Method and device for identifying abnormalities in starting and stopping of wind turbine generator sets

Abstract

This disclosure provides a method and apparatus for identifying start-up and shutdown anomalies in wind turbine generator sets. The method includes: acquiring operating data of the wind turbine generator set within a preset time period; determining the number of start-up and shutdown operations of the wind turbine generator set within each preset time interval of the preset time period based on the operating data, wherein the number of start-up and shutdown operations within each preset time interval represents the sum of the number of start-up actions and the number of shutdown actions of the wind turbine generator set within the corresponding preset time interval; and identifying start-up and shutdown anomalies of the wind turbine generator set based on the number of start-up and shutdown operations.

Description

Technical Field

[0001] The present disclosure relates to the field of wind power generation, and in particular to a method and device for identifying abnormalities in starting and stopping of a wind power generator set. Background Art

[0002] When a wind turbine needs to shut down due to a fault or external working conditions, the wind turbine will initiate a shutdown strategy and switch the wind turbine from the operating state (i.e., power generation state) to the shutdown state. When the fault is eliminated or the external working conditions meet the starting requirements, the wind turbine will initiate a starting strategy and switch the wind turbine from the shutdown state or standby state to the operating state. Each time the wind turbine is started and shut down, the main control system, pitch system, electrical system and other systems of the wind turbine need to be in different opening and closing states to cooperate with each other. However, if the number of starts and stops is too frequent, it will lead to various adverse effects, such as reduced power generation performance of the wind turbine, increased load on the entire machine, and reduced life of switchgear and electrical equipment.

[0003] The reasons that usually lead to frequent starts and stops of wind turbines in a short period of time include the following four aspects: First, when the average wind speed is low, due to unstable wind conditions, when the wind speed changes from low to high, it can blow the wind turbine and connect it to the grid for power generation. However, if the turbulence is large in the short term and the wind speed decreases rapidly, the wind turbine will not have enough wind energy to support its power generation, so it will be cut out of the grid again and go into shutdown state, which will cause frequent starts and stops; second, due to a fault in the wind turbine itself, it will be shut down and restarted. If the fault cannot be repaired, there will be a problem of repeated starts and stops in a short period of time; Third, when the test personnel conduct special tests on site, there are frequent starts and stops caused by human start and stop; Fourth, when the average wind speed is high, in order to avoid excessive load on the wind turbine, when the wind speed or pitch angle (for example, pitch angle) exceeds a certain threshold, the shutdown operation will be initiated, causing the wind turbine to be disconnected from the grid again and enter a shutdown state, but the wind turbine will continue to determine whether the start command is met at this time. If the wind speed is still high and can blow the wind turbine, the wind turbine will be reconnected to the grid for power generation, and then it will be disconnected again due to excessive wind speed, resulting in frequent starts and shutdowns.

[0004] There are existing technical means to identify the second and third situations. For example, in the second situation, the wind turbine will automatically generate a fault file and notify the on-site personnel to deal with the fault problem; the frequent start and stop in the third situation is a manual operation, so it is not a problem of the wind turbine itself and does not need to be dealt with. However, for different wind speed conditions such as the first and fourth situations, it is difficult to effectively identify whether the wind turbine has abnormal start and stop, and it is difficult to effectively avoid frequent start and stop. Summary of the invention

[0005] The purpose of the embodiments of the present disclosure is to provide a method and device for identifying abnormal starts and shutdowns of a wind turbine generator set, so as to avoid too frequent starts and shutdowns and the adverse effects caused by them, and to achieve at least one of the following technical effects: improving the power generation performance of the wind turbine generator set, reducing the overall load of the wind turbine generator set, reducing the operating risks of the overall machine and subsystems of the wind turbine generator set, extending the service life of various components of the wind turbine generator set, and reducing the maintenance cost of the wind turbine generator set.

[0006] According to an embodiment of the present disclosure, a method for identifying start-stop anomalies of a wind generator set is provided, the method comprising: acquiring operating data of the wind generator set within a preset time period; determining the number of starts and stops of the wind generator set within each preset time interval in the preset time period based on the operating data, wherein the number of starts and stops within each preset time interval represents the sum of the number of start actions and the number of shutdown actions of the wind generator set within the corresponding preset time interval; identifying the start-stop anomalies of the wind generator set based on the start-stop numbers.

[0007] According to an embodiment of the present disclosure, a computer-readable storage medium storing a computer program is provided. When the computer program is executed by a processor, the method for identifying abnormalities during start-stopping as described above is implemented.

[0008] According to an embodiment of the present disclosure, a computing device is provided, the computing device comprising: a processor; a memory storing a computer program, and when the computer program is executed by the processor, the start-stop abnormality identification method as described above is implemented.

[0009] According to an embodiment of the present disclosure, a device for identifying start-stop abnormalities of a wind generator set is provided, and the device comprises: a data acquisition unit, configured to acquire operating data of the wind generator set within a preset time period; a data processing unit, configured to determine the number of starts and stops of the wind generator set within each preset time interval in the preset time period according to the operating data, wherein the number of starts and stops within each preset time interval represents the sum of the number of start actions and the number of shutdown actions of the wind generator set within the corresponding preset time interval; and a start-stop abnormality identification unit, configured to identify the start-stop abnormalities of the wind generator set according to the number of starts and stops.

[0010] By using the wind turbine start-stop abnormality identification method and device, a computer-readable storage medium storing a computer program, and a computing device according to the embodiments of the present disclosure, at least one of the following technical effects can be achieved: the distribution of the start-stop times of multiple wind turbines relative to the average wind speed can be intuitively observed, and the start-stop abnormality points can be easily identified, and any information related to the start-stop abnormality points can be obtained; the start-stop abnormalities of the wind turbine can be effectively identified, and the control parameters of the wind turbine corresponding to the start-stop abnormality points can be adjusted in a targeted manner, thereby effectively avoiding the start-stop abnormalities of the wind turbine and reducing the adverse effects caused by the start-stop abnormalities; the power generation performance of the wind turbine is improved, the whole machine load of the wind turbine is reduced, the operation risk of the whole machine and subsystems of the wind turbine is reduced, the service life of each component of the wind turbine is extended, and the maintenance cost of the wind turbine is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other objects and features of the present disclosure will become more apparent from the following description in conjunction with the accompanying drawings.

