PDF《wind energy conversion system》

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34 algorithm with small step size is slow in tracking the MPP, specially in rapid wind variations. On the other hand,

35 by increasing the step size to make the method faster, there will be unwanted oscillations around the maximum

36 point and the MPPT accuracy and e?ciency will be reduced [6, 14]. Two-step-value HCS algorithm is proposed

37 in [15, 16] in order to improve the performance of this method. Variable step size has also been considered to

38 overcome the aforementioned con?icting problem of conventional HCS method [17]. These methods, although

39 improve the performance of HCS algorithm, but do not completely present satisfactory performance, especially

40 in rapidly varying wind speeds. In addition to the aforementioned MPPT algorithms, some other methods have

41 been proposed in the literature. Fuzzy logic control has been employed for implementing variable-speed HCS

42 2 HESHMATIAN et al./Turk J Elec Eng &

Comp Sci algorithm [18, 19]. Some e?orts have been made in order to use neural networks for realization of MPPT in

1 WECSs [20, 21]. Sliding mode control (SMC) is also applied to WECSs in [19, 22] for maximizing the captured

2 energy.

3 In this paper, a control structure is proposed for a standalone WECS. This control scheme includes

4 an improved MPPT algorithm which eliminates the aforementioned problems of the P&

O method in case of

5 tracking the optimum point. Also, the system is designed based on Vienna recti?er because of its high e?ciency

6 and good performance and is controlled by a multi-objective MPC strategy for fast and accurate tracking of the

7 optimal reference point obtained from the MPPT algorithm. A secondary control objective is considered in the

8 cost function in order to limit the switching frequency and hence, reduce the power losses. The proposed control

9 scheme shows great performance and is advantageous over conventional approaches in terms of extracting the

10 maximum possible power and transferring it to the load by providing fast and accurate response, especially for

11 wind speed pro?les with fast variations.

12 2. System con?guration and modeling

13 Figure

1 shows the overall con?guration of the studied WECS with the designed control scheme. In this section,

14 modeling and analysis of the main parts are presented.

15 Figure 1. Studied WECS. 2.1. Wind turbine modeling

16 The mechanical output power for a variable-speed wind turbine is given by the following expression.

17 Pm =

1 2 cpρAV

3 w (1)

3 HESHMATIAN et al./Turk J Elec Eng &

Comp Sci where Pm is the mechanical power, Vw is the wind speed and ρ represents the air density. A is the swept area

1 by the blades and is given by A = πR2 where R is the radius of the blades. cp represents the power coe?cient

2 and is described by a non-linear function of the blades pitch angle (β ) and the tip speed ratio (λ) as expressed

3 in (2) and (3) [23]. The ratio between the linear velocity of tip of the blades and the e?ective wind speed around

4 them is called the tip speed ratio and is given in (4). Table

1 shows the coe?cients in (2).

5 cp(λ, β) = ........