KR101032930B1 - The apparatus and method of wind speed estimator for wind turbine generation system - Google Patents

Abstract

PURPOSE: An optimum control type integrated management device for an aerogenerator using a digital wind speed estimation module and a variable speed control module and a method thereof are provided to estimate the speed of the wind blown from the front end of the aerogenerator using the output characteristic of the aerogenerator. CONSTITUTION: An optimum control type integrated management device for an aerogenerator using a digital wind speed estimation module and a variable speed control module comprises a digital wind speed estimation module(100), a variable speed control module(200), and an integrated management server(300). The digital wind speed estimation module measures the speed of rotation of a blade, a pitch angle, and the output power of a generator. The digital wind speed estimation module estimates the wind speed of the front end of the blade. The variable speed control module uses the wind speed information transmitted from the digital wind speed estimation module. The variable speed control module estimates the speed of the rotation in order to obtain the maximum power from the present speed of the wind. The variable speed control module controls the output of a DC-DC converter. The integrated management server collects the transmitted sensor data and estimated data through an LAN(Local Area Network) The integrated management server saves the collected data in the database.

Description

Integrated control system and method for optimal control of wind power generator using digital wind speed estimation module and variable speed control module {THE APPARATUS AND METHOD OF WIND SPEED ESTIMATOR FOR WIND TURBINE GENERATION SYSTEM}

The present invention relates to a wind turbine optimal control type integrated management apparatus and method through a digital wind speed estimation module and a variable speed control module.

Recently, wind power has attracted attention as an environmentally friendly alternative energy source for the depletion of fossil energy. In Western Europe, especially in Germany, the Netherlands, and Denmark, there have been many studies of wind power generation since the 1970s. As a result, several MW wind power systems have recently been commercialized.

Domestic research and development of wind power generation system is actively underway, with a large number of research institutes in Korea, and the development of domestic wind resources is possible due to the introduction of medium-large and large-scale domestic and overseas systems installed and operated in numerous regions including Jeju Island through government-supported energy projects. The situation is under review.

In particular, the wind farms installed in the Gunsan region on the west coast consisted of six 750KW models (NM48) from NEG-Micon of Denmark and four 850KW models (V52) of VESTAS from Denmark with a total cost of 7.9MW. It is designed to provide electricity for nearly 3,000 generations.

The height of each generator's hub is 45m and 49m, NM48 is stall control method and V52 is pitch control method.

Most wind farms consist of several decades of wind turbines and a meteorological tower for measuring wind speed and wind direction along height. The wind speed at the hub height of the turbine blade is closely related to the generating capacity, which assumes that the velocity profile at the hub height does not change with the height from the ground to the hub, but the location where the wind generator is installed and the weather tower are installed. Due to the change of topography between the locations, the wind speed at the hub of the actual wind turbine is difficult to know exactly.

Wind power generation systems have fixed-speed fixed-pitch, fixed-speed variable-pitch, variable-speed fixed-pitch and variable speed depending on the operating method. -Variable-speed Variable-Pitch is classified into variable speed control system. The variable speed system is a control method to obtain the maximum energy that the blade can obtain from the wind within the wind speed range from the cut-in speed to the rated wind speed. This requires accurate wind speed measurement at the blade hub stage.

However, in the case of a horizontal axis wind power generator, it is difficult to directly install the anemometer at the hub stage, and even if it is installed, the measured wind speed value is not accurate due to the rotation of the blade.

In addition, the wind power generator is the maximum output point that can produce the optimum output under each wind speed depending on the output characteristics of the blade, and to find the maximum output point, you need to know the exact wind speed.

However, due to the structure of the wind power generator, it is difficult to measure the wind speed by installing the anemometer at the front end of the wind power generator, and when installing the anemometer at the rear end, it is difficult to accurately measure the wind speed due to the difference in the air density generated by the turbulence of the blade and the mechanism. It is true.

In addition, in the case of small wind turbines, the controller occupies less than 10% of the total manufacturing cost of wind turbines, making it difficult to apply expensive sensors such as anemometers. .