[0012] Figure 1 is a flow chart of a method for identifying abnormalities in starting and stopping a wind turbine generator set according to an embodiment of the present disclosure;

[0013] Figure 2 is an operational flow chart of preprocessing operation data according to an embodiment of the present disclosure;

[0014] Figure 3 A flow chart is shown for determining the number of starts and stops of a wind turbine generator set within each preset time interval in a preset time period;

[0015] Figure 4 is an operational flow chart for determining average wind speed according to an embodiment of the present disclosure;

[0016] Figure 5 is a distribution diagram of the number of starts and stops of multiple wind turbine generator sets relative to the average wind speed according to an embodiment of the present disclosure;

[0017] Figure 6 is a distribution diagram of the start and stop times of multiple wind turbine generator sets relative to the average wind speed according to another embodiment of the present disclosure;

[0018] Figure 7 and Figure 8 is a schematic diagram of the operation data of a specific wind turbine generator set and the change of the ambient wind speed over time in a specific time period according to an embodiment of the present disclosure;

[0019] Fig. 9 A block diagram of a start-stop abnormality identification device according to an embodiment of the present disclosure is shown;

[0020] Fig.10 is a schematic diagram of a computing device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0021] In order to effectively identify whether a wind turbine generator set has frequent start and stop abnormalities under different wind speed conditions, the start and stop principles of low wind speed conditions and high wind speed conditions should be understood. In the present disclosure, the low wind speed condition may include a condition where the wind speed is lower than the wind speed when the wind turbine generator set is generating electricity normally or the number of start and stop times of the wind turbine generator set is small, and the high wind speed condition may include a condition where the wind speed is higher than the wind speed when the wind turbine generator set is generating electricity normally or the number of start and stop times of the wind turbine generator set is small.

[0022] The start-stop logic principle under different wind speed conditions is: when the wind turbine generator set is in standby state, the control system of the wind turbine generator set checks whether each subsystem (including the converter subsystem, pitch subsystem, communication subsystem, etc.) is operating normally. If each subsystem does not feedback abnormality, the control system issues instructions to turn on the converter subsystem, the water cooling subsystem and the pitch subsystem. The controller in the control system monitors in real time whether the generator speed reaches the generator speed start threshold, and the generator speed remains higher than the generator speed start threshold for a certain period of time. Only after the controller monitors that the above conditions are met, the wind turbine generator set will execute the starting action and enter the starting process. For example, the pitch subsystem executes the pitch action, enters the power generation state, connects the converter to the grid, and executes the power generation instruction issued by the controller. While generating electricity, the controller continuously monitors the generated power, generator speed, wind speed and pitch angle (i.e., pitch angle), until the generated power is too small (e.g., less than the generated power threshold), and the generator speed is too small (e.g., lower than the generator speed stop threshold) and lasts for a predetermined time (e.g., the duration of the generator speed remaining lower than the generator speed stop threshold reaches a predetermined time), the controller will cause the wind turbine generator set to perform a shutdown action, for example, the pitch subsystem performs a pitch action to cut the converter out of the grid; or, until the average wind speed is too large (the average wind speed is greater than the maximum wind speed threshold) and the pitch angle is greater than the maximum angle threshold, the controller will cause the wind turbine generator set to perform a shutdown action, for example, the pitch subsystem performs a pitch action to cut the converter out of the grid. When the generator stops completely and the pitch angle reaches the stop angle threshold, the wind turbine generator set enters a standby state. In this way, a start and stop process of the wind turbine generator set under different wind speed conditions is completed, and a start action and a stop action are completed respectively, that is, the number of starts and stops is two (the sum of one start and one stop).

[0023] For multiple wind turbines in the entire wind farm, due to the influence of factors such as different working parameters related to start and shutdown and different unit states, the number of starts and stops of multiple wind turbines and the interval time between each start and shutdown are also different. Usually, the number of starts and stops of a wind turbine in a year will reach about one thousand times, and some wind turbines even reach thousands of times. And because the vast majority of wind turbine start and shutdown operations are normal operations (that is, the number of starts and stops does not exceed the start and shutdown number threshold), therefore, for the operating conditions of hundreds of wind turbines in a wind farm or even thousands of wind turbines in several wind farms, it is difficult to identify wind turbines with abnormal start and shutdown.

[0024] In order to identify the abnormal start and shutdown of wind turbines under different wind speed conditions and overcome the adverse effects caused by frequent start and shutdown, the present invention proposes a method and device for identifying abnormal start and shutdown of wind turbines. According to the method and device for identifying abnormal start and shutdown disclosed in the present invention, the abnormal start and shutdown of wind turbines can be evaluated offline or online through innovative analysis and recognition algorithms, and the frequent abnormal start and shutdown caused by low wind speed and / or high wind speed conditions can be screened out. The number of frequent start and shutdown can be reduced through strategy optimization, and at least one of the following technical effects can be achieved: avoiding too frequent start and shutdown times and the adverse effects caused by them, improving the power generation performance of wind turbines, reducing the whole machine load of wind turbines, reducing the operation risks of the whole machine and subsystems of wind turbines, extending the service life of various components of wind turbines, and reducing the maintenance cost of wind turbines.

[0025] Figure 1 1 is a flow chart of a method for identifying abnormal start and stop of a wind turbine generator set according to an embodiment of the present disclosure. In an embodiment of the present disclosure, the wind turbine generator set may include one or more wind turbine generator sets. The wind turbine generator set may be all or part of the wind turbine generator sets on a single wind farm or multiple wind farms.

[0026] In step S11, the operation data of the wind turbine generator set within a preset time period is obtained. The preset time period can be a period of time that has passed, a period of time in the future, etc., for example, a period of time that has passed since the current time (for example, within the past year), a period of time that will pass since the current time (for example, within the next year). Among them, the period of time that has passed can be used for offline identification of start-up and shutdown anomalies, and the period of time in the future can be used for online identification of start-up and shutdown anomalies.

[0027] According to an embodiment of the present disclosure, the operation data includes operation status data of the wind turbine generator set, and the operation status data indicates the operation status of the wind turbine generator set. The operation status may include a starting state, a standby state, a power generation state, and a shutdown state. Switching from the standby state to the starting state indicates a starting action, and switching from the power generation state to the shutdown state indicates a shutdown action. In addition, the operation data may also include the number of the wind turbine generator set and / or the operation parameters of the wind turbine generator set.

[0028] In an embodiment of the present disclosure, the operating status data may be an operating status word that uses different numbers to represent the operating status of the wind turbine generator set. For example, operating status word 1 represents the shutdown state, operating status word 2 represents the standby state, operating status word 3 represents the starting state, and operating status word 4 represents the operating state, i.e., the power generation state.

[0029] In step S12, the number of starts and stops of the wind turbine generator set in each preset time interval in the preset time period is determined based on the operating data, wherein the number of starts and stops in each preset time interval represents the sum of the number of start actions and the number of shutdown actions of the wind turbine generator set in the corresponding preset time interval.

[0030] According to an embodiment of the present disclosure, the preset time period is evenly divided into a plurality of preset time intervals, and each preset time interval corresponds to the number of starts and stops. For example, the preset time period is last year (i.e., 8760 hours), and the preset time interval is 2 hours, that is, the preset time period may include 4380 preset time intervals, and within the last year, the number of starts and stops within each 2 hours represents the sum of the number of starts and stops of the wind turbine generator set within the corresponding 2 hours.

[0031] According to an embodiment of the present disclosure, step S12 may include sorting the operating status data in chronological order; grouping the sorted operating status data according to each preset time interval to generate preprocessed operating status data; and determining the number of starts and stops based on the change in the operating status indicated by the preprocessed operating status data.

