Data acquisition method, device and equipment of wind power generator and storage medium

Abstract

This application discloses a data acquisition method, apparatus, device, and storage medium for wind turbines, relating to the field of data acquisition technology. The method is applied to a monitoring and control system for wind turbines, which includes an independent acquisition module that does not occupy system data processing resources. The method includes: activating the independent acquisition module to acquire real-time operating data of the turbine; performing waveform recording processing on the operating data based on the independent acquisition module to obtain waveform recording data; and processing the waveform recording data and then storing it in a rolling manner according to a preset weight level. In this application, an independent acquisition module is used to acquire real-time operating data of the turbine under different operating conditions, avoiding a frequency lower than the frequency of acquiring and recording waveform recording data. This avoids the need for users to manually process operating data that cannot be processed in a timely manner, thereby improving the efficiency of data acquisition for wind turbines.

Description

Technical Field

[0001] This application relates to the field of data acquisition technology, and in particular to a data acquisition method, apparatus, equipment and storage medium for a wind turbine. Background Technology

[0002] Currently, the focus on wind turbines is no longer limited to fault records, power generation, and availability; greater attention is being paid to their performance and health status. This necessitates the testing of wind turbine characteristics. In particular, the collection, frequency characteristic testing, and analysis of wind turbine data are crucial.

[0003] Currently, the commonly used method for detecting and analyzing the frequency characteristics of wind turbine data acquisition is the fault recording method. This method utilizes two or more different frequencies combined with memory and hard disk recording. During operation, a higher first frequency is used to sample and store the data in memory immediately, while a lower second frequency is used to store some data on the hard disk. Different sampling frequencies are designed according to different data requirements. When a fault occurs, the memory data is stored on the hard disk, and a higher sampling frequency is also used to sample and store the data on the hard disk to ensure the density and accuracy of the data during a fault. However, in complex wind farms, the acquired wind turbine data is holistic data that requires processing. Lower data processing frequencies cannot meet the demands of higher acquisition and recording frequencies, leading to data accumulation and compromising real-time data acquisition. Furthermore, to avoid data accumulation, users need to manually process the recorded data, resulting in low data acquisition efficiency for wind turbines. Summary of the Invention

[0004] The main objective of this application is to provide a data acquisition method, device, equipment, and storage medium for wind turbines, aiming to solve the technical problem that existing technologies cause the accumulation of acquired and recorded waveform data, requiring users to manually process the recorded waveform data, resulting in low data acquisition efficiency for wind turbines.

[0005] To achieve the above objectives, this application provides a data acquisition method for a wind turbine generator. This method is applied to a monitoring and control system for the wind turbine generator, which includes an independent acquisition module that does not occupy system data processing resources. The data acquisition method for the wind turbine generator includes:

[0006] Activate the independent acquisition module to collect motor operating data in real time;

[0007] Based on the independent acquisition module, the operating data is processed by waveform recording to obtain waveform data;

[0008] The recorded waveform data is processed and then stored in a rolling manner according to a preset weight level.

[0009] Optionally, the step of processing the waveform data and then storing it in a rolling manner according to a preset weight level includes:

[0010] The recorded waveform data is classified according to the preset operating conditions to obtain the recorded waveform sub-data for each preset operating condition;

[0011] The recorded data is weighted according to a preset weight level to obtain datasets with different weight levels;

[0012] The dataset is stored in a rolling manner based on the weight levels.

[0013] Optionally, the step of performing weighted classification on the waveform data according to a preset weight level to obtain datasets with different weight levels includes:

[0014] The operating data of the motor at different frequencies is analyzed from the recorded sub-data.

[0015] Simulate the operating state of the motor at different frequencies based on the aforementioned operating sub-data;

[0016] Based on the operating status and preset weight levels, the recorded sub-data is weighted and classified to obtain datasets of different levels.

[0017] Optionally, the step of weighting the waveform data based on the operating state and preset weight levels to obtain datasets of different levels includes:

[0018] Based on the operating status, the elimination data that does not meet the preset state threshold is removed from the recorded sub-data to obtain the data to be classified.

[0019] Based on the operating status of the data to be classified and the preset weight level, the data to be classified is weighted and classified to obtain datasets of different levels.

