CN113027678B - Wind driven generator group data acquisition system, method and device - Google Patents

Abstract

A wind turbine generator group data acquisition system, method and device are provided. The data acquisition system comprises a data interface and a processor, wherein the data interface is connected with the wind generating set controller; the processor is configured to assign a unit variable acquired from the wind turbine generator set to each of the plurality of acquisition threads during each sampling interval, perform data acquisition on the unit variable assigned on each acquisition thread based on the sampling interval, adjust an ending time of each sampling interval based on an acquisition status on the plurality of acquisition threads at an expected ending time of each sampling interval, determine the time as a starting time of a next sampling interval, and store data of the unit variable acquired on each acquisition thread during each sampling interval at the end of the sampling interval.

Description

Wind turbine data acquisition system, method and device

technical field

The present application generally relates to the technical field of wind farm communication, and more particularly, to a data acquisition system, method and device for a wind turbine.

Background technique

In the prior art, a Supervisory Control And Data Acquisition (SCADA, Supervisory Control And Data Acquisition) system is usually used to monitor the working status of each wind turbine in a wind farm. Figure 1 shows a schematic structural diagram of a common wind turbine. As shown in FIG. 1 , a wind turbine 100 may include a tower 2 , a nacelle 6 mounted on the tower 2 , an impeller consisting of three blades 8 rotatably mounted to the nacelle 6 , and a rotor 4 . The impeller can obtain kinetic energy from the wind to propel the impeller to rotate. The impeller is coupled to a rotor 4 of a generator (not shown) via a main shaft, and drives the rotor 4 to rotate so that the generator generates electricity. The wind turbine 100 generally includes a main controller 10, which can realize automatic start, automatic direction adjustment, automatic speed regulation, automatic grid connection, automatic disconnection, automatic shutdown, automatic cable for the wind turbine 100 It can provide important control and protection functions such as unwinding, automatic recording and monitoring, and can provide various real-time control data and status data of wind turbines.

In a wind farm, the locations of the units are scattered. In order to utilize the wind resources reasonably, a certain distance must be maintained between the wind turbines to reduce mutual influence and interference. The existing wind power SCADA system mainly consists of the following two parts:

1) Local monitoring part: arranged in the control cabinet of each wind turbine tower. The local control of each wind turbine can monitor the running state of the wind turbine and collect the data generated by the wind turbine.

2) Central monitoring part: generally arranged in the wind farm control room. The staff can control and understand the operation and operation status of a certain type of wind turbine in the wind farm at any time according to the switching of the screen.

The data transmission between the local monitoring part and the central monitoring part mainly means that the lower computer control system can communicate the data, status and fault conditions of the lower computer with the upper computer in the central monitoring room through a dedicated data transmission device and interface circuit. At the same time, the upper computer can convey the control instructions to the lower computer, and the control system of the lower computer performs corresponding actions based on the control instructions, so as to realize the remote monitoring function.

Generally speaking, due to the large number of wind turbines distributed in large wind farms and the large flow of data information, the time delay of long-distance data transmission is as high as 7 seconds. This is unacceptable for real-time control of fans.

Therefore, it is necessary to quickly collect various real-time control data and status data of wind turbines (such as real-time wind speed, wind direction, pitch angle, azimuth angle, power generation, fan speed, fan power, etc.) A data acquisition scheme that can ensure the sampling correctness and time determinism of the collected data at the same time.

On the other hand, the basic communication framework of the current wind turbine is mainly divided into four layers, as shown in Figure 2, from the lowest layer to the highest layer are: logical terminal (indicated by 210 in Figure 2), perception and edge operations (Figure 2). 2), integrated management of field-level data (indicated by 230 in FIG. 2), and diagnostic operation and maintenance of field group-level massive data (indicated by 240 in FIG. 2). The logic terminal layer is the main control system with programmable logic controller (PLC) as the core, the field-level data comprehensive management layer includes various monitoring and management systems of the main control center, and the field-group-level massive data diagnosis and operation and maintenance layer is mainly in the A unified control center shall be established correspondingly in each region.

However, the current research work on the perception and edge computing layer is obviously insufficient, and a comprehensive edge computing product is needed to make up for the insufficient computing power of the logic control terminal, the insufficient granularity of field-level data collection, and the coarse control and management.

SUMMARY OF THE INVENTION

In order to at least solve the above problems in the prior art, the present application provides a data acquisition system, method and device for a wind turbine.

According to an aspect of the present invention, a wind turbine data acquisition system is provided, which is characterized by comprising a data interface and a processor, wherein the data interface is communicatively connected to the main controller of the wind turbine; the processor is used for The unit variables obtained from the wind turbines are assigned to each of the plurality of acquisition threads during each sampling interval, and data collection is performed on the unit variables assigned on each acquisition thread based on the The collection status of the expected end time of the interval on the multiple collection threads, adjust the end time of each sampling interval, and determine the time as the start time of the next sampling interval, and at the end of each sampling interval, store The data for the fleet variables collected on each collection thread during the sampling interval.

