JP2009091923A - Maximum power point follow-up control device for wind power generation device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a maximum power point follow-up control device for performing follow-up control of an optimum rotating speed to produce maximum output depending on a wind speed even when a wind mill parameter for a wind power generation device is changed. <P>SOLUTION: The maximum power point follow-up control device comprises a rotating position and speed measuring instrument for measuring the rotating position and speed of the rotor of a generator 3, a torque meter for detecting the rotating torque of the generator, a wind speed meter 8 for measuring a wind speed, a parameter identifier 11, an optimum rotating speed setter 12, and a speed controller 13, a current controller 17 and a coordinate converter 16. The parameter identifier 11 uses an input/output measured value for the generator and wind speed information to identify the loss coefficient of a wind mill being operated with sequential minimum square estimation. The optimum rotating speed setter 12 uses the identified loss coefficient to determine an optimum rotating speed equivalent to a maximum power point. The controllers 13, 17 control the determined optimum rotating speed as a target value to actualize the maximum power point follow-up control of the wind power generation device. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a control device that enables high-efficiency power generation for a wind turbine generator, and more particularly to a device that controls the maximum power point by online identification of wind turbine parameters.

  In recent years, development of natural energy has been promoted from the viewpoint of raising awareness of environmental conservation due to global warming, rising prices of fossil fuels, and resource depletion. Among them, wind power generation is a power generation system that uses environmentally friendly wind energy, and is being actively built around the world. In Japan as well, the government is encouraging the introduction of wind power generation with the goal of 3 million kW of wind power generation by 2010.

  However, while wind energy is inexhaustible clean energy, wind power is an unstable energy source that is extremely variable, and the power generated by wind power changes in proportion to the third power of the wind speed and is highly irregular. There is a problem that the energy density is only about 1/800 compared with the water energy which is a fluid, the wind speed is irregular, and the power generation efficiency is poor.

Since wind turbines have different optimum rotational speeds for each wind speed to obtain the maximum output depending on the characteristics, the wind turbine can be controlled at a variable speed to obtain the maximum output to obtain more generated power. It is effective to drive at a speed that is possible.
Until now, in order to determine the optimum rotation speed, a method using a hill climbing method, a method using a characteristic parameter of a windmill, and the like have been proposed.

  In Patent Document 1, a wind speed sensor is provided, and the output current of the generator is controlled so as to be a current operating point corresponding to a torque operating point at which the shaft output is maximized according to the wind speed, so that the maximum is obtained at an arbitrary wind speed. A wind turbine generator is disclosed in which a wind turbine is operated at a torque operating point at which the shaft output can be obtained and the generated power can be collected in a battery with high efficiency.

However, in the method of determining the rotational speed command value using the windmill parameters given in advance, when the windmill characteristics change due to ice and snow sticking to the windmill blade, the given windmill parameters are assumed to be actual. Because of the difference, maximum power point tracking control cannot be achieved.
Therefore, in order to obtain the optimum load amount and rotation speed corresponding to the amount of wind energy for each wind speed, a data table obtained in advance by design calculation is prepared, and errors of various coefficients due to differences in wind speed are taken into account. Correction is performed, and control is performed in combination with feedforward control that determines the wind speed versus rotational speed and the wind speed versus shaft output.
However, this correction is accumulated based on results, and it is a problem that correction is required for each wind turbine generator.

Patent Document 2 discloses a control method for a wind turbine generator that forcibly changes the amount of load that can be supplied as electric power, detects the change in the rotational speed of the windmill that occurs at this time, and determines the axial output of the windmill. ing.
The disclosed method uses the fact that the load amount matches the shaft output of the windmill when the rotation speed does not change even if the load amount is forcibly changed.

The disclosed method does not rely on a design data table that must be verified and corrected for errors, but it accumulates the actual values in the actual device and ensures that the maximum output point of the wind turbine can be tracked in a short time. Efficient operation can be performed.
However, in the disclosed method, in order to obtain the extreme value of the characteristic curve, it is necessary to give an unnecessary load amount fluctuation to prevent efficient energy use and disturb smooth operation.
JP 2002-315396 A JP 2004-129395 A

  Therefore, the problem to be solved by the present invention is to provide a maximum power point tracking control device for a wind turbine generator that performs tracking control of an optimum rotational speed that can obtain a maximum output according to the wind speed even if the wind turbine parameter changes. Is to provide.