[0020] Optionally, the step of removing discarded data whose operating state does not meet a preset state threshold from the waveform data based on the operating state to obtain data to be classified includes:

[0021] Filter out the data to be eliminated from the recorded sub-data whose operating state does not meet the preset state threshold, and obtain the sub-data to be classified;

[0022] Based on the data to be phased out, the test operation status of the motor is simulated again;

[0023] Based on the test operation status, the eliminated data is removed from the data to be eliminated, and the erroneous analysis data is obtained.

[0024] The sub-data to be classified is integrated with the erroneous analysis data to obtain the data to be classified.

[0025] Optionally, the step of storing the dataset in a rolling manner based on the weight level includes:

[0026] Based on the weight levels, the datasets are stored sequentially into a preset storage space;

[0027] If the preset storage space is full, the data to be compared with the lowest weight level is selected from the preset storage space.

[0028] Compare the weight levels of the data to be compared with those of the dataset;

[0029] If the weight level of the dataset is higher than the weight level of the data to be compared, then the data to be compared is deleted, and the dataset is stored in the preset storage space.

[0030] Optionally, before the step of comparing the weight levels of the data to be compared with the weight levels of the dataset, the method further includes:

[0031] Based on the datasets with the same weight level, the adaptability of the motor under different preset operating conditions is determined;

[0032] The preset working conditions are labeled with an adaptation level based on the fitness level;

[0033] After the step of comparing the weight levels of the data to be compared with the weight levels of the dataset, the method further includes:

[0034] If the weight levels of the dataset and the data to be compared are equal, then the annotation adaptation levels of the preset working conditions corresponding to the dataset and the preset working conditions corresponding to the data to be compared are compared.

[0035] If the annotation adaptation level of the data to be compared is lower than the annotation adaptation level of the dataset, then the data to be compared is deleted, and the dataset is stored in the preset storage space.

[0036] Furthermore, to achieve the above objectives, this application also provides a data acquisition device for a wind turbine generator. The data acquisition device is controlled by a monitoring and control system for the wind turbine generator. The monitoring and control system includes an independent acquisition module that does not occupy system data processing resources. The data acquisition device for the wind turbine generator comprises:

[0037] The acquisition module is used to activate the independent acquisition module and collect the motor's operating data in real time.

[0038] The waveform recording module is used to perform waveform recording processing on the running data based on the independent acquisition module to obtain waveform recording data;

[0039] The storage module is used to process the recorded waveform data and then store it in a rolling manner according to a preset weight level.

[0040] In addition, to achieve the above objectives, this application also proposes a data acquisition device for a wind turbine, the device comprising: a memory, a processor, and a data acquisition program for the wind turbine stored in the memory and executable on the processor, the data acquisition program for the wind turbine being configured to implement the steps of the data acquisition method for the wind turbine as described above.

[0041] In addition, to achieve the above objectives, this application also proposes a storage medium storing a data acquisition program for a wind turbine generator, wherein when the data acquisition program for the wind turbine generator is executed by a processor, it implements the steps of the data acquisition method for the wind turbine generator as described above.

[0042] This application provides a data acquisition method, apparatus, device, and storage medium for wind turbines. Compared with existing technologies that result in the accumulation of acquired and recorded waveform data, requiring manual processing by the user and leading to low data acquisition efficiency for wind turbines, this application activates an independent acquisition module to acquire the motor's operating data in real time. Based on the independent acquisition module, the operating data is processed using waveform recording to obtain waveform data. The processed waveform data is then stored in a rolling manner according to a preset weight level. In this application, by acquiring the motor's operating data in real time through an independent acquisition module and recording the operating data, waveform data can be processed at the highest frequency, and the processed waveform data is stored in a rolling manner. That is, in this application, an independent acquisition module is used to acquire the motor's operating data under different operating conditions in real time, avoiding a frequency of waveform data processing lower than the frequency of acquiring and recording the motor's operating data. Therefore, it avoids the need for the user to manually process operating data that cannot be processed in a timely manner, thereby improving the efficiency of data acquisition for wind turbines. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of this application;

[0046] Figure 2 This is a flowchart illustrating the first embodiment of the data acquisition method for wind turbine generators according to this application;

[0047] Figure 3 This is a flowchart illustrating the second embodiment of the data acquisition method for wind turbine generators in this application;

[0048] Figure 4 This is a flowchart illustrating the third embodiment of the data acquisition method for wind turbine generators in this application;

[0049] Figure 5 This is a schematic diagram of the structural configuration of the data acquisition device for the wind turbine generator in this application.

[0050] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0051] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0052] Reference Figure 1 , Figure 1 This is a schematic diagram of the data acquisition device structure of a wind turbine generator in the hardware operating environment involved in the embodiments of this application.