The processor can also be used for: at the start time of each sampling interval, each acquisition thread is allocated a group of unit variables for data collection, and for each acquisition thread, if the currently assigned unit variables are When data is collected and the time interval from the expected end time of the current sampling interval is greater than the preset threshold, the collection thread is allocated the next set of unit variables for data collection.

The processor can also be used for: if there is an overtime collection thread that has not completed the data collection of the unit variable in the multiple threads at the expected end time of the current sampling interval, then the end time of the current sampling interval is postponed to the overtime The time at which the data collection of the unit variables on the collection thread ends, and this time is determined as the start time of the next sampling interval, if all the For data collection of unit variables, the end time of the current sampling interval is advanced to the time when the data collection of the last unit variable is completed, and this time is determined as the start time of the next sampling interval.

The processor may also be configured to: count the number of sampling intervals in which data collection is performed, and perform a first correction operation when the count value is equal to or greater than a preset value, wherein the first correction operation may include: if the current sampling The data collection of all unit variables has been completed on the multiple collection threads before the expected end time of the interval, then the end time of the current sampling interval is corrected to the expected end time of the current sampling interval, and this time is determined as the next Start time of the sampling interval and restart counting, if data collection for all unit variables is not completed on the plurality of collection threads before the expected end time of the current sampling interval, continue the current counting.

The processor may also be configured to perform a second correction operation after performing the data collection for a predetermined time, wherein the second correction operation may include resetting the start time of the next sampling interval.

The processor can also be used to parse the data of the generator set variables obtained from the wind power generator set, and provide the parsed data of the generator set variables according to the allocated acquisition thread for data acquisition.

According to another aspect of the present invention, there is provided a wind turbine data collection method, comprising: assigning a unit variable acquired from a wind turbine to each of a plurality of collection threads during a current sampling interval; Data collection is performed on the unit variables allocated on each collection thread during the interval; based on the collection status on the multiple collection threads at the expected end time of the current sampling interval, the end time of the current sampling interval is adjusted, and the time is determined is the start time of the next sampling interval; and when the current sampling interval ends, the data of the unit variables collected on each collection thread during the current sampling interval are stored.

The step of allocating unit variables may include: at the start time of the current sampling interval, assigning a group of unit variables to each acquisition thread for data collection, and for each acquisition thread, if the data for the currently allocated unit variables is completed. Collection and the time interval from the expected end time of the current sampling interval is greater than the preset threshold, then the collection thread is allocated the next set of unit variables for data collection.

The step of determining the start time of the next sampling interval may include: if there is a timeout acquisition thread that has not completed the data acquisition of the unit variable in the multiple threads at the desired end time of the current sampling interval, then the end of the current sampling interval The time delay is the time when the data collection of the unit variable on the time-out collection thread ends, and this time is determined as the start time of the next sampling interval, if the multiple collections are performed before the expected end time of the current sampling interval If the data collection of all unit variables has been completed on the thread, the end time of the current sampling interval is advanced to the time when the data collection of the last unit variable is completed, and this time is determined as the start time of the next sampling interval.

The step of determining the start time of the next sampling interval may further include: counting the number of sampling intervals in which data collection is performed; when the count value is equal to or greater than a preset value, performing a first correction operation, wherein the first correction operation Can include: if the data collection of all unit variables has been completed on the plurality of acquisition threads before the expected end time of the current sampling interval, then the end time of the current sampling interval is corrected to the expected end time of the current sampling interval, and This time is determined as the start time of the next sampling interval and the counting is restarted, and if the data collection of all the unit variables is not completed on the plurality of collection threads before the expected end time of the current sampling interval, the counting is continued.

The step of determining the start time of the next sampling interval may further include: after performing the data collection for the predetermined time, performing a second correction operation, wherein the second correction operation may include: performing a second correction operation on the start time of the next sampling interval reset.

According to another aspect of the present invention, there is provided a wind turbine data acquisition device, characterized in that the device comprises: a variable allocation module configured to perform a data acquisition on each of a plurality of acquisition threads during each sampling interval The acquisition thread assigns the unit variables obtained from the wind turbine; the data acquisition module is configured to perform data acquisition on the unit variables assigned on each acquisition thread based on the sampling interval; the sampling interval adjustment module is configured to perform data acquisition based on the data acquisition module in the The collection status of the expected end time of each sampling interval on the multiple collection threads, the end time of each sampling interval is adjusted, and the time is determined as the start time of the next sampling interval; the storage module is configured as At the end of each sampling interval, the data for the fleet variables collected on each acquisition thread during that sampling interval is stored.

According to another aspect of the present invention, a computer-readable storage medium is provided, wherein computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by a processor, the aforementioned data collection is realized method.

