On the Selection of Wind Turbine Generator based on ...

2013 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology

On the Selection of Wind Turbine Generator based on ARMA Time Series M.T.Askari1,2*, M.Z.A. Ab Kadir1, H.Hizam1, J.Jasni1 Universiti Putra Malaysia, Malaysia, Electrical& Electronic Dept. 2 Department of Electrical Engineering, Semnan Branch, Islamic Azad University, Semnan, Iran 1

Selecting the compatible WTG can be considered as one of most important factors to increase the profit of wind GENCOs. Therefore the investors can ask their consulters to consider this parameter in their study. In this article, the effect of different types of WTG has been compared and simulated in a specific area.

Abstract - By increasing the global warming and environmental challenges the industrial countries commit to reduce the gas emissions and develop the renewable energy based on the Kyoto protocol and also renewable portfolio standard (RPS). Among them, wind resources are more important because of the progress in the technology of its facilities to convert wind to electricity. Wind turbines manufacturer companies improve the efficiency of their facilities during the last decade. On the other hand, the wind generator firms and investors intend to select a suitable wind turbine generators (WTGs) based on the wind distribution in a specific region. In this study, Output power (MW) of four different types of WTGs has been compared. The wind speed data has been collected for 10 years from 2000 to 2009 from Canada Swift Current region. Then, the model of wind speed has been predicted with ARMA time series. In the next stage, the output power of each WTG has been determined with probability method. Finally, the suitable WTG is recommended according to the output power produced.

The rest of this paper is organized in the following order. Section (II) describes the technique for modeling the wind farm and proposed a framework for calculating the amount of WTGs output. The case study has been given in section (III). The results have been discussed in section (IV). Finally, the last section is devoted to conclusion. II.

A. Hourly wind modeling with ARMA time series An Autoregressive moving average (ARMA) time series [6] was used as the wind speed model to simulate the hourly wind speeds. In which, the wind speed time series is modelled by Equations (1) and (2).

Keywords— Renewable Energy, WTG, ARMA time series

I.

INTRODUCTION

related to the decreasing availability of fossil fuel and the increase of environmental problems has motivated the authorities to develop sources of renewable energy[1]. Due to the fast growth in the technology of wind turbines, wind generation attracts most attention compared to other methods of generating clean power[2]. Wind power is an abundant, widely distributed energy resource that has zero fuel cost, zero emissions and zero water use. Wind’s challenges are largely related to its variable nature – wind speed and direction can change by the season, day, hour and minute [3, 4]. Wind’s challenges are largely related to its variable nature wind speed and direction can change by the season, day, hour and minute. For electricity grid operators the variability of wind sometimes too much wind is blowing and at others too little makes it difficult to integrate wind into a grid that was not designed for fluctuations. Moreover, surplus wind power cannot be stored, given current technology. Although the policy makers try to provide a suitable conditions due to participate the wind energy in power market, the wind plants are not able to compete with conventional power plants. The major reasons are include the intermittent nature of wind that causes to reduce the reliability of power systems and also high capital investment of wind plants that the barrier to develop it[5].

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MODELING THE WIND RESOURCES

yt =(OWt − μt )/δt

(1)

n m y = ∑ ϕ y +α − ∑ θ α i t −i t j t− j t i =1 j =1

(2)

Where y, is the time series value at time t, ϕ (i = 1, 2, 3, ..., n ) , and θ (i = 1, 2, 3,..., m ) are the autoi i regressive and moving average factors of the model respectively.{α } is a normal white noise process with zero t mean and a variance of σ a2 (i.e. αt ∈NID (0,σ a2 ) ), where NID denotes Normally Independently Distributed. The simulated wind speed SW, at each hour can be obtained from the historical mean wind speed µt and its standard deviation δt [6-8], SWt = μt +δt × yt

42

(3)


One of the most important advantages of this method is the correlation with previous hours. For a specific area, after hourly modeling of the wind speed with ARMA time series the occurrence probability of each scenario of wind speed has determined. After that, by using the power curve of wind turbine can estimate the output of wind turbine generator (WTG) for each scenario. Figure 1 illustrates the power curves used to determine the power production by each wind turbine. From this figure, it can be seen that a wind generator will begin to generate electricity at the cut-in speed (Vci) and will trip at the cut-out speed (Vco) for its own safety. The maximum power will be generated between the rated speed (Vr) and cut-out speed. There is a nonlinear relationship between the power output and the wind between the cut-in speed Vci and the rated speed Vr. Equation 4 provides a mathematical expression to calculate the power generated by a WTG. The terms A, B, and C are constant values that can be calculated by Equation 5, Equation 6, and Equation 7 [9-12].