In order to solve the above problems, the maximum power point tracking control method for a wind turbine generator according to the present invention identifies a loss factor of an operating wind turbine by successive least squares estimation using measured system input output and wind speed information. Then, the optimum rotational speed corresponding to the maximum power point is determined using the identified loss coefficient, and the wind turbine generator is controlled using the designated optimum rotational speed as a target value.
With the control method of the present invention, even when parameter fluctuation occurs, it is possible to identify the exact parameter and set the optimum rotation speed to perform wind power generation, and achieve maximum power point tracking control of the wind power generator. A more efficient power generation output can be obtained.

  The maximum power point tracking control device for a wind turbine generator according to the present invention is a wind turbine generator in which a rotating shaft of a generator is connected to a rotating shaft of a windmill, and a pulse width adjustment (PWM) converter device is connected to a stator of the generator. A rotational position speed measuring device that measures the rotational position and rotational speed of the generator rotor, a torque meter that detects the rotational torque of the generator, an anemometer that measures the wind speed, a parameter identifier, and an optimum rotational speed A setting device, a speed controller, a current controller, and a coordinate converter are provided.

The parameter identifier calculates wind turbine parameters based on the input rotational speed, rotational torque and wind speed information of the generator rotor, and supplies them to the optimum rotational speed setter.
The optimum rotational speed setter calculates the wind turbine rotational speed corresponding to the maximum power point from the wind speed information and the wind turbine parameters, and supplies it as a target value for the speed controller.
The speed controller inputs the rotation speed of the generator rotor, calculates a dq axis current command value by control calculation, and supplies the dq axis current command value to the current controller as a current command value.
The current controller calculates and calculates a dq-axis voltage command value for the dq-axis current measurement value input via the coordinate converter, and outputs a voltage command to the PWM converter device via the coordinate converter. Supply as value.
The PWM converter device controls the rotation of the generator.

The maximum power point tracking control device of the present invention can perform wind power generation by tracking the maximum power point of the windmill.
In addition, the parameter identifier can obtain | require a windmill parameter sequentially by sequential type | formula least square estimation.

Hereinafter, a maximum power point tracking control device of a wind turbine generator according to the present invention will be described in detail with reference to the drawings.
FIG. 1 is a main configuration diagram of a wind turbine generator provided with a maximum power point tracking control device according to the present embodiment.

In FIG. 1, the rotor of the induction generator 3 is connected to the shaft of the windmill 1 via the speed increaser 2. When the windmill 1 is rotated by _wind energy P _wind , the induction generator 3 is rotated by the windmill 1. It generates an AC power P _out in response to the induction generator drive frequency and the speed ω _w.
A PWM converter 4 is connected to the stator of the induction generator 3, and variable frequency AC power P _out generated by the induction generator 3 is converted into DC power. The PWM (pulse width adjustment) converter 4 is connected to the PWM inverter 5 via a capacitor, converts the DC power of the PWM converter 4 into AC power of a fixed frequency and supplies it to the power system 6.

Wind energy P _w obtained from the wind turbine 1 is transmitted to the induction generator (IG) 3 via a speed increaser second amplification ratio R _n. The induction generator 3 is speed-controlled by the PWM converter 4 using an accurate position and rotational speed measured by a rotary encoder (not shown).
In the wind power generator of this embodiment, the maximum power point tracking control device 7 is attached, and the optimum rotational speed is determined while identifying the parameters of the wind turbine online, and the generator is vector controlled so that the maximum output is always obtained. To do.

