KR20160025060A - Wind farm control system and control method thereof - Google Patents

Abstract

According to an aspect of the present invention, the wind farm control system of the present invention can comprise: a wind turbine transition state detecting unit connected to one or more wind turbines to acquire transition state information from each wind turbine; a wind turbine fatigue load detecting unit connected to one or more wind turbines to acquire fatigue load information from each wind turbine; a met mast for measuring the wind conditions such as the speed and the direction of the wind moving toward the wind farm made of the one or more wind turbines; and a wind farm control unit for acquiring the information of the wind turbine transition state detecting unit and the fatigue load detecting unit, conducting a cluster analysis according to the pertinent information, making the one or more wind turbines into groups, and performing a control sequence of the wind turbines according to the wind conditions for each group.

Description

Technical Field [0001] The present invention relates to a wind turbine control system,

The present invention relates to a wind turbine control system, and more particularly, to a wind turbine control system capable of reducing a calculation load of a control system through cluster analysis and a wind turbine control system using the same And a control method.

A wind turbine is a place where a lot of wind turbines are installed to turn an electric generator by natural wind on land or on the sea to get energy. Germany, the United States, and Denmark, and a small-scale power generation complex was established in Korea.

It is important that the wind farm should be operated in accordance with grid linkage standards as well as producing and supplying the power required by the grid operator, and it is important to maximize the power generation within the range permitted by the grid operator. There is also a need to minimize the mechanical load reduction of the power plant to minimize energy production costs and to reduce maintenance costs. In order to achieve this, environmental information such as the wind direction and direction of the wind blowing to the wind turbine is acquired through facilities such as a meteorological instrument, and accordingly, an optimal operation control is performed for each of the wind turbines .

In terms of operation of the wind turbine system, it is accompanied by a lot of computing load for system simulation and real-time operation due to its large size. In other words, according to the environment information transmitted in real time through the wind-up meteorological tower, each wind turbine should receive the operation control signal for each zone and location through the simulation process for the optimal operating environment, Therefore, a large-scale data processing apparatus is required for such an operation.

However, the computing device responsible for such computation load must increase in proportion to the number of wind power generation devices requiring control, but there is a limit to increase the computing devices. In addition, there is a problem that the operation speed may not follow the wind turbine immediately to control the wind turbine according to the increasing wind speed in real time as the operation amount increases.

Korean Registered Patent No. 10-1297082 (Registered on August 31, 2013)

The present embodiment has been conceived in view of the above points. It is an object of the present invention to provide a wind turbine control system that employs a statistical analysis method to reduce the computational load and reduce the computational load, Provides a power generation complex control method.

A wind turbine control system according to an aspect of the present invention includes a wind turbine transition state sensing unit connected to at least one wind turbine to acquire transition state information from each of the wind turbines; A wind turbine fatigue load sensing unit coupled to at least one or more wind turbines to obtain fatigue load information from each of the wind turbines; A meteorological meteorological tower for measuring a wind condition such as a wind speed and a direction of a wind running toward a wind turbine constructed of at least one or more wind turbines; And a wind turbine control unit for acquiring information of the wind turbine transition state sensing unit and the fatigue load sensing unit and performing a cluster analysis according to the information to group the at least one wind turbine, And a power generation complex control section.

The wind turbine transition state sensing unit and the fatigue load sensing unit may be installed individually in the respective wind turbines or may be installed inside the control unit of the wind power generator.

The wind farm controller may be communicatively coupled to the at least one wind turbine via an Ethernet network.

Also, the wind power generator control unit may be connected to a plurality of wind farms through an Internet network so as to be able to communicate with each other.

The wind power plant control method according to the present embodiment includes the steps of: acquiring the weather forecast information from the wind-up meteorological tower; Simulating wind turbine operation using the acquired wind direction information; Obtaining current state information of the wind turbine; Performing cluster analysis using current state information of the obtained wind turbine; Grouping wind turbines that are in a similar state according to the wind speed, through cluster analysis; And outputting a wind turbine control command in accordance with the weather forecast information for each group.

The cluster analysis step may also include sensing each wind turbine fatigue load; Sensing each wind turbine transition state; And transmitting fatigue load and transition state information of each wind turbine to the memory unit.

According to the present embodiment as described above, the same group of wind turbines placed in similar wind and mechanical conditions is clustered and controlled by the same control command for each group, thereby reducing the computational load of the processing unit .

In addition, since a plurality of wind turbines can be controlled only by a computing device having a limited number of processing units, it is possible to secure the economical efficiency of the apparatus configuration and to expect an improvement in computation speed due to a drop in computational load. Wind turbine control is possible.

