Wind power plant intelligent early warning method and system based on operation state monitoring

Abstract

The invention relates to a wind power plant intelligent early warning method and system based on operation state monitoring, and the method comprises the steps: obtaining the current external wind power data of a wind power plant and the current operation state data of a wind turbine generator in the wind power plant, and generating a current in-cabin estimated disturbance range; acquiring operation vibration detection data and operation noise detection data in a cabin of the wind turbine generator, generating a current operation estimated disturbance value, and judging whether the current operation estimated disturbance value belongs to the current cabin estimated disturbance range or not; and if the judgment result is no, generating a current operation early warning instruction, and guiding a wind turbine generator maintainer to maintain the cabin of the wind turbine generator according to the current operation early warning instruction. According to the invention, monitoring and early warning of the cabin of each wind turbine generator in the wind power plant are realized based on the external wind power data, the operation state data and the actual detection data in the cabin, early warning of possible faults is realized in advance, and the fault rate is further reduced.

Description

Technical Field

[0001] The present application relates to the field of data acquisition technology, and in particular to an intelligent early warning method and system for a wind farm based on operation status monitoring. Background Art

[0002] A wind farm, or wind power plant, is a facility that installs multiple large-scale grid-connected wind turbines in a site with good wind energy resources. The turbines are arranged in an array according to the terrain and main wind direction to form a fleet that supplies power to the grid. Depending on the site, they can be divided into offshore wind farms and onshore wind farms.

[0003] For onshore wind farms, monitoring during their operation is crucial. For example, the invention patent with a publication date of September 2, 2022 and publication number CN115002003A discloses a system and method for monitoring the operating status of a wind farm central monitoring system, including an intranet status monitoring system and an extranet status monitoring system for monitoring the central monitoring system of a wind farm. The intranet status monitoring system and the extranet status monitoring system are respectively connected to the centralized control Ethernet communication.

[0004] Although the technical solution in the above patent document can monitor the operating status of the central monitoring system of the wind farm and determine possible problems of the system in advance to avoid large-scale failures such as system crashes, its monitoring of the wind farm is not comprehensive enough. It only monitors the status of the internal and external networks. Similar to other monitoring methods in the prior art, it only judges whether a failure may occur by paying attention to the operating parameters themselves. However, parts are often damaged due to factors such as vibration disturbances, but the damage is not to the extent that it affects operation. At this time, relying only on operating parameters for fault warning will cause warning delays. Often when the warning is issued, the components are completely damaged, which may seriously lead to major failures. Summary of the invention

[0005] Based on this, it is necessary to provide a wind farm intelligent early warning method and system based on operating status monitoring, which can comprehensively consider the disturbance effects of external wind interference and vibration interference caused by normal operation on the cabin, and monitor and warn the cabins of each wind turbine set in the wind farm based on external wind data, operating status data and actual detection data in the cabin, in order to solve the above technical problems.

[0006] The technical solution of the present invention is as follows:

[0007] An intelligent early warning method for a wind farm based on operation status monitoring, the method comprising:

[0008] Acquire current external wind power data of the wind farm and current operating status data of the wind turbines in the wind farm, and generate a current estimated disturbance range in the cabin according to the current external wind power data and the current operating status data;

[0009] Acquire operation vibration detection data and operation noise detection data in the nacelle of the wind turbine generator set, and generate a current operation estimated disturbance value according to the operation vibration detection data and the operation noise detection data, wherein the current operation estimated disturbance value is a value used to indicate the influence of the operation of the wind turbine generator set and external wind force on the nacelle;

[0010] Determining whether the current operation estimated disturbance value falls within the current cabin estimated disturbance range;

[0011] If the judgment is no, a current operation warning indication is generated, and the wind turbine maintenance personnel are guided to perform maintenance on the nacelle of the wind turbine according to the current operation warning indication.

[0012] Optionally, the cabin includes a plurality of vibration detection areas, each of which includes a vibration detection point, and each of which includes a vibration detection sensor. The operation vibration detection data includes the sum of data detected by each activated vibration detection sensor, wherein after the activated vibration detection sensor performs vibration detection, operation vibration data of a detection point is correspondingly obtained;

[0013] The cabin also includes a noise detection area, in which a plurality of noise collection sensors are arranged, and the noise collection sensors are used for noise collection;

[0014] Acquiring operation vibration detection data and operation noise detection data in the nacelle of the wind turbine generator set, and generating a current operation estimated disturbance value according to the operation vibration detection data and the operation noise detection data, including:

[0015] Acquire a current wind force level corresponding to the current external wind force data, and generate a detection point activation quantity according to the current wind force level;

[0016] Selecting a detection point to be activated from each of the vibration detection points according to the number of detection point activations;

[0017] Controlling the vibration detection sensors corresponding to the detection points to be activated to start and obtaining the running vibration data of the detection points detected by the activated vibration detection sensors;

[0018] Generate an operation vibration interference value according to the operation vibration data of each detection point;

[0019] Calculating the distance between the noise collection sensor and the detection point to be activated according to each of the detection points to be activated, and setting the noise collection sensor to be activated, wherein the selected noise collection sensor is the noise sensor to be activated;

[0020] Starting the noise collection sensor to be activated and acquiring operating noise detection data;

[0021] generating an operating noise interference value according to the operating noise detection data;

[0022] The operation vibration disturbance value and the operation noise disturbance value are added together to generate a current operation estimated disturbance value.

[0023] Optionally, selecting a detection point to be activated from each of the vibration detection points according to the number of detection point activations includes:

[0024] Acquire historical maintenance data of each vibration detection area in the cabin according to the number of activations of the detection points;

[0025] Extracting the historical maintenance number of each vibration detection area from the historical maintenance data, arranging the historical maintenance number corresponding to each vibration detection area in descending order, and generating a maintenance number set, wherein one vibration detection area corresponds to one historical maintenance number;

[0026] Filter out vibration detection areas with the same number of activations as the detection points from the maintenance number set, and set them as detection areas to be activated;

[0027] A corresponding detection point to be activated is selected according to the detection area to be activated.

[0028] Optionally, generating an operation vibration interference value according to the operation vibration data of each detection point includes:

[0029] Calculating a detection point fluctuation value according to the detection point operation vibration data, wherein one detection point fluctuation value corresponds to one detection point operation vibration data;

[0030] The fluctuation values ​​of each detection point are added together to generate the operation vibration interference value.

[0031] Optionally, calculating a detection point fluctuation value according to the running vibration data of one detection point includes:

[0032] Calculate the maximum vibration frequency, the minimum vibration frequency and the average vibration frequency according to the vibration data of the detection point operation;

[0033] Generating a vibration frequency distribution curve according to the vibration data of the detection point operation, and screening out an increasing frequency segment and a decreasing frequency segment from the vibration frequency distribution curve;

[0034] Calculate the slopes of the rising frequency segment and the falling frequency segment, take the absolute value of each slope and record it as the fluctuation slope;

[0035] The detection point fluctuation value is generated according to the maximum vibration frequency, the minimum vibration frequency, the average vibration frequency and the fluctuation slope.