In the wind turbine generator shown in FIG. 1, when the radius of the windmill blade is R ₀ , the blade rotation area S is πR ₀ ² . If wind wind speed V _w is blow windmills, when the air density and [rho, if the energy of the wind is all converted to the wind turbine rotor torque, the input energy P _wind is represented by the following formula (1) .
^{_{P wind = ρπR 0 2 V w}} 3/2 (1)
Further, the wind turbine input torque T _{wind at} this time is expressed by the following equation (2).
^{_{T wind = ρπR 0 3 V w}} 2/2 (2)

The wind turbine output coefficient C _p and the wind turbine torque coefficient C _t are used as indices representing the general characteristics of the wind turbine. The indices for the propeller type wind turbine are represented by the following equations (3) and (4).
C _t = αλ ² + βλ + γ (3)
C _p = λC _t = P _w / P _wind (4)
Here, α, β, and γ are coefficients determined by the size, shape, number, and pitch angle of the blades, λ = R ₀ ω _w / V _w is the blade peripheral speed ratio, ω _w is the rotational angular velocity of the windmill, and P _w is This is the output energy of the windmill.

When the torque T _w that can actually be taken out from the wind turbine using the wind turbine torque coefficient C _t is expressed by the following equation (5).
^{_{T w = C t ρπR 0 3}} V w 2/2 (5)
It becomes.
The equation of motion of the windmill when it receives this windmill torque is expressed by the following equation (6).
T _w = T _wind −T _f
= J _w dω _w / dt + T _lw (6)
Where T _f is the wind turbine loss torque, J _w is the wind turbine inertia moment, and T _lw is the wind turbine load torque.

The wind turbine loss torque T _f is given by the following equation (7).
T _f = K ₀ V _w ² + K ₁ V _w ω _w + K ₂ ω _w ² (7)
Here, K ₀ , K ₁ , and K ₂ are wind turbine loss coefficients and are expressed by the following equations (8), (9), and (10).
K ₀ = ρSR ₀ (1-γ) / 2 (8)
K ₁ = ρSR ₀ ² ζ / 2 (9)
K ₂ = ρSR ₀ ³ ξ / 2 (10)
Here, γ, ζ, and ξ are coefficients determined by the size, shape, number, and pitch angle of the wind turbine blades.
In other words, the windmill loss coefficient is a coefficient determined by the shape of the windmill, and is an approximate parameter expressing that the windmill loss torque is convex downward.

The loss torque T _{f of the} windmill can be expressed by the following equation (11) from the equations (2) and (5).
T _f = T _wind −T _w
= (1-C _t ) T _wind (11)
When the speed increase ratio R _n is used, the relationship between the rotational speed ω _{w of the} wind turbine and the rotational speed ω _{m of} the generator, and the relationship between the torque T _{lw of the} wind turbine and the torque T _lm of the generator is expressed by the following equation (12). The
ω _m = R _n ω _w (12)
T _lm = −T _lw / R _n (13)
From equation (6), the equation of motion of the windmill can be expressed by the following equation (14).
T _wind = J _w dω _w / dt + T _f + T _lw (14)
Finally, the mechanical input P _w to the generator is expressed by the following equation (15).
P _w = T _w ω _w = −T _m ω _m (15)

FIG. 2 is a diagram showing the wind turbine output characteristics for each wind speed. In the graph, the horizontal axis indicates the rotational speed ω _w of the wind turbine rotor, and the vertical axis indicates the output P _wind of the wind turbine for each _wind speed.
The wind turbine has an optimum rotational speed at which the maximum output can be obtained depending on the wind speed. Therefore, in order to obtain more electric power, it is preferable to operate the wind power generator by selecting a rotation speed at which the maximum output can be obtained for each wind speed.
Therefore, the optimum rotational speed at which the output of the windmill is maximized is obtained from the basic equation of the windmill. The wind turbine output P _w in the steady state, that is, the input to the generator is a cubic function of the wind turbine rotational speed ω _w as shown in the following equation (16) from the relationship of the equations (6) and (15).
P _w = (T _wind −T _f ) ω _{w
= (ΡSR 0/2-K} 0) V w 2 ω w -K 1 V w ω w 2 -K 2 ω w 3 (16)