[0032] In step S13, the abnormal start and stop of the wind turbine generator set is identified according to the start and stop times. According to the embodiment of the present disclosure, the abnormal start and stop indicates that the start and stop times of the wind turbine generator set are too many. Figures 5 to 8 How to identify abnormal start and shutdown of a wind turbine generator set is further described.

[0033] Figure 2 is an operational flow chart of preprocessing operation data according to an embodiment of the present disclosure.

[0034] In step S21, the number of the wind turbine generator set and the operating status data within a preset time period are extracted from the operating data. According to an embodiment of the present disclosure, the wind turbine generator set may include one or more wind turbine generator sets, and the operating data may include the number of the wind turbine generator set and the operating status data of each wind turbine generator set within a preset time period. Accordingly, the number of one or more wind turbine generator sets and the operating status data of each wind turbine generator set within a preset time period may be extracted from the operating data.

[0035] In step S22, for each wind turbine generator set, the operating status data is sorted in chronological order. According to an embodiment of the present disclosure, based on the number of each wind turbine generator set, the operating status data corresponding to the number can be sorted in chronological order. By sorting the operating status data in chronological order, it is convenient to determine the number of starts and stops of each wind turbine generator set in each preset time interval in the preset time period.

[0036] In step S23, for each wind turbine generator set, the sorted operating status data is grouped according to each preset time interval. Thus, the number of starts and stops of each wind turbine generator set in each preset time interval in the preset time period can be further determined. In the embodiment of the present disclosure, the operating status data of each wind turbine generator set is grouped into m operating status data groups according to each preset time interval, recorded as [L1...L(i-1), Li, L(i+1)...Lm], wherein 1 to m respectively represent the data group number of each group of data in the operating status data group, i and m are both natural numbers, m represents the maximum value of the group number and is equal to the length of the preset time period divided by the value of the preset time interval, 1≤i≤m. For example, the preset time period is last year (i.e., 8760 hours), and the preset time interval is 2 hours, then m=4380.

[0037] Each set of data may include one or more data points. For example, the data points included in each set of data may be recorded as n, where n is a natural number greater than or equal to 1. For example, the i-th set of operating status data is [S1…S(j-1), Sj, S(j+1)…Sn], where 1 to n represent the serial numbers of the data points, 1≤j≤n, and j and n are both natural numbers. In the embodiments of the present disclosure, S1 to Sn represent the operating states corresponding to the data points, respectively. For example, Sj=1 represents the shutdown state, Sj=2 represents the standby state, Sj=3 represents the starting state, and Sj=4 represents the operating state, i.e., the power generation state. For example, the change in the operating state can be analyzed starting from the first data point.

[0038] In the embodiment of the present disclosure, the number of data points in each group of data is related to the sampling frequency when acquiring the operating status data. For example, if the sampling frequency is 1 Hz, each group of data grouped according to the above embodiment includes 7200 data points, that is, n=7200.

[0039] The following will be combined Figure 3 The present invention describes a process of determining the number of starts and stops of a wind turbine generator set within each preset time interval in a preset time period.

[0040] After grouping the sorted running status data according to each preset time interval, you can start executing Figure 3 The operation flow shown in the figure can be determined starting from the first set of data L1 (ie, data set number i=1).

[0041] like Figure 3 As shown, in step S31, it is determined whether the data group number i is less than the maximum group number m. If not, step S32 is executed; if yes, step S39 is executed.

[0042] In step S32, the i-th group of operating status data [S1...S(j-1), Sj, S(j+1)...Sn] is obtained.

[0043] In step S33, it is determined whether the sequence number j of the data point is less than n. If so, step S34 is executed; otherwise, step S38 is executed.

[0044] In step S34, it is determined whether S(j-1) to Sj indicate switching from the standby state to the start-up state. For example, it can be determined whether S(j-1) to Sj is changing from S(j-1)=2 to Sj=3. If so, step S36 is executed; otherwise, step S35 is executed.

[0045] In step S35, it is determined whether S(j-1) to Sj indicate switching from the power generation state to the shutdown state. For example, it can be determined whether S(j-1) to Sj change from S(j-1)=4 to Sj=1. If so, step S36 is executed; otherwise, step S37 is executed.

[0046] In step S36, the number of starts and stops Ki of the wind turbine generator set may be counted. As above, if it is determined that S(j-1) to Sj indicates switching from the standby state to the starting state or S(j-1) to Sj indicates switching from the power generation state to the shutdown state, the number of starts and stops Ki is incremented, that is, Ki=Ki+1, otherwise the number of starts and stops Ki remains unchanged. Step S37 may be executed after step S36.

[0047] In step S37, the sequence number j of the data point is incremented (i.e., j=j+1), so that the change of the running state can be analyzed for the next data point. In step S38, the data group number i is incremented, i.e., i=i+1, so that the change of the running state can be analyzed for the next set of running state data.

[0048] After analyzing the changes of all the operation status data, step S39 may be executed to output the number of starts and stops of the wind turbine generator set in each preset time interval in the preset time period [K1…Ki…Km]. For example, the number of starts and stops of the wind turbine generator set in each preset time interval in the preset time period [K1…Ki…Km] may be stored and output in the form of a list. The list may also include the number of the wind turbine generator set.

[0049] Reference Figure 2 and Figure 3 The described operation flow may also be referred to as data screening, through which data dimensionality reduction processing may be achieved, for example, determining the number of starts and stops of a wind turbine generator set within each preset time interval in a preset time period. The above operation may be performed for all or part of the wind turbine generator sets on a single wind farm or multiple wind farms.

[0050] Figure 4 is an operational flow chart of determining average wind speed according to an embodiment of the present disclosure.

[0051] The start-stop abnormality identification method according to an embodiment of the present disclosure may further include: obtaining the ambient wind speed of the wind turbine generator set within a preset time period (S41); and determining the average wind speed within each preset time interval according to the ambient wind speed (S42).

[0052] In step S41, the ambient wind speed of the wind turbine generator set within a preset time period may be obtained by a wind measuring device such as a wind measuring radar. In an embodiment of the present disclosure, the ambient wind speed of the wind turbine generator set within the preset time period may be included in the operation data of the wind turbine generator set within the preset time period. Therefore, the ambient wind speed may be extracted from the operation data.

[0053] In step S42, the Figure 2 The data grouping method shown is used to group the ambient wind speed. For example, the ambient wind speed can be sorted in chronological order, and the sorted ambient wind speed corresponds to the sorted operating status data in chronological order. Then, the sorted ambient wind speed is grouped according to each preset time interval to generate a preprocessed ambient wind speed. In an embodiment of the present disclosure, the ambient wind speed of each wind turbine generator set is grouped into m ambient wind speed groups according to each preset time interval, denoted as [V1…V(i-1), Vi, V(i+1)…Vm], wherein 1 to m represent the data group number of each group of data in the ambient wind speed data group, respectively, i and m are both natural numbers, m represents the maximum value of the group number and is equal to the length of the preset time period divided by the value of the preset time interval, 1≤i≤m. The m ambient wind speed groups correspond to the m operating status data groups described above.