[0036] Optionally, calculating the distance between the noise collection sensor and the detection point to be activated according to each of the detection points to be activated, and setting the noise collection sensor to be activated includes:

[0037] Calculating the distance between each noise collection sensor and the detection point to be activated, and generating a sensor spacing data group, wherein one noise collection sensor corresponds to one sensor spacing data group, each sensor spacing data group includes a plurality of actual detection point spacings, and the distance between one noise collection sensor and one detection point to be activated is one actual detection point spacing;

[0038] Calculating the sum of the distances between each noise collection sensor and each detection point to be activated according to each sensor spacing data group, and generating a total distance between detection points, wherein one noise collection sensor corresponds to one total distance between detection points;

[0039] The noise collection sensor corresponding to the total distance between detection points with the smallest value is set as the noise collection sensor to be activated.

[0040] Optionally, generating an operating noise interference value according to the operating noise detection data includes:

[0041] generating a noise data frequency curve according to the operating noise detection data;

[0042] Acquiring a total curve length of the noise data frequency curve, and screening out an abnormal frequency curve from the noise data frequency curve, wherein the abnormal frequency curve is a curve outside a normal frequency range in the noise data frequency curve;

[0043] Calculating the abnormal curve length of the abnormal frequency curve;

[0044] The ratio of the abnormal curve length to the total curve length is calculated, and an operation noise interference value is generated.

[0045] Optionally, obtaining current external wind power data of the wind farm and current operating status data of wind turbines in the wind farm, and generating a current estimated disturbance range in the cabin according to the current external wind power data and the current operating status data, includes:

[0046] Acquire current external wind data of the wind farm, extract current wind force level from the current external wind data, and acquire current wind disturbance coefficient according to the current wind force level;

[0047] Acquiring the usage time of the cabin, and generating a disturbance increase coefficient according to the usage time;

[0048] Acquire current operating status data of wind turbines in the wind farm, compare the current operating status data with preset normal operating status data, and filter out normal operating status data that matches the current operating status data;

[0049] Acquiring an initial disturbance range according to the conventional operating state data;

[0050] The current estimated disturbance range in the cabin is generated according to the current wind disturbance coefficient, disturbance amplification coefficient and initial disturbance range.

[0051] Optionally, a wind farm intelligent early warning system based on operation status monitoring is also provided, the system comprising:

[0052] A status data analysis module, used to obtain the current external wind power data of the wind farm and the current operating status data of the wind turbines in the wind farm, and generate the current estimated disturbance range in the cabin according to the current external wind power data and the current operating status data;

[0053] an operation disturbance generating module, used for acquiring operation vibration detection data and operation noise detection data in the nacelle of the wind turbine generator set, and generating a current operation estimated disturbance value according to the operation vibration detection data and the operation noise detection data, wherein the current operation estimated disturbance value is a value used to represent the influence of the operation of the wind turbine generator set and the external wind force on the nacelle;

[0054] An operation disturbance judgment module is used to judge whether the current operation estimated disturbance value falls within the current cabin estimated disturbance range;

[0055] The operation warning generation module is used to generate a current operation warning indication if the judgment is no, and guide the wind turbine maintenance personnel to repair the nacelle of the wind turbine according to the current operation warning indication.

[0056] Optionally, the cabin includes a plurality of vibration detection areas, each of which includes a vibration detection point, and each of which includes a vibration detection sensor. The operation vibration detection data includes the sum of data detected by each activated vibration detection sensor, wherein after the activated vibration detection sensor performs vibration detection, operation vibration data of a detection point is correspondingly obtained;

[0057] The cabin also includes a noise detection area, in which a plurality of noise collection sensors are arranged, and the noise collection sensors are used for noise collection;

[0058] The operation disturbance generation module is also used for:

[0059] Obtain the current wind force level corresponding to the current external wind force data, and generate the number of detection point activations according to the current wind force level; select the detection point to be activated from each of the vibration detection points according to the number of detection point activations; control the vibration detection sensors corresponding to each of the detection points to be activated to start and obtain the operation vibration data of the detection points detected by each of the vibration detection sensors after activation; generate an operation vibration interference value according to the operation vibration data of each of the detection points; calculate the distance between the noise collection sensor and the detection point to be activated according to each of the detection points to be activated, and set the noise collection sensor to be activated, wherein the selected noise collection sensor is the noise sensor to be started; start the noise collection sensor to be activated and obtain the operation noise detection data; generate the operation noise interference value according to the operation noise detection data; add the operation vibration interference value and the operation noise interference value to generate the current operation estimated disturbance value.

[0060] Optionally, the operation disturbance generating module is further used for:

[0061] According to the number of activated detection points, historical maintenance data of each vibration detection area in the cabin is obtained; the historical maintenance number of each vibration detection area is extracted from the historical maintenance data, the historical maintenance number corresponding to each vibration detection area is arranged in descending order, and a maintenance number set is generated, wherein one vibration detection area corresponds to one historical maintenance number; vibration detection areas with the same number of activated detection points as the detection points are selected from the maintenance number set, and are set as detection areas to be activated; and corresponding detection points to be activated are selected according to the detection areas to be activated.

[0062] Optionally, the operation disturbance generation module is also used to: calculate the detection point fluctuation value based on the detection point operation vibration data, wherein one detection point fluctuation value corresponds to one detection point operation vibration data; add the detection point fluctuation values ​​and generate the operation vibration interference value.

[0063] The operation disturbance generation module is also used to calculate the operation vibration disturbance value by the following formula:

[0064]

[0065] Among them, Rd is the operating vibration interference value, αi is the vibration influence coefficient of the i-th vibration detection area, N is the number of detection point fluctuation values, and Fdi is the i-th detection point fluctuation value.

[0066] Optionally, the operation disturbance generation module is further used to: calculate the maximum vibration frequency, the minimum vibration frequency and the average vibration frequency according to the operation vibration data of the detection point; generate a vibration frequency distribution curve according to the operation vibration data of the detection point, and screen out an increasing frequency segment and a decreasing frequency segment from the vibration frequency distribution curve; calculate the slope of the increasing frequency segment and the decreasing frequency segment, take the absolute value of each slope and record it as a fluctuation slope; generate a detection point fluctuation value based on the following formula according to the maximum vibration frequency, the minimum vibration frequency, the average vibration frequency and the fluctuation slope:

[0067]

[0068] Among them, Fd is the fluctuation value of the detection point, β1 is the first vibration coefficient, far is the average vibration frequency, fs is the pre-stored standard vibration frequency, β2 is the second vibration coefficient, fmax is the maximum vibration frequency, fmin is the minimum vibration frequency, β3 is the third vibration coefficient, n is the number of fluctuation slopes, Kj is the jth fluctuation slope, and Ksj is the pre-stored standard slope.