1 is a schematic block diagram of a wind power plant control system according to the present embodiment;
2 is a view schematically showing the configuration of a wind power generation system control system and a wind power generation terminal according to the present embodiment,
FIG. 3 is a diagram for collectively explaining the process of predicting the rising time and the process of allocating the power generation demand,
4 is a view showing a state in which wind farms according to the present embodiment are grouped for cluster analysis,
5 and 6 are flowcharts schematically illustrating a method of controlling a wind power plant according to the present embodiment.

Hereinafter, a wind turbine control system according to the present embodiment and a wind turbine control method using the same will be described with reference to the drawings. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

In addition, the same or corresponding components are denoted by the same reference numerals regardless of the reference numerals, and redundant explanations thereof will be omitted. For convenience of explanation, the size and shape of each constituent member shown may be exaggerated or reduced have.

On the other hand, terms including an ordinal number such as a first or a second may be used to describe various elements, but the constituent elements are not limited by the terms, and the terms may refer to a constituent element from another constituent element It is used only for the purpose of discrimination.

FIG. 1 is a schematic block diagram of a wind turbine control system according to the present embodiment. FIG. 2 is a schematic view of the configuration of a wind turbine control system and a wind turbine terminal according to the present embodiment. And FIG. 4 is a view illustrating a grouping of the wind farms according to the present embodiment for cluster analysis. FIGS. 5 and 6 are views FIG. 2 is a flowchart schematically showing a method of controlling a wind power generation complex.

1 and 2, the wind turbine control system according to the present embodiment may include a wind-up meteorological tower 10, a wind turbine 20, and a wind turbine control unit 100.

The wind-up meteorological tower (10, Met Mast) acquires environmental information related to the wind, and the front end of the wind power generator complex 20 according to the present embodiment can be disposed. Here, wind wind is a concept involving wind speed and wind direction, and may include other elements for wind. It is necessary for the wind-pollution meteorological tower 10 to acquire the relevant weather information before the wind reaches the wind turbine located at the front of the wind power generator complex 20 and to transmit the related information to the wind power generator control unit 100. The information of the wind speed measuring meteorological tower 10 is transmitted to the processing unit 110 provided in the wind power generator control unit 100 and can be used as basic data for controlling the wind turbines WT through hardware and software processing.

The wind power generation complex 20 can generate electricity using a wind blowing in a certain region by arranging a plurality of wind turbines (WT) in parallel or in series. The wind turbines WT installed in the wind power generation complex 20 can be configured to have the same mechanical characteristics. The reason why the wind turbine (WT) is configured to have the same mechanical characteristics is to standardize the control command output through the wind power generator control unit 100 as much as possible. If wind power generation complex 20 is constructed with wind turbines (WTs) of different specifications, it is necessary to perform additional calculation for control, so that immediate control command output may become difficult.

The wind turbine control unit 100 controls a plurality of wind turbines WT installed in the wind turbine 20. The wind power generator control unit 100 may include a processing unit 110, an Ethernet UI 120, an Internet UI 130, and a memory unit 140. At this time, the wind power generation controller 100 may be connected to the at least one wind turbine (WT) through an Ethernet network via the Ethernet UI 120 to be able to communicate with the at least one wind turbine (WT) May be communicatively coupled to the power generation complexes (not shown).

The processing unit 110 performs an ultra-short term prediction on a wind condition to be input to the wind turbines WT in real time and allocates power generation requirements to the wind turbines based on the prediction result. Thus, the wind turbines (WT) are controlled to produce the allocated power generation requirements.

At the same time, the processing unit 110 can perform a grouping operation according to the current state of the plurality of wind turbines (WT) through cluster analysis. The state information of the wind turbines WT may include transition state information and fatigue load information obtained from the wind turbine transition state sensing unit 200 and the wind turbine fatigue load sensing unit 300.

That is, in the wind turbine transition state sensing unit 200, the wind turbine WT operates stably according to the control command, and the operation mode of the wind turbine WT is changed by the new control command in accordance with the change of the wind turbine, Information of the state in which the free energy of the wind turbine WT takes a maximum value during the process of entering the state. At this time, it is possible to group similar wind turbines (WTs) having a transition region among a plurality of wind turbines (WT) through the acquired information.

In addition, the wind turbine fatigue load sensing unit 300 can sense structural deformation or fatigue in accordance with load due to wind pressure, centrifugal force or the like during operation of the wind turbine WT. Such fatigue loading data can be obtained for each wind turbine (WT) that is variably controlled in accordance with the wind speed, so that the wind turbines (WT) having similar fatigue load can be grouped and controlled.

The wind turbine transition state sensing unit 200 and the fatigue load sensing unit 300 may be installed individually in the respective wind turbines WT and may be installed in the wind turbine control unit 100 have.

