Method and device for judging running state of fan, electronic equipment and storage medium

Abstract

The invention relates to a draught fan operation state judgment method and device, electronic equipment and a storage medium, and the method comprises the steps that at least one current operation parameter of a target draught fan used for wind power generation in a preset extreme environment and at least one actual environment parameter of the current environment are obtained; fusing the at least one current operation parameter and the at least one actual environment parameter to obtain fused multi-dimensional data; and by taking the multi-dimensional data as an index, querying the fan operation state meeting a preset matching condition in a preset database to obtain a matching result, and determining the current operation state of the target fan based on the matching result, or inputting the multi-dimensional data into a pre-constructed state recognition model to recognize the current operation state. Therefore, the technical problem that the actual operation state of the fan is difficult to reflect comprehensively due to low fault diagnosis accuracy caused by dependence on a single type of data source and incomplete monitoring information in the related technology is solved.

Description

Technical Field

[0001] The present application relates to the technical field of wind power generation, and in particular to a method, device, electronic device and storage medium for determining the operating status of a wind turbine. Background Art

[0002] As a renewable energy source, wind power generation has been widely used around the world. As the core component of wind power generation, the monitoring of the operating status of wind turbines is crucial to ensure power generation efficiency and equipment safety.

[0003] In related technologies, by monitoring the operating status of the wind turbine, the wind turbine can be maintained in a timely manner to ensure power generation efficiency and equipment safety.

[0004] However, in the related technologies, wind turbine operating status monitoring mainly relies on a single type of data source, such as vibration signals, temperature signals or electrical signals. The monitoring method of a single data source has problems such as incomplete information and low fault diagnosis accuracy. It is difficult to fully reflect the actual operating status of the wind turbine, resulting in errors in the monitoring results, making it difficult to effectively ensure power generation efficiency and equipment safety. Summary of the invention

[0005] The present application provides a method, device, electronic device and storage medium for determining the operating status of a fan to solve the technical problem in the related art that the method relies on a single type of data source, the monitoring information is incomplete, the fault diagnosis accuracy is low, and it is difficult to fully reflect the actual operating status of the fan.

[0006] The first aspect of the present application provides a method for determining the operating status of a wind turbine, comprising the following steps: obtaining at least one current operating parameter of a target wind turbine for wind power generation under a preset extreme environment and at least one actual environmental parameter of the current environment; fusing the at least one current operating parameter and the at least one actual environmental parameter to obtain fused multi-dimensional data; using the multi-dimensional data as an index, querying a preset database for a wind turbine operating status that meets a preset matching condition to obtain a matching result, and determining the current operating status of the target wind turbine based on the matching result, or inputting the multi-dimensional data into a pre-built state recognition model to identify the current operating status.

[0007] Optionally, in one embodiment of the present application, querying the preset database for the fan operating status that meets the preset matching conditions to obtain the matching result includes: determining whether there is a fan operating status that meets the preset matching conditions in the preset database; if the fan operating status exists, taking the fan operating status as the matching result; if the fan operating status does not exist, extracting at least one data feature from the multidimensional data, and assigning a value to each data feature to obtain the weight of each data feature in different fan operating conditions, and generating the matching result based on the weight.

[0008] Optionally, in one embodiment of the present application, after determining the current operating state of the target wind turbine based on the matching result, it also includes: associating the current operating state and the multidimensional data corresponding to the current operating state; and storing the associated current operating state and multidimensional data in the preset database.

[0009] Optionally, in one embodiment of the present application, before inputting the multidimensional data into a pre-built state recognition model to identify the current operating state, it also includes: based on the at least one current operating parameter and the at least one actual environmental parameter, screening out multiple groups of historical parameter data that meet preset deviation conditions in a preset historical database, and generating a historical parameter data set, wherein each group of historical parameter data includes historical operating parameters, historical environmental parameters and corresponding historical operating states obtained at the same time; and obtaining the state recognition model based on the historical parameter data set.

[0010] Optionally, in one embodiment of the present application, after identifying the current operating state, it also includes: associating the at least one current operating parameter, the at least one actual environment parameter and the current operating state; storing the associated at least one current operating parameter, the at least one actual environment parameter and the current operating state in the preset historical database.