[0069] The operation disturbance generation module is also used for: calculating the distance between each of the noise collection sensors and the detection point to be activated, and generating a sensor spacing data group, wherein one of the noise collection sensors corresponds to one sensor spacing data group, each of the sensor spacing data group includes multiple actual detection point spacings, and the distance between one of the noise collection sensors and one of the detection points to be activated is one actual detection point spacing; calculating the sum of the distances between each of the noise collection sensors and each of the detection points to be activated according to each of the sensor spacing data groups, and generating a total detection point spacing, wherein one of the noise collection sensors corresponds to one total detection point spacing; and setting the noise collection sensor corresponding to the detection point spacing with the smallest value as the noise collection sensor to be activated.

[0070] The operation disturbance generation module is also used to: generate a noise data frequency curve according to the operation noise detection data; obtain the total curve length of the noise data frequency curve, and screen out abnormal frequency curves from the noise data frequency curve, wherein the abnormal frequency curve is a curve outside the normal frequency range of the noise data frequency curve; calculate the abnormal curve length of the abnormal frequency curve; calculate the ratio of the abnormal curve length to the total curve length, and generate an operation noise interference value.

[0071] The status data analysis module is also used to: obtain the current external wind data of the wind farm, extract the current wind force level from the current external wind data, and obtain the current wind disturbance coefficient according to the current wind force level; obtain the usage time of the cabin, and generate a disturbance increase coefficient according to the usage time; obtain the current operating status data of the wind turbines in the wind farm, and compare the current operating status data with the preset normal operating status data, and filter out the normal operating status data that matches the current operating status data; obtain the initial disturbance range according to the normal operating status data; generate the current estimated disturbance range in the cabin according to the current wind disturbance coefficient, the disturbance increase coefficient and the initial disturbance range.

[0072] Optionally, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned wind farm intelligent early warning method based on operation status monitoring when executing the computer program.

[0073] Optionally, a computer-readable storage medium is also provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned wind farm intelligent early warning method based on operation status monitoring are implemented.

[0074] The technical effects achieved by the present invention are as follows:

[0075] The above-mentioned wind farm intelligent early warning method and system based on operation status monitoring successively obtain the current external wind data of the wind farm and the current operation status data of the wind turbines in the wind farm, and generate the current estimated disturbance range in the cabin according to the current external wind data and the current operation status data; obtain the operation vibration detection data and the operation noise detection data in the cabin of the wind turbine, and generate the current operation estimated disturbance value according to the operation vibration detection data and the operation noise detection data, wherein the current operation estimated disturbance value is a value used to indicate the influence of the operation of the wind turbine and the external wind force in the cabin; judge whether the current operation estimated disturbance value falls within the current estimated disturbance range in the cabin; if the judgment is no, generate a current operation early warning indication, and guide the wind turbine maintenance personnel to repair the cabin of the wind turbine according to the current operation early warning indication. In the present invention, in order to determine whether the wind force outside the wind farm and the operating data of the wind turbine will have a direct or indirect impact on the components in the nacelle of the wind turbine, the current external wind force data of the wind farm and the current operating status data of the wind turbine in the wind farm are first obtained. Under the condition that the wind turbine operates normally, the disturbance in the nacelle caused by a certain external wind force and the corresponding operating status will be within a normal range. This range can be used as a reference for determining whether it will affect the operation of various components in the nacelle of the wind turbine, that is, the current estimated disturbance range in the nacelle is generated according to the current external wind force data and the current operating status data. That is, the current estimated disturbance range in the cabin is generated according to the current external wind force data and the current operating status data, and then the operating vibration detection data and the operating noise detection data in the cabin of the wind turbine are obtained, and the current operating estimated disturbance value is generated according to the operating vibration detection data and the operating noise detection data, and the current operating estimated disturbance value is set to indicate the magnitude of the influence of the operation of the wind turbine and the external wind force in the cabin, and it is expressed by a specific numerical value to realize digital wind farm management, and then, it is determined whether the current operating estimated disturbance value belongs to the current estimated disturbance range in the cabin; if the judgment is yes, that is, it is determined that the current operating estimated disturbance value belongs to the current estimated disturbance range in the cabin, and no early warning is required at this time, therefore, a current operating safety indication is generated, and the wind turbine is controlled to continue to operate according to the operating safety indication. If the judgment is no, that is, it is judged that the current operation estimated disturbance value does not fall within the current cabin estimated disturbance range, a current operation warning indication is generated, and the wind turbine maintenance personnel are guided to repair the cabin of the wind turbine according to the current operation warning indication.Compared with the warning method in the prior art that only analyzes whether a fault may occur based on the operating parameters themselves, the present application takes into account the potential risks caused by the interference of operation and external wind on the cabin. Usually, when the operating parameters are normal, some components in the cabin may have shifted or partially damaged. The situation where such components are not completely damaged will not affect normal operation. However, once the time is too long, it will lead to complete damage of the components, and then cause the unit to fail and shut down or cause other large losses. Therefore, the present application comprehensively considers the impact of external wind interference and vibration interference caused by normal operation on the cabin, and realizes monitoring and early warning of the cabin of each wind turbine in the wind farm based on external wind data, operating status data and actual detection data in the cabin, thereby realizing early warning of possible failures and reducing the failure rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a schematic diagram of a flow chart of a wind farm intelligent early warning method based on operation status monitoring in one embodiment;

[0077] Figure 2 The figure is a structural block diagram of a wind farm intelligent early warning system based on operation status monitoring in one embodiment. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0079] In one embodiment, a terminal is provided, which is used to: obtain current external wind data of a wind farm and current operating status data of wind turbines in the wind farm, and generate a current estimated disturbance range in the cabin according to the current external wind data and the current operating status data; obtain operating vibration detection data and operating noise detection data in the cabin of the wind turbine, and generate a current operating estimated disturbance value according to the operating vibration detection data and the operating noise detection data, wherein the current operating estimated disturbance value is a value used to indicate the influence of the operation of the wind turbine and external wind force on the cabin; determine whether the current operating estimated disturbance value falls within the current estimated disturbance range in the cabin; if not, generate a current operating warning indication, and guide wind turbine maintenance personnel to perform maintenance on the cabin of the wind turbine according to the current operating warning indication.

[0080] The terminal may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices.

[0081] In one embodiment, Figure 1 As shown, a wind farm intelligent early warning method based on operation status monitoring is provided, and the method comprises:

[0082] Step S100: obtaining current external wind power data of the wind farm and current operating status data of the wind turbines in the wind farm, and generating a current estimated disturbance range in the cabin according to the current external wind power data and the current operating status data;

[0083] Step S200: acquiring operation vibration detection data and operation noise detection data in the nacelle of the wind turbine generator set, and generating a current operation estimated disturbance value according to the operation vibration detection data and the operation noise detection data, wherein the current operation estimated disturbance value is a value used to represent the influence of the operation of the wind turbine generator set and the external wind force on the nacelle;

[0084] Step S300: determining whether the current operation estimated disturbance value is within the current cabin estimated disturbance range;

[0085] Step S400: If the judgment is no, a current operation warning indication is generated, and the wind turbine maintenance personnel are guided to perform maintenance on the nacelle of the wind turbine according to the current operation warning indication.