Domestic Patent Registration Publication No. 10-0830518 (announced May 21, 2008)

In order to solve the above problems, the present invention can estimate the wind speed blowing from the front end of the wind power generator using the output characteristics of the wind power generator, can be compatible with the existing wind power generator as well as the small wind power generator, It is an object of the present invention to provide an optimal control and integrated management system for a wind generator through a digital wind speed estimation module and a variable speed control module that can obtain the maximum power at the same wind speed within a short time based on the estimated wind speed.

In order to achieve the above object, the optimum control system for a wind power generator using a digital wind speed estimation module and a blade speed change module according to the present invention

The blade rotated according to the change of wind speed, the generator located at the rear end of the blade to generate wind energy by direct current, rectifier for rectifying the voltage accumulated in the generator, and the voltage rectified through the rectifier according to the usage capacity. DC-DC converter to down or boost, and DC-DC converter to convert the voltage down or boosted from DC to AC integrated management device for optimal control of wind power output power by converting wind power into electrical energy Made of (1),

The wind turbine optimal control type integrated management device (1) is a digital wind speed estimation module (100) for estimating the front wind speed of the blade after measuring the rotational speed, pitch angle and output power of the generator according to the change in wind speed, and

By using the wind speed information transmitted from the digital velocity estimation module estimates the optimum rotation speed (W _mopt) for obtaining the maximum power at the current wind speed and the rotation speed measured by the RPM sensor (W _mact) to track the optimum rotational speed of the Variable speed control module 200 for controlling the output of the DC-DC converter,

Collect sensor data and estimated data transmitted from digital wind speed estimation module and variable speed control module through LAN, store collected data in DB for a long time, and display in graph form and numeric form on PC monitor, This is achieved by configuring the integrated management server 300 to provide data using a web program on request.

In addition, the optimal control method of the wind power generator through the digital wind speed estimation module and the blade speed variable module according to the present invention

Wind speed estimation step (S100) for estimating the wind speed of the blade front end of the wind generator,

Variable speed control step (S200) for controlling the maximum output by varying the rotational speed of the wind generator to the optimum rotational speed,

It is achieved by the integrated management step (S300) of collecting, storing, and displaying data generated in the wind speed estimation step and the variable speed control step.

As described above, in the present invention, the wind speed blowing from the front end of the wind power generator can be estimated using the output characteristics of the wind power generator, and the maximum power can be obtained based on the estimated front wind speed. Efficient management and information can be provided when requesting data from the outside, and if an abnormality occurs in the wind turbine, it can effectively deal with the problem through the analysis data from the monitoring server.

곡선을 도시한 그래프,
도 8은 본 발명에 따른 최대값을 갖는 포인트들에 해당하는 X축 데이터와 풍속과의 관계그래프를 생성한 전력곡선그래프,
도 9는 본 발명에 따른 X축에 풍속값을 1개의 입력뉴런으로 사용하고, Y축에 최적의 W_mopt값으로 1개의 뉴런이 사용한 학습데이터를 도시한 일실시예도,
도 10은 본 발명에 따른 디지털 풍속 추정 모듈과 블레이드 속도가변모듈을 통한 풍력 발전기 최적 제어 방법을 도시한 순서도,
도 11은 본 발명에 따른 풍속추정단계(S100)를 구체적으로 도시한 순서도,
도 12는 본 발명에 따른 가변속제어단계(S200)를 구체적으로 도시한 순서도,
도 13은 본 발명에 따른 통합관리단계(S300)를 구체적으로 도시한 순서도.1 is a block diagram showing the components of the integrated control device for optimal control of the wind power generator through the digital wind speed estimation module and the variable speed control module according to the present invention,
2 is a block diagram showing the components of the digital wind speed estimation module 100 according to the present invention;
3 is a block diagram showing the components of the variable speed control module 200 according to the present invention;
Figure 4 is an embodiment showing that the digital wind speed estimation module 100, the variable speed control module 200 according to the present invention is formed of one PCB substrate,
5 is a configuration diagram showing the components of the wind power generator according to the present invention;
6 is a graph showing a power coefficient curve according to the present invention;
7 is in accordance with the present invention

Graph showing the curve,
8 is a power curve graph for generating a graph of the relationship between the X-axis data and the wind speed corresponding to the points having the maximum value according to the present invention;
FIG. 9 is a diagram illustrating learning data used by one neuron with an optimal W _mopt value on the Y axis and a wind speed value on the X axis according to the present invention;
10 is a flowchart illustrating a method for optimally controlling a wind power generator using a digital wind speed estimation module and a blade speed change module according to the present invention;
11 is a flowchart specifically showing a wind speed estimation step S100 according to the present invention;
12 is a flowchart specifically showing a variable speed control step S200 according to the present invention;
Figure 13 is a flow chart showing in detail the integrated management step (S300) according to the present invention.