According to another aspect of the present invention, a computer device is provided, comprising a readable medium storing computer program instructions, wherein the computer program instructions include instructions for executing the aforementioned data collection method.

beneficial effect

By applying the wind turbine data acquisition system, method and device according to the exemplary embodiment of the present invention, by directly collecting data from the main controller of the wind turbine, various real-time control and status data of the wind turbine can be obtained quickly, accurately and in real time, In this way, it is possible to synthesize real-time wind speed, wind direction, pitch angle, azimuth angle, power generation, wind turbine speed, wind turbine power and other information to evaluate the overall operating status of the wind turbine. Predicting the situation makes the operation, control and planning of the fan more reasonable.

In addition, by applying the wind turbine data acquisition system, method and device according to the exemplary embodiments of the present invention, the problems of insufficient computing power of the logic control terminal and insufficient granularity of field-level data acquisition are also effectively overcome.

Description of drawings

The above and/or other aspects of the exemplary embodiments will be described below with reference to the accompanying drawings, in which:

Figure 1 shows a schematic structural diagram of a wind turbine;

Figure 2 shows the basic communication framework of a wind turbine of the prior art;

3 shows a block diagram of a data acquisition system according to an exemplary embodiment of the present invention;

4 shows a block diagram of a data acquisition apparatus according to an exemplary embodiment of the present invention;

5 shows a schematic diagram of multi-threaded parallel real-time acquisition according to an exemplary embodiment of the present invention;

FIG. 6 shows a data acquisition timing diagram according to an exemplary embodiment of the present invention;

FIG. 7 shows a flowchart of a data acquisition method according to an exemplary embodiment of the present invention.

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings, throughout which the same or similar elements will be designated by the same or similar reference numerals.

Detailed ways

The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of exemplary embodiments of the present invention as defined by the claims and their equivalents. The description includes various specific details to assist in that understanding, but these details are considered to be exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions may be omitted for clarity and conciseness.

FIG. 3 shows a block diagram of a data acquisition system 300 according to an exemplary embodiment of the present invention. For example only, the data acquisition system 300 may be a portable case in hardware structure, and can be carried by the operator into the tower of the wind turbine 100 . The data acquisition system 300 includes a data interface 301 and a processor 302 . The data interface 301 is directly connected to the main controller 10 (eg, PLC) through a switch to obtain data of various turbine variables of the wind turbine 100 . However, it should be understood that the data collection system 300 may also take any other form. The processor 302 may be used to perform the data collection method described later below with reference to FIG. 7 .

FIG. 4 shows a block diagram of a data acquisition apparatus 400 according to an exemplary embodiment of the present invention. 4 , the data collection apparatus 400 may include: a variable allocation module 410 , a data collection module 420 , a sampling interval adjustment module 430 and a storage module 440 .

The variable assignment module 410 according to an exemplary embodiment of the present invention may assign the turbine variables acquired from the wind turbine to each of the plurality of acquisition threads during each sampling interval, thereby enabling multi-threading of the turbine variable data Parallel real-time acquisition. This will be described in detail below with reference to FIG. 5 .

FIG. 5 shows a schematic diagram of multi-threaded parallel real-time acquisition according to an exemplary embodiment of the present invention.

Referring to FIG. 5, in an embodiment of the present invention, when data collection is performed, a separate connection channel can be established for each unit variable obtained from the main controller of the wind turbine (eg, Beckhoff PLC) (FIG. 5 Connecting channel 1, connecting channel 2, connecting channel 3, connecting channel 4, . Independent collection. The collected data can be sent to the data storage and analysis device, so that the device can evaluate the overall situation of the wind turbine to obtain the current operating status of the wind turbine, and at the same time, according to these data, it can be used for the future operation of the wind turbine. predict.

When the number of collection threads is equal to or more than the number of unit variables, the variable allocation module 410 may allocate a separate collection thread for each unit variable to perform data collection.

However, when the number of acquisition threads is less than the number of group variables, the variable assignment module 410 may assign group variables to each thread in turn. Specifically, at the start time of each sampling interval, the variable allocation module 410 may first allocate a set of unit variables to each acquisition thread to perform data collection, and the set of unit variables may include one unit variable, or may include Preset multiple unit variables. Then, for each acquisition thread, if the acquisition thread has completed the data acquisition of the currently allocated unit variables and the time interval from the expected end time of the current sampling interval is greater than the preset time threshold, the variable allocation module 410 may The thread assigns the next set of crew variables for data collection. For example, suppose there are 5 acquisition threads, thread 1, thread 2, thread 3, thread 4, and thread 5. When the current sampling interval begins, the variable assignment module 410 may assign Crew Variable 1, Crew Variable 2, Crew Variable 3, Crew Variable 4, and Crew Variable 5 to Thread 1, Thread 2, Thread 3, Thread 4, and Thread 5, respectively. Data collection, if the data collection for unit variable 3 is first completed on thread 3, and the end time from the current sampling interval is still longer (for example, greater than the preset time threshold), then the variable allocation module 410 can assign the next A crew variable 6 is assigned to thread 3 for data collection, and so on. In this way, each collection thread can be effectively utilized, reducing the possibility of a thread being too idle or too busy, thereby ensuring that system resources are fully utilized.