By using the following equation (17) and the following equation (18) indicating this solution, the wind turbine is obtained by obtaining a rotation speed at which the value of the equation obtained by partial differentiation of the equation (16) with respect to the wind turbine rotation speed ω _w becomes zero. An optimum rotational speed ω _w ^{opt at} which the output P _w becomes a maximum value can be obtained.
dP _w / dω _w
= (ΡSR ₀ / 2-K ₀ ) V _w ² −2K ₁ V _w ω _w −3K ₂ ω _w ² = 0 (17) ω _w ^opt
= (-K ₁ V _w + ((K ₁ V _w ) ² -3K ₂ (ρSR ₀ ³ / 2-K ₀ ) V _w ² ) ^1/2 ) / 3K ₂
(18)

The optimum generator rotational speed ω _m ^opt is expressed by the following equation (19), where R _n is the speed increasing ratio.
ω _m ^opt = R _n ω _w ^opt (19)
Therefore, the generator is controlled so that the optimum generator rotational speed ω _m ^opt can be obtained from the wind speed and the wind turbine loss coefficient based on the equations (18) and (19), and the wind turbine is adjusted to the optimum rotational speed ω _w ^opt . By making it follow, maximum power point tracking control can be achieved.

In order to obtain the optimum rotational speed in a state where the windmill parameters can change, such as ice and snow sticking to the blade, it is necessary to know the windmill parameters at that time.
The least square method is often used for off-line identification in which input / output signal sequences are observed and recorded and parameters are identified away from the system. In the offline identification using the least square method, calculation is performed when N pieces of data are collected. However, when new data is additionally observed, the entire calculation must be performed again.

On the other hand, each time new data is obtained, a new estimate is obtained by using the data obtained sequentially after the system starts to operate and updating the estimate from the data obtained so far at each time point. Iterative least squares estimation can be used.
The identification method using recursive least square estimation is an on-line identification method in the sense of giving parameter estimates every moment.
Sequential least square estimation is obtained by iteratively transforming least square estimation into a calculation formula.

In the successive least square estimation, when an input signal u that changes to some extent with time is completely observed in a linear discrete-time system with one input u and one output y whose output is disturbed by the observation noise e. Then, it can be expressed by the following equation (20).
_{y (k) + a 1 y} (k-1) + ··· + a n y (kn)
= B ₀ u (k) + b ₁ u (k-1) +... + B _n u (kn) + e (k) (20)
Here, a ₁ , a ₂ ,..., A _n , b ₀ , b ₁ ,... B _{n correspond} to parameters such as a wind turbine loss coefficient in this embodiment, and parameter identification is {y (k)} { The purpose is to determine these parameters by observing u (k)}.

When Expression (20) is expressed as a vector, it can be expressed by the following Expression (21).
y = Ωα + e (21)
here,
y = [y (n), y (n + 1),... y (N)] ^T
_{α = [- a 1, -a} 2, ···, -a n, b 0, b 1, ··· b n] T
e = [e (n), e (n + 1),..., e (N)] ^T

である。
このとき、Ｎ時点までの観測値列{ｙ(k)}{ｕ(k)}を得ると、パラメータαの最小二乗推定値^αは次式（２２）で与えられる。
^α＝[Ω^TΩ]^-1Ω^Tｙ （２２）

It is.
At this time, when the observation value sequence {y (k)} {u (k)} up to the time N is obtained, the least square estimated value ^ α of the parameter α is given by the following equation (22).
^ α = [Ω ^T Ω] ^-1 Ω ^T y (22)

In order to clarify that it is a matrix or vector created based on the observation values up to N time points, it is written as shown in Expression (23) with a subscript N.
[Ω ^T Ω] ^-1 ≡P _N
Ω ^T y _N ≡b _N (23)
Also, let z _{N be} the lowest column of the matrix Ω.
z _N ≡ [y (N-1), y (N-2), ..., y (Nn), u (N), u (N-1), ..., u (Nn)] ^T
Then, an estimation equation such as the following equation (24) is obtained.
^ α _N = ^ α _N−1 −k _N (z _N ^T ^ α _N−1 −y (N)) (24)
here,
k _N = P _N-1 z _N (1 + z _N ^T P _N-1 z _N ) ^-1 (25)
Also,
P _N = P _N-1 −P _N-1 z _N (1 + z _N ^T P _N-1 z _N ) ⁻¹ z _N ^T P _N-1 (26)

Expressions (24) to (26) are parameter estimation expressions of the iterative calculation method, and ^ α ₁ , P ₁ are obtained if ^ α ₀ , P ₀ , z ₁ are given as initial values, and new By using z ₂ created by adding data, ^ α ₂ and P ₂ can be obtained. Thereafter, this repeated calculation is continued each time new observation data is obtained.