[0011] Optionally, in one embodiment of the present application, before fusing the at least one current operating parameter and the at least one actual environmental parameter to obtain fused multidimensional data, it also includes: preprocessing the current operating parameter and the actual environmental parameter to obtain the current operating parameter and actual environmental parameter that meet preset validity conditions.

[0012] Optionally, in one embodiment of the present application, it also includes: judging whether the current operating state belongs to a faulty operating state or an abnormal operating state; if the current operating state belongs to the faulty operating state or the abnormal operating state, determining the potential fault source of the target fan based on the current operating parameters, the actual environmental parameters and the historical operating parameters and historical environmental parameters within a preset time period, and generating an abnormal report for the target fan.

[0013] The second aspect of the present application provides a device for determining the operating status of a wind turbine, comprising: an acquisition module for acquiring at least one current operating parameter of a target wind turbine for wind power generation under a preset extreme environment and at least one actual environmental parameter of the current environment; a fusion module for fusing the at least one current operating parameter and the at least one actual environmental parameter to obtain fused multi-dimensional data; a determination module for using the multi-dimensional data as an index to query a preset database for a wind turbine operating status that meets a preset matching condition, obtain a matching result, and determine the current operating status of the target wind turbine based on the matching result, or input the multi-dimensional data into a pre-built status recognition model to identify the current operating status.

[0014] Optionally, in one embodiment of the present application, the determination module includes: a judgment unit, used to judge whether there is a fan operating state that meets the preset matching condition in the preset database; a first determination unit, used to use the fan operating state as the matching result when the fan operating state exists; and a second determination unit, used to extract at least one data feature from the multidimensional data when the fan operating state does not exist, and assign a value to each data feature to obtain the weight of each data feature under different fan operating states, and generate the matching result based on the weight.

[0015] Optionally, in one embodiment of the present application, it also includes: a first association module, used to associate the current operating state and the multidimensional data corresponding to the current operating state; a first storage module, used to store the associated current operating state and multidimensional data in the preset database.

[0016] Optionally, in one embodiment of the present application, it also includes: a first processing module, used to screen out multiple groups of historical parameter data that meet preset deviation conditions in a preset historical database based on the at least one current operating parameter and the at least one actual environmental parameter, and generate a historical parameter data set, wherein each group of historical parameter data includes historical operating parameters, historical environmental parameters and corresponding historical operating states obtained at the same time; a construction module, used to obtain the state recognition model based on the historical parameter data set.

[0017] Optionally, in one embodiment of the present application, it also includes: a second association module, used to associate the at least one current operating parameter, the at least one actual environmental parameter and the current operating state; a second storage module, used to store the associated at least one current operating parameter, the at least one actual environmental parameter and the current operating state in the preset historical database.

[0018] Optionally, in one embodiment of the present application, it further includes: a second processing module, used to pre-process the current operating parameters and the actual environmental parameters to obtain the current operating parameters and the actual environmental parameters that meet preset validity conditions.

[0019] Optionally, in one embodiment of the present application, it also includes: a judgment module, used to judge whether the current operating state belongs to a faulty operating state or an abnormal operating state; a generation module, used to determine the potential fault source of the target fan based on the current operating parameters, the actual environmental parameters and the historical operating parameters and historical environmental parameters within a preset time period, and generate an abnormal report of the target fan when the current operating state belongs to the faulty operating state or the abnormal operating state.

[0020] A third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for determining the operating status of a wind turbine as described in the above embodiment.

[0021] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method for determining the operating status of a wind turbine as described in the above embodiment.

[0022] A fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the above method for determining the operating status of a wind turbine.