[0054] Then, the average wind speed within each preset time interval can be determined based on the preprocessed ambient wind speed. For example, each group of ambient wind speeds in the ambient wind speed group (e.g., each of V1 to Vm) can be averaged to generate a corresponding average wind speed. For example, the average wind speed of the i-th group is determined to be Vi' based on the i-th group of ambient wind speeds Vi. Therefore, the corresponding average wind speed group [V1'...V(i-1)',Vi',V(i+1)'...Vm'] is determined based on the m ambient wind speed groups [V1...V(i-1),Vi,V(i+1)...Vm].

[0055] According to an embodiment of the present disclosure, the average wind speed group [V1'...V(i-1)', Vi', V(i+1)'...Vm'] can be stored and output in the form of a list. Optionally, the average wind speed group [V1'...V(i-1)', Vi', V(i+1)'...Vm'] can be stored and output together with the number of starts and stops of the wind turbine generator set in each preset time interval in a preset time period [K1...Ki...Km].

[0056] According to an embodiment of the present disclosure, the start-stop abnormality of the wind turbine generator set can be identified according to the start-stop number of the wind turbine generator set in each preset time interval in the preset time period. The process of identifying the start-stop abnormality may include displaying a distribution diagram of the start-stop number of the wind turbine generator set in each preset time interval in the preset time period relative to the average wind speed. For example, a visual chart may be used to display the distribution diagram.

[0057] In an embodiment of the present disclosure, the wind turbine generator set may include a plurality of wind turbine generator sets, and the distribution graph may include a distribution graph of the start and stop times of the plurality of wind turbine generator sets relative to an average wind speed.

[0058] Figure 5 is a distribution diagram of the start and shutdown times of multiple wind turbine generator sets relative to the average wind speed according to an embodiment of the present disclosure.

[0059] like Figure 5 As shown, symbols of different shapes represent the number of starts and stops of wind turbines with different unit numbers within each preset time interval in a preset time period. Two wind turbines (unit numbers are 140605044 and 140605065) are used as an example for illustration, but the present invention is not limited thereto, and a distribution diagram of the number of starts and stops of more or one wind turbine relative to the average wind speed may also be displayed, for example, a distribution diagram of the number of starts and stops of all or part of the wind turbines on a single wind farm or multiple wind farms relative to the average wind speed may be displayed.

[0060] According to an embodiment of the present disclosure, a start-stop number threshold can be set according to the overall performance of the wind turbine generator set to identify the start-stop number greater than the start-stop number threshold as a start-stop abnormal point. Identifying the start-stop abnormality of the wind turbine generator set according to the start-stop number can also include: comparing the start-stop number with the start-stop number threshold; identifying the start-stop number greater than the start-stop number threshold as a start-stop abnormal point.

[0061] exist Figure 5 In the embodiment shown, the start-stop number threshold is set to 4. The start-stop number less than the start-stop number threshold can be identified as a normal start-stop point. The start-stop number greater than the start-stop number threshold can be identified as an abnormal start-stop point. Figure 5 As shown, the vast majority of start and stop times are less than or equal to the start and stop times threshold, which are normal start and stop points. However, the start and stop times in the dotted box are greater than the start and stop times threshold, and are therefore identified as abnormal start and stop points. The abnormal start and stop points are mainly distributed in wind speed sections with relatively low average wind speeds (e.g., less than 4 m / s) and relatively high average wind speeds (e.g., greater than 15 m / s). Wind conditions with relatively low average wind speeds can be called low wind speed conditions, and wind conditions with relatively high average wind speeds can be called high wind speed conditions.

[0062] According to another embodiment of the present disclosure, identifying start-up and shutdown anomalies of a wind turbine generator set based on the number of start-up and shutdown times may also include: based on a distribution diagram of the number of start-up and shutdown times of multiple wind turbine generator sets relative to the average wind speed, identifying at least one of the number of start-up and shutdown times, the average wind speed, the specific wind turbine generator set and the specific time interval corresponding to the start-up and shutdown abnormality point.

[0063] For example, based on Figure 5 The displayed distribution diagram further identifies at least one of the number of starts and shutdowns, the average wind speed, the specific wind turbine generator set and the specific time interval corresponding to the abnormal start and shutdown point. Figure 6 4 is a distribution diagram of the start and stop times of multiple wind turbine generator sets relative to the average wind speed according to another embodiment of the present disclosure.

[0064] like Figure 6 As shown, a start-stop abnormal point can be selected in the distribution diagram of the start-stop times of multiple wind turbines relative to the average wind speed, so that the start-stop times count (count = 5.000), the average wind speed wind_mean (wind_mean = 3.54 m / s), the unit number wtid (wtid = 140605065) of the specific wind turbine and the starting time ts_start (ts_start = 2018-02-20 10:00:00) of the specific time interval corresponding to the selected start-stop abnormal point can be further displayed in the distribution diagram. Figure 6The information further displayed in the figure can identify the number of starts and shutdowns (e.g., 5), the average wind speed (e.g., 3.54 m / s), the specific wind turbine generator set (e.g., the unit number is 140605065) and the specific time interval (e.g., 2 hours starting from 2018-02-20 10:00:00) corresponding to the selected start and shutdown abnormal point.

[0065] Figure 6 The illustrated embodiments are merely examples, but the present invention is not limited thereto, and at least one of the number of starts and stops, the average wind speed, the specific wind turbine generator set, and the specific time interval corresponding to all the abnormal start and shutdown points in the distribution diagram may be displayed, or at least one of the number of starts and stops, the average wind speed, the specific wind turbine generator set, and the specific time interval corresponding to one or more abnormal start and shutdown points among all the abnormal start and shutdown points may be selectively displayed according to user needs.

[0066] Reference Figure 5 and Figure 6 The illustrated embodiment can display the distribution of the start and shutdown times of multiple wind turbines relative to the average wind speed in a visual manner, so that the distribution of the start and shutdown times of multiple wind turbines relative to the average wind speed can be intuitively observed, and abnormal start and shutdown points can be easily identified. Any information related to the abnormal start and shutdown points can also be obtained, for example, at least one of the start and shutdown times corresponding to the abnormal start and shutdown points, the average wind speed, a specific wind turbine and a specific time interval.

[0067] According to an embodiment of the present disclosure, identifying start-up and shutdown anomalies of a wind turbine generator set based on the number of start-up and shutdown times may also include: displaying the changes in operating data of a specific wind turbine generator set corresponding to the start-up and shutdown anomaly point and / or the ambient wind speed over time within a specific time period, wherein the specific time period includes a specific time interval corresponding to the start-up and shutdown anomaly point.

[0068] During the operation of a wind turbine generator set, the time period when abnormal start and shutdown occurs is often not limited to a single time interval, but involves multiple continuous time intervals. For example, the unit number of the specific wind turbine generator set corresponding to the first abnormal start and shutdown point is 140605065, and the first specific time interval is 2 hours starting from 2018-02-20 10:00:00; the unit number of the specific wind turbine generator set corresponding to the second abnormal start and shutdown point is 140605065, and the second specific time interval is 2 hours starting from 2018-02-20 12:00:00. Therefore, it may be necessary to perform timing analysis on multiple abnormal start and shutdown points. The specific time period used for timing analysis at least includes a specific time interval, that is, the range of the specific time period may be greater than or equal to the range of the specific time interval, and the specific time interval may include one or more time intervals.

