SET2011, 10th

International Conference on Sustainable Energy Technologies, İstanbul, TÜRKİYE, 4-7 Sep. 2011

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RELIABILITY ASSESSMENT OF WIND ENERGY SYSTEM

CONSIDERING TURBINE DIMENSIONS

B. Kekezoglu1

, A. Erduman1, A. Durusu

1, İ. Nakir

1, M.Tanriöven

1

1Yildiz Technical University, Electrical & Electronic Faculty, İstanbul

Abstract

The importance of renewable energy sources has increased rapidly due to run out of fossil fuels and environmental effects of

conventional energy production systems. As one of the renewable energy sources, wind energy has considered to be a leading

alternative energy source by reason of vast energy potential of wind. Due to random characteristics of wind speed, electric

generation reliability of wind energy system is affected considerably both in stand-alone and grid connected configurations. The

disadvantage could be minimized by making use of the additional wind energy potential. For this purpose, there are three basic

methods i.e. increasing the hub height, rotor diameter and generator rated power, which can be utilized. In this study, reliability

indices are calculated based on the measured field wind and load data for Bandırma, Turkey. As a result, the effects of hub

heights for different types of wind turbines on reliability are examined. Eventually, the most appropriate options for the Bandırma

region are presented.

Keywords: Wind Energy, Wind Turbine Dimensions, Reliability

1. Introduction

The penetration of wind energy conversation systems (WECS) to electric power grids are on the increase. One of the

main goals of modern power systems is to supply the power with very high reliability level. Due to the random

nature of wind speeds, the power out of WECS continuously fluctuates. This feature has a negative effect on the

reliability. Since then, the reliability aspects of renewable energy sources have growing importance in power

systems.

In the literature, many studies conducted on the reliability of wind turbines. P. J. Tavner et. al. realized the prediction

of reliability of large wind turbines by using grouped wind data [1]. O. Ozgener and L. Ozgener have performed

reliability analysis of a 1,5kW wind turbine on Izmir, Turkey [2]. B. Hahn et al. presented the reliability of wind

turbines in Germany [3]. Haitao Guo et. al. achieved reliability analysis of wind turbines with incomplete data

collection by using three parameter Weibull function [4]. Johan Ribrant and Lina Margareta Bertling studied

reliability performance of the different components within the wind turbine statically [5]. F. Spinato et. al. analyzed

the reliability of wind turbine parts [6]. The effects of wind speed on wind turbines availability are investigated by S.

Faulstich et.al [7].All of these studies shows the significance of reliability of wind energy conversation systems.

The percent of wind energy generation in Turkey’s electrical power system is on the raise. Therefore, reliability

analysis of wind turbines is necessity for Turkey.

In this study, reliability indices are calculated based on the measured field wind and load data for Bandırma, Turkey.

Three different wind turbine classes with variety of hub height, among the commercially available ones, are taken

into account to shown the performance and reliability terms of Bandırma. Eventually, the most appropriate options

for the Bandırma region are presented.

2. Background and Notations

Electrical power output of a wind turbine has a critically importance on reliability. Mathematical background for

calculation of wind turbine power output and performance/reliability terms is given below.

E-mail: bkekez@yildiz.edu.tr

SET2011, 10th

International Conference on Sustainable Energy Technologies, İstanbul, TÜRKİYE, 4-7 Sep. 2011

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2.1. Wind Turbine Output Power

Electrical power output of a Wind turbine is obtained from a wind generator which is driven by the mechanical

energy of rotor blades. Herein, the power output is given as a function of wind speed, [8],

(1)

where, is power coefficient, is swept area and is the air density.

The reliability of a wind power plant related to wind turbine output power. Power generation of a wind turbine can

be improved by increasing the tower height. Correlation between wind speeds at different heights is shown below.

where is wind speed at tower height , is wind speed at tower height and is the roughness coefficient. In

this study roughness coefficient value is 0,14.