[0023] The embodiment of the present application can fuse the current operating parameters of the target fan under the preset extreme environment and the actual environmental parameters of the current environment to obtain the fused multi-dimensional data, and realize more accurate fan status determination through the multi-dimensional data. The fan operating status that meets the preset matching conditions is queried in the preset database with the multi-dimensional data as the index to obtain the matching result, and the current operating status of the target fan is determined based on the matching result, or the multi-dimensional data is input into the pre-built state recognition model to identify the current operating status, thereby improving the accuracy and reliability of fan status monitoring, and effectively identifying potential faults by comprehensively and accurately monitoring the operating status of the fan, thereby improving the operating efficiency and safety of the fan. Thus, the technical problem in the related art that it relies on a single type of data source, the monitoring information is not comprehensive, the fault diagnosis accuracy is low, and it is difficult to fully reflect the actual operating status of the fan is solved.

[0024] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0026] Figure 1 A flow chart of a method for determining the operating state of a fan provided according to an embodiment of the present application;

[0027] Figure 2 It is a schematic diagram of the principle of a method for determining the operating state of a fan according to an embodiment of the present application;

[0028] Figure 3 A schematic diagram of the structure of a device for determining the operating state of a fan provided according to an embodiment of the present application;

[0029] Figure 4 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0031] The following describes the operation state determination method, device, electronic device and storage medium of the fan of the embodiment of the present application with reference to the accompanying drawings. In view of the technical problem that the related technology mentioned in the above background technology relies on a single type of data source, the monitoring information is not comprehensive, the fault diagnosis accuracy is low, and it is difficult to fully reflect the actual operation state of the fan, the present application provides an operation state determination method of the fan, in which the current operation parameters of the target fan under the preset extreme environment and the actual environmental parameters of the current environment can be fused to obtain the fused multi-dimensional data, and a more accurate fan state determination is achieved through the multi-dimensional data, and the fan operation state that meets the preset matching conditions is queried in the preset database with the multi-dimensional data as an index to obtain the matching result, and the current operation state of the target fan is determined based on the matching result, or the multi-dimensional data is input into a pre-built state recognition model to identify the current operation state, thereby improving the accuracy and reliability of fan state monitoring, and effectively identifying potential faults by comprehensively and accurately monitoring the operation state of the fan, and improving the operation efficiency and safety of the fan. Thereby, the technical problem in the related technology that it relies on a single type of data source, the monitoring information is incomplete, the fault diagnosis accuracy is low, and it is difficult to fully reflect the actual operating status of the fan is solved.

[0032] Specifically, Figure 1 A schematic flow chart of a method for determining the operating status of a fan provided in an embodiment of the present application.

[0033] like Figure 1 As shown, the method for determining the operating state of the fan includes the following steps:

[0034] In step S101, at least one current operating parameter of a target wind turbine for wind power generation under a preset extreme environment and at least one actual environmental parameter of a current environment are obtained.

[0035] It is understandable that wind turbines used for wind power generation can capture the kinetic energy of wind, use wind power to drive the turbine blades to rotate, and then drive the generator to generate electricity. Wind power generation is widely used as a clean energy and renewable energy. Monitoring the operating status of wind turbines and promptly resolving wind turbine failures are effective means to ensure power supply.

[0036] However, in some extreme environmental conditions, such as extreme temperatures, strong winds, sandstorms / salt spray corrosion, high humidity and precipitation, seismic activity and lightning strikes, the probability of fan failure is greater, and the detection accuracy of some sensors used to obtain fan status parameters will be reduced. Therefore, there is a high probability of misjudgment when judging the fan operating status only through a single data source.

[0037] Therefore, the embodiments of the present application can utilize a variety of data detection methods in some extreme environments to obtain at least one current operating parameter of the target fan, such as mechanical data and electrical data during the operation of the fan, and obtain at least one actual environmental parameter of the current environment of the target fan, such as meteorological data.

[0038] Among them, the preset extreme environment can be set accordingly by technical personnel in this field according to actual conditions, and no specific limitation is made here.

[0039] In step S102, at least one current operating parameter and at least one actual environment parameter are fused to obtain fused multi-dimensional data.

[0040] During the actual execution process, the embodiment of the present application can fuse at least one current operating parameter and at least one actual environmental parameter to obtain multi-dimensional data that can reflect the status of the wind turbine.

[0041] Among them, the fusion methods may include multiple methods, for example, statistical methods (such as mean, median), machine learning algorithms (such as principal component analysis PCA, support vector machine SVM), deep learning models (such as neural networks), etc.