Referring back to FIG. 4 , the data collection module 420 in an exemplary embodiment according to the present invention may perform data collection on the unit variables allocated on each collection thread based on the sampling interval.

Here, the sampling interval refers to a preset time period for collecting the data of all the unit variables to be collected. The data collection module 420 may complete one data collection of all plant variables during one sampling interval.

The data collection module 420 typically performs multiple data collections of plant variables at fixed sampling time intervals. However, due to the influence of various factors (for example, a large amount of data for a certain unit variable, interference factors, machine failure factors, etc., resulting in longer data collection time for one or some unit variables, etc.), a fixed sampling interval is used. This makes it possible that the current sampling interval ends and the next sampling interval begins on one or some threads that the data collection of the unit variables cannot be completed, which will result in incorrect sampling data and affect the sampling time. certainty.

In this regard, in this embodiment of the present invention, the sampling interval adjustment module 430 may be used to adjust the sampling interval end time, and determine this time as the start time of the next sampling interval, and try to ensure that the current data collection for all unit variables is completed before or at the end of the current sampling interval. Here, the expected end time of the current sampling interval refers to the end time of the fixed sampling interval corresponding to the current data collection. The end time of the sampling interval. As an example only, assuming that the current data collection is the Nth data collection, the time length of the fixed sampling interval is span, and the data collection start time is 0, then the expected end time of the current sampling interval corresponding to the current data collection is N×span.

In an exemplary embodiment of the present invention, if there is a timeout collection thread that has not completed the data collection of the unit variable among the multiple threads at the expected end time of the current sampling interval, the sampling interval adjustment module 430 may adjust the current sampling interval The end time delay is the time when the data collection of the unit variable on the timeout collection thread ends, and this time is determined as the start time of the next sampling interval. The sampling interval adjustment module 430 may advance the end time of the current sampling interval to the completion time of the last unit variable if the data collection for all unit variables has been completed on the plurality of collection threads before the expected end time of the current sampling interval The time of data collection, and this time is determined as the start time of the next sampling interval. The adjustment operation of the sampling interval adjustment module 430 will be described in more detail below in conjunction with FIG. 6 .

FIG. 6 shows an example of a data acquisition timing diagram according to an exemplary embodiment of the present invention.

Referring to FIG. 5 , it is assumed that the data collection device 400 uses 5 collection threads (ie, thread 1, thread 2, thread 3, thread 4 and thread 5 shown in FIG. 6 ), time T0 is the start time of data collection, and the dotted line The sampling interval shown (the solid line and the dashed line at T3 in Figure 5 overlap) is a fixed sampling interval (for example, 20ms), time T0 to time T1 are the fixed sampling interval corresponding to the first data collection and time T1 is the first The expected end time of the second data collection, time T1 to time T2 is the fixed sampling interval corresponding to the second data collection and time T2 is the expected end time of the second data collection, and time T2 to time T3 are the same as the third time. The fixed sampling interval corresponding to the data collection and the time T3 is the expected end time of the third data collection.

As shown in FIG. 6 , the numbers on each thread represent the group variables allocated by the variable assignment module 410 for the thread during the corresponding sampling interval, for example, the numbers 1-6 between T0 and T1 in thread 1 represent the variable assignments, respectively The 6 variables allocated by the module 410 to thread 1 during the first sampling interval, the numbers 1-6 between T0 and T1 in thread 2 represent the variables allocated by the variable allocation module 410 to thread 2 during the first sampling interval, respectively. 6 variables, etc. It should be understood that the number indicating the group variable on each thread only indicates the serial number of the group variable assigned to that thread, and the group variables assigned to each thread during the same sampling interval are different from each other.

As can be seen from Figure 6, when the first data collection starts from time T0, during the fixed sampling interval from time T0 to time T1, the units assigned to thread 1, thread 2, thread 4 and thread 5 can be completed. Variable data collection, and at the end time T1 of the fixed sampling interval (that is, the expected end time of the 1st sampling interval corresponding to the 1st data collection), the data collection of the 6th unit variable allocated on thread 3 has not yet Finish. At this time, the sampling interval adjustment module 430 according to an exemplary embodiment of the present invention may delay the actual end time of the first sampling interval to the data collection end time T1' of the sixth unit variable on the time-out thread 3, and The time T1' is determined as the start time of the second sampling interval corresponding to the second data collection.

Similarly, when the 2nd data collection starts from time T1', at the end time T2 of the fixed sampling interval corresponding to the 2nd data collection (ie, the expected end time of the 2nd sampling interval), thread 1 can be completed , Thread 3, Thread 4, and Thread 5 are assigned data collection of the unit variables, and at the end time T2 of the fixed sampling interval, the data collection of the 6th unit variable assigned on thread 2 has not been completed. At this time, the sampling interval adjustment module 430 according to an exemplary embodiment of the present invention may delay the actual end time of the second sampling interval to the data collection end time T2' of the sixth unit variable on the thread 2 that has timed out, and The time T2' is determined as the start time of the third sampling interval corresponding to the third data collection.

