 2006. Wind Energy Systems, [15]
Keoppl GW. 1982. Putnam’s power from the wind. New York: Van Nostrand Reinhold.
[16] Troen, I., Lundtang, E. 1988. European wind atlas.
Roskilde, Denmark: Riso National Laboratory. [17]
M. Amr, H. Petersen, SM. Habali. Assessment of wind farm economics in relation to site wind resources applied
to sites in Jordan. Solar Energy 1990; 45: 167 –75.
[18] Justis, C.G. Traduit et adapté par. Plazy J. L. 1982.
Vent et performances des éoliennes, édition S.C.M, Paris.
[19] DeMoor, G. 1983. Les théories de la turbulence dans la
couche-limite atmosphérique. Ministère des transports. Direction de la meteorology.
[20] Hladik, J. 1984. Energétique éolienne. Chauffage éolien.
Production d’électricité. Pompage, Masson. [21]
S.W. Mohod, M.V. Aware. Laboratory development of wind turbine simulator using variable speed induction
motor . International Journal of Engineering, Science and Technology Vol. 3, N
o
. 5, 2011, pp. 73-82. [22]
H.S. Bagiorgas,
M.N. Assimakopoulos,
D. Theoharopoulos,
D. Matthopoulos
and G.K.
Mihalakakou. Electricity generation using wind energy conversion system in the area of Western Greece.
Energy conversion and management 48. 2007.1640- 1655.
[23] T. Petru, T. Thiringer. Modelling of wind turbines for
power system studies, IEEE Trans. Energy Convers. 17 November 4 2002 1132
–1139. [24]
Muljadi, E., Pierce, K., Migliore, P., 1998. Control strategy for variable speed stall regulated wind turbines,
in: Proceedings of the American Control Conference, Philadelphia.
[25] Sathyajith, M. 2006. Wind Energy Fundamentals,
Resource Analysis and Economics, Springer. [26]
WR. Powell. An analytical expression for the average output power of a wind machine. Solar Energy 1981;
26: 77 –80.
[27] R. Chedid, H. Akiki, S. Rahman. A decision support
technique for the design of hybrid solar –wind power
systems. IEEE Trans Energy Convers 1998; 131:76 –
83. [28]
MJM. Stevens, PT. Smulders. The estimation of the parameters of the Weibull wind speed distribution for
wind energy utilization purposes. Wind Eng 1979; 3:132
–45. [29]
R. Pallabazzer. Previsional estimation of the energy output of wind generators. Renew Energy 2004;29:
413 –20
[30] Hu Ssu-yuan and Jung-ho Cheng. Performance
evaluation of paring between sites and wind turbine. Renewable energy 32. 2007. 1934-1947.
[31] H. Nfaoui, J. Bahraui. Wind energy potential in
Morocco. Renewable Energy 1991; 1:1 –8.
[32] A. El-Mallah, AM. Soltan. A nomogram for estimating
capacity factors of wind turbines using site and machine characteristics. Sol Wind Technol 1989; 6: 633
–5. [33]
JL. Torres, E. Prieto, A. Garcia, M. De Blas, F. Ramirez, A. De Francisco. Effects of the model selected for the
power curve on the site effectiveness and the capacity factor of a pitch regulated wind turbine. Solar Energy
2003; 74: 93 –102.
[34] GE Energy. 2005. 1.5 MW Series Wind Turbines.
Product brochure.
Available at:
http:www.gepower.comprod_servproductswind_turbi nes endownloadsge_15_brochure.pdf
Volume 56
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No.5, October 2012
47
Comprehensive Exploration for Proposing Hybrid Scheme upon Congestion Avoidance Algorithms
Wasai Shadab Ansari , Ijaz Ali Shoukat
, Atif M. AlAmri , Abdullah Al-Dhelaan , Mohsin
Iftikhar , Mudassar Ayub, Mohammad Serajuddin
College of Computer Information Sciences, King Saud University P. O. Box. 51178, Riyadh 11543, Kingdom of Saudi Arabia KSA.
ABSTRACT
Congestion free services are ultimate preference of every network consumer and service providers. Variety of parameters
like packet dropping rate, latency, jitter, throughput, bandwidth, fair response of resources, link utilization and queue length are
responsible to fabricate or reduce congestion. Current TCP model for high speed networks is unstable and ineffectual due to
slow response, large window size and fairness issues. The ideal and positive utilization of indicated factors can reduce
congestion up to ideal strength with enhanced fairness. These entire factors cannot be handled with single congestion handling
technique but a joint committee of congestion techniques can manage all these constraints. We considered packet loss as a
primary congestion and fairness metric that differs with already conveyed hybrid congestion techniques that utilize delay as
primary metric. We reviewed several congestion algorithms to find out most essential parameters to negate congestion in
packet switched networks among the above mentioned parameters. We proposed a hybrid congestion handling
technique after performing sufficient comparison with already conveyed hybrid congestion management techniques. Our
propose hybrid congestion management technique ECN + IFRC is empirically superior to exiting hybrid congestion
management techniques in some extents.
General Terms
Network Communication, Congestion and Flow Control, Networks Protocols, Network Services and measurements
Keywords
Congestion Avoidance, QoS, Latency, Jitter, Hybrid Congestion Schemes
1. INTRODUCTION
Most of applications require heavy contents with rapid transfer rate under the requirement of bulky bandwidth. In order to
manage bandwidth and fair response there is need to manage congestion. The information regarding the congested situation
collected by the sender may not be accurately reflecting the actual situation of congestion. This type of non-accurate
information may caused worst situation because the network behaves like a black box for newly joined source network node
or machine, therefore in order to get fresh network situation the newly joined node initiates first request with small sending rate
and increases the sending rate in next subsequent requests. The newly joined node may require the issuance of many requests in
order get complete network situation. These types of network requests creates extra load on networks that may lead to
congestion. The solution of this kind of congestion issue requires to judge the load on the behalf of hop counts and RTT
rate ratio as discussed by Ijaz A. Shoukat and Iftikhar M. According to their opinions, network path is substantially
amended with sufficient increment in hop counts and RTT values when congestion occurs
[1] . Congestion management is
reliant on four mutual algorithms Efficient Retransmission, sluggish Start, Congestion Avoidance, Quick Recovery and all
these algorithms can be implemented under generic congestion handling protocols
[2] : 1
Buffer based Congestion Protocol
-
in which every node sends the packet to its downward node close to it if and only if the receiving node has some buffer
capacity. 2
Rate Based Congestion Protocol
– in which transmission rate is measured through both incoming and
outgoing packet streams among the neighboring nodes by calculating the weight function. 3
Priority Based Congestion Protocol
[3] – it deals with measuring hope’s congestion
severity through packet arrival rate and priority index of node depending upon the weight of fairness.
Congestion investigation and control management can be done in three ways: 1 Detection, 2 Notification and 3
Adjustment of transmission rate
[3] .
Any control protocol that deals only with either index delay based indicator or loss
based indicator cannot perform ideally against real time streaming video applications in a high speed network because
for real time video streaming delays are not tolerated by users. Standard TCP mechanism and its several relatives like TCP
New-Reno, TCP Illinois etc., are not sufficiently enough to deal with streaming application
[4 ]. Our concern is with QoS of
remote servers in network environment that mostly get engaged with congestion. We proposed a hybrid congestion handling
technique that employs the ideal utilization of all congestion parameters to get enhanced result avoid congested situation.
2. LITERATURE REVIEW