2.2. Wind Turbine Performance and Reliability Indices

Before the installation of a wind turbine, performance and reliability terms must be analyzed. Some of these terms

are explained below.

2.2.1. Capacity Factor (CF)

One of the important parameters used to measure the efficiency of energy generation systems is the capacity factor.

The capacity factor for a wind turbine can be given as in Equation (3).

(3)

where, is the annual energy output of the turbine and is the rated wind turbine power. The

capacity factor can be rearranged based on the Weibull pdf as below [8]:

(4)

where, is the shape factor and is the scale factor, , and are cut-in, rated and cut-out wind speeds

respectively. The symbol, represent the incomplete gamma function.

2.2.2. Loss of Load Expectations (LOLE)

is the total time period which the power generation cannot meet the load demand. is especially

considered for stand-alone applications. is calculated by using Equation (5) [9].

where is the time step, is the number of time step, is the power generation available in time period , is

the load demand in time period .

SET2011, 10th

International Conference on Sustainable Energy Technologies, İstanbul, TÜRKİYE, 4-7 Sep. 2011

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2.2.3. Availability

Availability is a reliability term which shows the effectiveness of wind turbines. By using availability factor, the

most suitable wind turbine can be matched to sites. Availability is defined as the percent of total time of power

generation on a time period. Availability is given as Equation (6) [9],

where, is the total power generation time, is the time period that turbine is unavailable.

3. Case Study

In this paper, five daily measured load data is used for reliability analysis of Bandırma, TURKEY, which is in

northwestern of Turkey and one of region with the highest wind speed potential. In the case study, load values are

assumed constant for each month. The measured load profile of Bandırma is shown in Figure 1 (a).

(a)

(b)

Figure 1. (a) Load Profile of Bandırma

(b) Wind Speed Characteristic of Bandırma

Conventional Weibull probability density function (pdf) is the most commonly used function to represent the wind

distributions for the site of which wind regime has a unimodal characteristic. Weibull pdf is defined with two factors:

the shape factor, k and the scale factor, c. The shape factor is 1,3786 and the scale factor is 7,0857 for examined site

at 85m hub heigth. Wind speed characteristics of Bandırma is given in Figure 1 (b).

In this study, three different wind turbine classes, among the commercially available ones, are taken into account to

shown the performance and reliability terms of Bandırma. The wind turbines options and their technical parameters

are given in Table 1. In the case study, wind turbines are examined for three different hub heights (85m, 100m,

115m).

Table 1. Turbine Technical Properties

TURBINE Rated Power

(kW)

Rotor Diameter

(m)

Vcut_in

(m/s)

Vrated

(m/s)

Vcut_out

(m/s)

Generator

Type

Turbine 1 3000 82 3 15 28 Synchronous

Turbine 2 3000 111 3 13 25 Asynchronous

Turbine 3 1500 82,5 3,5 13 25 Asynchronous

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International Conference on Sustainable Energy Technologies, İstanbul, TÜRKİYE, 4-7 Sep. 2011

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4. Results

In this paper, capacity factor, loss of load expectations and availability terms are calculated for Bandırma. The

results are given below.

In table 2, capacity factor of investigated wind turbines at different hub heights are shown.

Table 2. Capacity Factors of Examined Wind Turbines at Different Hub Heights

Turbine 1 Turbine 2 Turbine 3

85m 100m 115m 85m 100m 115m 85m 100m 115m

Capacity

Factor 0,4848 0,5036 0,5200 0,5646 0,5848 0,6024 0,5125 0,5318 0,5486

As can be seen from the table, Turbine 2 has the highest capacity factor at all hub heights. In other words, Turbine 2

is the most efficient choice for Bandırma in these three wind turbines.

