A wind turbine generator system and method of operation monitoring

Abstract

The application discloses an operation monitoring system and method of a wind turbine generator set, relates to the technical field of wind power generation, and solves the technical problem that the wind turbine generator set is difficult to discover abnormal conditions of the wind turbine generator in time, and the operation monitoring efficiency of the wind turbine generator set is reduced; the operation monitoring system comprises the following steps: acquiring geographical condition data of a region where the wind turbine generator set is located; analyzing parts where each wind turbine generator is located based on the geographical condition data to obtain a terrain influence factor; acquiring wind power data of the region where the wind turbine generator set is located, and analyzing each wind turbine generator corresponding abnormal analysis priority based on the wind power data and the terrain influence factor; acquiring detection data corresponding to each wind turbine generator in turn based on the order from large to small of the abnormal analysis priority, and analyzing the operation state of the wind turbine generator based on the detection data to obtain a monitoring result; and the monitoring and analysis efficiency of the wind turbine generator set is increased.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wind power generation, and particularly relates to an operation monitoring system and method of a wind turbine generator system. BACKGROUND

[0002] To cope with global climate change and the severe situation of energy shortage and energy supply safety, wind energy, as a renewable energy, has a continuously improved position in the energy strategy of various countries due to its characteristics of cleanness, safety and sustainability. In recent years, wind energy has developed rapidly and begun to play an important role in energy supply. With the rapid development of wind energy development in China, a large number of megawatt new wind turbine generators have been put into large-scale production and operation in recent years, and the problems of quality and operation reliability are more prominent.

[0003] The failure mechanism of a wind turbine is very complex, and there are many influencing factors. Real-time monitoring of a single wind turbine can be implemented, and for a wind turbine system, a large amount of data needs to be processed, and it is difficult to simultaneously monitor each wind turbine in the wind turbine system in real time. Further, it is difficult to discover abnormal conditions of the wind turbine in time, and thus the operation monitoring efficiency of the wind turbine system is reduced. Therefore, a wind turbine system operation monitoring system and method are needed. SUMMARY

[0004] The application provides a wind turbine system operation monitoring system and method, which solves the technical problem that it is difficult to discover abnormal conditions of a wind turbine in time in the prior art, and the operation monitoring efficiency of the wind turbine system is reduced.

[0005] To achieve the above purpose, the application adopts the following technical scheme:

[0006] In a first aspect, a wind turbine system operation monitoring method is provided, comprising:

[0007] Obtaining geographical condition data of a region where the wind turbine system is located; analyzing a part where each wind turbine is located based on the geographical condition data to obtain a terrain influence factor;

[0008] Obtaining wind data of the region where the wind turbine system is located, and analyzing each wind turbine corresponding abnormal analysis priority based on the wind data and the terrain influence factor;

[0009] Based on the order from large to small of the abnormal analysis priority, detection data corresponding to each wind turbine is obtained in sequence, and the operation state of the wind turbine is analyzed based on the detection data to obtain a monitoring result.

[0010] Based on the above technical scheme, in the operation monitoring method of the wind turbine generator set provided in the application, geographical condition data of a region where the wind turbine generator set is located is obtained; topographic influence factors are obtained by analyzing parts of each wind turbine generator based on the geographical condition data; abnormal analysis priorities corresponding to each wind turbine generator are obtained by analyzing the current wind data and the topographic influence factors; detection data corresponding to each wind turbine generator is obtained in turn according to the order from large to small of the abnormal analysis priorities, and the operation state of the wind turbine generator is analyzed based on the detection data to obtain a monitoring result; the data of each wind turbine generator is analyzed in turn by judging the influence of the current environmental state on each wind turbine generator in the wind turbine generator set; and the abnormal condition of the wind turbine generator can be found in time, thereby increasing the monitoring and analysis efficiency of the wind turbine generator set.

[0011] In combination with the first aspect, in a possible implementation manner, the topographic influence factors are obtained based on the geographical condition data, including:

[0012] Terrain data and surface attribute data in the geographical condition data are extracted; a wind simulation model of the region where the wind turbine generator is located is constructed based on the terrain data and the surface attribute data; wind of a plurality of set test wind speeds and test wind directions is simulated in the wind simulation model; and a simulated wind speed at each position of the wind turbine generator is obtained during the simulation process;

[0013] The ratio of a plurality of simulated wind speeds to the test wind speed at the same position of the wind turbine generator is calculated, and the ratio is marked as a wind speed attenuation factor; the variance of each wind speed attenuation factor is taken as the topographic influence factor of the wind turbine generator.