Although FIG. 6 only illustrates the operation of the sampling interval adjustment module 430 to delay the end time of the sampling interval, it should be understood that the present application is not limited thereto. The sampling interval adjustment module 430 may also advance the end time of the sampling interval. For example, in conjunction with the situation in Figure 6, if all the unit variables allocated to thread 1, thread 2, thread 3, thread 4 and thread 5 have been completed before the end time T2 of the fixed sampling interval corresponding to the second data collection , the sampling interval adjustment module 430 can advance the end time of the second sampling interval to the time when the data collection of the last unit variable is completed, and determine this time as the start time of the next sampling interval. At this time, Unlike the situation shown in Figure 6, T2' will precede T2.

Through the above adjustment operation of the sampling interval adjustment module 430, since the end time of the sampling interval of each data collection is sometimes advanced and sometimes delayed, it can be roughly guaranteed that the number of data collection times and the collection amount of the data set within a same period of time are the same, which not only ensures the sampling The time determinism can also ensure the correctness of sampling.

It should be understood, however, that when performing sampling interval adjustments, the sampling interval adjustment module 430 may only perform the above adjustments when the time actually consumed by one data acquisition for all unit variables is close to the fixed sampling interval, and when performing one data acquisition on all unit variables When the actual time consumed by the collection is much longer than the fixed sampling interval (for example, when the actual consumption time exceeds the fixed sampling interval by a predetermined time length), in order to ensure the correctness of the sampling, the time determinism of the sampling can be abandoned without adjusting the sampling interval.

Optionally, in addition to the above sampling interval adjustment operation, the sampling interval adjustment module 430 according to an exemplary embodiment of the present invention may also perform a correction operation after performing the sampling interval adjustment for a period of time.

Specifically, the sampling interval adjustment module 430 may include a counting module (not shown). A counting module (not shown) may count the number of sampling intervals during which data collection module 420 performs data collection. The sampling interval adjustment module 430 may perform the first correction operation when the count value of the count module (not shown) is equal to or greater than the preset value. Here, the first correction operation may include: if the data collection of all the unit variables has been completed on the multiple collection threads before the expected end time of the current sampling interval, the sampling interval adjustment module 430 may correct the end time of the current sampling interval be the expected end time of the current sampling interval (rather than the time to complete the data collection of the last unit variable), and determine this time as the start time of the next sampling interval and cause the counting module (not shown) to restart counting; Whereas, if the data collection for all unit variables is not completed on the plurality of collection threads before the expected end time of the current sampling interval, the sampling interval adjustment module 430 may cause the counting module (not shown) to continue the current counting.

6 , assuming that the preset value is 3, when the data collection module 420 performs the third data collection, the count value of the counting module (not shown) is 3, and the sampling interval adjustment module 430 can first determine Whether data collection for all unit variables has been completed on multiple collection threads (ie, thread 1 to thread 5) before the expected end time T3 of the third sampling interval, if so, the sampling interval adjustment module 430 may 3 The end time of the sampling interval is corrected to the expected end time T3 instead of the completion time of the data collection of the last unit variable, as shown in Figure 6. At this time, the sampling interval adjustment module 430 may reset the count value of the counting module (not shown) to zero and restart counting after completing the calibration, waiting for the next calibration opportunity.

However, if the data collection for all unit variables has not been completed at the expected end time T3 of the third sampling interval, the sampling interval adjustment module 430 may still delay the end time of the current sampling interval to the time when the data collection of the last unit variable ends. time, and determine this time as the start time of the next sampling interval. At this time, the counting module (not shown) may continue to count the number of sampling intervals during which the data collection module 420 performs data collection (ie, the count value becomes 4), and the sampling interval adjustment module 430 may determine the fourth sampling interval after that Whether the above correction can be performed, and if not, a similar determination is continued for subsequent sampling intervals and the counting module (not shown) continues the current count until a correction is performed.

In this way, after a period of sampling interval adjustment, the actual end time of the sampling interval can be consistent with the expected end time again, so even if the start time of some sampling intervals is adjusted, it can be guaranteed that the same period of time The number of times of data collection and the same amount of data collection within the length (for example, the period from T0 to T3) are the same, which further ensures the time determinism of sampling and the correctness of sampling.

In addition, the sampling interval adjustment module 430 can also perform a comprehensive correction after performing data collection for a period of time. Specifically, after the data collection for a predetermined time is performed by the data collection module 420, the sampling interval adjustment module 430 may perform the second correction operation. Here, the second correcting operation may include resetting the start time of the next sampling interval regardless of the acquisition state of the data acquisition module 420 during the current sampling interval. That is to say, in the exemplary embodiment of the present invention, every predetermined time, the data collection module 420 will directly terminate the current data collection process regardless of whether the data collection of the unit variables has been completed at this time, and restart a new round data collection operation. Furthermore, in the exemplary embodiment of the present invention, the time interval for performing the second correction operation is much longer than the time interval for performing the first correction operation.