[0042] Taking data fusion using a neural network model, by fusing the blade angle (operating parameter) and wind speed (environmental parameter) of the wind turbine to predict the power generation of the wind turbine as an example, the embodiment of the present application can select a suitable neural network architecture, such as a multi-layer perceptron (MLP), with blade angle and wind speed as input features and power generation as the output target. The neural network model is trained using the historical data of the above data, and the model parameters are adjusted using the back propagation algorithm. After the model training is completed, the current blade angle and actual wind speed data are input, and the model will output the predicted power generation. This power generation is the result of the fusion of multidimensional data of wind turbine operating parameters and environmental parameters.

[0043] Optionally, in one embodiment of the present application, before fusing at least one current operating parameter and at least one actual environmental parameter to obtain fused multidimensional data, it also includes: preprocessing the current operating parameters and the actual environmental parameters to obtain current operating parameters and actual environmental parameters that meet preset validity conditions.

[0044] It is understandable that noise and redundant information will have a greater impact on subsequent data fusion and analysis, resulting in lower accuracy of the final state determination.

[0045] Therefore, the embodiment of the present application can pre-process the acquired data after acquiring at least one current operating parameter and at least one actual environmental parameter to eliminate noise and redundant information.

[0046] First, the embodiments of the present application can perform data cleaning on the acquired data to eliminate quality problems such as noise, missing values, duplicates, etc. in the data.

[0047] For example, for missing data, embodiments of the present application can use filling methods (such as mean filling, median filling, mode filling, interpolation, etc.) or deletion methods (directly deleting records or variables containing missing values) for processing. For outliers (or noise), embodiments of the present application can use statistical methods (such as Z-score, IQR, etc.) to identify outliers and decide whether to retain, delete or correct them. For duplicate data, embodiments of the present application can perform data duplication checks and delete or merge these records. For logically erroneous data, embodiments of the present application can perform data logic checks and make corresponding corrections.

[0048] Furthermore, the embodiments of the present application can perform normalization processing on the clarified data. Data normalization is to scale the data so that it falls into a small specific interval (such as 0 to 1 or -1 to 1) so that data of different magnitudes or units can be compared or further processed.

[0049] The embodiment of the present application can also perform feature extraction on the acquired data to extract information or attributes useful for subsequent analysis from the original data. Among them, the feature extraction method can be selected accordingly according to the actual situation, for example, calculating the statistics of the data (such as mean, variance, median, mode, skewness, kurtosis, etc.) as features; using deep learning models (such as convolutional neural network CNN, recurrent neural network RNN, etc.) to automatically learn feature representation from the original data, etc.

[0050] It should be noted that in the process of data cleaning and feature extraction, it is also necessary to pay attention to eliminating redundant information. The embodiment of the present application can check and delete duplicate records or variables, use dimensionality reduction techniques such as principal component analysis (PCA), map the original data to a low-dimensional space, and try to retain the information of the original data. In this way, redundant information can be eliminated while reducing the data dimension.

[0051] In step S103, using the multidimensional data as an index, the fan operating status that meets the preset matching conditions is queried in the preset database to obtain a matching result, and the current operating status of the target fan is determined based on the matching result, or the multidimensional data is input into a pre-built status recognition model to identify the current operating status.

[0052] After obtaining the multi-dimensional data that can be used to describe the operating status of the wind turbine, the embodiment of the present application can determine the operating status of the target wind turbine in a variety of ways.

[0053] In some embodiments, the current operating state can be determined by data matching. First, the embodiment of the present application can construct a database for the mapping relationship between multidimensional data and operating state, wherein the mapping relationship can be obtained from a big data platform or based on historical data of the wind turbine. The embodiment of the present application can use multidimensional data as an index to search for the mapping relationship in the database, and then obtain the operating state that matches the multidimensional data.

[0054] In other embodiments, the current operating state may be identified by a pre-built state identification model, that is, the current operating state may be obtained by using the pre-built state identification model with multi-dimensional data as input.