[0014] In combination with the first aspect, in a possible implementation manner, the plurality of simulated wind speeds and the test wind speed, and the wind speed attenuation factor corresponding to the simulated wind speed and the test wind speed at each position of the wind turbine generator are obtained; a wind speed attenuation factor query table is constructed based on the test wind speed, the test wind direction, the position of the wind turbine generator and the wind speed attenuation factor.

[0015] In combination with the first aspect, in a possible implementation manner, the abnormal analysis priorities corresponding to each wind turbine generator are obtained based on the wind data and the topographic influence factors, including:

[0016] A plurality of wind speed data and a plurality of wind direction data in the wind data are extracted;

[0017] A wind speed fluctuation score for representing wind speed fluctuation is obtained based on the wind speed data;

[0018] A wind direction fluctuation score for representing wind direction fluctuation is obtained based on the wind direction data;

[0019] obtaining an actual wind speed closest to a current time in wind speed data, and an actual wind direction closest to the current time in wind direction data; querying a corresponding wind speed attenuation factor in a wind speed attenuation factor table based on the actual wind speed, the actual wind direction, and a position of each wind turbine;

[0020] obtaining a corresponding terrain influence factor of each wind turbine, and an actual wind speed in the wind speed data; and substituting the actual wind speed, the terrain influence factor, the wind speed fluctuation score, the wind direction fluctuation score, and the wind speed attenuation factor into an abnormality analysis function to obtain an abnormality analysis priority for representing a possibility of abnormal operation of the wind turbine.

[0021] In combination with the first aspect, in a possible implementation manner, one obtaining manner of the wind speed fluctuation score includes:

[0022] extracting a plurality of actual wind speeds in wind speed data; sorting the actual wind speeds according to their corresponding collection times in chronological order, and fitting the actual wind speeds into a wind speed change curve FS(t); the fitting manner includes an interpolation method, etc.; and obtaining the wind speed fluctuation score SP through a formula:

[0023]

[0024] is an average wind speed, that is, an average value of FS(t) in 0 to T, which can be obtained through the following manner

[0025]

[0026] is an average change rate, that is, an average value of the wind speed change rate in 0 to T, which can be obtained through the following manner

[0027]

[0028] ; t ∈ [0, T].

[0029] In combination with the first aspect, in a possible implementation manner, one obtaining manner of the wind direction fluctuation score includes:

[0030] extracting a plurality of actual wind directions in wind speed data; calculating cosine values of the actual wind directions; sorting the cosine values according to their corresponding collection times in chronological order, and fitting the cosine values into a wind direction change curve FX(t); the fitting manner includes an interpolation method, etc.; and obtaining the wind direction fluctuation score XP through a formula:

[0031]

[0032] ​​​The average cosine value, i.e. the average value of FX(t) from 0 to T, can be obtained by:

[0033]

[0034] The average cosine value change rate of the wind direction, i.e. the average value of the wind direction cosine value change rate from 0 to T, can be obtained by:

[0035]

[0036] ; t∈[0, T].

[0037] In combination with the first aspect, in a possible implementation manner, the abnormality analysis function is:

[0038] YF = H f (SP, XP, FSY, QFS) + H d (DXY) + SYL

[0039] wherein YF is a numerical value corresponding to the abnormality analysis priority; H f is a set wind factor quantification function, SP is a wind speed volatility score, XP is a wind direction volatility score, FSY is a wind speed attenuation factor, and QFS is a current actual wind speed; H d is a set terrain factor quantification function, DXY is a terrain influence factor, and SYL is a usage aging quantification value.

[0040] In combination with the first aspect, in a possible implementation manner, the usage aging quantification value is obtained by:

[0041] obtaining historical usage data of the wind turbine, extracting the number of abnormalities and the average abnormality level in the historical usage data, and marking them as M and PYD respectively; and calculating the usage aging quantification value by the formula

[0042]

[0043]

[0044] In combination with the first aspect, in a possible implementation manner, the monitoring result is obtained by analyzing the running state of the wind turbine based on the detection data, and includes:

[0045] ​​extract vibration data and electrical data in the monitoring data; the vibration data includes vibration amplitude and vibration frequency of each blade of the wind turbine in a set time period; the electrical data includes current and voltage of components in the wind turbine; input the vibration data and electrical data into the trained state analysis model to obtain the monitoring result corresponding to the wind turbine; the monitoring result includes normal or abnormal level; the state analysis model is obtained by artificial intelligence model training.