First, as illustrated in FIG. 5, the wind power generator described in the present invention rectifies a blade rotated according to a change in wind speed, a generator positioned at a rear end of the blade to generate wind energy by direct current, and a voltage accumulated in the generator. It consists of rectifier, DC-DC converter to down or boost the voltage rectified through the rectifier according to the usage capacity, and inverter to convert voltage down or boosted through DC-DC converter from DC to AC. do.

And, the digital wind speed estimation module 100 according to the present invention is connected to one side of the blade, the generator is configured to measure the pitch angle (β) of the blade, the rotational speed (ω _r ) of the blade, the output power (Pg) of the generator .

Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings.

1 is a block diagram showing the components of the integrated control apparatus for optimal control of the wind power generator through the digital wind speed estimation module and the variable speed control module according to the present invention, which is largely the digital wind speed estimation module 100 and the variable speed control module ( 200), the integrated management server 300.

First, the digital wind speed estimation module 100 according to the present invention will be described.

The digital wind speed estimation module 100 measures the rotational speed, pitch angle, and output power of the generator according to the change of the wind speed, and serves to estimate the wind speed at the front end of the blade, which is an RPM sensor 110 and a voltage transformer. The producer 120, the current transducer 130, the counter unit 140, the first A / D converter 150, the second A / D converter 160, the blade torque estimation unit 170, the wind speed estimation unit 180 , The first data transmission unit 190.

The RPM sensor 110 serves to measure the rotational speed of the blade or the rotational speed of the generator rotated by the wind power introduced into the wind generator.

The voltage transducer 120 is connected to the generator output terminal and serves to measure the amount of change in the voltage converted by the wind.

The current transducer 130 is connected to the generator output stage and serves to measure the amount of change in the current converted by the wind.

The counter 140 plays a role of converting a pulse type signal output from the RPM sensor into a digital signal value.

The first A / D converter 150 is connected to the voltage transducer to convert an analog voltage signal output from the voltage transducer into a digital signal value.

The second A / D converter 160 is connected to the current transducer to convert an analog voltage signal output from the current transducer into a digital signal value.

The blade torque estimator 170 calculates the torque T _g of the generator calculated from the rotation speed and the rotation speed measured by the RPM sensor using the wind turbine model, and the output current and voltage of the generator. _a ) to estimate;

The wind speed estimating unit 180 serves to estimate the wind speed through a tip speed ratio obtained by using a forging or non-forging function calculated from a torque estimated by a blade torque estimating unit and a power coefficient curve.

The first data transmitter 190 transmits the measured data and the estimated wind speed data from the RPM sensor and the voltage & current transmitter to the variable speed control module 200 and the integrated management server 300.

It is composed of a LAN line or an RS232 cable, and is connected to the variable speed control module 200 and the integrated management server 300 to transmit data.

Next, the variable speed control module 200 according to the present invention will be described.

The variable speed control module 200 estimates an optimum rotation speed (W _mopt ) for obtaining the maximum power at the current wind speed by using the wind speed information transmitted from the digital wind speed estimation module and measures the rotation speed (W _mact ) measured from the RPM sensor. To control the output of the DC-DC converter to track the optimum rotational speed, which is the first data receiving unit 210, the counter unit 220, the optimum speed estimation unit 230, PI (Proportional plus Integral) The controller unit 240, the PWM control unit 250, and the data transmission unit 260.

The variable speed control module 200 according to the present invention uses the wind speed data estimated from the digital wind speed estimation module and the rotation speed data measured by the RPM sensor to change the rotation speed of the wind power generator to rotate at the optimum rotation speed. It has a role to control.