[0053] like Figure 1As shown, the data acquisition device for this wind turbine may include: a processor 1001, such as a central processing unit (CPU), wherein the processor 1001 may include a monitoring and control system for the wind turbine (not shown) and an independent acquisition module (not shown), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk storage device. Alternatively, the memory 1005 may be a storage device independent of the aforementioned processor 1001.

[0054] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the data acquisition equipment for wind turbines and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0055] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a data acquisition program for the wind turbine.

[0056] exist Figure 1 In the data acquisition device of the wind turbine generator shown, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the data acquisition device of the wind turbine generator of this application can be set in the data acquisition device of the wind turbine generator. The data acquisition device of the wind turbine generator calls the data acquisition program of the wind turbine generator stored in the memory 1005 through the processor 1001 and executes the data acquisition method of the wind turbine generator provided in the embodiment of this application.

[0057] This application provides a data acquisition method for a wind turbine generator, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of a data acquisition method for a wind turbine generator according to this application.

[0058] It should be noted that the execution subject of this embodiment can be a data acquisition device for a wind turbine with a monitoring and control system for the wind turbine. The data acquisition device for the wind turbine can be an electronic device such as a personal computer, smartphone, or tablet computer, or other devices that can achieve the same or similar functions. This embodiment does not limit this. In this embodiment and the following embodiments, the data acquisition device for the wind turbine is used as an example to illustrate the data acquisition method for the wind turbine of this application.

[0059] In this embodiment, the data acquisition method for the wind turbine is applied to the monitoring and control system of the wind turbine. The monitoring and control system is equipped with an independent acquisition module that does not occupy system data processing resources. The data acquisition method for the wind turbine includes:

[0060] Step S10: Start the independent acquisition module to collect the motor's operating data in real time.

[0061] It should be noted that the motor can be a wind turbine, and the operating data of the motor can be the operating time, average speed, operating temperature, and other data of the wind turbine under various operating conditions.

[0062] It should be noted that the independent acquisition module can operate independently of the monitoring main control system's waveform recording process on the wind turbine, so that the monitoring main control system can use the highest frequency for data processing. This ensures that the data processing frequency is the same as or higher than the data acquisition and waveform recording frequency, thus avoiding the accumulation of waveform recording data.

[0063] In practice, the frequency of collecting wind turbine operating data using an independent acquisition module can be at the millisecond level, so as to achieve real-time acquisition of motor operating data, thereby collecting more accurate motor operating data and facilitating more accurate subsequent detection of wind turbine characteristics.

[0064] Step S20: Based on the independent acquisition module, perform waveform recording processing on the running data to obtain waveform recording data.

[0065] It should be noted that the waveform recording frequency for processing the running data can be provided by the independent acquisition module. That is, the running data can be recorded using the preset waveform recording frequency in the independent acquisition module to avoid occupying the resources needed for data processing. This can increase the frequency of processing waveform data and reduce the accumulation of waveform data.

[0066] It should be noted that performing waveform processing on the operating data can obtain a real-time power waveform diagram of the wind turbine, which makes it convenient for users to subsequently call upon the waveform data to analyze the operating characteristics of wind power generation.

[0067] In the specific implementation, after acquiring real-time operating data of the wind turbine using an independent acquisition module, waveform recording is performed on the operating data to obtain waveform data. After classifying the waveform data, it is stored in a rolling manner according to the level of the waveform data to perform preliminary screening. This allows users to directly obtain high-value data from the waveform data storage space when they need to analyze the characteristics of the wind turbine in the future. The high-value data can include parameters of high operating efficiency of the wind turbine under various operating conditions, etc., without being specifically limited.

[0068] Step S30: After processing the recorded waveform data, it is stored in a rolling manner according to a preset weight level.

[0069] It should be noted that data processing of the waveform recording data can yield waveform recording data for each operating condition. This facilitates the subsequent analysis of the wind turbine's operating characteristics under a specific condition, allowing direct access to the corresponding waveform recording data. Furthermore, data processing can also remove data with low weight levels from the waveform recording data. In other words, data reflecting low wind turbine operating efficiency or unsuitable for analyzing the wind turbine's operating characteristics can be deleted from the waveform recording data to be stored or from the stored waveform recording data. This preliminary processing of the waveform recording data during the acquisition phase reduces the need for more intensive data in subsequent analysis of wind turbine characteristics, making the analysis results more convincing.