[0046] In a second aspect, the present application provides a wind turbine operation monitoring device, comprising: a processor and a storage medium; the storage medium comprises instructions, and the processor is configured to execute the instructions to implement the method described in the first aspect and any possible implementation manner of the first aspect. The wind turbine operation monitoring device can be an electronic device or a chip in an electronic device.

[0047] In a third aspect, the present application provides a wind turbine operation monitoring system, comprising: a data acquisition module, an analysis and sorting module, an abnormality analysis module, a display module and a database; wherein,

[0048] The data acquisition module is configured to obtain geographical condition data of the area where the wind turbine is located, and obtain monitoring data, wherein the monitoring data includes vibration data and electrical data.

[0049] The analysis and sorting module is configured to analyze the parts where each wind turbine is located based on the geographical condition data to obtain terrain influence factors, obtain wind data of the area where the wind turbine is located, and analyze the abnormality analysis priority of each wind turbine based on the wind data and the terrain influence factors.

[0050] The abnormality analysis module is configured to obtain the detection data corresponding to each wind turbine in turn based on the order from large to small of the abnormality analysis priority, and analyze the operation state of the wind turbine based on the detection data to obtain the monitoring result.

[0051] The display module is configured to display the monitoring result and the monitoring data.

[0052] The database is configured to store a plurality of data generated in the system.

[0053] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores instructions, and when the instructions are executed on the wind turbine operation monitoring device, the wind turbine operation monitoring device executes the method described in the first aspect and any possible implementation manner of the first aspect.

[0054] In a fifth aspect, the present application provides a computer program product comprising instructions which, when the computer program product is executed on an operation monitoring device of a wind turbine generator system, cause the operation monitoring device of the wind turbine generator system to perform the method as described in the first aspect and any possible implementation of the first aspect.

[0055] The present application provides a wind turbine generator system operation monitoring system and method, which can obtain geographical condition data of an area where the wind turbine generator system is located, analyze each wind turbine generator part according to the geographical condition data to obtain a terrain influence factor, analyze each wind turbine generator corresponding abnormal analysis priority according to current wind data and the terrain influence factor, obtain detection data of each wind turbine generator in turn according to the order of the abnormal analysis priority from large to small, and analyze the operation state of the wind turbine generator based on the detection data to obtain a monitoring result, and analyze the data of each wind turbine generator in turn by judging the influence of the current environment state on each wind turbine generator in the wind turbine generator system, so that the abnormal condition of the wind turbine generator can be found in time, and the monitoring and analysis efficiency of the wind turbine generator system is increased.

[0056] It should be understood that the description of technical features, technical solutions, beneficial effects or similar language in the present application does not imply that all features and advantages can be realized in any single embodiment. On the contrary, it can be understood that the description of a feature or a beneficial effect means that the specific technical feature, technical solution or beneficial effect is included in at least one embodiment. Therefore, the description of technical features, technical solutions or beneficial effects in the specification does not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions and beneficial effects described in the embodiments can be combined in any appropriate manner. Those skilled in the art will understand that the embodiments can be implemented without one or more specific technical features, technical solutions or beneficial effects of a specific embodiment. In other embodiments, additional technical features and beneficial effects can be identified in specific embodiments that do not embody all embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creating any creative labor.

[0058] Figure 1 A schematic diagram of the steps of a wind turbine generator system operation monitoring method in the present application;

[0059] Figure 2A module connection diagram of an operation monitoring system of a wind turbine generator set. DETAILED DESCRIPTION

[0060] The technical solutions of the present application will be described clearly and completely below in conjunction with embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0061] Referring to Figure 1 , in a first aspect, an operation monitoring method of a wind turbine generator set is provided, comprising:

[0062] obtaining geographical condition data of an area where the wind turbine generator set is located; the geographical condition data is topographic data and surface attribute data of the area where the wind turbine generator set is located; the topographic data includes topographic features such as valleys and altitude features; the surface attribute data includes types and distribution of surface vegetation; based on the geographical condition data, a topographic influence factor is obtained by analyzing each part where the wind turbine generator is located; the topographic influence factor is the influence of the geographical condition of the location of the wind turbine generator on wind transmission stability; the greater the value of the topographic influence factor, the greater the influence of the geographical condition of the location of the wind turbine generator on wind transmission stability;