The first data receiver 210 serves to receive data transmitted from the digital wind speed estimation module.

It consists of LAN line or RS232 cable and is connected to digital wind speed estimation module to receive data.

Here, data refers to data measured from RPM sensors and voltage & current transducers, and estimated wind speed data.

The counter 220 serves to convert the pulse signal output from the RPM sensor for measuring the rotational speed of the blade into a digital signal.

The optimum speed estimator 230 estimates the optimum rotation speed by using the learned wind speed data received from the digital wind speed estimation module as input of a neural network learned to output the optimum rotation speed W _mopt corresponding to the wind speed when the wind speed is input. do.

Here, the learned neural network generates a graph of the relationship between the X-axis data and the wind speed corresponding to the points having the maximum value in the power coefficient curve of FIG. 6 and the power curve of FIG. It is used as a learning input.

The PI (Proportional plus Integral) controller 240 is an optimum speed value (W _mopt ) estimated by the wind speed and the wind power generator power curve table estimated from the wind speed estimation calculation control unit and the actual speed value of the current blade (Wmact) After calculating the difference between the error (error), and generates a reference voltage signal of the PWM controller to reduce the error (error).

The PWM controller 250 receives the comparator reference voltage of the PWM controller from a PI (Proportional plus Integral) controller, compares it with a reference sawtooth signal through an internal operation of the comparator, and then controls the output of the generator. It controls the duty ratio.

The second data transmitter 260 serves to transmit data about the optimum rotation speed W _mopt estimated from the variable speed control module and the current control output to the integrated management server.

It is composed of a LAN line or RS232 cable, and is connected to the integrated management server 300 to transmit data.

Next, the integrated management server 300 according to the present invention will be described.

The integrated management server 300 collects sensor data and estimated data transmitted from the digital wind speed estimation module and the variable speed control module through a LAN, stores the collected data in a DB for a long time, and displays a graph and a number on a PC monitor. In this case, the remote user serves to provide data by using a web program when requesting data. The second data receiving unit 310, the display unit 320, the data storage unit 330, and the web service unit ( 340.

The second data receiver 310 transmits sensor data (RPM sensor, current & voltage transducer, etc.) and estimated data (wind speed, optimum rotation speed, etc.) transmitted from a remote digital wind speed estimation module and a variable speed control module using LAN communication. ) To receive

The display unit 320 serves to display the collected data in graph form or numeric form on a monitor.

The data storage unit 330 serves to record the data received in a DB file produced using a SQL server in order.

The web service unit 340 serves to provide a desired service by a web program operated on a web browser when a service request is made remotely.

Hereinafter, a detailed process of the integrated wind turbine generator control method using the digital wind speed estimation module and the variable speed control module according to the present invention will be described.

The wind speed estimation step (S100) of estimating the wind speed of the blade front of the wind generator, the variable speed control step (S200) of controlling the maximum output by varying the rotation speed of the wind generator to the optimum rotation speed, wind speed estimation step and variable speed control Collecting and storing the data generated in the step, it consists of an integrated management step (S300) to be displayed on the PC monitor.

[Wind velocity estimation step (S100)]

First, the rotational speed of the blade and the current and voltage of the generator are measured (S110).

It measures the rotational speed of the blade rotated by the wind through the RPM sensor, measures the amount of change in voltage converted by the wind through the voltage transducer, and measures the amount of change in the current converted by the wind through the current transducer. do.

Subsequently, the torque is calculated using the measured rotation speed, the current and voltage of the generator (S120).

At this time, the torque calculation equation is used as Equation 1 below.

) 및 현재 풍력발전기의 회전속도(

)를 추정하게 된다(S130). The measured rotational speed (ω _r ) and the calculated generator torque (T _g ) are then calculated using the Kalman filter algorithm and the torque of the current wind turbine (

) And current wind speed

) Is estimated (S130).

Torque and rotational speed of wind turbines are estimated using the Kalman filter.

로 이루어져 추정치를 구하는 알고리즘이다.
Here, the Kalman filter algorithm

An algorithm that calculates the estimates.