[0070] In practical implementation, data processing of recorded waveform data can involve classifying the recorded waveform data according to the operating conditions of the wind turbine, assigning weight levels to the classified data, and then storing the recorded waveform data in a rolling manner according to the preset weight levels. This involves storing recorded waveform data with high weight levels and deleting data with low weight levels. The data with low weight levels can be recorded waveform data to be stored or already stored data. This initial screening of the operating data during the data acquisition stage avoids the need to analyze data with little value when analyzing the characteristics of the wind turbine later.

[0071] It should be noted that using an independent acquisition module to collect wind power generation operation data and perform waveform recording processing on the operation data to obtain waveform data not only enables high-frequency acquisition and waveform recording of operation data, but also does not occupy the resources for data processing before storing waveform data, avoiding the accumulation of waveform data. It can also automatically analyze and classify the waveform data, avoiding manual processing of waveform data by users and improving the efficiency of data acquisition from wind turbines.

[0072] It should be noted that due to complex on-site wind conditions, frequent power curtailment, and personnel deployment during wind turbine operation, the operating conditions of wind turbines are complex and changeable. Using an independent data acquisition module can automatically adjust the frequency of data acquisition based on changes in operating conditions, which can avoid collecting empty data and save energy.

[0073] This embodiment provides a data acquisition method for wind turbines. Compared with existing technologies that result in the accumulation of acquired and recorded waveform data and require manual processing by the user, leading to low data acquisition efficiency for wind turbines, this application activates an independent acquisition module to acquire the motor's operating data in real time. Based on the independent acquisition module, the operating data is processed using waveform recording to obtain waveform data. After data processing, the waveform data is stored in a rolling manner according to a preset weight level. In this application, by acquiring the motor's operating data in real time through an independent acquisition module and obtaining waveform data through waveform recording, the waveform data can be processed at the highest frequency, and the processed waveform data is stored in a rolling manner. That is, in this application, an independent acquisition module is used to acquire the motor's operating data under different operating conditions in real time, avoiding a frequency of waveform data processing lower than the frequency of acquiring motor operating data and recording operating data. Therefore, it can avoid the need for the user to manually process operating data that cannot be processed in a timely manner, thereby improving the efficiency of wind turbine data acquisition.

[0074] refer to Figure 3 , Figure 3 This is a flowchart illustrating the second embodiment of the data acquisition method for wind turbine generators in this application.

[0075] Based on the above embodiments, in this embodiment, in order to facilitate the subsequent acquisition of waveform data and the monitoring of wind turbines based on the waveform data, step 30 includes:

[0076] Step S01: Classify the waveform data according to the preset working conditions to obtain waveform sub-data for each preset working condition.

[0077] It should be noted that the operational data obtained from wind power generation varies under different operating conditions, and these conditions are complex and variable. Furthermore, the collected operational data is acquired based on a timeline, resulting in a chaotic overall dataset. Subsequent analysis of the wind turbine's operating characteristics under various conditions requires filtering the recorded waveform data. The more data collected, the lower the filtering efficiency. To improve the efficiency of subsequent utilization of the waveform data, it is first categorized according to preset operating conditions before storage. Since the amount of real-time acquired waveform data is small, it can be categorized in real-time according to preset operating conditions, and the accuracy of this categorization is higher than that of storing the data first and then filtering it.

[0078] In practical implementation, since the operating conditions of a wind turbine can change multiple times within a second, there may be multiple waveform data segments for different operating conditions at a given moment. That is, the system first acquires the preset operating conditions set by the user on the front-end interface, and then classifies the waveform data in real time according to these preset conditions to obtain waveform data segments for each operating condition. If the waveform data at a certain moment corresponds to only one preset operating condition, then that waveform data is directly classified into that preset operating condition category. After classifying the waveform data according to the preset operating conditions, a weighting level can also be assigned to the waveform data.

[0079] Step S02: The recorded data is processed according to a preset weight level to obtain datasets with different weight levels.

[0080] It should be noted that since the waveform data contains both data points indicating low and high wind turbine operating efficiency, to facilitate data collection focusing on either type of efficiency, the waveform data can be weighted according to preset weight levels to obtain datasets with different weight levels. Specifically, if the user needs to analyze the reasons for low wind turbine operating efficiency based on the waveform data, the lower the operating efficiency, the higher the weight; conversely, if the user needs to analyze how to improve wind turbine operating efficiency based on the waveform data, the higher the operating efficiency, the higher the weight, thus better meeting the user's needs.