[0063] obtaining wind data of the area where the wind turbine generator set is located; the wind data is wind data of the area where the wind turbine generator set is located; based on the wind data and the topographic influence factor, an abnormal analysis priority corresponding to each wind turbine generator is obtained; the abnormal priority represents the probability of abnormality of the wind turbine generator under the current condition; the greater the probability, the greater the value of the corresponding abnormal analysis priority; the greater the influence of the current environmental state on the wind turbine generator, the greater the probability of abnormality of the wind turbine generator, and the wind turbine generator is analyzed preferentially;

[0064] based on the order from large to small of the abnormal analysis priority, detection data corresponding to each wind turbine generator is obtained in turn, and based on the detection data, the operation state of the wind turbine generator is analyzed to obtain a monitoring result.

[0065] Based on the above technical scheme, in the operation monitoring method of the wind turbine generator set provided in the application, geographical condition data of a region where the wind turbine generator set is located is obtained; topographic influence factors are obtained by analyzing parts where each wind turbine generator is located according to the geographical condition data; abnormal analysis priorities corresponding to each wind turbine generator are obtained by analyzing the current wind data and the topographic influence factors; detection data corresponding to each wind turbine generator is obtained in turn according to the order from large to small of the abnormal analysis priorities, and the operation state of the wind turbine generator is analyzed based on the detection data to obtain a monitoring result; the data of each wind turbine generator is analyzed in turn by judging the influence of the current environmental state on each wind turbine generator in the wind turbine generator set; so that the abnormal condition of the wind turbine generator can be found in time, and the monitoring and analysis efficiency of the wind turbine generator set is increased.

[0066] In a possible implementation manner, the topographic influence factors are obtained based on the geographical condition data, including: terrain data and surface attribute data in the geographical condition data are extracted; the terrain data includes terrain features such as valleys and altitude features; the surface attribute data is the type and distribution of surface vegetation; a wind simulation model of the region where the wind turbine generator set is located is constructed based on the terrain data and the surface attribute data; the wind simulation model can be constructed by combining fluid mechanics with a WRF (Weather Research and Forecasting) model; winds with a plurality of set test wind speeds and test wind directions are simulated in the wind simulation model; the test wind speed and the test wind direction are set by relevant test personnel; a plurality of continuous wind speeds and wind directions can also be generated by AI simulation; and a simulated wind speed at each position of the wind turbine generator is obtained in the simulation process; the simulated wind speed is the wind speed of the test wind speed and the test wind direction reaching the position of the wind turbine generator after being affected by the terrain in the wind simulation model;

[0067] The ratio of the plurality of simulated wind speeds at the position of the same wind turbine generator to the test wind speed is calculated, and the ratio is marked as a wind speed attenuation factor; the variance of each wind speed attenuation factor is taken as the topographic influence factor of the wind turbine generator.

[0068] In a possible implementation manner, the plurality of simulated wind speeds and the test wind speed, and the wind speed attenuation factor of each position of the wind turbine generator corresponding to the simulated wind speed and the test wind speed are obtained; a wind speed attenuation factor query table is constructed based on the test wind speed, the test wind direction, the position of the wind turbine generator, and the wind speed attenuation factor;

[0069] For example, part of the wind speed attenuation factor query table is as follows:

[0070] Wind turbine ID Wind turbine location Wind speed Wind direction Wind speed attenuation factor W001 (115.085，30.780) 7.8 240 0.8813 W001 (115.085，30.780) 7.9 240 0.8868 W001 (115.085，30.780) 8.0 240 0.8963 W001 (115.085，30.780) 8.1 240 0.8986 W001 (115.085，30.780) 8.2 240 0.9012

[0071] Wherein, the wind speed is a test wind speed, the wind direction is a test wind direction, the wind turbine ID is a unique ID assigned to each wind turbine in the wind turbine group; the wind turbine position is represented by longitude and latitude, and can also be represented by other ways.

[0072] In a possible implementation, the abnormal analysis priority corresponding to each wind turbine is analyzed based on the wind data and the terrain influence factor, and includes:

[0073] Extract a plurality of wind speed data and a plurality of wind direction data in the wind data; the wind speed data includes the actual wind speed of the area where the wind turbine group is located collected at a plurality of collection time points; the wind direction data includes the actual wind direction corresponding to the actual wind speed in the wind speed data;

[0074] The wind speed fluctuation score is analyzed based on the wind speed data to represent the wind speed fluctuation; the wind speed fluctuation score is an evaluation of the actual wind speed fluctuation or stability according to the change of the actual wind speed, and the higher the wind speed fluctuation score, the more serious the wind speed fluctuation in the set time period, and the more unstable the wind speed.