[0069] Figure 5 and Figure 6 A distribution diagram of the number of starts and stops relative to the average wind speed is shown to facilitate identification of abnormal start and stop points. According to the above distribution diagram, a timing diagram can be further displayed for abnormal start and stop points, that is, the operation data of a specific wind turbine generator set corresponding to the abnormal start and stop point and / or the change of the ambient wind speed over time in a specific time period are displayed. In an embodiment of the present disclosure, the operation data of a specific wind turbine generator set may include a generator speed and / or a pitch angle.

[0070] Figure 7 and Figure 8 It is a schematic diagram of the operation data of a specific wind turbine generator set and the change of the ambient wind speed over time in a specific time period according to an embodiment of the present disclosure.

[0071] exist Figure 7 In the embodiment shown, the average wind speed in the specific time interval corresponding to the abnormal start-stop point is about 3.5 m / s, and the specific time period includes the specific time interval corresponding to the abnormal start-stop point. For example, the specific time period is from 0:00 to 1:00 p.m. on February 20, 2018. Figure 7 As shown in the figure, the ambient wind speed fluctuates over time within the low wind speed range of 0 to 6 m / s. Since the specific wind turbine generator set corresponding to the abnormal start-stop point frequently starts and stops with the change of the ambient wind speed, the generator speed and pitch angle also increase and decrease repeatedly with the change of the ambient wind speed. Such frequent and repeated changes will have an adverse effect on the performance of the specific wind turbine generator set, for example, the power generation performance of the wind turbine generator set will be reduced, the load of the whole machine will be increased, and the life of the switchgear and electrical equipment will be reduced.

[0072] exist Figure 8 In the embodiment shown, the average wind speed in the specific time interval corresponding to the abnormal start-stop point is about 16 m / s, and the specific time period includes the specific time interval corresponding to the abnormal start-stop point. For example, the specific time period is about 3:00 to 8:00 am on August 11, 2018. Figure 8 As shown in the figure, the ambient wind speed fluctuates over time within the large wind speed range of 10m / s to 20m / s. Since the specific wind turbine generator set corresponding to the abnormal start-stop point frequently starts and stops with the change of the ambient wind speed, the generator speed and pitch angle also increase and decrease repeatedly with the change of the ambient wind speed. Such frequent and repeated changes will have an adverse effect on the performance of the specific wind turbine generator set, for example, the power generation performance of the wind turbine generator set will be reduced, the load of the whole machine will be increased, and the life of the switchgear and electrical equipment will be reduced.

[0073] Based on the operation data of the specific wind turbine generator set corresponding to the abnormal start-stop point and the change of the ambient wind speed over time in a specific time period (for example, Figure 7 and Figure 8The timing change diagram shown in the figure can analyze the operating environment and operating conditions of the specific wind turbine generator set corresponding to the abnormal start and shutdown points from the time dimension, so as to effectively adjust the control parameters of the specific wind turbine generator set and effectively avoid frequent start and shutdown.

[0074] As mentioned above, by displaying the distribution diagram of the number of starts and shutdowns relative to the average wind speed and / or the changes in operating data and ambient wind speed over time in a specific time period, information related to the wind turbine generator set can be analyzed from a specific dimension, which is conducive to quickly evaluating the operating conditions of the wind turbine generator set and facilitating the efficient identification of start and shutdown anomalies of the wind turbine generator set.

[0075] After identifying the abnormal point of start-stop, the control parameters of the specific wind turbine generator set corresponding to the abnormal point of start-stop can be adjusted. According to an embodiment of the present disclosure, the interval time threshold between the start action and the shutdown action can be extended for the specific wind turbine generator set corresponding to the abnormal point of start-stop. The interval time threshold can represent the minimum time interval between the start action and the shutdown action. For example, the interval time threshold between the start action and the shutdown action corresponding to the abnormal point of start-stop is 15 minutes, that is, as long as the interval time between the current start action and the last shutdown action is greater than or equal to 15 minutes or the interval time between the current shutdown action and the last start action is greater than or equal to 15 minutes, the current start action or shutdown action is allowed to be executed, which leads to too frequent start-stop. Therefore, in order to forcibly reduce the number of starts and stops, the interval time threshold between the start action and the shutdown action can be appropriately extended, for example, the interval time threshold is extended to 20 minutes.

[0076] In addition, identifying the start-stop abnormality of a wind turbine generator set according to the number of starts and stops may also include: identifying the wind condition corresponding to the start-stop abnormal point according to the average wind speed corresponding to the start-stop abnormal point. Thus, the information associated with the start-stop abnormal point can be analyzed in a targeted manner based on the wind condition, and the specific wind turbine generator set corresponding to the start-stop abnormal point can be controlled in a targeted manner to avoid frequent starts and stops. According to an embodiment of the present disclosure, the control parameters of the specific wind turbine generator set corresponding to the start-stop abnormal point can be adjusted according to the wind condition corresponding to the start-stop abnormal point. The control parameters may include any parameters related to the start-stop of a specific wind turbine generator set, for example, the average wind speed threshold of a specific wind turbine generator set, the generator speed start threshold, the duration threshold for the generator speed to remain higher than the generator speed start threshold, and the interval time between the start action and the shutdown action.

[0077] According to an embodiment of the present disclosure, the wind condition corresponding to the start-stop abnormal point may include a high wind speed condition and / or a low wind speed condition. In an embodiment of the present disclosure, a minimum average wind speed and / or a maximum average wind speed may be set. When the average wind speed corresponding to the start-stop abnormal point is less than the minimum average wind speed, the wind condition corresponding to the start-stop abnormal point may be identified as a low wind speed condition. When the average wind speed corresponding to the start-stop abnormal point is greater than the maximum average wind speed, the wind condition corresponding to the start-stop abnormal point may be identified as a high wind speed condition. For example, Figures 5 to 8 In the illustrated embodiment, the minimum average wind speed may be set to 4 m / s, and the maximum average wind speed may be set to 15 m / s. Figure 5 and Figure 6 The start-stop abnormal points in the two dotted boxes are distributed in low wind speed conditions and high wind speed conditions respectively.

[0078] According to an embodiment of the present disclosure, adjusting the control parameters of a specific wind turbine generator set corresponding to the abnormal start-stop point according to the wind condition corresponding to the abnormal start-stop point includes: if the wind condition corresponding to the abnormal start-stop point is a high wind speed wind condition, setting an average wind speed threshold of the specific wind turbine generator set so that the specific wind turbine generator set is allowed to perform a start-up action when the average wind speed is less than the average wind speed threshold. Figure 5 and Figure 6 The distribution diagram shown and Figure 8 The time series change diagram is shown, and it can be seen that under high wind speed conditions, strong winds can easily blow a specific wind turbine generator set, that is, it is very easy to meet the starting conditions, so an additional judgment of the average wind speed can be added for the specific wind turbine generator set. By additionally setting the average wind speed threshold of the specific wind turbine generator set, the specific wind turbine generator set is allowed to perform the starting action only when the average wind speed is less than the average wind speed threshold, thereby greatly reducing the number of starts of the specific wind turbine generator set, and correspondingly reducing the abnormal start and stop of the specific wind turbine generator set under high wind speed conditions.