According to this configuration, the processing unit 110 can calculate the optimal power generation demand and distribute it to each wind turbine WT according to the current state of the wind and wind turbines WT. That is, the processing unit 110 can perform a simulation operation using the acquired information, and distribute the entire power generation required by the system operator to the wind power generation complex 20 to the wind turbines WT. For example, when the total power generation requirement given to the wind power generation complex 20 is 100 kW, 10 kW for the first wind turbine WT11, 8 kW for the second wind turbine WT12, , 1 kW to the nth wind turbine (WTnn), and the like, in consideration of the position of the wind turbines (WT) and the wind direction.

Meanwhile, the processor 110 may cluster the wind turbines (WT) in a similar state by cluster analysis of the information obtained from the wind turbine transition state sensing unit 200 and the wind turbine fatigue load sensing unit 300.

Cluster analysis techniques can be divided into partitioning approach and hierarchical approach.

The segmentation approach can be based on the Euclidean distance measure, which is a way to determine K segments that optimize the category function. A K-means algorithm can be used among the partitioning algorithms used to cluster numeric attribute data. To use this algorithm, enter the K value to which you want to divide into several groups. Then, the algorithm assigns K average points once, and all data is looked at one by one and assigned to the group corresponding to the nearest average point. Then, the data can be reassigned to the nearest group again by slightly changing the average points. This process is repeated until the scale function indicating the cluster state no longer changes. If the cluster function is no longer changed, the group of states is designated as the result of clustering.

The hierarchical approach is a bottom-up approach in which each data point is initially set as a cluster and then split or merged based on the distance between the two pairs. When all the points belong to one large cluster The history information can be maintained. This is called agglomerative hierarchical clustering, and is a method of clustering objects that are relatively close together. This algorithm first assumes that all n data are n different groups. Thereafter, the process of merging the two closest similar groups and reducing the number of groups is repeated until the total number of groups reaches K, and K groups are searched. In addition, the process of merging or separating the clusters can be simplified by using a dendrogram of the two-dimensional drawing. In the clustering process, once an object belongs to another cluster, it can be prevented from belonging to another cluster.

According to the present embodiment, the processing unit 110 can define environment information of wind turbines (WT) as multivariate data and perform cluster analysis by defining a major part of the total variation thereof as a main component.

For example, the set of control commands according to the p environment variables for n wind turbines (WT) are denoted as X ₁ , X ₂ , ... , X _p are observed, the characteristics representative of each wind turbine (WT) can be expressed by the following equation.

By performing the cluster analysis as described above, several grouped clusters can be derived through the dimension reduction. This cluster can be represented by a representative value of the main component as described above. In this case, the values constituting each cluster are the explanatory power (R-square, R ² ) that each principal component can express through the described total dispersion percentage value based on the eigenvalue which is an index describing the entire nonlinear model The number of clusters that can be operated within the allowable range of the entire nonlinear model can be determined.

FIG. 3 is a diagram for explaining the process of predicting the rising of the wind and the process of allocating the generation demand. In FIG. 3, the concept of a prediction window is introduced.

The prediction window is a section from the wind-induced information measured by the wind-up meteorological tower 10 until the wind enters the wind power generator complex 20 and then flows out. The predicted window also includes previously measured winds in addition to the winds currently measured by the wind-up meteorological tower 10, which can be used for all wind turbine (WT) And to predict the mechanical load.

The processing unit 110 predicts the wind speed and the mechanical load for all the wind turbines WT provided in the wind power generation complex through the prediction window in real time and assigns the generation demand amounts to the wind turbines WT based on the prediction result .

3,

1) t and _k in the wind impinging on the WT (wind turbine) -11,12 is punghwang the wind incident to the measuring meteorological tower 10 to t _k before,

2) at t _k is incident on the measurement punghwang vapor tower 10, the wind which enters the WT-21,22 are in t _k-1 previously, at t _k-1 and the wind incident to the WT-11,12,

3) incident from t _k to t _k-2 is the wind before punghwang measuring vapor container (10) to be incident on the WT-31,32, and then at t _k-2 incident on the WT-11,12, t _{k- 1} to WT-21 and 22, respectively.

FIG. 4 is a diagram showing a processing unit 110 grouping a plurality of wind turbines WT according to the present embodiment.

As shown in the figure, the wind power generator control unit 100 may be connected to the wind power generator complex 20, wherein the wind power generator complexes 20 may include a plurality of wind turbines WTnm. In this case, n means a phase-shifted column and m means a row. Accordingly, the wind turbine of one row and one row can be expressed as WT11, and the wind turbine of row three, column four can be expressed as WT34 or the like. For the sake of understanding, the wind turbine control system according to the present embodiment has a total of twelve wind turbines (WT). However, the present invention is not limited thereto, and the number of wind turbines can be increased or decreased as needed.

As shown in the figure, when the wind is blown in a direction in which the wind turbine (WT31) in the third row and the first row is positioned at the frontmost end, the wind-up meteorological tower (10) senses this and sends the related information to the wind power generator control unit It can send out.