At the end of each sampling interval, the data collected for the fleet variables on each acquisition thread during the sampling interval may be stored in the storage module 440 .

In addition, in an exemplary embodiment of the present invention, the data acquisition device 400 may further include: a data analysis module (not shown), configured to analyze the data of the unit variables obtained from the main controller of the wind turbine. , and according to the collection thread allocated by the variable allocation module 410, the parsed data of the unit variables is provided to the data collection module 410 for data collection. A data parsing module (not shown) can parse the data of the unit variables to have a preset data type, format and structure. Here, the preset data type, format and structure can be parsed by various protocols and platforms via, for example, virtual machines, so that the data collection apparatus 400 of the exemplary embodiment of the present invention can be applicable/compatible with various protocols and platforms .

For example only, a data parsing module (not shown) may define the variable format as follows:

variable name: variable type: variable length.

In the exemplary embodiment of the present invention, the naming principle and structure of the variable type are completely consistent with the data structure definition in the main controller of the wind turbine and cover all variable type definitions and syntax formats of the main controller, and need to define to specific actual variables. For example, assuming a PLC is used as the master controller 10, the variables can be defined as follows:

a) Simple variables

Global variables defined in Twincat2 start with '.', and Global variables in Twincat3 are defined in strict accordance with this format, while other domain variables use domain name + variable name.

b) structure

The definition of the structure is '.'+structure name+specific variable name in Twincat2, 'Globle'+structure name+specific variable name in Twincat3, and other domain variables use domain name+structure name+variable name .

In addition, the variable length is used to define whether the variable is a single variable or data, the single variable length is 1, and the data is defined by the actual size, for example, the following example shows two variable formats:

Single variable: .wind_speed:REAL:1

Structure: gh_control.FB_GH_CONTROLLER_1.speed_set_point:REAL:1

The following table 1 shows all the data types of the group variables when the Beckhoff PLC is used as the master controller 10.

【Table 1】

It should be understood that the analysis processing of the data analysis module (not shown) is described above by taking PLC as an example for easy understanding, but the present application is not limited thereto. When the wind turbine uses other types of main controllers, the data parsing module (not shown) can perform adaptive and corresponding analysis on the data of the turbine variables according to the specific type of the main controller used.

In addition, in the exemplary embodiment of the present invention, the storage module 440 should follow the following storage principles when saving data: simple and easy to read, distinguish between single variables and data, store column names, store timestamps, and customize file size. By way of example only, the above principles can be implemented as follows:

Simple and easy to read: Store the collected data of unit variables in text (for example, in txt or csv2 file format), which is conducive to subsequent automatic analysis and manual analysis.

Distinguish between single variables and data: single variables can be stored directly as strings, numbers can be stored as [value 1, value 2, value 3] for example.

Storage column name: The first line of the file is a list of fields for users to view.

Storage timestamp: The first column of data is the data collection time, which is convenient for data time synchronization and checking the data collection frequency and data validity.

Customize the file size: Customize the file size of the stored result, so as to prevent the tool from crashing when the tool is opened with a too large file, and the problem of too many files that are not easy to manage when saving a too small file.

In addition, although not shown in FIG. 4 , the data collection apparatus 400 may further include at least one of a data processing module (not shown) and a communication module (not shown). The data processing module (not shown) can perform some simple preprocessing and analysis on the collected and stored data while the data collection device 400 is collecting data (eg, performing fast filtering to address class, simple fault diagnosis and simple analysis report), so that when the remote main control center obtains the data of each wind turbine under management, it is more convenient and fast to analyze such data based on the preprocessed and analyzed data. Further processing is performed, thereby reducing the computational burden of the main control center, and improving the computational efficiency and data analysis accuracy of the main control center. The communication module (not shown) can send the collected and stored unit variable data and/or the data processing module (not shown) to preprocess and analyze the unit variable data at a preset period or as instructed. main control center. For example, when the operator cannot directly retrieve the above-mentioned data from the data acquisition device 400 due to various reasons, the communication module (not shown) can send such data to the main control center.

In addition, although the composition of the data acquisition device 400 and the specific operations of each of its constituent modules (eg, the variable allocation module 410, the data acquisition module 420, the sampling interval adjustment module 430, and the storage module 440, etc.) are shown in a modular manner, It should be understood that this is for ease of explanation only. For example, the operations performed by the various modules of the data collection apparatus 400 may be completed by the processor of the data collection system 300 of FIG. 3 , or each module of the data collection apparatus 400 may be further subdivided into more modules to perform corresponding operations, or Several modules in the data collection device 400 can be integrated with each other into one module to perform corresponding operations. Furthermore, it should be understood that the above-described modules may be implemented in software, hardware, firmware, or a combination of two or more of software, hardware, and firmware.

FIG. 7 shows a flowchart of a data acquisition method according to an exemplary embodiment of the present invention. Figure 7 shows a flow of data collection performed by the data collection system 300 during one sampling interval, and the data collection system 300 may perform a similar flow at each sampling interval.