[0079] According to an embodiment of the present disclosure, adjusting the control parameters of a specific wind turbine generator set corresponding to the abnormal start-stop point according to the wind condition corresponding to the abnormal start-stop point includes: if the wind condition corresponding to the abnormal start-stop point is a low wind speed wind condition, increasing the generator speed start threshold of the specific wind turbine generator set, and / or increasing the duration threshold for the generator speed of the specific wind turbine generator set to remain higher than the generator speed start threshold. For example, referring to Figure 5 and Figure 6 The distribution diagram shown and Figure 7The time series variation diagram is shown, and it can be seen that under low wind speed conditions, the wind speed fluctuates greatly and there is more turbulence in the short term, resulting in insufficient wind energy to continuously support the power generation of a specific wind turbine generator set. Therefore, the specific wind turbine generator set starts and stops frequently. In order to reduce the abnormal start and stop of a specific wind turbine generator set under low wind speed conditions, the generator speed start threshold of the specific wind turbine generator set can be increased, so that the start action is allowed only when the generator speed is higher than the increased generator speed start threshold. Optionally, the duration threshold for the generator speed of the specific wind turbine generator set to remain higher than the generator speed start threshold can also be increased, so that the start action is allowed only when the generator speed remains higher than the generator speed start threshold for more than the increased duration threshold. In this way, the start and stop abnormalities can be effectively reduced by increasing the start requirements of the specific wind turbine generator set under low wind speed conditions.

[0080] As described above, the abnormal start and shutdown of the wind turbine generator set can be effectively identified, and the control parameters of the wind turbine generator set corresponding to the abnormal start and shutdown points can be adjusted in a targeted manner, thereby effectively avoiding the abnormal start and shutdown of the wind turbine generator set and reducing the adverse effects caused by the abnormal start and shutdown.

[0081] According to an embodiment of the present disclosure, the above start-stop abnormality identification method can be adopted in an offline and / or online manner. For example, the historical operation data and historical ambient wind speed of the wind turbine generator set can be obtained in an offline manner, the start-stop abnormality of the wind turbine generator set can be identified in an offline manner, and the control parameters of the wind turbine generator set can be set or adjusted in an offline manner based on the result of the offline identification, thereby reducing the start-stop abnormality. Optionally, the real-time operation data and real-time ambient wind speed of the wind turbine generator set can be obtained in real time online, the start-stop abnormality of the wind turbine generator set can be identified in an online manner, and the control parameters of the wind turbine generator set can be set or adjusted in real time online based on the result of the online identification, thereby reducing the start-stop abnormality. In addition, in order to execute the above start-stop abnormality identification method online, many real-time processing requirements such as real-time computing resources and real-time data feedback are required.

[0082] According to an embodiment of the present disclosure, a start-stop abnormality identification device for a wind turbine generator set is provided, and the start-stop abnormality identification device can execute the above start-stop abnormality identification method.

[0083] Fig. 9 FIG. 2 is a block diagram of a start-stop abnormality identification device 2 according to an embodiment of the present disclosure. The start-stop abnormality identification device 2 may include a data acquisition unit 21 , a data processing unit 22 , and a start-stop abnormality identification unit 23 .

[0084] The data acquisition unit 21 is configured to acquire the operation data of the wind turbine generator set within a preset time period. According to an embodiment of the present disclosure, the data acquisition unit 21 can be implemented by any device or module (e.g., data monitoring equipment, data storage) in the wind turbine generator set for monitoring and recording operation data.

[0085] The data processing unit 22 is configured to determine the number of starts and stops of the wind turbine generator set in each preset time interval in the preset time period according to the operation data, wherein the number of starts and stops in each preset time interval represents the sum of the number of start actions and the number of stop actions of the wind turbine generator set in the corresponding preset time interval. According to an embodiment of the present disclosure, the data processing unit 22 can be implemented by any device or module (e.g., a main control system, a central processing unit, etc.) in the wind turbine generator set for processing operation data.

[0086] The start-stop abnormality identification unit 23 is configured to identify the start-stop abnormality of the wind turbine generator set according to the start-stop number. According to the embodiment of the present disclosure, the start-stop abnormality identification unit 23 can be implemented by any device or module (e.g., main control system, central processing unit, etc.) in the wind turbine generator set for processing operation data. Optionally, the start-stop abnormality identification unit 23 may also include a display unit (not shown) for displaying reference Figure 5 and Figure 6 Described distribution map and / or reference Figure 7 and Figure 8 The display unit can be realized by a display in a wind turbine generator set or other device with display function.

[0087] In addition, the start-stop abnormality identification device 2 may further include a control parameter adjustment unit 24. The control parameter adjustment unit 24 may be configured to adjust the control parameters of a specific wind turbine generator set corresponding to the start-stop abnormal point for the wind conditions corresponding to the start-stop abnormal point. Optionally, the control parameter adjustment unit 24 may be configured to extend the interval time threshold between the start action and the shutdown action for the specific wind turbine generator set corresponding to the start-stop abnormal point. According to an embodiment of the present disclosure, the interval time threshold may represent the minimum time interval between the start action and the shutdown action. According to an embodiment of the present disclosure, the control parameter adjustment unit 24 may be implemented by a controller or control module (e.g., a main control system, a central processing unit, a generator controller, a pitch controller, etc.) in the wind turbine generator set for controlling the operation of various components of the wind turbine generator set.

[0088] Please refer to the above combined Figures 1 to 8 The start-stop abnormality identification method described is used to understand the specific details of the corresponding processing performed by the start-stop abnormality identification device 2 and its various units, which will not be repeated here.

[0089] According to an embodiment of the present disclosure, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed, the start-stop abnormality identification method according to the embodiment of the present disclosure is implemented. In an embodiment of the present disclosure, the computer-readable storage medium may carry one or more computer programs, and when the computer program is executed, the start-stop abnormality identification method according to the embodiment of the present disclosure is implemented. Figures 1 to 8 All the steps described, for example, the following steps: obtaining operating data of a wind turbine generator set within a preset time period; determining the number of starts and stops of the wind turbine generator set within each preset time interval in the preset time period based on the operating data, wherein the number of starts and stops within each preset time interval represents the sum of the number of start actions and the number of shutdown actions of the wind turbine generator set within the corresponding preset time interval; identifying start and shutdown anomalies of the wind turbine generator set based on the number of starts and stops; adjusting control parameters of a specific wind turbine generator set corresponding to the start and shutdown abnormal point for wind conditions corresponding to the start and shutdown abnormal point; and extending the interval time threshold between the start action and the shutdown action for the specific wind turbine generator set corresponding to the start and shutdown abnormal point.