[0075] The wind direction fluctuation score is analyzed based on the wind direction data to represent the wind direction fluctuation; the wind direction fluctuation score is an evaluation of the actual wind direction fluctuation or stability according to the change of the actual wind direction, and the higher the wind direction fluctuation score, the more serious the wind direction fluctuation in the set time period, and the more unstable the wind direction.

[0076] The actual wind speed closest to the current time in the wind speed data is obtained; and the actual wind direction closest to the current time in the wind direction data; the corresponding wind speed attenuation factor is queried in the wind speed attenuation factor table based on the actual wind speed, the actual wind direction and the position of each wind turbine;

[0077] Obtain the terrain influence factor corresponding to each wind turbine, and the current actual wind speed in the wind speed data; the actual wind speed, the terrain influence factor, the wind speed fluctuation score, the wind direction fluctuation score and the wind speed attenuation factor are substituted into the abnormal analysis function to obtain the abnormal analysis priority for representing the possibility of abnormal operation of the wind turbine.

[0078] In a possible implementation, one of the ways to obtain the wind speed fluctuation score includes:

[0079] Extract a plurality of actual wind speeds in the wind speed data; sort the actual wind speeds according to the order of their corresponding collection time, and fit into a wind speed change curve FS(t); the fitting method includes interpolation method and the like; the wind speed fluctuation score SP is calculated by the formula:

[0080]

[0081] The wind speed fluctuation score SP is calculated; wherein, The average wind speed, i.e., the average value of FS(t) from 0 to T, can be obtained by

[0082]

[0083] The average change rate, i.e., the average value of the change rate of the wind speed from 0 to T, can be obtained by

[0084]

[0085] ; t∈[0, T].

[0086] The wind direction fluctuation score SP is calculated by the above formula. When the difference between the actual wind speed and the average wind speed in the set time period is greater, or the difference between the change rate of the actual wind speed and the average change rate is greater, it indicates that the fluctuation of the wind speed is stronger and the stability is weaker, and the corresponding wind speed fluctuation score is set to be greater. The subsequent wind turbine is more complex in the case of being affected by the wind speed.

[0087] In one possible implementation, one way of obtaining the wind direction fluctuation score includes:

[0088] A plurality of actual wind directions in the wind speed data are extracted, the cosine values of the actual wind directions are calculated, the cosine values are sorted according to the order of the collection time corresponding to the cosine values, and a wind direction change curve FX(t) is fitted, the fitting method including an interpolation method, etc. The wind direction fluctuation score XP is calculated by the formula:

[0089]

[0090] The average cosine value, i.e., the average value of FX(t) from 0 to T, can be obtained by

[0091]

[0092] The average change rate of the wind direction cosine value, i.e., the average value of the change rate of the wind direction cosine value from 0 to T, can be obtained by

[0093]

[0094] ; t∈[0, T].

[0095] ​​​The wind direction fluctuation score XP is calculated by the above formula. When the difference between the cosine value of the actual wind direction and the average cosine value in the set time period is larger, or the difference between the change rate of the actual wind direction cosine value and the change rate of the average cosine value is larger, it indicates that the wind direction fluctuation is stronger, the change is more complex, and the stability is weaker, and the corresponding wind direction fluctuation score is set to be larger. It can be understood that the wind direction fluctuation score is essentially similar in meaning to the wind speed fluctuation score. Only two directions of the wind are evaluated respectively. The subsequent wind turbine is more complex in the case of being affected by the wind direction.

[0096] In a possible implementation, the anomaly analysis function is:

[0097] YF = H f (SP, XP, FSY, QFS) + H d (DXY) + SYL

[0098] Wherein, YF is an anomaly analysis priority; H f is a set wind factor quantification function, SP is a wind speed fluctuation score, XP is a wind direction fluctuation score, FSY is a wind speed attenuation factor, and QFS is a current actual wind speed; H d is a set terrain factor quantification function, DXY is a terrain influence factor; SYL is a use aging quantification value; The embodiment comprehensively analyzes various factors affecting the wind turbine by the anomaly analysis function, and further obtains the anomaly analysis priority of the corresponding wind turbine. The larger the value of the anomaly analysis priority, the greater the degree of influence of the anomaly analysis priority on the current environment, the higher the probability of abnormality, and the priority of the detection data of the wind turbine corresponding to the anomaly analysis priority is analyzed. It is convenient to find the abnormal state of the wind turbine in time.