으로 전개한 단조·비단조 함수에 관한 제2연산알고리즘(수학식 2-4)으로 정리한다(S140).
Subsequently, the torque (Pa) calculation algorithm (Equation 2-1) and the main speed ratio (λ) calculation of the torque and rotational speed of the current wind generator estimated by the Kalman filter algorithm are absorbed by the turbine from the wind energy. The first computational algorithm (Equation 2-3) for the monotonic and non-forged functions calculated by the algorithm (Equation 2-2)

It sums up by the 2nd algorithm (Equation 2-4) regarding the forging and non-forging function developed by (S140).

(2)

(2)

(3)

(3)

(4)

(4)

으로 전개한 단조·비단조 함수에 관한 제2연산알고리즘을 나타낸 것이며, Pa는 풍력에너지로부터 터빈에 의해 흡수되는 에너지를 나타낸 것이고, R는 회전자의 반경(m), ρ는 공기의 밀도로 약 1.25[kg/㎥]이며, λ는 주속비(tip-speed ratio)를 나타내며, Cp(λ,β)는 전력계수를 나타낸 것이다.
Here, Equation 2-1 represents the energy (Pa) calculation algorithm is absorbed by the turbine from the wind energy, Equation 2-2 represents the circumferential speed ratio (λ) calculation algorithm, Equation 2- 3 represents a first operation algorithm for a monotonic / non-forged function, and Equation 2-4

Shows the second algorithm for the forging and non-forging functions developed by the equation, Pa is the energy absorbed by the turbine from wind energy, R is the radius of the rotor (m), and ρ is the air density. 1.25 [kg / ㎥], λ represents the tip-speed ratio, Cp (λ, β) represents the power coefficient.

로 정리된 단조·비단조 함수에 관한 제2연산알고리즘을 통해 주속비(Tip speed ratio)(λ)를 연산한 후, 풍속추정연산알고리즘(수학식 3)에 대입시켜 현재 풍속을 추정한다(S150).next,

Calculate the tip speed ratio (λ) through the second algorithm for the forging and non-forging functions, and then substitute the wind speed estimation algorithm (Equation 3) to estimate the current wind speed (S150). ).

곡선을 얻을 수가 있게 된다.That is, when the y-axis Cp (λ, β) of the power coefficient curve Cp-λ shown in FIG. 6 provided in the blade design is divided by the value of the power of the x-axis λ, as shown in FIG. ,

You can get a curve.

로 정리된 단조·비단조 함수에 관한 제2연산알고리즘에서 연산된 값은 도 7의 y축 값으로 이에 해당하는 λ를 얻게 되면 다음의 수학식 3(풍속추정연산알고리즘)에 의해 풍속을 추정하게 된다.Because of

The value calculated in the second operation algorithm for the monotonic and non-forged functions summarized as is the y-axis value of FIG. 7, and when λ corresponding thereto is obtained, the wind speed is estimated by the following equation 3 (wind velocity estimation algorithm). do.

Here, Equation 3 shows the wind speed estimation algorithm.

-λ곡선은 피치값에 따라 단조 또는 비단조 함수가 된다. 일반적으로 수학식 2(현재 풍속추정연산알고리즘)에서 연산된 값에 의해 λ를 얻기 위해서는 각 피치각에

-λ곡선에 대한 테이블 데이터가 필요하게 된다.That is, in FIG.

The −λ curve becomes a monotonic or non-monostatic function depending on the pitch value. In general, in order to obtain λ by the value calculated in Equation 2 (current wind speed estimation algorithm),

You will need table data for the -λ curve.

-λ를 효율적으로 처리하기 위해 인공신경망을 이용한다.In this case, as the table data, in the present invention,

Artificial neural networks are used to efficiently process -λ.

값이 신경망의 입력으로 사용되기 때문에 입력뉴런이 2개가 사용되며, 출력에는 1개의 뉴런이 사용되며 λ가 출력된다.
Here, the training data used in the artificial neural network learning can be obtained from FIG. 7, calculated by the pitch angle and the equation (2).

Since the value is used as the input of the neural network, two input neurons are used, one neuron is used for output, and λ is output.