[0086] In this embodiment, in order to determine whether the wind force outside the wind farm and the operating data of the wind turbine will have a direct or indirect impact on the components in the nacelle of the wind turbine, the current external wind force data of the wind farm and the current operating status data of the wind turbine in the wind farm are first obtained. Under the condition that the wind turbine operates normally, the disturbance in the nacelle caused by a certain external wind force and the corresponding operating status will be within a normal range. This range can be used as a reference for determining whether it will affect the operation of various components in the nacelle of the wind turbine, that is, the current estimated disturbance range in the nacelle is generated according to the current external wind force data and the current operating status data. That is, the current estimated disturbance range in the cabin is generated according to the current external wind force data and the current operating status data, and then the operating vibration detection data and the operating noise detection data in the cabin of the wind turbine are obtained, and the current operating estimated disturbance value is generated according to the operating vibration detection data and the operating noise detection data, and the current operating estimated disturbance value is set to indicate the magnitude of the influence of the operation of the wind turbine and the external wind force in the cabin, and it is expressed by a specific numerical value to realize digital wind farm management, and then, it is determined whether the current operating estimated disturbance value belongs to the current estimated disturbance range in the cabin; if the judgment is yes, that is, it is determined that the current operating estimated disturbance value belongs to the current estimated disturbance range in the cabin, and no early warning is required at this time, therefore, a current operating safety indication is generated, and the wind turbine is controlled to continue to operate according to the operating safety indication. If the judgment is no, that is, it is judged that the current operation estimated disturbance value does not belong to the current cabin estimated disturbance range, then a current operation warning indication is generated, and the wind turbine maintenance personnel are guided to repair the cabin of the wind turbine according to the current operation warning indication. Compared with the warning method in the prior art that only analyzes whether a fault may occur based on the operating parameters themselves, the present application considers the potential risks that may be caused by the interference of operation and external wind force in the cabin. Usually, when the operating parameters are normal, some components in the cabin may have shifted or partially damaged. Such incomplete damage to the components will not affect normal operation, but once the time is too long, it will cause complete damage to the components, which will lead to the unit failure shutdown or other large losses. Therefore, the present application comprehensively considers the impact of external wind interference and vibration interference caused by normal operation on the cabin, and realizes the monitoring and warning of the cabins of each wind turbine in the wind farm based on external wind data, operating status data and actual detection data in the cabin, so as to realize early warning of possible faults and reduce the failure rate.

[0087] In one embodiment, the cabin includes a plurality of vibration detection areas, each of which includes a vibration detection point, and each of which includes a vibration detection sensor. The operation vibration detection data includes the sum of data detected by each activated vibration detection sensor, wherein after the activated vibration detection sensor performs vibration detection, operation vibration data of a detection point is correspondingly obtained;

[0088] The cabin also includes a noise detection area, in which a plurality of noise collection sensors are arranged, and the noise collection sensors are used for noise collection;

[0089] Step S200: acquiring operation vibration detection data and operation noise detection data in the nacelle of the wind turbine generator set, and generating a current operation estimated disturbance value according to the operation vibration detection data and the operation noise detection data, including:

[0090] Step S210: obtaining the current wind force level corresponding to the current external wind force data, and generating the number of detection point activations according to the current wind force level;

[0091] Step S220: selecting a detection point to be activated from each of the vibration detection points according to the number of detection point activations;

[0092] Step S230: controlling the vibration detection sensors corresponding to the detection points to be activated to start and obtaining the running vibration data of the detection points detected by the activated vibration detection sensors;

[0093] Step S240: generating an operation vibration interference value according to the operation vibration data of each detection point;

[0094] Step S250: calculating the distance between the noise collection sensor and the detection point to be activated according to each of the detection points to be activated, and setting the noise collection sensor to be activated, wherein the selected noise collection sensor is the noise sensor to be activated;

[0095] Step S260: starting the noise collection sensor to be activated and acquiring operation noise detection data;

[0096] Step S270: generating an operating noise interference value according to the operating noise detection data;

[0097] Step S280: Add the operating vibration disturbance value and the operating noise disturbance value to generate a current operating estimated disturbance value.

[0098] In this embodiment, in order to achieve all-round refined detection under the maximum interference condition, multiple vibration detection areas are set in the cabin, and each of the vibration detection areas is provided with a vibration detection point, and one of the vibration detection points is provided with a vibration detection sensor, and the operating vibration detection data includes the sum of the data detected by each vibration detection sensor after activation. Furthermore, in order to prevent the redundancy of detection data and save monitoring resources, and considering that when the external wind force is small or the operating power is low, the disturbance is small, so it is not necessary to start all sensors at this time, that is, only a part of the vibration detection sensors need to be activated. Therefore, it is set that after a vibration detection sensor is activated to perform vibration detection, the corresponding operating vibration data of a detection point is obtained. At the same time, by setting a noise detection area in the cabin, a plurality of noise collection sensors are provided in the noise detection area, and the noise collection sensor is used for noise collection. Next, the current wind force level corresponding to the current external wind force data is obtained, and the number of detection point activations is generated according to the current wind force level. Specifically, after the current wind force level is extracted from the current external wind force data, the current wind force level is compared with a pre-stored standard wind force level database, in which a plurality of wind force test groups are pre-stored, and each wind force test group includes a standard wind force level and a standard number of detection points. Therefore, the standard wind force level that is the same as the current wind force level is screened out, and the corresponding number of standard detection points is set as the number of detection point activations. For example, if the number of detection point activations is 3, then 3 vibration detection sensors need to be activated. Next, the detection point to be activated is selected from each of the vibration detection points according to the number of detection point activations, and the vibration detection sensors corresponding to each of the detection points to be activated are controlled to start and obtain the detection point operation vibration data detected by each of the vibration detection sensors after activation. Then, an operation vibration interference value is generated according to the operation vibration data of each detection point, and the operation vibration interference value is used to indicate the degree of influence of the vibration generated by the wind turbine during operation on the vibration detection area corresponding to each detection point to be activated in the cabin. Then, a noise monitoring point is set according to each detection point to be activated, so that noise detection is performed according to the vibration detection point, and the linkage monitoring of vibration and noise in the selected area is realized. Then, a noise acquisition sensor is selected from the noise detection area according to the noise monitoring point, and then the noise sensor to be started in the noise detection area is started and the operation noise detection data is obtained; and an operation noise interference value is generated according to the operation noise detection data, and the operation noise interference value is used to indicate the degree of influence of the noise generated by the wind turbine during operation on the noise detection area corresponding to each noise monitoring point in the cabin.Finally, the operating vibration interference value and the operating noise interference value are added together to generate the current operating estimated disturbance value, so as to consider the noise and vibration analysis in the cabin, and then determine whether a fault problem may occur, thereby achieving early warning.