[0090] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In an embodiment of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a computer program that may be used by or in conjunction with an instruction execution system, device or device. The computer program contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof. The computer-readable storage medium may be contained in any device; it may also exist alone without being assembled into the device.

[0091] The above has been combined Figures 1 to 8 The start-stop abnormality identification method according to the embodiment of the present disclosure is described. Fig.10 A computing device according to an embodiment of the present disclosure is described.

[0092] Fig.10 is a schematic diagram of a computing device according to an embodiment of the present disclosure.

[0093] Reference Fig.10According to an embodiment of the present disclosure, the computing device 3 may include a memory 31 and a processor 32. A computer program 33 is stored in the memory 31. When the computer program 33 is executed by the processor 32, the start-stop abnormality identification method according to an embodiment of the present disclosure is implemented.

[0094] In the embodiment of the present disclosure, when the computer program 33 is executed by the processor 32, the reference Figures 1 to 8 All operations of the described start-stop abnormality identification method, such as the following operations: obtaining operating data of a wind turbine generator set within a preset time period; determining the number of starts and stops of the wind turbine generator set within each preset time interval in the preset time period based on the operating data, wherein the number of starts and stops within each preset time interval represents the sum of the number of start actions and the number of shutdown actions of the wind turbine generator set within the corresponding preset time interval; identifying the start-stop abnormality of the wind turbine generator set based on the number of starts and stops; adjusting the control parameters of the specific wind turbine generator set corresponding to the start-stop abnormal point for the wind condition corresponding to the start-stop abnormal point; and extending the interval time threshold between the start action and the shutdown action for the specific wind turbine generator set corresponding to the start-stop abnormal point.

[0095] Fig.10 The computing device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0096] The above has been referred to Figures 1 to 10 The wind turbine generator set start-up and shutdown abnormality identification method and device, computer-readable storage medium, and computing device according to the embodiments of the present disclosure are described. However, it should be understood that: Fig. 9 The start-stop abnormality identification device and its units or modules shown in the figure can be respectively configured as software, hardware, firmware or any combination of the above items to perform specific functions. Fig.10 The computing device shown in is not limited to including the components shown above, but some components may be added or deleted as needed, and the above components may also be combined.

[0097] By using the wind turbine start-stop abnormality identification method and device, computer-readable storage medium, and computing device according to the embodiments of the present disclosure, at least one of the following technical effects can be achieved: the distribution of the start-stop times of multiple wind turbines relative to the average wind speed can be intuitively observed, and the start-stop abnormality points can be easily identified, and any information related to the start-stop abnormality points can be obtained; the start-stop abnormalities of the wind turbine can be effectively identified, and the control parameters of the wind turbine corresponding to the start-stop abnormality points can be adjusted in a targeted manner, thereby effectively avoiding the start-stop abnormalities of the wind turbine and reducing the adverse effects caused by the start-stop abnormalities; the power generation performance of the wind turbine is improved, the whole machine load of the wind turbine is reduced, the operation risk of the whole machine and subsystems of the wind turbine is reduced, the service life of each component of the wind turbine is extended, and the maintenance cost of the wind turbine is reduced.

[0098] The control logic or function performed by each component or controller in the control system can be represented by a flow chart or similar diagram in one or more figures. These figures provide representative control strategies and / or logic, which can be implemented using one or more processing strategies (such as event-driven, interrupt-driven, multi-tasking, multi-threading, etc.). Therefore, the various steps or functions shown can be executed in the order shown, executed in parallel, or omitted in some cases. Although not always clearly shown, it will be appreciated by those of ordinary skill in the art that one or more steps or functions shown can be repeatedly executed according to the specific processing strategy used.

[0099] While the present disclosure has been shown and described with reference to preferred embodiments, it will be understood by those skilled in the art that various modifications and variations may be made to these embodiments without departing from the spirit and scope of the disclosure as defined by the appended claims.

Claims

1. A method for identifying abnormalities in the start and stop of a wind turbine generator set, characterized in that: The start-stop abnormality identification method comprises: Obtaining the operating data of the wind turbine generator set within a preset time period; Determine the number of starts and stops of the wind turbine generator set in each preset time interval in the preset time period according to the operation data, wherein the number of starts and stops in each preset time interval represents the sum of the number of starts and the number of stops of the wind turbine generator set in the corresponding preset time interval; Identifying start and stop abnormalities of the wind turbine generator set according to the start and stop times; Wherein, the start-stop abnormality identification method further includes: obtaining the ambient wind speed of the wind turbine generator set in the preset time period; determining the average wind speed in each preset time interval according to the ambient wind speed; Wherein, identifying the start and shutdown abnormality of the wind turbine generator set according to the start and shutdown times includes: displaying a distribution diagram of the start and shutdown times relative to the average wind speed.

2. The method for identifying abnormalities of start and stop according to claim 1, characterized in that: The operating data includes operating status data of the wind turbine generator set, and the operating status data indicates an operating status of the wind turbine generator set.

3. The method for identifying abnormalities during start-up and shutdown according to claim 2, characterized in that: Determining the start and stop times of the wind turbine generator set according to the operation data includes: Sorting the operating status data in chronological order; Grouping the sorted operating status data according to each preset time interval to generate pre-processed operating status data; The number of starts and stops is determined according to a change in the operating state indicated by the pre-processed operating state data.

4. The method for identifying abnormalities of start and stop according to claim 2, characterized in that: The operating state includes a starting state, a standby state, a power generation state and a shutdown state. Switching from the standby state to the starting state indicates a starting action, and switching from the power generation state to the shutdown state indicates a shutdown action.

5. The method for identifying abnormalities during start-up and shutdown according to claim 1, characterized in that: The wind turbine generator set comprises a plurality of wind turbine generator sets. The distribution graph includes a distribution graph of the start and shutdown times of the plurality of wind turbine generator sets relative to the average wind speed.

6. The method for identifying abnormalities during start-up and shutdown according to claim 5, characterized in that: Identifying the start-up and shutdown abnormalities of the wind turbine generator set according to the start-up and shutdown times also includes: Comparing the number of starts and stops with a start and stop number threshold; A start-stop number greater than the start-stop number threshold is identified as a start-stop abnormal point.

7. The method for identifying abnormalities of starting and stopping according to claim 6, characterized in that: Identifying the start-up and shutdown abnormalities of the wind turbine generator set according to the start-up and shutdown times also includes: Based on the distribution diagram, at least one of the number of starts and stops, the average wind speed, the specific wind turbine generator set and the specific time interval corresponding to the start and stop abnormal point is identified.

8. The method for identifying abnormalities during start-up and shutdown according to claim 7, characterized in that: Identifying start and shutdown anomalies of the wind turbine generator set according to the start and shutdown times also includes: displaying changes in operating data of the specific wind turbine generator set and / or ambient wind speed over time within a specific time period, wherein the specific time period includes the specific time interval.