[0055] Optionally, in one embodiment of the present application, a fan operating state that meets a preset matching condition is queried in a preset database to obtain a matching result, including: determining whether a fan operating state that meets the preset matching condition exists in the preset database; if a fan operating state exists, taking the fan operating state as the matching result; if a fan operating state does not exist, extracting at least one data feature from the multidimensional data, and assigning a value to each data feature to obtain a weight of each data feature under different fan operating conditions, and generating a matching result based on the weight.

[0056] In the actual execution process, there may be two matching results when using the database for query matching.

[0057] The first one is that there is a matching wind turbine operating status in the database. In this case, the embodiment of the present application can directly output the corresponding current operating status.

[0058] In the second group, there is no matching wind turbine operating status in the database. At this time, the embodiment of the present application can extract at least one data feature from the multidimensional data, and sort the data features according to the intensity of their impact on the wind turbine status, and then assign a value to each data feature according to the sorting result to obtain the weight of each data feature under different wind turbine operating conditions, and generate a matching result based on the weight.

[0059] For example, an embodiment of the present application can pre-set a corresponding data range for each wind turbine operating state, calculate the product between each data feature and the weight, sum all products to obtain a state value, and thereby determine the wind turbine operating state based on the data range within which the state value falls.

[0060] Optionally, in one embodiment of the present application, after determining the current operating state of the target wind turbine based on the matching result, it also includes: associating the current operating state and the multidimensional data corresponding to the current operating state; and storing the associated current operating state and multidimensional data in a preset database.

[0061] Furthermore, in the embodiment of the present application, when there is no matching wind turbine operating status in the database and the corresponding current operating status is obtained by extracting data features, etc., the current operating status and the corresponding multidimensional data are associated to obtain a mapping relationship, and the mapping relationship, the current operating status and the corresponding multidimensional data are stored in the database for the next call.

[0062] Optionally, in one embodiment of the present application, before inputting multidimensional data into a pre-built state recognition model to identify the current operating state, it also includes: based on at least one current operating parameter and at least one actual environmental parameter, screening out multiple groups of historical parameter data that meet preset deviation conditions in a preset historical database, and generating a historical parameter data set, wherein each group of historical parameter data includes historical operating parameters, historical environmental parameters and corresponding historical operating states obtained at the same time; and obtaining a state recognition model based on the historical parameter data set.

[0063] As a possible implementation method, the embodiment of the present application can construct a state recognition model and perform model training using historical parameter data as a training set.

[0064] Among them, there are certain requirements for the selection of historical parameter data.

[0065] Since the amount of historical parameter data is large, a lot of time and resources will be wasted in model training each time it is used. Therefore, the embodiment of the present application can first screen the historical parameter data, that is, select data that does not deviate much from at least one current operating parameter and at least one actual environmental parameter.

[0066] For example, if the wind speed data of the actual environmental parameters is level 3, the wind speed data selected from the historical parameter data that meets the preset deviation condition can be level 2-4, and among all the historical parameter data in the same group whose wind speed data is in the range of level 2-4, the data with a large deviation from other parameters in the same group of historical parameter data are screened out. For example, if the blade speed in the current operating parameters is 19, and the blade speed in a group of historical parameter data with wind speed data at level 2 is 30, then this group of historical parameter data does not meet the deviation condition, and thus the group of historical parameter data is screened out.

[0067] Through the above screening method, the embodiment of the present application can obtain multiple groups of historical parameter data and generate a historical parameter data set, and then use the historical parameter data set to perform model training, so as to obtain a model that can be used to determine the operating status of the wind turbine.

[0068] Optionally, in one embodiment of the present application, after identifying the current operating state, it also includes: associating at least one current operating parameter, at least one actual environmental parameter and the current operating state; storing the associated at least one current operating parameter, at least one actual environmental parameter and the current operating state in a preset historical database.

[0069] Furthermore, after identifying the current operating state, the embodiments of the present application can associate at least one current operating parameter, at least one actual environmental parameter, and the current operating state to obtain a corresponding mapping relationship, and store the mapping relationship and the associated at least one current operating parameter, at least one actual environmental parameter, and the current operating state in a preset historical database so that it can be called during subsequent model training to realize the self-learning process of the model.