Fig. 2. Flowchart of determining the WTG’s output

The aim of this study is to compare and designate the suitable WTG. Therefore the average of WTGs outputs calculate and give as criteria to compare the different types of WTGs. 0 ⎧ ⎪ ⎪ PGewind = ⎨ Pr ( A+ B×WS + C ×WS 2 ) ⎪ Pr ⎪⎩

A=

B =

C=

Vci ≤WS ≤Vr Vr ≤WS ≤Vco 3⎫ ⎪ ⎬ ⎪ ⎭

3 ⎫ ⎧⎪ ⎡ V +V ⎤ ci r ⎥ − (3V +V ) ⎪⎬ 4( ) V V + ⎢ ⎨ ci r ⎪ (Vci −Vr ) 2 ⎩⎪ ci r ⎣⎢ 2Vr ⎥⎦ ⎭ 1

⎡ V +V ⎪⎧ ci r ⎨2− 4 ⎢ ⎢⎣ 2Vr (Vci −Vr ) 2 ⎪⎩ 1

3⎫ ⎤ ⎪ ⎥ ⎬ ⎥⎦ ⎪ ⎭

Case Study

The Centennial Wind Power Facility is SaskPower facility situated in the hills 25 kilometres southeast of the town of Swift Current, Saskatchewan. Four different of types of WTGs has been selected with various outputs. It should be noted that all types of wind turbine have been manufactured in the same company. The Swift Current wind data are used in this article has been gathered from the Canada’s National Climate Archive since 2000 until 2009. The histogram has been shown in figure (3).

0≤WS ≤Vci orWS ≥Vco

⎧⎪ ⎡ V +V ⎤ ci r ⎥ ⎨Vci (Vci +Vr ) − 4VciVr ⎢ 2 (Vci −Vr ) ⎩⎪ ⎣⎢ 2Vr ⎦⎥ 1

III.

(4)

(5)

Table I illustrates the specifications of various types of Vestas Company.

(6)

TABLE I. Characteristics of Vestas Company turbines (7)

Turbine

V_ci

V_co

Power

Rotor diameter

Default height

[MW]

The flowchart of this paper has been indicated in Figure 2.

Fig. 1. Power curve of a wind turbine

43

Vestas V80

4

25

1.8

80

67

Vestas V80

4

25

2

80

67

Vestas V66

4

25

2

66

67

Vestas V66

4

25

1.75

66

67


economic aspects and just focus on this problem by considering the adequacy of wind farms. This article can be completed by considering the Microeconomics discussion that dealing with the decision making and performance.

Histogram of wind speed 900 800 700 600 500 400 300 200 100 0

F re q u e n c y

TABLE II. Average output power WTGrated power[MW]

V80-1.8

V80-2

V66-2

V66-1.75

Average output power [ MW]

0.3721

0.3952

0.2774

0.2687

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69

TABLE III. Comparison of different types of WTGs output in Swift current region

Wind speed

Fig. 3. Histogram graph of wind speed in Swift Current region

WTG

WTGrated power[MW]

V80-1.8

V80-2

V66-2

V66-1.75

Annual Wind power [ GW]

270.547

287.342

201.692

195.366

0.1 0.09 0.08

V.

Probablity

0.07

By increasing the global warming and environmental challenges the industrial countries commit to reduce the gas emissions and develop the renewable energy based on the Kyoto protocol and also renewable portfolio standard (RPS). Among them, wind resources are most important because of the progress in the technology to convert wind to electricity. Wind turbines manufacturer companies improve the efficiency of their facilities during the last decade. On the other hand, the wind generator firms and investors intend to select a suitable wind turbine generators (WTGs) based on the wind distribution in a specific region. In this article wind power generation for a specific region has been modeled for four different types of WTGs. The wind speed data has been collected for ten years from 2000 to 2009 from Canada Swift Current region. Then, the model of wind speed has been predicted with ARMA time series. In the next stage, the output power of each WTG has been determined with probability method. And finally, the suitable wind turbine has been offered according to the output power of WTGs. The results in this article shows the importance of this concepts as an important parameters which can be considered in planning of wind farms.

0.06 0.05 0.04 0.03 0.02 0.01

1

1

1

1

1

1

1

0.99

0.98

0.86

0.54

0.23

0

0.06

0

0

0 0

CONCLUSION

Wind Turbine Output (Pu)

Fig. 4. Histogram graph of electricity generated with Vestas V80 wind turbine in Swift Current region IV. Results and Discussion The main objective of this study select the suitable wind turbine according to the amount of electricity generated with different types of WTGs. Since, the technical features of various WTGs company differ from each other, in this article, only the Vestas turbines have been investigated. And also the Swift Current wind farm as a windy region has been studied in this article. The results have been shown in the Table II. The average output electricity power has been calculated for each type of wind turbines. The outputs are only related to one turbine. In each farm there are more than one WTG, for example in Swift Current there are 83 wind turbines V801.8MW. According to the results has been given in this study can be concluded that the planner can increase the amount of electricity from wind turbine around 0.0231MW for each turbine by substituting the V80-2MW with the current turbine. In this case the annual average electricity generated can increase to 16795 MV. The annual output of WTGs has been indicated in Table III. In this article did not consider the

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