Since the initial value z ₁ can be determined by the accumulation of input / output data, the calculation can be started with the time when n pieces of data are acquired as the initial time, but the initial values ^ α ₀ and P ₀ are determined in advance. If the initial estimate is not given, it will be determined appropriately. Depending on how the initial value is determined, convergence may be delayed, so an appropriate value is selected empirically.

FIG. 3 shows a configuration diagram of the wind turbine generator of the present embodiment.
Wind energy obtained from a wind turbine not shown in FIG. 3 is transmitted to the squirrel-cage induction generator 3 through a speed increaser (speed increase ratio R _n ) (not shown).
The anemometer 8 acquires the wind speed information V _w and supplies it to the parameter identifier 11 and the optimum speed setter 12.

The parameter identifier 11 is an arithmetic circuit that identifies the parameters of the wind turbine from the relationship between the wind energy input to the wind turbine and the output of the rotor rotation output. The parameter identifier 11 is obtained by estimating from the rotational speed ω _w of the induction generator 3 measured by the rotary encoder installed in the induction generator 3 and the torque T _{m of} the rotor in addition to the wind speed information V _w. The rotational torque T _w of the wind turbine rotor connected to the rotor shaft is sequentially input, and the wind turbine loss coefficients K ₀ , K ₁ , K ₂ are estimated using the sequential least square estimation method and supplied to the optimum speed setter 12. To do.

The optimum speed setter 12 determines the optimum rotational speed ω _m ^opt using the input wind speed information V _w and the identified wind turbine loss coefficient, supplies it to the speed controller 13 for control calculation, and The set value is supplied to the subtractor 14 that calculates the deviation.
The speed controller 13 cooperates with the proportional coefficient unit 15 of the gain K _Pw to execute a proportional integral equation (PI) control calculation to generate operation signals i _d ^* and i _q ^*, and to the current controller 17. Supply.

The coordinate conversion circuit 16 is a circuit that performs coordinate conversion between the d, q orthogonal coordinates and the stator coordinates, and inputs the rotor rotation angle θ _m of the induction generator 3 measured by the rotary encoder, and the current value i _a on the stator coordinates. , I _b , i _c are converted into current values i _d , i _q on the dq orthogonal coordinate axes and supplied to the current controller 17.
The current controller 17 performs a PI operation on the dq axis current deviation to obtain dq axis command voltages V _d ^* and V _q ^* , and supplies them to the coordinate conversion circuit 16, where the stator The command voltage V _abc ^* on the coordinates is transformed and supplied to the PWM converter 4 to vector-control the induction generator 3.

When the equation of motion of the vector-controlled induction generator is expressed as an equivalent DC machine, the following equation (27) is obtained.
J _m dω _w / dt + D _m ω _m + T _l ′ = T _e = K _T i _qs ^* (27)
Here, D _m is a braking coefficient of the induction generator, and J _m is an inertia coefficient of the induction generator. T _l ′ is an equivalent disturbance torque expressed by the following equation:
T _l '= PT _L −K _T i _iq
K _T is an electromotive force coefficient expressed by the following equation.
K _T = 3PL _m / 4L _r (L _r i _dr ^* + L _m i _ds ^* )

The command torque T _m ^* is determined by the PI controller using the deviation e _w = ω _r −ω _r ^* between the command speed ω _r ^* and the actual speed ω _r as shown in the following equation (28).
^{_{T m * = K Pw e w}} + K Iw ∫e w dt (28)
Here, K _Iw is an integral gain.
The torque command current i _qs ^* is determined by the following equation (29) using the command torque T _m ^* .
i _qs ^* = (J _m dω _r ^* / dt + D _m ω _m ^* + T _lm −α e _w ) / K _T (29)
Here, K _T is a torque output coefficient, and α is a feedback gain.