[0070] Optionally, in one embodiment of the present application, it also includes: judging whether the current operating state is a faulty operating state or an abnormal operating state; if the current operating state is a faulty operating state or an abnormal operating state, determining the potential fault source of the target fan based on the current operating parameters, actual environmental parameters and historical operating parameters and historical environmental parameters within a preset time period, and generating an abnormal report for the target fan.

[0071] During the actual execution process, the embodiment of the present application can determine the potential fault source of the target wind turbine based on the current operating parameters, actual environmental parameters, and all historical data within a certain period of time from the current moment to the present when the current operating state is in a faulty operating state or an abnormal operating state.

[0072] For example, the rapidly increasing / decreasing values ​​in the data are filtered out, and based on the filtering results, the problematic data is determined to obtain the potential source of the fault.

[0073] The embodiment of the present application can generate an abnormality report of the target wind turbine according to the potential fault source, so that the technicians can perform rapid maintenance.

[0074] Combination Figure 2 As shown, the working principle of the method for determining the operating status of the fan in the embodiment of the present application is described in detail with an embodiment.

[0075] like Figure 2 The structure shown is involved in the actual application of the embodiment of the present application.

[0076] Among them, the embodiment of the present application may include: a data acquisition module 1, a data preprocessing module 2, a multi-dimensional data fusion module 3, a monitoring and diagnosis module 4 and a visualization module 5.

[0077] Specifically, the data acquisition module 1 can collect heterogeneous data such as mechanical data, electrical data, and meteorological data during the operation of the fan in real time through multiple sensors.

[0078] Data preprocessing module 2: It can clean, normalize and extract features of the collected heterogeneous data, eliminate noise and redundant information, and provide a high-quality data foundation for subsequent data fusion and analysis.

[0079] Multidimensional data fusion module 3: Multidimensional data fusion technology can be used to express the pre-processed mechanical data, electrical data and meteorological data in a multi-dimensional manner to comprehensively reflect the operating status of the wind turbine.

[0080] Monitoring and diagnosis module 4: Based on the integrated multi-dimensional data, through machine learning and artificial intelligence algorithms, it can monitor the operating status of the fan in real time, identify and diagnose potential faults and abnormal conditions.

[0081] Visualization module 5: It can provide a friendly user interface, display the operating status, historical data and fault diagnosis results of the fan in a graphical way, and provide intuitive decision support for operation and maintenance personnel.

[0082] In summary, the embodiments of the present application can comprehensively and accurately monitor the operating status of the wind turbine, effectively identify potential faults, and improve the operating efficiency and safety of the wind turbine. It has broad application prospects and important practical significance in the field of wind power generation.

[0083] According to the method for determining the operating state of a fan proposed in the embodiment of the present application, the current operating parameters of the target fan under a preset extreme environment and the actual environmental parameters of the current environment can be integrated to obtain the integrated multi-dimensional data, and a more accurate determination of the fan state can be achieved through the multi-dimensional data. The fan operating state that meets the preset matching conditions is queried in the preset database with the multi-dimensional data as an index to obtain a matching result, and the current operating state of the target fan is determined based on the matching result, or the multi-dimensional data is input into a pre-built state recognition model to identify the current operating state, thereby improving the accuracy and reliability of fan state monitoring, and effectively identifying potential faults by comprehensively and accurately monitoring the operating state of the fan, thereby improving the operating efficiency and safety of the fan. Thus, the technical problem in the related art that it relies on a single type of data source, the monitoring information is incomplete, the fault diagnosis accuracy is low, and it is difficult to fully reflect the actual operating state of the fan is solved.

[0084] Next, a device for determining the operating status of a wind turbine according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0085] Figure 3 It is a block diagram of a device for determining the operating status of a wind turbine according to an embodiment of the present application.

[0086] like Figure 3 As shown, the wind turbine operation status determination device 10 includes: an acquisition module 100 , a fusion module 200 and a determination module 300 .

[0087] Specifically, the acquisition module 100 is used to acquire at least one current operating parameter of a target wind turbine for wind power generation under a preset extreme environment and at least one actual environmental parameter of the current environment.