[0081] Further, in this embodiment, step S02 includes:

[0082] Step S021: Analyze the operating sub-data of the motor at different frequencies from the recorded sub-data;

[0083] Step S022: Simulate the operating state of the motor at different frequencies based on the operating sub-data;

[0084] Step S023: Based on the operating status and preset weight levels, the recorded sub-data is weighted and classified to obtain datasets of different levels.

[0085] It should be noted that since the operating frequency of wind turbines varies with changes in the wind environment, and even slight changes in the wind can cause changes in the frequency of wind turbines, when collecting wind turbine operating data, the waveform data can be further subdivided according to different frequencies of the wind turbine to obtain operating sub-data at different frequencies. The operating state of the wind turbine can then be simulated based on the operating sub-data. Data that does not meet the operating conditions is removed from the waveform data based on the operating state. After filtering the waveform data, the data is weighted and classified according to the operating state to obtain data at different operating state levels, that is, datasets with different weight levels.

[0086] It should be noted that since the waveform data is obtained after being categorized according to preset operating conditions, different preset operating conditions can be simulated sequentially when simulating the operation of a wind turbine based on the waveform data, making the operating status of each preset operating condition more accurate.

[0087] Further, the step of weighting the recorded sub-data based on the operating state and preset weight levels to obtain datasets of different levels includes:

[0088] Step S0231: Based on the operating state, remove the eliminated data that does not meet the preset state threshold from the recorded sub-data to obtain the data to be classified;

[0089] Step S0232: Based on the running status corresponding to the data to be classified and the preset weight level, the data to be classified is weighted and classified to obtain datasets of different levels.

[0090] It should be noted that the preset state threshold can be the boundary between unstable and stable operation states set by the user according to their needs, so as to facilitate real-time filtering of the waveform data required by the user.

[0091] In practical implementation, if a user needs to analyze the characteristics of unstable operation of wind power generation, they can use preset state thresholds and simulated wind turbine operating states to delete data corresponding to stable operating states from the waveform recording data and obtain data to be classified corresponding to unstable operating states; if a user needs to analyze the characteristics of stable operation of wind turbine, they can use preset state thresholds and simulated wind turbine operating states to delete data corresponding to unstable operating states from the waveform recording data and obtain data to be classified corresponding to stable operating states.

[0092] In the specific implementation, after obtaining the data to be classified, the data to be classified is then weighted according to the corresponding running status to obtain datasets of different levels.

[0093] Optionally, to avoid significant errors in the simulated wind turbine's operating state, the waveform data required by the user is deleted. In this embodiment, step S0231 further includes:

[0094] Step Sa1: Filter out the data to be eliminated from the recorded sub-data whose operating state does not meet the preset state threshold, and obtain the sub-data to be classified.

[0095] Step Sa2: Based on the data to be eliminated, simulate the test operation status of the motor again;

[0096] Step Sa3: Based on the test operation status, remove the eliminated data from the data to be eliminated to obtain the erroneous analysis data;

[0097] Step Sa4: Integrate the sub-data to be classified with the erroneous analysis data to obtain the data to be classified.

[0098] It should be noted that, based on the data to be eliminated in the first screening, a second simulation is performed on the wind turbine to obtain the test operation status of the test error. Based on the test status, the data to be eliminated is filtered out from the data that was obtained by erroneous simulation, so as to avoid the deletion of data that meet the preset state threshold due to erroneous analysis.

[0099] Optionally, the obsolete data that needs to be deleted can also be stored separately for comprehensive analysis of wind turbines. Separate storage can be achieved by storing the obsolete data and the data to be classified in the same storage space, or by storing them in two separate storage spaces. There is no specific limitation on the specific storage method.

[0100] In the specific implementation, data that fails to meet the preset state threshold in operation are filtered out from the waveform data and sub-data to be classified is obtained. The operation state of the wind turbine is simulated again based on the data to be eliminated to obtain the test operation state of the wind turbine. The test operation state of the wind turbine can be simulated more than once to improve the accuracy of the data to be classified. Based on the test operation state, erroneous analysis data due to simulation errors is obtained from the data to be eliminated and integrated with the sub-data to be classified to obtain the data to be classified.

[0101] Step S03: The dataset is stored in a rolling manner based on the weight level.

[0102] It should be noted that, based on the weight level, data with a higher weight level can replace data with a lower weight level in the stored recorded data, so that the stored waveform data is data with a weighted set. This not only allows more data required by users to be stored in a limited storage space, but also makes the stored waveform data denser, making the subsequent analysis of the characteristics of wind turbines more accurate.