[0099] Furthermore, the vibration sensor may be a microphone, a piezoelectric ceramic sheet, an acceleration sensor or other sensors capable of acquiring vibration signals. The noise collection sensor may be a microphone.

[0100] In one embodiment, step S220: selecting a detection point to be activated from each of the vibration detection points according to the number of detection point activations, comprises:

[0101] Step S221: acquiring historical maintenance data of each vibration detection area in the cabin according to the number of activated detection points;

[0102] Step S222: extracting the historical maintenance quantity of each vibration detection area from the historical maintenance data, arranging the historical maintenance quantity corresponding to each vibration detection area in descending order, and generating a maintenance number set, wherein one vibration detection area corresponds to one historical maintenance quantity;

[0103] Step S223: selecting vibration detection areas having the same number of activations as the detection points from the maintenance number set, and setting them as detection areas to be activated;

[0104] Step S224: selecting a corresponding detection point to be activated according to the detection area to be activated.

[0105] In this embodiment, in order to prevent the detection of more useless data when detecting data, it is necessary to select the detection point to be activated. When selecting, first obtain the historical maintenance data of each vibration detection area in the cabin according to the number of detection point activations. The historical maintenance data is the maintenance data left by each vibration detection area in the previous maintenance and early warning process. Then, extract the historical maintenance number of each vibration detection area from the historical maintenance data, arrange the historical maintenance number corresponding to each vibration detection area in descending order, and generate a maintenance number set. In the maintenance number set, the values ​​from the first historical maintenance number to the last historical maintenance number decrease in sequence, that is, the largest value is the first historical maintenance number. Then, filter out the vibration detection area with the same number of detection point activations from the maintenance number set, and set it as the detection area to be activated. For example, there are 5 historical maintenance numbers in the maintenance number set. When the number of detection point activations is 3, 3 are filtered out from the maintenance number set. Then, select the corresponding detection point to be activated according to the detection area to be activated. With such a configuration, when the wind force is at its maximum and the operation interference is at its maximum, all vibration detection sensors in the vibration detection area are controlled to perform detection. If the wind force is not at its maximum and the interference is not at its maximum, all vibration detection sensors do not need to work simultaneously. At this time, through steps S221 to S224, the detection area most prone to failure in the historical data is screened out. In this way, when all vibration detection sensors are not turned on, only the area most prone to failure is detected, which saves detection resources on the one hand and finds the failure point and predicts the trend with the highest probability on the other hand.

[0106] In one embodiment, step S240: generating an operation vibration interference value according to the operation vibration data of each detection point includes:

[0107] Step S241: calculating a detection point fluctuation value according to the detection point operation vibration data, wherein one detection point fluctuation value corresponds to one detection point operation vibration data;

[0108] Step S242: Add the fluctuation values ​​of each detection point to generate the operation vibration interference value.

[0109] In this embodiment, the operating vibration interference value is calculated using the following formula:

[0110]

[0111] Among them, Rd is the operating vibration interference value, αi is the vibration influence coefficient of the i-th vibration detection area, N is the number of detection point fluctuation values, and Fdi is the i-th detection point fluctuation value.

[0112] In this embodiment, the vibration detection area includes a first hub detection area, a second hub detection area, a spindle detection area, a gearbox detection area and a bulkhead detection area. The vibration detection sensor corresponding to the vibration detection point of the first hub detection area is set on the outer side of the hub; the vibration detection sensor corresponding to the vibration detection point of the second hub detection area is set on the hub and is located opposite to the vibration detection point of the first hub detection area; the vibration detection sensor corresponding to the vibration detection point of the spindle detection area is set on the outer side of the spindle; the vibration detection sensor corresponding to the vibration detection point of the gearbox detection area is set on the gearbox; the vibration detection sensor corresponding to the vibration detection point of the bulkhead detection area is set on the inner wall of the cabin. Obviously, different vibration detection areas are used to detect areas where different components are located. Specifically, the vibration influence coefficients corresponding to the first hub detection area, the second hub detection area, the spindle detection area, the gearbox detection area and the bulkhead detection area are α1-α5 respectively. The vibration influence coefficient is preset and set according to the components in the vibration detection area.

[0113] In one embodiment, calculating a detection point fluctuation value according to the running vibration data of the detection point includes:

[0114] Step S2411: Calculating the maximum vibration frequency, the minimum vibration frequency and the average vibration frequency according to the vibration data of the detection point operation;

[0115] Step S2412: generating a vibration frequency distribution curve according to the running vibration data of the detection point, and screening out an ascending frequency segment and a descending frequency segment from the vibration frequency distribution curve;

[0116] Step S2413: Calculate the slopes of the rising frequency segment and the falling frequency segment, and take the absolute value of each slope and record it as the fluctuation slope;

[0117] Step S2414: Generate the detection point fluctuation value based on the following formula according to the maximum vibration frequency, the minimum vibration frequency, the average vibration frequency and the fluctuation slope:

[0118]

[0119] Among them, Fd is the fluctuation value of the detection point, β1 is the first vibration coefficient, far is the average vibration frequency, fs is the pre-stored standard vibration frequency, β2 is the second vibration coefficient, fmax is the maximum vibration frequency, fmin is the minimum vibration frequency, β3 is the third vibration coefficient, n is the number of fluctuation slopes, Kj is the jth fluctuation slope, and Ksj is the pre-stored standard slope.

[0120] Furthermore, the calculation formula of the fluctuation value of each detection point is as follows:

[0121]

[0122] Among them, Fdi is the fluctuation value of the i-th detection point, β1 is the first vibration coefficient, fari is the average vibration frequency corresponding to the i-th detection point operating vibration data, fsi is the standard vibration frequency corresponding to the pre-stored i-th detection point operating vibration data, β2 is the second vibration coefficient, fmaxi is the maximum vibration frequency corresponding to the i-th detection point operating vibration data, fmini is the minimum vibration frequency corresponding to the i-th detection point operating vibration data, β3 is the third vibration coefficient, n is the number of fluctuation slopes, Kj is the j-th fluctuation slope, and Ksj is the standard slope corresponding to the i-th detection point operating vibration data.

[0123] The first vibration coefficient, the second vibration coefficient and the third vibration coefficient are all preset. The first vibration coefficient represents the proportion occupied by the average fluctuation, the second vibration coefficient represents the proportion occupied by the difference between the maximum and minimum frequency values, and the third vibration coefficient represents the proportion occupied by the frequency variation.

[0124] In one embodiment, step S250: calculating the distance between the noise collection sensor and the detection point to be activated according to each detection point to be activated, and setting the noise collection sensor to be activated, comprises:

[0125] Step S251: Calculate the distance between each noise collection sensor and the detection point to be activated, and generate a sensor spacing data group, wherein one noise collection sensor corresponds to one sensor spacing data group, each sensor spacing data group includes a plurality of actual detection point spacings, and the distance between one noise collection sensor and one detection point to be activated is one actual detection point spacing;

[0126] Step S252: calculating the sum of the distances between each noise collection sensor and each detection point to be activated according to each sensor spacing data group, and generating a total distance between detection points, wherein one noise collection sensor corresponds to one total distance between detection points;

[0127] Step S253: setting the noise collection sensor corresponding to the total distance between detection points with the smallest value as the noise collection sensor to be activated.