9. The method for identifying abnormalities during start-up and shutdown according to claim 8, characterized in that: The operating data of the specific wind turbine generator set includes a generator rotation speed and / or a pitch angle of the specific wind turbine generator set.

10. The method for identifying abnormalities of start and stop according to claim 6, characterized in that: Identifying the start-up and shutdown abnormalities of the wind turbine generator set according to the start-up and shutdown times also includes: The wind condition corresponding to the abnormal start-stop point is identified according to the average wind speed corresponding to the abnormal start-stop point.

11. The method for identifying abnormalities of starting and stopping according to claim 10, characterized in that: The start-stop abnormality identification method further includes: adjusting the control parameters of the specific wind turbine generator set corresponding to the start-stop abnormality point according to the wind condition corresponding to the start-stop abnormality point.

12. The method for identifying abnormalities of start and stop according to claim 11, characterized in that: The wind condition includes a high wind speed condition and / or a low wind speed condition.

13. The method for identifying abnormalities of start and stop according to claim 12, characterized in that: Adjusting the control parameters of the specific wind turbine generator set corresponding to the abnormal start-stop point according to the wind condition corresponding to the abnormal start-stop point includes: If the wind condition corresponding to the start-stop abnormal point is a high wind speed condition, an average wind speed threshold of the specific wind turbine generator set is set so that the specific wind turbine generator set is allowed to start when the average wind speed is less than the average wind speed threshold.

14. The method for identifying abnormalities of starting and stopping according to claim 12, characterized in that: Adjusting the control parameters of the specific wind turbine generator set corresponding to the abnormal start-stop point according to the wind condition corresponding to the abnormal start-stop point includes: If the wind condition corresponding to the start-stop abnormal point is a low wind speed condition, the generator speed start threshold of the specific wind turbine generator set is increased, and / or the duration threshold for the generator speed of the specific wind turbine generator set to remain higher than the generator speed start threshold is increased.

15. The method for identifying abnormalities of start and stop according to claim 6, characterized in that: The start-stop abnormality identification method further includes: For a specific wind turbine generator set corresponding to the abnormal start-stop point, the interval time threshold between the start-up action and the shutdown action is extended.

16. The method for identifying abnormalities of starting and stopping according to any one of claims 1 to 15, characterized in that: The start-stop abnormality identification method is performed in an offline and / or online manner.

17. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the start-stop abnormality identification method as described in any one of claims 1 to 16 is implemented.

18. A computing device, characterized in that: The computing device comprises: processor; A memory stores a computer program, and when the computer program is executed by a processor, the method for identifying start-stop abnormalities as described in any one of claims 1 to 16 is implemented.

19. A device for identifying abnormalities in starting and stopping a wind turbine generator set, characterized in that: The start-stop abnormality identification device comprises: A data acquisition unit is configured to acquire operation data of the wind turbine generator set within a preset time period; a data processing unit configured to determine, based on the operating data, the number of starts and stops of the wind turbine generator set in each preset time interval in the preset time period, wherein the number of starts and stops in each preset time interval represents the sum of the number of starts and the number of stops of the wind turbine generator set in the corresponding preset time interval; a start-stop abnormality identification unit, configured to identify the start-stop abnormality of the wind turbine generator set according to the start-stop number; The data acquisition unit is further configured to: acquire the ambient wind speed of the wind turbine generator set within the preset time period; determine the average wind speed within each preset time interval according to the ambient wind speed; The start-stop abnormality identification unit is further configured to: display a distribution diagram of the start-stop times relative to the average wind speed.

20. The start-stop abnormality identification device according to claim 19, characterized in that: The operating data includes operating status data of the wind turbine generator set, and the operating status data indicates an operating status of the wind turbine generator set.

21. The start-stop abnormality identification device according to claim 20, characterized in that: The data processing unit is further configured to: Sorting the operating status data in chronological order; Grouping the sorted operating status data according to each preset time interval to generate pre-processed operating status data; The number of starts and stops is determined according to a change in the operating state indicated by the pre-processed operating state data.

22. The start-stop abnormality identification device according to claim 20, characterized in that: The operating state includes a starting state, a standby state, a power generation state and a shutdown state. Switching from the standby state to the starting state indicates a starting action, and switching from the power generation state to the shutdown state indicates a shutdown action.

23. The start-stop abnormality identification device according to claim 19, characterized in that: The wind turbine generator set comprises a plurality of wind turbine generator sets. The distribution graph includes a distribution graph of the start and shutdown times of the plurality of wind turbine generator sets relative to the average wind speed.

24. The start-stop abnormality identification device according to claim 23, characterized in that: The start-stop abnormality identification unit is further configured to: Comparing the number of starts and stops with a start and stop number threshold; A start-stop number greater than the start-stop number threshold is identified as a start-stop abnormal point.

25. The start-stop abnormality identification device according to claim 24, characterized in that: The start-stop abnormality identification unit is further configured to: Based on the distribution diagram, at least one of the number of starts and stops, the average wind speed, the specific wind turbine generator set and the specific time interval corresponding to the start and stop abnormal point is identified.

26. The start-stop abnormality identification device according to claim 25, characterized in that: The start-stop abnormality identification unit is further configured to: display the operation data of the specific wind turbine generator set and / or the change of the ambient wind speed over time in a specific time period, wherein the specific time period includes the specific time interval.

27. The start-stop abnormality identification device according to claim 26, characterized in that: The operating data of the specific wind turbine generator set includes a generator rotation speed and / or a pitch angle of the specific wind turbine generator set.

28. The start-stop abnormality identification device according to claim 24, characterized in that: The start-stop abnormality identification unit is further configured to: The wind condition corresponding to the abnormal start-stop point is identified according to the average wind speed corresponding to the abnormal start-stop point.

29. The start-stop abnormality identification device according to claim 28, characterized in that: The start-stop abnormality identification device further includes a control parameter adjustment unit configured to adjust the control parameters of a specific wind turbine generator set corresponding to the start-stop abnormality point according to the wind condition corresponding to the start-stop abnormality point.

30. The start-stop abnormality identification device according to claim 29, characterized in that: The wind condition includes a high wind speed condition and / or a low wind speed condition.

31. The start-stop abnormality identification device according to claim 30, characterized in that: The control parameter adjustment unit is further configured to: If the wind condition corresponding to the start-stop abnormal point is a high wind speed condition, an average wind speed threshold of the specific wind turbine generator set is set so that the specific wind turbine generator set is allowed to start when the average wind speed is less than the average wind speed threshold.

32. The start-stop abnormality identification device according to claim 30, characterized in that: The control parameter adjustment unit is further configured to: If the wind condition corresponding to the start-stop abnormal point is a low wind speed condition, the generator speed start threshold of the specific wind turbine generator set is increased, and / or the duration threshold for the generator speed of the specific wind turbine generator set to remain higher than the generator speed start threshold is increased.

33. The start-stop abnormality identification device according to claim 24, characterized in that: The start-stop abnormality identification device further includes: a control parameter adjustment unit configured to extend the interval time threshold between the start-up action and the shutdown action for a specific wind turbine generator set corresponding to the start-stop abnormality point.

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