[0103] In the actual implementation, the weight levels of the current dataset are compared with the data with the lowest weight level in the stored dataset. Data with higher weight levels is stored, while data with lower weight levels is deleted.

[0104] refer to Figure 4 , Figure 4 This is a flowchart illustrating the third embodiment of the data acquisition method for wind turbine generators in this application.

[0105] Based on the above embodiments, in this embodiment, step S03 further includes:

[0106] Step S1: Based on the weight levels, the dataset is stored sequentially in a preset storage space;

[0107] Step S2: If the preset storage space is full, then filter the data to be compared with the lowest weight level from the preset storage space.

[0108] Step S3: Compare the weight levels of the data to be compared with those of the dataset;

[0109] Step S4: If the weight level of the dataset is higher than the weight level of the data to be compared, then delete the data to be compared and store the dataset in the preset storage space.

[0110] It should be noted that in order to continue automatically collecting wind turbine operating data even after the preset storage space is full, the dataset can be compared with one or more sets of data with the lowest total weight among the already stored waveform data. Data with low weight is deleted, so that the preset storage space can always store waveform data closely related to the user's needs.

[0111] In the specific implementation, if the preset storage space is full, the lowest weighted data to be compared is selected from the waveform data already stored in the preset storage space. First, the size of the data to be compared and the dataset can be compared. If the data to be compared is smaller than the dataset, the lowest weighted data (excluding the data to be compared) from the stored waveform data can be selected and incorporated into the data to be compared. Then, the weighted level of the data to be compared is compared with the weighted level of the dataset. Data with higher weights is selected from the data to be compared and stored in the preset storage space, while data with lower weights is deleted. For example, if the weighted level of the dataset is higher than that of the data to be compared, the data to be compared is deleted. If some data in the data to be compared has a higher weighted level than the data in the dataset and some has a lower weighted level than the data in the dataset, the data with lower weights and the dataset can be deleted. Alternatively, the data to be compared and the dataset can be deleted together; the specific method is not limited.

[0112] Optionally, when comparing the data to be compared with the dataset in terms of weight, there may be cases where sets have the same weight level. Therefore, in this embodiment, before step S3, the method further includes:

[0113] Step S3b: Based on the datasets with the same weight level, determine the adaptability of the motor under different preset operating conditions;

[0114] Step S4b: Label the preset working condition with an adaptation level based on the fitness.

[0115] In the specific implementation, the fitness of the wind turbine under different preset operating conditions is determined based on the dataset with the same weight level, and the preset operating conditions are ranked by weight according to the fitness to determine the fitness level of the preset operating conditions. The fitness can be the operating stability of the wind turbine. That is, the more stable the wind turbine is, the higher the operating stability (the higher the fitness).

[0116] After the step of comparing the weight levels of the data to be compared with the weight levels of the dataset, the method further includes:

[0117] Step S5b: If the weight levels of the dataset and the data to be compared are equal, then compare the annotation adaptation levels of the preset working conditions corresponding to the dataset and the preset working conditions corresponding to the data to be compared.

[0118] Step S6b: If the annotation adaptation level of the data to be compared is lower than the annotation adaptation level of the dataset, then the data to be compared is deleted, and the dataset is stored in the preset storage space.

[0119] It should be noted that since different operating conditions can also affect the operating status of wind turbines, when the weight levels of the dataset and the data to be compared are equal, it is also possible to determine which set of data to delete based on the preset labeling adaptation level of the operating conditions, so as to ensure that the data stored in the preset storage space is closer to the user's needs.

[0120] In practical implementation, if a user needs to analyze the stable operation characteristics of a wind turbine, and the weight levels of the dataset and the data to be compared are equal, then the label fitness levels of the dataset and the data to be compared can be compared. Data with lower label fitness levels is deleted, and data with higher label fitness levels is stored in a preset storage space. For example, if the label fitness level of the data to be compared is lower than that of the dataset, it means that the fitness of the preset operating condition to which the data to be compared belongs is less than that of the preset operating condition to which the dataset belongs. Therefore, the data to be compared can be deleted, and the dataset can be stored in the preset storage space.