[0088] The fusion module 200 is used to fuse at least one current operating parameter and at least one actual environment parameter to obtain fused multi-dimensional data.

[0089] The determination module 300 is used to use the multidimensional data as an index to query the fan operating status that meets the preset matching conditions in the preset database, obtain the matching result, and determine the current operating status of the target fan based on the matching result, or input the multidimensional data into a pre-built state recognition model to identify the current operating status.

[0090] Optionally, in one embodiment of the present application, the determination module 300 includes: a determination unit, a first determination unit and a second determination unit.

[0091] The judging unit is used to judge whether there is a fan operating state that meets the preset matching condition in the preset database.

[0092] The first determining unit is configured to use the fan operating state as a matching result when the fan operating state exists.

[0093] The second determination unit is used to extract at least one data feature from the multidimensional data in the absence of the wind turbine operating status, and assign a value to each data feature to obtain the weight of each data feature under different wind turbine operating states, and generate a matching result based on the weight.

[0094] Optionally, in one embodiment of the present application, the device 10 for determining the operating state of a wind turbine further includes: a first association module and a first storage module.

[0095] The first associating module is used to associate the current running state with the multi-dimensional data corresponding to the current running state.

[0096] The first storage module is used to store the associated current operating status and multi-dimensional data into a preset database.

[0097] Optionally, in one embodiment of the present application, the device 10 for determining the operating state of a wind turbine further includes: a first processing module and a construction module.

[0098] Among them, the first processing module is used to screen out multiple groups of historical parameter data that meet the preset deviation conditions in the preset historical database based on at least one current operating parameter and at least one actual environmental parameter, and generate a historical parameter data set, wherein each group of historical parameter data includes historical operating parameters, historical environmental parameters and corresponding historical operating status obtained at the same time.

[0099] A building block is used to obtain a state recognition model based on a historical parameter data set.

[0100] Optionally, in one embodiment of the present application, the device 10 for determining the operating state of a wind turbine further includes: a second association module and a second storage module.

[0101] The second associating module is used to associate at least one current operating parameter, at least one actual environment parameter and the current operating state.

[0102] The second storage module is used to store the associated at least one current operating parameter, at least one actual environmental parameter and the current operating state in a preset historical database.

[0103] Optionally, in one embodiment of the present application, the device 10 for determining the operating state of a wind turbine further includes: a second processing module.

[0104] The second processing module is used to pre-process the current operating parameters and the actual environmental parameters to obtain the current operating parameters and the actual environmental parameters that meet the preset validity conditions.

[0105] Optionally, in one embodiment of the present application, the device 10 for determining the operating state of a wind turbine further includes: a determination module and a generation module.

[0106] The judging module is used to judge whether the current operation state is a faulty operation state or an abnormal operation state.

[0107] A generation module is used to determine the potential fault source of the target fan based on the current operating parameters, actual environmental parameters and historical operating parameters and historical environmental parameters within a preset time period when the current operating state is a faulty operating state or an abnormal operating state, and generate an abnormal report for the target fan.

[0108] It should be noted that the above explanation of the embodiment of the method for determining the operating status of a fan is also applicable to the device for determining the operating status of a fan in this embodiment, and will not be repeated here.

[0109] According to the operating state determination device of the wind turbine proposed in the embodiment of the present application, the current operating parameters of the target wind turbine under the preset extreme environment and the actual environmental parameters of the current environment can be integrated to obtain the integrated multi-dimensional data, and a more accurate wind turbine state determination can be achieved through the multi-dimensional data. The wind turbine operating state that meets the preset matching conditions is queried in the preset database with the multi-dimensional data as an index to obtain the matching result, and the current operating state of the target wind turbine is determined based on the matching result, or the multi-dimensional data is input into a pre-built state recognition model to identify the current operating state, thereby improving the accuracy and reliability of wind turbine state monitoring, and effectively identifying potential faults by comprehensively and accurately monitoring the operating state of the wind turbine, thereby improving the operating efficiency and safety of the wind turbine. Thus, the technical problem in the related art that it relies on a single type of data source, the monitoring information is incomplete, the fault diagnosis accuracy is low, and it is difficult to fully reflect the actual operating state of the wind turbine is solved.