[0099] Specifically, the wind factor quantification function is used to quantify the influence of wind-related factors on the wind turbine. In the embodiment, a form of the wind factor quantification function is given as follows:

[0100]

[0101] Wherein, δ1 is a quantitative adjustment factor in the wind factor quantitative function, the growth ratio of the quantification can be set by adjusting the size of δ1, δ1>0, the specific value is set according to experience, δ1=0.5 in the embodiment; WFS is a set unit wind speed, used to remove the unit of the current actual wind speed, the unit wind speed in the embodiment is set to 1m / s; α1 is a weight coefficient corresponding to the wind speed fluctuation score; α2 is a weight coefficient corresponding to the wind direction fluctuation score, α1 and α2 are mainly used to adjust the size of the influence of the wind speed fluctuation score and the wind direction fluctuation score on the wind turbine; the specific value is set according to experience, and can also be obtained according to the following method: obtaining a plurality of wind speed attenuation factors of the position of the wind turbine under a plurality of test wind speeds and test wind directions when the wind simulation model is simulated, calculating the variance of each wind speed attenuation factor under the same test wind direction and different test wind speeds as the wind direction influence variance, obtaining the maximum value of each wind direction influence variance as the wind direction influence ratio, calculating the variance of each wind speed attenuation factor under the same test wind speed and different test wind directions as the wind speed influence variance, obtaining the maximum value of each wind speed influence variance as the wind speed influence ratio, and the ratio of the wind speed influence ratio to the sum of the wind speed influence ratio and the wind direction influence ratio is taken as the weight coefficient α1 corresponding to the wind speed fluctuation score; the ratio of the wind direction influence ratio to the sum of the wind speed influence ratio and the wind direction influence ratio is taken as the weight coefficient α2 corresponding to the wind direction fluctuation score.

[0102] The wind factor quantitative function is used for quantifying each wind-related influence factor, and the greater the values of the wind speed fluctuation score and the analysis fluctuation score, or the greater the relative wind speed of the area where the wind turbine is located, the more unstable the current wind state, the more complex the influence on the wind turbine, and the greater the influence on the wind turbine. The probability of abnormality of the wind turbine under the current condition is higher, and therefore the corresponding quantification result is set to be larger.

[0103] Specifically, the terrain factor quantitative function is used for quantifying the influence of terrain on the wind turbine, and a form of a terrain factor quantitative function in the embodiment is as follows:

[0104]

[0105] Wherein, δ2 is a quantitative adjustment factor in the terrain factor quantitative function, the growth ratio of the quantification can be set by adjusting the size of δ2, δ2>0, the specific value is set according to experience, δ2=0.5 in the embodiment.

[0106] The embodiment realizes quantification of the influence of the terrain on the wind at the location of the wind turbine through the terrain factor quantification function. The wind at the location of the wind turbine is greatly affected by the terrain and topography of the corresponding area, that is, the greater the terrain influence factor, the more complex the change of the wind speed and direction at the location of the wind turbine, and it is difficult to accurately grasp, at this time, the wind turbine is more complexly affected, and the probability of abnormality is greater, so the corresponding quantification value is set to be greater.

[0107] In a possible implementation, the obtaining of the usage aging quantification value includes:

[0108] The historical usage data of the wind turbine is obtained, and the number of exceptions and the average exception level in the historical usage data are extracted and marked as M and PYD respectively. The usage aging quantification value is calculated through the formula

[0109]

[0110] The usage aging quantification value is calculated.

[0111] The embodiment calculates the usage aging quantification value of the wind turbine through the above formula. The more the historical maintenance times of the wind turbine, and the higher the average level of failure, the higher the probability of abnormality, especially in a complex environment. Therefore, the corresponding usage aging quantification value is set to be greater.