Table 3. LOLE Index at Different Hub Heights

Turbine 1 Turbine 2 Turbine 3

85m 100m 115m 85m 100m 115m 85m 100m 115m

January 33 33 33 12 12 3 15 12 12

February 63 63 60 69 63 63 63 63 63

March 57 54 54 39 33 33 39 36 33

April 60 60 57 57 54 48 51 51 51

May 96 96 93 84 84 81 84 84 84

June 72 72 72 69 69 66 66 66 66

July 63 63 60 48 48 36 45 45 45

August 102 102 102 99 93 90 96 93 90

September 39 39 39 21 21 18 24 21 18

October 36 33 30 12 12 12 12 12 12

November 51 51 51 48 48 45 48 48 48

December 30 30 30 24 24 21 27 27 27

As will be seen from Table 3, LOLE Index values decreased with rising hub height for all turbines. Turbine 1 has the

worst LOLE value because of its power output characteristic. The most successful turbine in terms of load

expectation is Turbine 2.

Figure 2. LOLE Index at Different Hub Heights

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12

LOLE

(h

)

Month

100-120

80-100

60-80

40-60

20-40

0-20

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International Conference on Sustainable Energy Technologies, İstanbul, TÜRKİYE, 4-7 Sep. 2011

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LOLE index values of examined wind turbines for variety of hub height are graphically shown in Figure 2. In the

figure, it is clearly seen that LOLE index is increasing in summer months. Moreover, especially in autumn and

winter, a large part of load could not be supplied.

Availability levels of investigated wind turbines for variety of hub heights are presented in Table 4.

Table 4. Turbine Availability at Different Hub Heights

Turbine 1 Turbine 2 Turbine 3

85m 100m 115m 85m 100m 115m 85m 100m 115m

January 97,5 97,5 97,5 97,5 97,5 97,5 97,5 97,5 97,5

February 100 100 100 92,5 92,5 90 90 90 90

March 100 100 100 90 90 90 90 90 90

April 100 100 100 95 95 95 92,5 92,5 95

May 100 100 100 80 80 80 77,5 77,5 80

June 97,5 97,5 97,5 82,5 82,5 82,5 82,5 82,5 82,5

July 100 100 100 100 100 100 100 100 100

August 97,5 97,5 97,5 72,5 72,5 72,5 72,5 72,5 72,5

September 100 100 100 95 95 95 95 95 95

October 100 100 100 97,5 97,5 97,5 97,5 97,5 97,5

November 97,5 97,5 97,5 90 87,5 87,5 90 87,5 87,5

December 100 100 100 100 100 100 100 100 100

As will be seen from the table, Turbine 1 is the best one for availability term. By reason of its low cut-in speed and

high cut-out speed, Turbine 1 generates electrical output power in a wide range of wind speed. By the rising hub

height, Availability values of Turbine 2 and Turbine 3 increased in March and April. However, Availability values

of Turbine 2 and Turbine 3 increased in November, despite the rising hub height. The reason is that, Turbine 2 and

Turbine 3 have low cut-out speed.

5. Conclusions

In this study, reliability indices are calculated based on the measured field wind and load data for Bandırma, Three

different wind turbine classes with variety of hub height, among the commercially available ones, are taken into

account to shown the performance and reliability terms of Bandırma. As a result, the effects of hub heights for

different types of wind turbines on reliability are examined and the most appropriate options are presented.

According to results, Turbine 2 is the most efficient choice for Bandırma in these three wind turbines with the

highest capacity factor. LOLE Index values decreased with rising hub height for all turbines and the highest LOLE

values calculated for summer months. The most suitable choice for Bandırma is Turbine 2 in terms of LOLE.

Finally, Turbine 1 is the best wind turbine in the sense of availability and also availability term is affected by the hub

height.

Acknowledgements

Authors would like to thank the Public Research Support Group (KAMAG) of the Scientific and Technological

Research Council of Turkey (TUBITAK) for full financial support of the project namely the National Power Quality

Project of Turkey, Project No: 105G129.

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International Conference on Sustainable Energy Technologies, İstanbul, TÜRKİYE, 4-7 Sep. 2011

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