[0128] In this embodiment, the distance between each noise collection sensor and the detection point to be activated is calculated in sequence to realize the generation of the sensor spacing data group, so that one noise collection sensor corresponds to one sensor spacing data group, each of the sensor spacing data groups includes multiple actual detection point spacings, and the distance between one noise collection sensor and one detection point to be activated is one actual detection point spacing, and then the sum of the distances between each noise collection sensor and each detection point to be activated is calculated according to each sensor spacing data group, and the total detection point spacing is generated, wherein one noise collection sensor corresponds to one total detection point spacing, and finally the noise collection sensor corresponding to the total detection point spacing with the smallest value is set as the noise collection sensor to be activated. In this way, the noise collection sensor to be activated is not fixed, but is set according to the detection point to be activated, and the noise collection sensor to be activated has the smallest sum of distances from each detection point to be activated, that is, it is closest to each detection point to be activated, so that data loss caused by long distance can be avoided to the greatest extent. Due to cost considerations, it is extremely difficult to set up enough noise collection sensors. Therefore, through steps S251 to S253, the noise collection sensor closest to each detection point to be activated is set among a limited number of noise collection sensors, so as to maximize high-precision noise data collection and improve the accuracy of data warning.

[0129] In one embodiment, step S270: generating an operating noise interference value according to the operating noise detection data includes:

[0130] Step S271: generating a noise data frequency curve according to the operating noise detection data;

[0131] Step S272: obtaining the total length of the noise data frequency curve, and screening out abnormal frequency curves from the noise data frequency curve, wherein the abnormal frequency curve is a curve outside the normal frequency range of the noise data frequency curve;

[0132] Step S273: calculating the abnormal curve length of the abnormal frequency curve;

[0133] Step S274: Calculate the ratio of the abnormal curve length to the total curve length, and generate an operating noise interference value.

[0134] In this embodiment, the historical data collected by the noise collection sensor to be activated when each component corresponding to the detection point to be activated is working is extracted in advance, and the normal frequency data of the wind turbine when working normally is screened out based on the historical data, and the normal frequency data includes the normal frequency maximum value and the normal frequency minimum value, and the normal frequency maximum value and the normal frequency minimum value form a normal frequency range. Therefore, first, a noise data frequency curve is generated according to the operating noise detection data, and then the total curve length of the noise data frequency curve is obtained, and an abnormal frequency curve is screened out from the noise data frequency curve, wherein the abnormal frequency curve is a curve outside the normal frequency range in the noise data frequency curve. Next, the abnormal curve length of the abnormal frequency curve is calculated, and finally the ratio of the abnormal curve length to the total curve length is calculated, and the operating noise interference value is generated. It should be noted that,

[0135] On the other hand, when the operation vibration interference value and the operation noise interference value are added, different weight values ​​can be set for the operation vibration interference value and the operation noise interference value, respectively, so as to achieve custom settings. For example, the operation vibration interference value and the operation noise interference value correspond to the first weight and the second weight, respectively. When maintenance is required, for maintenance workers, the vibration impact is less than the noise impact. At this time, the weight corresponding to the operation noise interference value is set to be greater than the weight corresponding to the operation vibration interference value.

[0136] In one embodiment, step S100: obtaining current external wind data of the wind farm and current operating status data of the wind turbines in the wind farm, and generating a current estimated disturbance range in the cabin according to the current external wind data and the current operating status data, including:

[0137] Step S110: acquiring current external wind data of the wind farm, extracting a current wind force level from the current external wind force data, and acquiring a current wind disturbance coefficient according to the current wind force level;

[0138] Step S120: obtaining the usage time of the cabin, and generating a disturbance increase coefficient according to the usage time;

[0139] Step S130: obtaining current operating status data of wind turbines in the wind farm, comparing the current operating status data with preset normal operating status data, and screening out normal operating status data that matches the current operating status data;

[0140] Step S140: acquiring an initial disturbance range according to the normal operation status data;

[0141] Step S150: Generate the current estimated disturbance range in the cabin according to the current wind disturbance coefficient, the disturbance amplification coefficient and the initial disturbance range.

[0142] In this embodiment, the current external wind data of the wind farm is obtained, and the current wind level is extracted from the current external wind data. The current wind level is a value indicating the size of the wind outside the cabin. The larger the current wind level, the greater the wind. The wind speed and wind direction are detected by a preset wind monitoring sensor, and then the wind speed and wind direction are classified according to the detected wind speed and wind direction, so as to generate the current wind level. The current wind level is compared with the standard wind level database. The standard wind level database pre-stores a plurality of wind test groups. The standard wind level included in each wind test group also corresponds to a different standard wind disturbance coefficient. The standard wind disturbance coefficient corresponding to the standard wind level that is the same as the current wind level is set as the current wind disturbance coefficient. Then, the used time of the cabin is obtained, and a disturbance amplification coefficient is generated according to the used time. The used time is proportional to the disturbance amplification coefficient. The longer the used time is, the greater the disturbance amplification coefficient is. The disturbance amplification coefficient is obtained by dividing the used time by the rated total life of the cabin. Next, the current operating state data of the wind turbines in the wind farm is obtained, and the current operating state data is compared with the preset normal operating state data, and the normal operating state data matching the current operating state data is screened out. The normal operating state data is pre-stored, and the number is multiple.

[0143] Data on the working status of the wind turbine is collected in advance, and data on the corresponding vibration detection area and noise detection area in the cabin are collected at the same time, and a corresponding relationship between the operating data and the noise and vibration data in the cabin is established based on the collected data.

[0144] Generally speaking, for the operating state with the same parameters, the data collected in the vibration detection area and the noise detection area in the cabin are not exactly the same. Therefore, after the data collection is completed, the data is summarized and analyzed, and a range value is generated after the analysis is completed. For the same motion parameter, the vibration data and noise data in the cabin are a range value, that is, the standard disturbance range. The standard disturbance range can be understood as the impact of the vibration generated by the wind turbine during operation on the vibration in the cabin.

[0145] Furthermore, the standard disturbance range value may be generated in the same manner as the current operation estimated disturbance value.

[0146] Therefore, the normal operation state data matching the current operation state data are first screened out, and then the standard disturbance range corresponding to the screened normal operation state data is set as the initial disturbance range. Furthermore, considering the reduction of anti-interference ability caused by the current actual wind force and the increase of usage time, the disturbance coefficient according to the current wind force and the disturbance amplification coefficient are added, and then multiplied by the initial disturbance range to finally generate the current estimated disturbance range in the cabin.

[0147] Furthermore, the current operating status data includes but is not limited to real-time power output, cumulative power generation, maximum power point tracking status, impeller speed, generator speed and generator temperature.