[0121] This application also provides a data acquisition device for a wind turbine, wherein the data acquisition device is controlled by a monitoring and control system for the wind turbine, and the monitoring and control system is equipped with an independent acquisition module that does not occupy system data processing resources. (See reference...) Figure 5 The data acquisition device for the wind turbine includes:

[0122] The acquisition module 501 is used to start the independent acquisition module to collect the motor's operating data in real time;

[0123] The waveform recording module 502 is used to perform waveform recording processing on the running data based on the independent acquisition module to obtain waveform recording data;

[0124] The storage module 503 is used to process the recorded waveform data and then store it in a rolling manner according to a preset weight level.

[0125] Optionally, the storage module 503 is further configured to classify the waveform recording data according to the preset working conditions to obtain waveform recording sub-data for each preset working condition; to perform weight classification processing on the waveform recording sub-data according to the preset weight level to obtain datasets with different weight levels; and to perform rolling storage of the datasets based on the weight level.

[0126] Optionally, the storage module 503 is further configured to analyze the operating sub-data of the motor at different frequencies from the waveform recording sub-data; simulate the operating state of the motor at different frequencies based on the operating sub-data; and perform weight classification on the waveform recording sub-data based on the operating state and a preset weight level to obtain datasets of different levels.

[0127] Optionally, the storage module 503 is further configured to, based on the operating state, remove the eliminated data corresponding to the preset state threshold from the recorded sub-data to obtain data to be classified; and, based on the operating state corresponding to the data to be classified and the preset weight level, classify the data to be classified by weight to obtain datasets of different levels.

[0128] Optionally, the storage module 503 is further configured to: filter out data from the waveform recording sub-data that does not meet the preset state threshold and obtain sub-data to be classified; simulate the test operation state of the motor again based on the data to be classified; remove the eliminated data from the data to be classified based on the test operation state and obtain erroneous analysis data; and integrate the sub-data to be classified with the erroneous analysis data to obtain data to be classified.

[0129] Optionally, the storage module 503 is further configured to store the dataset sequentially into a preset storage space based on the weight level; if the preset storage space is full, then filter the data to be compared with the lowest weight level from the preset storage space; compare the weight level of the data to be compared with the weight level of the dataset; if the weight level of the dataset is higher than the weight level of the data to be compared, then delete the data to be compared and store the dataset into the preset storage space.

[0130] Optionally, the storage module 503 is further configured to: determine the adaptability of the motor under different preset operating conditions based on the datasets with the same weight level; label the preset operating conditions with an adaptability level based on the adaptability; if the weight level of the dataset and the data to be compared are equal, compare the labeled adaptability levels of the preset operating conditions corresponding to the dataset and the preset operating conditions corresponding to the data to be compared; if the labeled adaptability level of the data to be compared is lower than the labeled adaptability level of the dataset, delete the data to be compared and store the dataset in the preset storage space.

[0131] The specific implementation of the data acquisition device for the wind turbine in this application is basically the same as the embodiments of the data acquisition method for the wind turbine described above, and will not be repeated here.

[0132] This application provides a storage medium that stores one or more programs, which can be executed by one or more processors to implement the steps of the data acquisition method for wind turbines described above.

[0133] The specific implementation of the storage medium in this application is basically the same as the various embodiments of the data acquisition method for wind turbines described above, and will not be repeated here.

[0134] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one, etc." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0135] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0136] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of this application.

[0137] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural changes made based on the content of this application’s specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A data acquisition method for a wind turbine generator, characterized in that, The data acquisition method for the wind turbine is applied to the monitoring and control system of the wind turbine. The monitoring and control system is equipped with an independent acquisition module that does not occupy system data processing resources. The data acquisition method for the wind turbine includes: Activate the independent acquisition module to collect motor operating data in real time; Based on the independent acquisition module, the operating data is processed by waveform recording to obtain waveform data, wherein the waveform data includes data of low operating efficiency of the wind turbine and data of high operating efficiency of the wind turbine. The recorded waveform data is processed and then stored in a rolling manner according to a preset weight level; The step of processing the waveform data and then storing it in a rolling manner according to a preset weight level includes: The recorded waveform data is classified according to preset operating conditions to obtain waveform sub-data for each preset operating condition; The recorded wave data is processed according to a preset weight level to obtain datasets with different weight levels. If it is necessary to analyze the reasons for the low operating efficiency of the wind turbine, the lower the operating efficiency, the higher the weight level. If it is necessary to analyze how to improve the operating efficiency of the wind turbine, the higher the operating efficiency, the higher the weight level. The dataset is stored in a rolling manner based on the weight levels.