Finally, when the wind speed estimation is completed, the measured data and the estimated wind speed data are transmitted from the RPM sensor and the voltage & current transmitter to the variable speed control module 200 and the integrated management server 300 through the first data transmission unit. S160).

At this time, UART communication is used for the transmission method.

[Variable Speed Control Step ( S200 )]

First, the wind speed data estimated from the digital wind speed estimation module is received (S210).

It consists of LAN line or RS232 cable and is connected to digital wind speed estimation module to receive data.

Subsequently, the received estimated wind speed data is input to a pre-learned artificial neural network to estimate an optimum rotation speed W _mopt at the current wind speed (S220).

Here, the learning data of the learned artificial neural network is the power coefficient curve of FIG. 6 given in the blade design and the power curve of FIG. 8 obtained from the energy (Pa) calculation algorithm absorbed by the turbine from the wind energy such as Equation 2-1. Refers to the relationship between the X-axis data and the wind speed corresponding to the points with the maximum value in the graph, and uses it as learning data.

That is, the learning data uses one input neuron as the current wind speed value (X axis), and the output uses one neuron as the optimal W _mopt value corresponding to the current wind speed.

For example, as shown in FIG. 9, the learning data uses a wind speed value as one input neuron on the X axis and one neuron as an optimal W _mopt value on the Y axis.

Subsequently, when the optimum speed is estimated, a difference value (steady state error) between the estimated optimum speed and the current speed value measured by the RPM sensor is calculated (S230).

Subsequently, the steady state error is used as an input of the PI controller, and the reference voltage of the PWM controller is adjusted so that the steady state error is zero in the PI controller (S240).

That is, a reference voltage is generated so that the current speed tracks the optimum speed.

Subsequently, the duty ratio is adjusted by comparing the reference voltage output from the PI controller with the PWM reference sawtooth signal through the PWM controller (S250).

At this time, as the duty ratio is adjusted, the rotation speed of the wind turbine is variable.

Finally, the data about the optimum rotation speed W _mopt estimated from the variable speed control module and the current control output are transmitted to the integrated management server through the second data transmitter 260 (S260).

It is composed of a LAN line or RS232 cable, and is connected to the integrated management server 300 to transmit data.

[Integrated management step (S300)]

First, sensor data (RPM sensor, current & voltage transducer, etc.) transmitted from a remote digital wind speed estimation module and a variable speed control module using LAN communication in the second data receiver 310, and estimation data (wind speed, optimal rotation speed). Etc.) (S310).

Subsequently, the data collected by the display unit 320 is displayed on the monitor in the form of a graph or a number (S320).

Subsequently, the data storage unit 330 sequentially records the data received in the DB file produced using the SQL server (S330).

Subsequently, when a service request is made remotely through the web service unit 340, a desired service is provided by a web program operated on a web browser (S340).

100: digital wind speed estimation module 110: RPM sensor
120: voltage transducer 130: current transducer
140: counter 150: 1A / D converter
160: 2 A / D converter 170: blade torque estimate
180: wind speed estimation unit 109: first data transmission unit
200: variable speed control module 300: integrated management server

Claims (6)