Referring to FIG. 7 , in step S710 , the unit variables obtained from the wind turbine may be assigned to each of the plurality of acquisition threads during the current sampling interval.

Specifically, at the start time of the current sampling interval, a group of unit variables can be allocated to each collection thread for data collection, and for each collection thread, if the collection thread has completed the data of the currently allocated unit variables Collection and the time interval from the expected end time of the current sampling interval is greater than the preset threshold, then the collection thread can be assigned the next set of unit variables for data collection.

In step S720, data collection may be performed on the unit variables allocated on each collection thread during the current sampling interval.

In step S730, based on the collection status on the plurality of collection threads at the expected end time of the current sampling interval, the end time of the current sampling interval may be adjusted and determined as the start time of the next sampling interval.

Specifically, if there is a timeout collection thread that has not completed the data collection of the unit variables in the multiple threads at the expected end time of the current sampling interval, the end time of the current sampling interval can be postponed to the time limit on the timeout collection thread. The time at which the data collection of the unit variables of the current sampling interval ends, and this time is determined as the start time of the next sampling interval; For data collection, the end time of the current sampling interval can be advanced to the time when the data collection of the last unit variable is completed, and this time can be determined as the start time of the next sampling interval.

In addition, in an exemplary embodiment of the present invention, in step S720, the number of sampling intervals in which data collection is performed may also be counted, and the first correction operation is performed when the count value is equal to or greater than a preset value. Here, the first correction operation may include: if the data collection of all the unit variables has been completed on the plurality of collection threads before the expected end time of the current sampling interval, then correcting the end time of the current sampling interval to the current sampling interval the expected end time of the current sampling interval, and determine this time as the start time of the next sampling interval and cause the counting module (not shown) to restart counting; If the data collection of all unit variables is not completed, the counting can continue.

Furthermore, in an exemplary embodiment of the present invention, if data collection for a predetermined time has been performed, a second correction operation may also be performed. Here, the second correcting operation may include resetting the start time of the next sampling interval regardless of the acquisition state during the current sampling interval.

Then, in step S740, the data of the unit variables collected on each collection thread during the current sampling interval may be stored at the end of the current sampling interval.

Optionally, although not shown in FIG. 7 , before step S710, the data of the unit variables obtained from the wind turbine may be parsed first, and the parsed unit may be provided according to the collection thread assigned in step S710 The variable data is collected in step S720. Here, the data of the unit variables can be parsed to have preset data types, formats and structures that can be parsed by various protocols.

The more specific implementation of each step of the above data acquisition method has been described in detail above with reference to FIGS. 3 to 6 , so for the sake of brevity, repeated descriptions will not be repeated here.

By applying the wind turbine data acquisition system, method and device according to the exemplary embodiments of this aspect, by directly acquiring data from the main controller of the wind turbine, various real-time control data of the wind turbine can be obtained quickly, accurately and in real time, and integrated The real-time wind speed, wind direction, pitch angle, azimuth angle, power generation, fan speed, fan power and other information can evaluate the overall operating status of the fan, and can also predict the future operating status of the fan based on these data and information at the same time. It makes the operation, control and planning of the fan more reasonable, which is conducive to improving the real-time performance of the fan control, and also effectively makes up for the insufficient computing power of the logic control terminal and the insufficient granularity of field-level data collection.

The above data collection method can be implemented by computer program instructions recorded on a computer readable storage medium, the computer program instructions implementing the data collection method when executed by a processor or other type of computing device. The storage medium may also include program instructions, data files, data structures, etc. alone or a combination of data files, data structures, etc. and program instructions. Examples of computer-readable storage media include magnetic media (eg, hard disks, floppy disks, and magnetic tapes), optical media (eg, CD ROM disks and DVDs), magneto-optical media (eg, optical disks), and memory media specially configured to store and execute program instructions. A hardware device (eg, read only memory (ROM), random access memory (RAM), flash memory, etc.). Examples of program instructions include machine code (eg, produced by a compiler) and files containing higher-level code that can be executed by a computer using an interpreter. The described hardware devices may be configured to function as one or more software units to perform the operations and methods described above, and vice versa. Furthermore, the computer-readable storage medium can be distributed among computer systems connected through a network, and the computer-readable code or program instructions can be stored and executed in a distributed fashion.

Although the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those skilled in the art that form and detail may be made therein without departing from the spirit and scope of the invention as defined in the claims various changes on.