Further, the command voltages v _d ^* and v _q ^* of the dq axes become values represented by the following expressions (30) and (31) by the PI controller using the current deviations e _id and e _iq .
^{_{v d * = K Pi e id}} + K Ii ∫e id dt (30)
^{_{v q * = K Pi e iq}} + K Ii ∫e iq dt (31)
Here, K _Pi is a proportional gain, K _Ii is an integral gain for the current ^{_{deviation, e id = i d * -i}} d is d-axis current ^{_{deviation, e iq = i q * -i}} q is q-axis current deviation to the current deviation It is.
Note that the rotor position θ _m and the rotational speed ω _m can be accurately measured by a rotary encoder.

The speed control of the cage induction generator can be performed by the method described above. In order to avoid power loss due to motor operation when the squirrel-cage generator follows the command value, the q-axis current command value i _q ^* has a limit of −20 A ≦ i _q ^* ≦ 0 A, for example. It is preferable.

In this example, sensitivity analysis was performed in order to know how the uncertainty and fluctuation of the parameter affect the output power.
Sensitivity analysis includes a method in which only one variable is changed to see the output fluctuation, and a Monte Carlo simulation in which all the variables are changed simultaneously to evaluate the influence on the output.

FIG. 4 is a table showing device parameters of the wind turbine power generation system subjected to sensitivity analysis and simulation. The target device is a system that generates power by driving an induction generator with a windmill having a blade having a radius of 0.95 m.
The wind speed was fixed at 8 m / s, and the influence of fluctuations in the wind turbine parameters on the output was evaluated for 5 seconds excluding the transient state immediately after startup. The rotational speed of the windmill follows the command value.

5 (a), (b), and (c) show that each variable of the wind turbine parameters K ₀ , K ₁ , and K ₂ used for obtaining the optimum rotational speed changes within an accurate value range of ± 20%. It shows the influence on the rotation speed when The degree of influence was evaluated by expressing the deviation between the rotational speed command value and the optimum rotational speed in%.
6 (a), 6 (b), and 6 (c) show the effect on the output power when the wind turbine parameters K ₀ , K ₁ , and K ₂ are changed from accurate values within a range of ± 20%. Is a graph representing the output power at the optimum rotational speed as a percentage.

5 (a), (b), and (c), it is K ₀ that has the greatest influence of the error on the command rotational speed among the wind turbine parameters. Particularly, when K ₀ becomes excessive, the error of the command rotational speed is large. It turns out that it expands to the low speed side. FIG. 6A shows that the result appears remarkably in the output power error. Therefore, K ₀ requires the highest accuracy when performing parameter identification.
However, when observing FIGS. 6 (a), (b), and (c), K ₁ and K ₂ of the wind turbine parameters have an output power of 95% or more even if there is an error of about ± 10%. It can be seen that the influence of the parameter identification error on the output is relatively light. Furthermore, it has been found that if the identification error can be suppressed to about ± 5% even if K ₀ is included, the influence on the output power is slight.

Fig. 7 is a graph showing the results of Monte Carlo simulation. When all wind turbine parameters K ₀ , K ₁ , K ₂ are changed at random within the range of ± 20% from the exact value, the effect on the output power is optimized. It is drawing represented by the% display which sets the output electric power in rotation speed to 100%. The horizontal axis is the command rotational speed calculated using the wind turbine parameters including an error from an accurate value, and the graph represents the output power when operating at the command rotational speed.
If the command rotational speed error is within ± 10%, the output power can be reduced within 10%. If the command rotational speed error is within ± 5%, the output power decrease is 1% or less. I understand.

Furthermore, the effectiveness of the method of the present invention was confirmed by performing a simulation.
FIG. 8 is a diagram showing a simulation result. In the simulation, immediately after starting the wind turbine generator, the speed is controlled by the command speed calculated using the wind power parameter including the initial error, and after 5 seconds, the determined parameter is applied based on the method of the present invention. The generator speed is controlled using the commanded speed.