The blade rotated according to the change of wind speed, the generator located at the rear end of the blade to generate wind energy by direct current, rectifier for rectifying the voltage accumulated in the generator, and the voltage rectified through the rectifier according to the usage capacity. DC-DC converter to down or boost, and DC-DC converter to convert the voltage down or boosted from DC to AC integrated management device for optimal control of wind power output power by converting wind power into electrical energy In (1),
The wind turbine optimal control type integrated management device (1) is a digital wind speed estimation module (100) for estimating the front wind speed of the blade after measuring the rotational speed, pitch angle and output power of the generator according to the change in wind speed, and
By using the wind speed information transmitted from the digital velocity estimation module estimates the optimum rotation speed (W _mopt) for obtaining the maximum power at the current wind speed and the rotation speed measured by the RPM sensor (W _mact) to track the optimum rotational speed of the Variable speed control module 200 for controlling the output of the DC-DC converter,
Collect sensor data and estimated data transmitted from digital wind speed estimation module and variable speed control module through LAN, store collected data in DB for a long time, and display in graph form and numeric form on PC monitor, Optimal control type integrated management device for a wind turbine with a digital wind speed estimation module and a variable speed control module, characterized in that the integrated management server 300 provides data using a web program on request.
The method of claim 1, wherein the digital wind speed estimation module 100
RPM sensor 110 for measuring the rotational speed of the blade or the rotational speed of the generator rotated by the wind power is installed inside the wind generator, and
A voltage transducer 120 connected to the generator output terminal and measuring a change amount of the voltage converted by the wind power;
A current transducer 130 connected to the generator output terminal and measuring a change amount of the current converted by wind power;
A counter unit 140 for converting a pulse type signal output from the RPM sensor into a digital signal value;
A first A / D converter 150 connected to the voltage transducer and converting an analog voltage signal output from the voltage transducer into a digital signal value;
A second A / D converter 160 connected to the current transducer and converting an analog voltage signal output from the current transducer into a digital signal value;
Blade torque estimator for estimating the torque (T _a ) of the blade (T _g ) of the generator calculated from the rotation speed, rotation speed, and generator output current and voltage measured using the wind turbine model. With 170,
Wind speed estimation unit 180 for estimating the wind speed through the tip speed ratio obtained by using the forged or non-forged function calculated from the torque and power coefficient curve estimated by the blade torque estimation unit,
Characterized in that the first data transmission unit 190 for transmitting the measured data and the estimated wind speed data from the RPM sensor and voltage & current transducer to the variable speed control module 200 and the integrated management server 300 Optimal control integrated wind turbine generator with digital wind speed estimation module and variable speed control module.
The variable speed control module 200 of claim 1,
A first data receiver 210 for receiving data transmitted from the digital wind speed estimation module;
Counter unit 220 for converting the pulse signal output from the RPM sensor for measuring the rotational speed of the blade into a digital signal,
An optimum speed estimator 230 for estimating an optimum rotation speed by inputting the wind speed data received from the digital wind speed estimation module as input of a neural network learned to output an optimum rotation speed W _mopt corresponding to the wind speed when the wind speed is input;
After calculating the error (difference) between the wind speed estimated from the wind speed estimation and control unit and the optimum speed value (W _mopt ) estimated by the power curve table of the wind turbine and the actual measured speed value (Wmact) of the blade, A PI (Proportional plus Integral) controller 240 for generating a reference voltage signal of the PWM controller to reduce errors;
The PWM control unit receives the comparator reference voltage of the PWM controller from the PI (Proportional plus Integral) controller, compares it with the reference sawtooth signal through the internal operation of the comparator, and adjusts the PWM duty ratio of the DC / DC converter that controls the generator output. 250,
The digital wind speed estimation module and the variable speed control module, comprising a second data transmission unit 260 which transmits data about the optimum rotation speed W _mopt estimated from the variable speed control module and the current control output to the integrated management server. Integrated control device for optimal control of wind power generators.
Wind speed estimation step (S100) for estimating the wind speed of the blade front end of the wind generator,
Variable speed control step (S200) for controlling the maximum output by varying the rotational speed of the wind generator to the optimum rotational speed,
In the integrated management step (S300) of collecting, storing, and displaying data generated in the wind speed estimation step and the variable speed control step, and displayed on a PC monitor,
The variable speed control step (S200)
Receiving the estimated wind speed data from the digital wind speed estimation module (S210);
Estimating the optimum rotational speed (W _mopt ) at the current wind speed by inputting the received estimated wind speed data to a pre-learned artificial neural network (S220);
If the optimum speed is estimated, calculating a difference value (steady state error) between the estimated optimum speed and the current speed value measured by the RPM sensor (S230);
Adjusting the reference voltage of the PWM controller so that the steady state error is used as an input of the PI controller and the steady state error is zero in the PI controller (S240);
Comparing the reference voltage output from the PI controller with the PWM reference sawtooth signal through the PWM controller to adjust the duty ratio (S250);
Digital wind speed estimation, comprising the step (S260) of transmitting the data about the optimum rotation speed (W _mopt ) and the current control output estimated from the variable speed control module via the second data transmission unit 260 to the integrated management server Optimal control type integrated management method of wind power generator through module and variable speed control module. delete delete

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