[0148] In one embodiment, Figure 2 As shown, a wind farm intelligent early warning system based on operation status monitoring is also provided, and the system includes:

[0149] A status data analysis module, used to obtain the current external wind power data of the wind farm and the current operating status data of the wind turbines in the wind farm, and generate the current estimated disturbance range in the cabin according to the current external wind power data and the current operating status data;

[0150] an operation disturbance generating module, used for acquiring operation vibration detection data and operation noise detection data in the nacelle of the wind turbine generator set, and generating a current operation estimated disturbance value according to the operation vibration detection data and the operation noise detection data, wherein the current operation estimated disturbance value is a value used to represent the influence of the operation of the wind turbine generator set and the external wind force on the nacelle;

[0151] An operation disturbance judgment module is used to judge whether the current operation estimated disturbance value falls within the current cabin estimated disturbance range;

[0152] The operation warning generation module is used to generate a current operation warning indication if the judgment is no, and guide the wind turbine maintenance personnel to repair the nacelle of the wind turbine according to the current operation warning indication.

[0153] In one embodiment, the cabin includes a plurality of vibration detection areas, each of which includes a vibration detection point, and each of which includes a vibration detection sensor. The operation vibration detection data includes the sum of data detected by each activated vibration detection sensor, wherein after the activated vibration detection sensor performs vibration detection, operation vibration data of a detection point is correspondingly obtained;

[0154] The cabin also includes a noise detection area, in which a plurality of noise collection sensors are arranged, and the noise collection sensors are used for noise collection;

[0155] The operation disturbance generation module is also used for:

[0156] Obtain the current wind force level corresponding to the current external wind force data, and generate the number of detection point activations according to the current wind force level; select the detection point to be activated from each of the vibration detection points according to the number of detection point activations; control the vibration detection sensors corresponding to each of the detection points to be activated to start and obtain the operation vibration data of the detection points detected by each of the vibration detection sensors after activation; generate an operation vibration interference value according to the operation vibration data of each of the detection points; calculate the distance between the noise collection sensor and the detection point to be activated according to each of the detection points to be activated, and set the noise collection sensor to be activated, wherein the selected noise collection sensor is the noise sensor to be started; start the noise collection sensor to be activated and obtain the operation noise detection data; generate the operation noise interference value according to the operation noise detection data; add the operation vibration interference value and the operation noise interference value to generate the current operation estimated disturbance value.

[0157] In one embodiment, the operation disturbance generation module is further used to:

[0158] According to the number of activated detection points, historical maintenance data of each vibration detection area in the cabin is obtained; the historical maintenance number of each vibration detection area is extracted from the historical maintenance data, the historical maintenance number corresponding to each vibration detection area is arranged in descending order, and a maintenance number set is generated, wherein one vibration detection area corresponds to one historical maintenance number; vibration detection areas with the same number of activated detection points as the detection points are selected from the maintenance number set, and are set as detection areas to be activated; and corresponding detection points to be activated are selected according to the detection areas to be activated.

[0159] The operation disturbance generation module is also used to: calculate the detection point fluctuation value according to the detection point operation vibration data, wherein one detection point fluctuation value corresponds to one detection point operation vibration data; add the detection point fluctuation values ​​and generate the operation vibration disturbance value.

[0160] In one embodiment, the operation disturbance generating module is further used to calculate the operation vibration disturbance value by the following formula:

[0161]

[0162] Among them, Rd is the operating vibration interference value, αi is the vibration influence coefficient of the i-th vibration detection area, N is the number of detection point fluctuation values, and Fdi is the i-th detection point fluctuation value.

[0163] In one embodiment, the operation disturbance generation module is further used to: calculate the maximum vibration frequency, the minimum vibration frequency and the average vibration frequency according to the detection point operation vibration data; generate a vibration frequency distribution curve according to the detection point operation vibration data, and screen out the rising frequency segment and the falling frequency segment from the vibration frequency distribution curve; calculate the slope of the rising frequency segment and the falling frequency segment, take the absolute value of each slope and record it as the fluctuation slope; generate the detection point fluctuation value based on the following formula according to the maximum vibration frequency, the minimum vibration frequency, the average vibration frequency and the fluctuation slope:

[0164]

[0165] Among them, Fd is the fluctuation value of the detection point, β1 is the first vibration coefficient, far is the average vibration frequency, fs is the pre-stored standard vibration frequency, β2 is the second vibration coefficient, fmax is the maximum vibration frequency, fmin is the minimum vibration frequency, β3 is the third vibration coefficient, n is the number of fluctuation slopes, Kj is the jth fluctuation slope, and Ksj is the pre-stored standard slope.

[0166] In one embodiment, the operation disturbance generation module is further used to: calculate the distance between each of the noise collection sensors and the detection point to be activated, and generate a sensor spacing data group, wherein one of the noise collection sensors corresponds to one sensor spacing data group, each of the sensor spacing data group includes multiple actual detection point spacings, and the distance between one of the noise collection sensors and one of the detection points to be activated is one actual detection point spacing; calculate the sum of the distances between each of the noise collection sensors and each of the detection points to be activated according to each of the sensor spacing data groups, and generate a total detection point spacing, wherein one of the noise collection sensors corresponds to one total detection point spacing; and set the noise collection sensor corresponding to the detection point total spacing with the smallest value as the noise collection sensor to be activated.

[0167] In one embodiment, the operation disturbance generation module is further used to: generate a noise data frequency curve according to the operation noise detection data; obtain the total curve length of the noise data frequency curve, and screen out abnormal frequency curves from the noise data frequency curve, wherein the abnormal frequency curve is a curve outside the normal frequency range of the noise data frequency curve; calculate the abnormal curve length of the abnormal frequency curve; calculate the ratio of the abnormal curve length to the total curve length, and generate an operation noise interference value.

[0168] In one embodiment, the status data analysis module is also used to: obtain the current external wind data of the wind farm, extract the current wind force level from the current external wind data, and obtain the current wind disturbance coefficient according to the current wind force level; obtain the usage time of the cabin, and generate a disturbance increase coefficient according to the usage time; obtain the current operating status data of the wind turbines in the wind farm, and compare the current operating status data with preset normal operating status data, and filter out the normal operating status data that matches the current operating status data; obtain the initial disturbance range according to the normal operating status data; generate the current estimated disturbance range in the cabin according to the current wind disturbance coefficient, the disturbance increase coefficient and the initial disturbance range.

[0169] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned wind farm intelligent early warning method based on operation status monitoring when executing the computer program.

[0170] In one embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned wind farm intelligent early warning method based on operation status monitoring are implemented.