FIG. 8A is a graph showing changes in the wind speed of the wind input to the target system.
FIG. 8B shows the error of the windmill parameters K ₀ , K ₁ , K ₂ . FIGS. 8C and 8D show the change in rotational speed and the change in output power, respectively.
As shown in FIG. 8B, the error of the windmill parameters K ₀ , K ₁ , K ₂ is large in the initial state, but after 5 seconds have passed, the parameter error is applied after the identification method of the present invention is applied. Is decreasing. Although there are several percent errors in K ₁ and K _2, the error in K ₀ is particularly small.

  As can be seen from FIGS. 8C and 8D, both the rotational speed and the output power have a deviation from the optimum value due to the influence of the initial error of the windmill parameter in the initial state. Therefore, the command rotational speed is in good agreement with the optimum speed, and the output power follows the maximum power point well.

The wind power generator maximum power point tracking control method and control device according to the present invention uses the wind turbine parameters identified online using the sequential least squares estimation method based on the measured values of the input and output, and the optimum command value is determined. Since the calculation and the speed control of the induction generator are performed, the wind turbine generator can be operated so as to always follow the maximum power point even when the wind turbine parameter fluctuates. The performance of the maximum power point tracking control method of the present invention was confirmed by simulation.
In this specification, the generator of the wind turbine generator is an induction generator. However, the maximum power point tracking control method of the present invention can also be applied when using a permanent magnet synchronous generator to which vector control can be applied. . Needless to say, it does not depend on the presence or absence of the gearbox.

It is a main machine block diagram showing the wind power generator provided with the maximum electric power point tracking control device concerning one example of the present invention. It is drawing which showed the windmill output characteristic for every wind speed in the windmill of a present Example. It is a block diagram of the wind power generator of a present Example. It is a table | surface which shows the apparatus parameter of the wind power generator used as the object of a sensitivity analysis and a simulation. It is a graph which shows the influence which it has on a rotational speed when changing a windmill parameter by 1 variable in a sensitivity analysis. It is a graph which shows the influence which it has on output electric power when changing a windmill parameter by 1 variable in sensitivity analysis. It is a graph which shows the result of a Monte Carlo simulation. It is a graph showing the simulation result of the maximum electric power point tracking control apparatus of the wind power generator of a present Example.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Windmill 2 Booster 3 Induction generator 4 PWM converter 5 PWM inverter 6 Electric power system 7 Maximum power point tracking control apparatus 8 Anemometer 11 Parameter identifier 12 Optimal speed setting device 13 Speed controller 14 Subtractor 15 Proportional coefficient device 16 Coordinate converter 17 Current controller

Claims (3)

  The wind turbine generator's input output measurement and wind speed information are used to identify the loss factor of the operating wind turbine by successive least squares estimation, and the optimum loss speed corresponding to the maximum power point is determined and specified using the identified loss factor. A method for controlling the maximum power point of a wind turbine generator, wherein the wind turbine generator is controlled using the optimum rotation speed as a target value.   Measures the rotational position and rotational speed of the generator rotor in a wind turbine generator in which the generator rotating shaft is connected to the wind turbine rotating shaft and the generator stator is connected to a pulse width adjustment (PWM) converter device. Rotational position speed measuring device, torque meter for detecting the rotational torque of the generator, anemometer for measuring wind speed, parameter identifier, optimum rotational speed setting device, speed controller, current controller, and coordinates A wind turbine parameter calculated on the basis of the rotational speed and rotational torque of the rotor and the wind speed input by the parameter identifier, and supplied to the optimal rotational speed setter, the optimal rotational speed setting The wind turbine calculates the wind turbine rotational speed corresponding to the maximum power point from the wind speed and the wind turbine parameters and supplies it as the target value of the speed controller. The speed controller inputs the rotational speed of the generator rotor and performs control calculation. The dq-axis current command value is calculated and supplied to the current controller as a current command value, and the current controller performs a control calculation on the dq-axis current measurement value input via the coordinate converter. The dq axis voltage command value is calculated and supplied to the PWM converter device as a voltage command value via the coordinate converter, and the PWM converter device controls the rotation of the generator to A maximum power point tracking control device for a wind turbine generator that performs wind power generation by tracking a power point.   The maximum power point tracking control device for a wind turbine generator according to claim 2, wherein the parameter identifier sequentially obtains the wind turbine parameters by successive least square estimation.

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