[0110] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0111] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0112] When the processor 402 executes the program, the method for determining the operating state of the wind turbine provided in the above embodiment is implemented.

[0113] Furthermore, the electronic device further comprises:

[0114] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0115] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0116] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0117] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0118] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0119] The processor 402 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0120] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above method for determining the operating status of a wind turbine is implemented.

[0121] The embodiment of the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements the method for determining the operating status of a wind turbine provided by the embodiment of the present invention.

[0122] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0123] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0124] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0125] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0126] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0127] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0128] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0129] The storage medium mentioned above may be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for determining the operating status of a fan, characterized in that: The following steps are involved: Acquire at least one current operating parameter of a target wind turbine for wind power generation under a preset extreme environment and at least one actual environmental parameter of a current environment; fusing the at least one current operating parameter and the at least one actual environmental parameter to obtain fused multi-dimensional data; Using the multidimensional data as an index, query the fan operating status that meets the preset matching conditions in the preset database to obtain a matching result, and determine the current operating status of the target fan based on the matching result, or input the multidimensional data into a pre-built status recognition model to identify the current operating status.

2. The method according to claim 1, characterized in that The step of querying the preset database for the wind turbine operating status that meets the preset matching condition to obtain the matching result includes: Determine whether there is a fan operating state that meets the preset matching condition in the preset database; If the fan operating state exists, taking the fan operating state as the matching result; If the wind turbine operating state does not exist, at least one data feature is extracted from the multidimensional data, and a value is assigned to each data feature to obtain a weight of each data feature under different wind turbine operating states, and the matching result is generated based on the weight.

3. The method according to claim 2, characterized in that After determining the current operating state of the target wind turbine based on the matching result, the method further includes: Associating the current running state with the multidimensional data corresponding to the current running state; The associated current operating status and multidimensional data are stored in the preset database.

4. The method according to claim 1, characterized in that: Before inputting the multi-dimensional data into a pre-built state recognition model to recognize the current operating state, the method further includes: Based on the at least one current operating parameter and the at least one actual environmental parameter, multiple groups of historical parameter data that meet a preset deviation condition are screened out in a preset historical database, and a historical parameter data set is generated, wherein each group of historical parameter data includes historical operating parameters, historical environmental parameters, and corresponding historical operating states acquired at the same time; The state recognition model is obtained based on the historical parameter data set.

5. The method according to claim 4, characterized in that After identifying the current operating state, the method further includes: Associating the at least one current operating parameter, the at least one actual environmental parameter, and the current operating state; The associated at least one current operating parameter, the at least one actual environmental parameter and the current operating state are stored in the preset historical database.

6. The method according to claim 1, characterized in that Before fusing the at least one current operating parameter and the at least one actual environmental parameter to obtain fused multi-dimensional data, the method further includes: The current operating parameters and the actual environmental parameters are preprocessed to obtain the current operating parameters and the actual environmental parameters that meet preset validity conditions.

7. The method according to claim 1, characterized in that Also includes: Determine whether the current operating state is a faulty operating state or an abnormal operating state; If the current operating state belongs to the faulty operating state or the abnormal operating state, the potential fault source of the target fan is determined based on the current operating parameters, the actual environmental parameters and the historical operating parameters and historical environmental parameters within a preset time period, and an abnormality report of the target fan is generated.

8. A device for determining the operating status of a fan, characterized in that: include: An acquisition module, used to acquire at least one current operating parameter of a target wind turbine for wind power generation under a preset extreme environment and at least one actual environmental parameter of a current environment; A fusion module, used for fusing the at least one current operating parameter and the at least one actual environment parameter to obtain fused multi-dimensional data; A determination module is used to use the multidimensional data as an index to query the fan operating status that meets the preset matching conditions in a preset database, obtain a matching result, and determine the current operating status of the target fan based on the matching result, or input the multidimensional data into a pre-built status recognition model to identify the current operating status.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for determining the operating status of a wind turbine as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for determining the operating status of a wind turbine as described in any one of claims 1 to 7.

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