[0112] In a possible implementation, the monitoring result obtained by analyzing the running state of the wind turbine based on the detection data includes: extracting vibration data and electrical data in the monitoring data; the vibration data includes the vibration amplitude and vibration frequency of each blade of the corresponding wind turbine in a set time period; the electrical data includes the current and voltage of the components in the corresponding wind turbine; the vibration data and electrical data are input into the trained state analysis model to obtain the corresponding monitoring result of the wind turbine; the monitoring result includes normal or abnormal level; the state analysis model is obtained by training an artificial intelligence model; specifically, a plurality of historical monitoring data and the monitoring result of the wind turbine analyzed by a person according to the vibration data and electrical data in the monitoring data are obtained, the monitoring result includes normal and abnormal levels, and the higher the abnormal level, the more serious the abnormality of the wind turbine; the vibration data and electrical data in the monitoring data and the corresponding monitoring result are integrated into a plurality of training data and test data; the artificial intelligence model is trained using the training data, and the trained artificial intelligence model is tested using the test data, to finally obtain a state analysis model with vibration data and electrical data as input and monitoring state as output; wherein the artificial intelligence model includes a deep neural network model.

[0113] In a second aspect, the present application provides a wind turbine operation monitoring device, comprising: a processor and a storage medium; the storage medium comprises instructions, and the processor is configured to execute the instructions to implement the method described in the first aspect and any possible implementation manner of the first aspect. The wind turbine operation monitoring device can be an electronic device or a chip in an electronic device.

[0114] Referring to Figure 2 In a third aspect, the present application provides a wind turbine operation monitoring system, comprising: a data acquisition module, an analysis and sorting module, an abnormality analysis module, a display module and a database; wherein,

[0115] The data acquisition module is configured to acquire geographical condition data of a region where the wind turbine is located, and acquire monitoring data, wherein the monitoring data comprises vibration data and electrical property data.

[0116] The analysis and sorting module is configured to analyze parts where each wind turbine is located based on the geographical condition data to obtain a terrain influence factor, acquire wind data of the region where the wind turbine is located, and analyze the terrain influence factor based on the wind data to obtain an abnormality analysis priority corresponding to each wind turbine.

[0117] The abnormality analysis module is configured to acquire detection data corresponding to each wind turbine in sequence based on the abnormality analysis priority in descending order, and analyze an operation state of the wind turbine based on the detection data to obtain a monitoring result.

[0118] The display module is configured to display the monitoring result and the monitoring data.

[0119] The database is configured to store a plurality of data generated in the system.

[0120] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores instructions, and when the instructions are executed on the wind turbine operation monitoring device, the wind turbine operation monitoring device executes the method described in the first aspect and any possible implementation manner of the first aspect.

[0121] In a fifth aspect, the present application provides a computer program product comprising instructions, and when the computer program product is executed on the wind turbine operation monitoring device, the wind turbine operation monitoring device executes the method described in the first aspect and any possible implementation manner of the first aspect.

[0122] Part of the data in the above formula is the value calculated by removing the dimension, and the formula is obtained by software simulation of a large amount of collected data to be closest to the real situation; the preset parameters and the preset threshold in the formula are set by the person skilled in the art according to the actual situation or obtained by a large amount of data simulation.

[0123] Working principle of the present application:

[0124] By acquiring geographical condition data of the area where the wind turbine generator set is located; analyzing the parts where each wind turbine is located according to the geographical condition data to obtain a terrain influence factor; analyzing each wind turbine corresponding abnormal analysis priority according to the current wind data and the terrain influence factor; acquiring each wind turbine corresponding detection data in order according to the order from large to small of the abnormal analysis priority, and analyzing the running state of the wind turbine based on the detection data to obtain a monitoring result; by judging the influence of the current environment state on each wind turbine in the wind turbine generator set, data analysis is performed on each wind turbine in order; so that the abnormal condition of the wind turbine can be found in time, and the monitoring and analysis efficiency of the wind turbine generator set is increased.

[0125] The above embodiments are only used to illustrate the technical method of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