[0171] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0172] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0173] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. An intelligent early warning method for a wind farm based on operation status monitoring, characterized in that: The method comprises: Acquire current external wind power data of the wind farm and current operating status data of the wind turbines in the wind farm, and generate a current estimated disturbance range in the cabin according to the current external wind power data and the current operating status data; Acquire operation vibration detection data and operation noise detection data in the nacelle of the wind turbine generator set, and generate a current operation estimated disturbance value according to the operation vibration detection data and the operation noise detection data, wherein the current operation estimated disturbance value is a value used to indicate the influence of the operation of the wind turbine generator set and external wind force on the nacelle; Determining whether the current operation estimated disturbance value falls within the current cabin estimated disturbance range; If the judgment is no, a current operation warning indication is generated, and the wind turbine maintenance personnel are guided to perform maintenance on the nacelle of the wind turbine according to the current operation warning indication.

2. The wind farm intelligent early warning method based on operation status monitoring according to claim 1 is characterized in that: The cabin includes a plurality of vibration detection areas, each of which includes a vibration detection point, and each of which includes a vibration detection sensor. The operation vibration detection data includes the sum of data detected by each vibration detection sensor after activation, wherein after vibration detection by an activated vibration detection sensor, operation vibration data of a detection point is obtained correspondingly; The cabin also includes a noise detection area, in which a plurality of noise collection sensors are arranged, and the noise collection sensors are used for noise collection; Acquiring operation vibration detection data and operation noise detection data in the nacelle of the wind turbine generator set, and generating a current operation estimated disturbance value according to the operation vibration detection data and the operation noise detection data, including: Acquire a current wind force level corresponding to the current external wind force data, and generate a detection point activation quantity according to the current wind force level; Selecting a detection point to be activated from each of the vibration detection points according to the number of detection point activations; Controlling the vibration detection sensors corresponding to the detection points to be activated to start and obtaining the running vibration data of the detection points detected by the activated vibration detection sensors; Generate an operation vibration interference value according to the operation vibration data of each detection point; Calculating the distance between the noise collection sensor and the detection point to be activated according to each of the detection points to be activated, and setting the noise collection sensor to be activated, wherein the selected noise collection sensor is the noise sensor to be activated; Starting the noise collection sensor to be activated and acquiring operating noise detection data; generating an operating noise interference value according to the operating noise detection data; The operation vibration disturbance value and the operation noise disturbance value are added together to generate a current operation estimated disturbance value.

3. The wind farm intelligent early warning method based on operation status monitoring according to claim 2 is characterized in that: Generating an operating vibration interference value according to the operating vibration data of each detection point includes: Calculating a detection point fluctuation value according to the detection point operation vibration data, wherein one detection point fluctuation value corresponds to one detection point operation vibration data; The fluctuation values ​​of each detection point are added together to generate the operation vibration interference value.

4. The wind farm intelligent early warning method based on operation status monitoring according to claim 3 is characterized in that: Calculating a detection point fluctuation value according to the running vibration data of the detection point includes: Calculate the maximum vibration frequency, the minimum vibration frequency and the average vibration frequency according to the vibration data of the detection point operation; Generating a vibration frequency distribution curve according to the vibration data of the detection point operation, and screening out an increasing frequency segment and a decreasing frequency segment from the vibration frequency distribution curve; Calculate the slopes of the rising frequency segment and the falling frequency segment, take the absolute value of each slope and record it as the fluctuation slope; The detection point fluctuation value is generated according to the maximum vibration frequency, the minimum vibration frequency, the average vibration frequency and the fluctuation slope.

5. The wind farm intelligent early warning method based on operation status monitoring according to claim 4 is characterized in that: Generating an operating noise interference value according to the operating noise detection data includes: generating a noise data frequency curve according to the operating noise detection data; Acquiring a total curve length of the noise data frequency curve, and screening out an abnormal frequency curve from the noise data frequency curve, wherein the abnormal frequency curve is a curve outside a normal frequency range in the noise data frequency curve; Calculating the abnormal curve length of the abnormal frequency curve; The ratio of the abnormal curve length to the total curve length is calculated, and an operation noise interference value is generated.

6. The wind farm intelligent early warning method based on operation status monitoring according to claim 1 is characterized in that: Acquiring current external wind power data of the wind farm and current operating status data of the wind turbines in the wind farm, and generating a current estimated disturbance range in the cabin according to the current external wind power data and the current operating status data, including: Acquire current external wind data of the wind farm, extract current wind force level from the current external wind data, and acquire current wind disturbance coefficient according to the current wind force level; Acquiring the usage time of the cabin, and generating a disturbance increase coefficient according to the usage time; Acquire current operating status data of wind turbines in the wind farm, compare the current operating status data with preset normal operating status data, and filter out normal operating status data that matches the current operating status data; Acquiring an initial disturbance range according to the conventional operating state data; The current estimated disturbance range in the cabin is generated according to the current wind disturbance coefficient, disturbance amplification coefficient and initial disturbance range.

7. An intelligent early warning system for wind farms based on operation status monitoring, characterized in that: The system comprises: A status data analysis module, used to obtain the current external wind power data of the wind farm and the current operating status data of the wind turbines in the wind farm, and generate the current estimated disturbance range in the cabin according to the current external wind power data and the current operating status data; an operation disturbance generating module, used for acquiring operation vibration detection data and operation noise detection data in the nacelle of the wind turbine generator set, and generating a current operation estimated disturbance value according to the operation vibration detection data and the operation noise detection data, wherein the current operation estimated disturbance value is a value used to represent the influence of the operation of the wind turbine generator set and the external wind force on the nacelle; An operation disturbance judgment module is used to judge whether the current operation estimated disturbance value falls within the current cabin estimated disturbance range; The operation warning generation module is used to generate a current operation warning indication if the judgment is no, and guide the wind turbine maintenance personnel to repair the nacelle of the wind turbine according to the current operation warning indication.

8. The wind farm intelligent early warning system based on operation status monitoring according to claim 7 is characterized in that: The cabin includes a plurality of vibration detection areas, each of which includes a vibration detection point, and each of which includes a vibration detection sensor. The operation vibration detection data includes the sum of data detected by each vibration detection sensor after activation, wherein after vibration detection by an activated vibration detection sensor, operation vibration data of a detection point is obtained correspondingly; The cabin also includes a noise detection area, in which a plurality of noise collection sensors are arranged, and the noise collection sensors are used for noise collection; The operation disturbance generation module is also used for: Obtain the current wind force level corresponding to the current external wind force data, and generate the number of detection point activations according to the current wind force level; select the detection point to be activated from each of the vibration detection points according to the number of detection point activations; control the vibration detection sensors corresponding to each of the detection points to be activated to start and obtain the operation vibration data of the detection points detected by each of the vibration detection sensors after activation; generate an operation vibration interference value according to the operation vibration data of each of the detection points; calculate the distance between the noise collection sensor and the detection point to be activated according to each of the detection points to be activated, and set the noise collection sensor to be activated, wherein the selected noise collection sensor is the noise sensor to be started; start the noise collection sensor to be activated and obtain the operation noise detection data; generate the operation noise interference value according to the operation noise detection data; add the operation vibration interference value and the operation noise interference value to generate the current operation estimated disturbance value.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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