The wind farm controller 100 may then perform a cluster analysis as described above to determine the current state of each wind turbine WT and to group the wind turbines WT that are in a similar state have. For example, the group 1 (G1) may be set as the third row and the first row as the wind turbine WT31, and the group 2 (G2) may be set as WT21, WT22, WT32, and the group G3 as WT11, WT12, WT33, WT34 , Group 4 (G4) can be set to WT13, WT23, WT24, and group 5 (G5) can be set to WT14. This grouping is a grouping using cluster analysis technique in which similar factors such as fatigue load and transition state of each wind turbine (WT) are similar.

The grouped information may be stored in the memory unit 140 and used only for control information of other wind farms. If the similar situation is repeated, the stored group information (WTs) according to the wind speeds of the wind turbines.

Hereinafter, the method of controlling the wind power plant according to the present embodiment will be described together with the flow charts of FIGS. 5 and 6. FIG.

The wind-up meteorological tower 10 installed at the front end of the wind power generation plant 20 in which the operation of the wind power generation plant is started acquires wind-condition information of winds blown toward the wind power generation plant 20 in real time, The power generation complex controller 100 can perform real-time transmission (S10).

Then, the wind power generation plant control section 100 can simulate the operation of the wind turbines (WT) and calculate the required power generation amount for each wind turbine to satisfy the total power generation output requirement based on the measured upwind information (S20 ).

Meanwhile, the present state of the wind turbines (WT) can be obtained through various sensing means (S30). For example, as shown in FIG. 6, in this embodiment, sensing the wind turbine fatigue load (S31) A step S32 of detecting the transition state of each wind turbine, and a step S33 of transmitting the information obtained in the steps S31 and S32 to the memory unit 140. [ The obtained information can be cluster analyzed in the processing unit 110 (S40), and the respective wind turbines WT can be grouped through the cluster analysis in the step S40 (S50).

In step S50, the processing unit 110 may set the wind turbines (WT) in the same mechanical environment and wind conditions to the same group, and the wind turbines (WT) set in the same group perform the power generation operation according to the same control command (S60).

According to the present embodiment as described above, the computation amount of the processing unit 110 is greatly reduced compared with the configuration in which each wind turbine WT is individually controlled in accordance with the wind direction information measured by the wind-up meteorological tower 10 , It is possible to control a larger number of wind turbines (WT) in the same processing unit (110).

In addition, since the amount of computation in the processing unit 110 is reduced, it is possible to control the optimum wind turbine (WT) in real time in conjunction with real-time data change due to the change in the wind direction.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, many modifications and changes may be made by those skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims. The present invention can be variously modified and changed by those skilled in the art, and it is also within the scope of the present invention.

10; Meteorological topography (met mast) 20; Wind power plant
WT11-34; Wind turbine 100; Wind power generator control unit
110; Processing unit 120; Ethernet UI
130; Internet UI 140; The memory unit
200; A wind turbine transition state detection unit 300; Wind turbine fatigue load sensing part

Claims (6)

A wind turbine transition state sensing unit connected to at least one or more wind turbines to acquire transition state information from each of the wind turbines;
A wind turbine fatigue load sensing unit coupled to at least one or more wind turbines to obtain fatigue load information from each of the wind turbines;
A meteorological meteorological tower for measuring a wind condition such as a wind speed and a direction of a wind running toward a wind turbine constructed of at least one or more wind turbines; And
A wind turbine generator for acquiring information of the wind turbine transition state sensing unit and the fatigue load sensing unit and performing a cluster analysis according to the information to group the at least one wind turbine and to perform a control sequence of the wind turbine according to the wind- And a control unit for controlling the wind turbine generator.
The method according to claim 1,
Wherein the wind turbine transition state sensing unit and the fatigue load sensing unit are individually installed in each wind turbine.
The method according to claim 1,
Wherein the wind turbine transition state sensing unit and the fatigue load sensing unit are installed inside the wind power generator control unit.
The method according to claim 1,
Wherein the wind farm control unit is communicably connected to the at least one wind turbine via an Ethernet network.
The method according to claim 1,
Wherein the wind farm control unit is communicably connected to a plurality of wind farms via an Internet network.
Obtaining the weather forecast information from the wind-up meteorological tower;
Simulating wind turbine operation using the acquired wind direction information;
Obtaining current state information of the wind turbine;
Performing cluster analysis using current state information of the obtained wind turbine;
Grouping wind turbines that are in a similar state according to the wind speed, through cluster analysis; And
And outputting a wind turbine control command in accordance with the weather forecast information for each group,
Wherein the cluster analysis step comprises:
Sensing each wind turbine fatigue load;
Sensing each wind turbine transition state; And
And transmitting the fatigue load and transition state information of each of the wind turbines to the memory unit.

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