Claims

1. A method for monitoring the operation of a wind turbine generator set, characterized in that, include: Obtain geographical condition data for the area where the wind turbine generator is located; The geographic condition data includes topographic data and surface attribute data; Based on geographical data, the terrain influence factors of each wind turbine location were analyzed, including: Extract terrain data and surface attribute data from the geographic condition data; construct a wind simulation model of the area where the wind turbine is located based on the terrain data and surface attribute data; simulate several set test wind speeds and test wind directions in the wind simulation model; and obtain the simulated wind speeds collected at the locations of each wind turbine during the simulation process. Calculate the ratio of several simulated wind speeds to the test wind speeds at the same location of the wind turbine, and label them as wind speed attenuation factors; use the variance of each wind speed attenuation factor as the terrain influence factor of the wind turbine. A wind speed attenuation factor lookup table is constructed based on the test wind speed, test wind direction, location of the wind turbine, and wind speed attenuation factor. Obtain wind data for the area where the wind turbines are located. Based on the wind data and topographic influence factor analysis, determine the anomaly analysis priority for each wind turbine, including: Extract several wind speed data points and several wind direction data points from the wind data; A wind speed volatility score is obtained based on wind speed data analysis to represent wind speed volatility. A wind direction volatility score is obtained based on wind direction data analysis to represent wind direction volatility. Obtain the actual wind speed closest to the current time from the wind speed data; and the actual wind direction closest to the current time from the wind direction data; and retrieve the corresponding wind speed attenuation factor from the wind speed attenuation factor table based on the actual wind speed, actual wind direction, and the location of each wind turbine. Obtain the terrain influence factor corresponding to each wind turbine, as well as the current actual wind speed in the wind speed data; substitute the actual wind speed, terrain influence factor, wind speed fluctuation score, wind direction fluctuation score, and wind speed attenuation factor into the anomaly analysis function to obtain the anomaly analysis priority used to represent the possibility of anomalies in the operation of the wind turbine. Based on the anomaly analysis priority from highest to lowest, the detection data corresponding to each wind turbine is obtained in sequence, and the operating status of the wind turbine is analyzed based on the detection data to obtain the monitoring results.

2. The method for monitoring the operation of a wind turbine generator set according to claim 1, characterized in that, The wind speed fluctuation score is obtained through the following methods: Extract several actual wind speeds from the wind speed data; sort the actual wind speeds according to their corresponding acquisition times, and fit them into a wind speed change curve FS(t); use the formula: The wind speed fluctuation score SP was calculated; where, Average wind speed; Let t be the average rate of change; t∈[0,T].

3. The method for monitoring the operation of a wind turbine generator set according to claim 1, characterized in that, The methods for obtaining the wind direction volatility score include: Extract several actual wind directions from the wind speed data; calculate the cosine value of each actual wind direction; sort the cosine values ​​according to the chronological order of their corresponding acquisition times, and fit them into a wind direction change curve FX(t); using the formula: The wind direction fluctuation score XP is calculated; where, The mean cosine value; Let be the rate of change of the mean cosine of the wind direction; t∈[0,T].

4. The method for monitoring the operation of a wind turbine generator set according to claim 1, characterized in that, The anomaly analysis function is: Where YF represents the priority of anomaly analysis; H f ( ) represents the set wind factor quantification function, SP represents the wind speed fluctuation score, XP represents the wind direction fluctuation score, FSY represents the wind speed attenuation factor, and QFS represents the current actual wind speed; H d ( ) represents the set terrain factor quantification function, DXY is the terrain influence factor; SYL is the aging quantification value used.

5. The method for monitoring the operation of a wind turbine generator set according to claim 4, characterized in that, The methods for obtaining aging quantification values ​​include: Obtain historical usage data of wind turbines, extract the number of anomalies and average anomaly level from the historical usage data, and label them as M and PYD, respectively; then use the formula... The aging quantification value was calculated.

6. The method for monitoring the operation of a wind turbine generator set according to claim 1, characterized in that, The monitoring results obtained by analyzing the operating status of the wind turbine based on the detection data include: Vibration and electrical data are extracted from the monitoring data; the vibration data includes the vibration amplitude and frequency of each blade of the corresponding wind turbine within a set time period; the electrical data includes the current and voltage of the components in the corresponding wind turbine; the vibration and electrical data are input into a trained state analysis model to obtain the monitoring results corresponding to the wind turbine; the monitoring results include normal or abnormal levels; the state analysis model is trained through an artificial intelligence model.

7. A wind turbine generator operation monitoring system, based on the operation monitoring method for a wind turbine generator according to any one of claims 1 to 6, characterized in that, include: The data acquisition module, analysis and sorting module, and anomaly analysis module; among them, Data acquisition module: used to acquire geographical condition data of the area where the wind turbine is located, and to acquire monitoring data, including vibration data and electrical data; Analysis and ranking module: Based on geographical condition data, analyze the location of each wind turbine to obtain terrain influence factors, obtain wind data of the area where the wind turbine is located, and obtain the anomaly analysis priority for each wind turbine based on wind data and terrain influence factors. Anomaly Analysis Module: Based on the anomaly analysis priority from highest to lowest, the module sequentially acquires the detection data corresponding to each wind turbine, and analyzes the operating status of the wind turbine based on the detection data to obtain monitoring results.

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