Claims (13)

1. A wind turbine data acquisition system, characterized in that, comprising a data interface and a processor, wherein, The data interface is communicatively connected with the main controller of the wind turbine; the processor is configured to assign the unit variables obtained from the wind turbine to each of the plurality of acquisition threads during each sampling interval, Data collection is performed on the unit variables allocated on each collection thread based on the sampling interval, Adjust the end time of each sampling interval based on the collection status on the plurality of collection threads at the expected end time of each sampling interval, and determine this time as the start time of the next sampling interval, At the end of each sampling interval, the data for the fleet variables collected on each acquisition thread during that sampling interval is stored. 2. The data acquisition system of claim 1, wherein the processor is also used for: At the start time of each sampling interval, each collection thread is assigned a set of unit variables for data collection, and For each collection thread, if the data collection for the currently allocated unit variables is completed and the time interval from the expected end time of the current sampling interval is greater than the preset threshold, the collection thread is allocated the next group of unit variables for data collection . 3. The data acquisition system of claim 1, wherein the processor is also used for: If at the expected end time of the current sampling interval, there is still an overtime collection thread in the multiple collection threads that has not completed the data collection of the unit variable, then the end time of the current sampling interval is postponed to the time limit of the unit variable on the overtime collection thread. The time when the data collection ends, and this time is determined as the start time of the next sampling interval, If the data collection for all unit variables has been completed on the multiple collection threads before the expected end time of the current sampling interval, the end time of the current sampling interval is advanced to the time when the data collection of the last unit variable is completed, and This time is determined as the start time of the next sampling interval. 4. The data acquisition system of claim 3, wherein the processor is also used for: counting the number of sampling intervals in which data collection is performed, and performing a first correction operation when the count value is equal to or greater than a preset value, Wherein, the first correction operation includes: If the data collection of all unit variables has been completed on the multiple collection threads before the expected end time of the current sampling interval, the end time of the current sampling interval is corrected to the expected end time of the current sampling interval, and the time Determined as the start time of the next sampling interval and restarts counting, If data collection for all fleet variables is not completed on the plurality of collection threads by the desired end time of the current sampling interval, the current count is continued. 5. The data acquisition system of claim 4, wherein the processor is further used for: After performing the data collection for a predetermined time, a second correction operation is performed, Wherein, the second correction operation includes: Reset the start time of the next sampling interval. 6. The data acquisition system of claim 1, wherein the processor is further configured to: The data of the unit variables obtained from the wind turbine is analyzed, and the parsed data of the unit variables is provided according to the assigned collection thread for data collection. 7. A data acquisition method for wind turbines, comprising: assigning unit variables obtained from the wind turbine to each of the plurality of acquisition threads during the current sampling interval; Perform data collection on the unit variables assigned on each collection thread during the current sampling interval; adjusting the end time of the current sampling interval based on the acquisition status on the plurality of acquisition threads at the expected end time of the current sampling interval, and determining that time as the start time of the next sampling interval; and At the end of the current sampling interval, the data of the unit variables collected on each collection thread during the current sampling interval is stored. 8. data acquisition method as claimed in claim 7 is characterized in that, the step of allocating unit variable comprises: At the start time of the current sampling interval, each collection thread is assigned a set of unit variables for data collection, and For each collection thread, if the data collection for the currently allocated unit variables is completed and the time interval from the expected end time of the current sampling interval is greater than the preset threshold, the collection thread is allocated the next group of unit variables for data collection . 9. The data collection method according to claim 7, wherein the step of determining the start time of the next sampling interval comprises: If at the expected end time of the current sampling interval, there is still an overtime collection thread in the multiple collection threads that has not completed the data collection of the unit variable, then the end time of the current sampling interval is postponed to the time limit of the unit variable on the overtime collection thread. The time when the data collection ends, and this time is determined as the start time of the next sampling interval, If the data collection for all unit variables has been completed on the multiple collection threads before the expected end time of the current sampling interval, the end time of the current sampling interval is advanced to the time when the data collection of the last unit variable is completed, and This time is determined as the start time of the next sampling interval. 10. The data collection method according to claim 9, wherein the step of determining the start time of the next sampling interval further comprises: Count the number of sampling intervals in which data collection is performed; When the count value is equal to or greater than the preset value, the first correction operation is performed, Wherein, the first correction operation includes: If the data collection of all unit variables has been completed on the multiple collection threads before the expected end time of the current sampling interval, the end time of the current sampling interval is corrected to the expected end time of the current sampling interval, and the time Determined as the start time of the next sampling interval and restarts counting, Continue counting if data collection for all fleet variables is not completed on the plurality of collection threads by the desired end time of the current sampling interval. 11. The data collection method according to claim 10, wherein the step of determining the start time of the next sampling interval further comprises: After performing the data collection for a predetermined time, a second correction operation is performed, Wherein, the second correction operation includes: Reset the start time of the next sampling interval. 12. A wind turbine data acquisition device, characterized in that, comprising: a variable assignment module configured to assign the unit variables obtained from the wind turbine to each of the plurality of acquisition threads during each sampling interval; a data acquisition module configured to perform data acquisition on the unit variables allocated on each acquisition thread based on the sampling interval; A sampling interval adjustment module configured to adjust the end time of each sampling interval based on the collection state of the data collection module on the plurality of collection threads at the expected end time of each sampling interval, and determine this time as the next the start time of the sampling interval; The storage module is configured to store, at the end of each sampling interval, the data of the unit variables collected on each collection thread during the sampling interval. 13. A computer-readable storage medium, wherein computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by a processor, any one of claims 7-11 is implemented The described data collection method.

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