2. The data acquisition method for a wind turbine generator as described in claim 1, characterized in that, The step of performing weighted classification on the waveform data according to a preset weight level to obtain datasets with different weight levels includes: The operating data of the motor at different frequencies is analyzed from the recorded sub-data. Simulate the operating state of the motor at different frequencies based on the aforementioned operating sub-data; Based on the operating status and preset weight levels, the recorded sub-data is weighted and classified to obtain datasets of different levels.

3. The data acquisition method for a wind turbine generator as described in claim 2, characterized in that, The step of weighting the recorded sub-data based on the operating state and preset weight levels to obtain datasets of different levels includes: Based on the operating status, the elimination data that does not meet the preset state threshold is removed from the recorded sub-data to obtain the data to be classified. Based on the operating status of the data to be classified and the preset weight level, the data to be classified is weighted and classified to obtain datasets of different levels.

4. The data acquisition method for a wind turbine generator as described in claim 3, characterized in that, The step of removing discarded data whose operating state does not meet a preset threshold from the recorded sub-data based on the operating state to obtain data to be classified includes: Filter out the data to be eliminated from the recorded sub-data whose operating state does not meet the preset state threshold, and obtain the sub-data to be classified; Based on the data to be phased out, the test operation status of the motor is simulated again; Based on the test operation status, the eliminated data is removed from the data to be eliminated, and the erroneous analysis data is obtained. The sub-data to be classified is integrated with the erroneous analysis data to obtain the data to be classified.

5. The data acquisition method for a wind turbine generator as described in any one of claims 1-4, characterized in that, The step of storing the dataset in a rolling manner based on the weight level includes: Based on the weight levels, the datasets are stored sequentially into a preset storage space; If the preset storage space is full, the data to be compared with the lowest weight level is selected from the preset storage space. Compare the weight levels of the data to be compared with those of the dataset; If the weight level of the dataset is higher than the weight level of the data to be compared, then the data to be compared is deleted, and the dataset is stored in the preset storage space.

6. The data acquisition method for a wind turbine generator as described in claim 5, characterized in that, Before the step of comparing the weight levels of the data to be compared with the weight levels of the dataset, the method further includes: Based on the datasets with the same weight level, the adaptability of the motor under different preset operating conditions is determined; The preset working conditions are labeled with an adaptation level based on the fitness level; After the step of comparing the weight levels of the data to be compared with the weight levels of the dataset, the method further includes: If the weight levels of the dataset and the data to be compared are equal, then the annotation adaptation levels of the preset working conditions corresponding to the dataset and the preset working conditions corresponding to the data to be compared are compared. If the annotation adaptation level of the data to be compared is lower than the annotation adaptation level of the dataset, then the data to be compared is deleted, and the dataset is stored in the preset storage space.

7. A data acquisition device for a wind turbine generator, characterized in that, The data acquisition device of the wind turbine is controlled by the monitoring and control system of the wind turbine. The monitoring and control system is equipped with an independent acquisition module that does not occupy system data processing resources. The data acquisition device of the wind turbine includes: The acquisition module is used to activate the independent acquisition module and collect the motor's operating data in real time. The waveform recording module is used to perform waveform recording processing on the operating data based on the independent acquisition module to obtain waveform recording data, wherein the waveform recording data includes data on low operating efficiency of the wind turbine and data on high operating efficiency of the wind turbine. The storage module is used to process the waveform data and then store it in a rolling manner according to a preset weight level. The storage module is also used to classify the waveform data according to preset working conditions and obtain waveform sub-data for each preset working condition. The recorded wave data is processed according to a preset weight level to obtain datasets with different weight levels. If it is necessary to analyze the reasons for the low operating efficiency of the wind turbine, the lower the operating efficiency, the higher the weight level. If it is necessary to analyze how to improve the operating efficiency of the wind turbine, the higher the operating efficiency, the higher the weight level. The dataset is stored in a rolling manner based on the weight levels.

8. A data acquisition device for a wind turbine generator, characterized in that, The data acquisition device for the wind turbine includes: a memory, a processor, and a data acquisition program for the wind turbine stored in the memory and executable on the processor. The data acquisition program for the wind turbine is configured to implement the steps of the data acquisition method for the wind turbine as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium stores a program for implementing a data acquisition method for a wind turbine generator. The program for implementing the data acquisition method for a wind turbine generator is executed by a processor to implement the steps of the data acquisition method for a wind turbine generator as described in any one of claims 1 to 6.

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