Abnormal yaw position identification method, device and controller for wind turbine generator system

Abstract

The disclosure provides a yaw position anomaly identification method, device and controller of a wind turbine generator set. The yaw position anomaly identification method comprises: acquiring a plurality of first yaw position data of a predetermined yaw time period with the same yaw direction; calculating a plurality of first yaw position change rates corresponding to the plurality of first yaw position data; acquiring a plurality of yaw time periods in which the yaw position data remains unchanged in the predetermined yaw time period based on the plurality of first yaw position change rates; and determining whether the yaw position of the wind turbine generator set is abnormal according to the plurality of yaw time periods, the first yaw position data of each yaw time period in the plurality of yaw time periods, and the yaw duration of each yaw time period. The yaw position anomaly identification method according to the embodiment of the disclosure can determine whether the yaw position of the wind turbine generator set is abnormal.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of wind power, and more particularly, to a yaw position abnormality identification method, device and controller for a wind turbine generator system. BACKGROUND

[0002] If the yaw position is abnormal (i.e., the yaw position data collected is abnormal) during the yaw process of the wind turbine generator system, on the one hand, the accumulation of yaw position errors will affect the precision of yaw-to-wind or the excessive twisting of the cable, thereby affecting the safety of the wind turbine generator system; on the other hand, if the yaw position remains for a long time frequently, a certain yaw error will be generated, affecting the stability of the yaw control. The above-mentioned yaw position abnormality is mostly caused by the abnormality of the yaw position sensor itself (for example, caused by the cam tooth jump of the yaw position sensor).

[0003] At present, the control system of the wind turbine generator system mostly only performs over-limit identification and protection on the upper and lower boundaries of the yaw position, and does not perform much abnormal identification and abnormal protection on the yaw position change law within the normal range. However, these abnormalities may affect the stability of the yaw control or the precision of the wind control, or even the safe operation of the unit. SUMMARY

[0004] One of the purposes of the exemplary embodiments of the present disclosure is to provide a yaw position abnormality identification method capable of identifying the yaw position abnormality of a wind turbine generator system.

[0005] According to a first aspect of the present disclosure, a yaw position abnormality identification method for a wind turbine generator system comprises: obtaining a plurality of first yaw position data of a predetermined yaw time period in the same yaw direction; calculating a plurality of first yaw position change rates corresponding to the plurality of first yaw position data; obtaining a plurality of yaw time periods in which the yaw position data remains unchanged in the predetermined yaw time period based on the plurality of first yaw position change rates; and determining whether the yaw position of the wind turbine generator system is abnormal according to the plurality of yaw time periods, the first yaw position data of each yaw time period in the plurality of yaw time periods, and the yaw duration of each yaw time period.

[0006] According to an embodiment of the present disclosure, determining whether the yaw position of the wind turbine generator system is abnormal according to the plurality of yaw time periods, the first yaw position data of each yaw time period in the plurality of yaw time periods, and the yaw duration of each yaw time period can comprise: determining at least one yaw time period that satisfies a preset condition based on the plurality of yaw time periods; and in response to determining that the first yaw position data before the start time of each yaw time period in the at least one yaw time period and the first yaw position data after the end time do not jump and the yaw duration of each yaw time period exceeds a first preset threshold, determining that the yaw position of the wind turbine generator system has yaw position retention abnormality.

[0007] According to an embodiment of the present disclosure, the step of determining at least one yaw time period satisfying the preset condition based on the plurality of yaw time periods can comprise: sorting the plurality of yaw time periods according to the chronological order of the respective start time; and merging the yaw time periods with the deviation between the end time of the previous yaw time period and the start time of the subsequent yaw time period within the preset time deviation to obtain the at least one yaw time period.

[0008] According to an embodiment of the present disclosure, the step of determining that there is no jump between the first yaw position data before the start time and the first yaw position data after the end time of each of the at least one yaw time period can comprise: calculating the position difference between the first yaw position data before the start time and the first yaw position data after the end time of each of the at least one yaw time period; and determining that there is no jump between the first yaw position data before the start time and the first yaw position data after the end time of each of the at least one yaw time period in response to the position difference being less than or equal to the second preset threshold.

[0009] According to an embodiment of the present disclosure, the step of obtaining the plurality of first yaw position data of the predetermined yaw time period with the same yaw direction can comprise: collecting the yaw position data at a predetermined time interval; and screening the plurality of first yaw position data of the predetermined yaw time period with the same yaw direction from the yaw position data.

[0010] According to an embodiment of the present disclosure, the step of screening the plurality of first yaw position data of the predetermined yaw time period with the same yaw direction from the yaw position data can comprise: screening the yaw position data of the plurality of segments in which at least one of the yaw flag position bit of the wind turbine generator, the grease state flag bit and the unmooring flag bit of the wind turbine generator, the left yaw action field or the right yaw action field is continuously true from the yaw position data; and removing the first n yaw position data in each segment to obtain the plurality of first yaw position data.

[0011] According to an embodiment of the present disclosure, the step of screening the plurality of first yaw position data of the predetermined yaw time period with the same yaw direction from the yaw position data can comprise: screening the yaw position data of the plurality of segments in which the left yaw action field or the right yaw action field of the wind turbine generator is continuously true and both the grease state flag bit and the unmooring flag bit are reset from the yaw position data; and removing the first n yaw position data in each segment to obtain the plurality of first yaw position data.

[0012] According to an embodiment of the present disclosure, the step of obtaining, based on the plurality of first yaw position change rates, a plurality of yaw time periods in which the yaw position data remains unchanged in the predetermined yaw time period can include: performing a sliding window maximum value processing on absolute values of the plurality of yaw position change rates with a predetermined sliding window length and a predetermined sliding step, to obtain an envelope line of the plurality of first yaw position change rates; screening the plurality of first yaw position change rates and the corresponding first yaw position data whose envelope line is less than a third predetermined threshold; cutting the first yaw position data according to whether the screened first yaw position data is continuous, to obtain a plurality of yaw segments in which the yaw position data remains unchanged, and determining, according to the plurality of yaw segments and a predetermined time interval, a plurality of yaw time periods corresponding to the plurality of yaw segments.

[0013] According to a second aspect of the present disclosure, a computer-readable storage medium stores instructions or programs which, when executed by a processor, implement the above-mentioned yaw position anomaly identification method of a wind turbine generator.

[0014] According to a third aspect of the present disclosure, a yaw position anomaly identification device of a wind turbine generator includes: a first obtaining unit configured to obtain a plurality of first yaw position data of a predetermined yaw time period in which yaw directions are the same; a first calculating unit configured to calculate a plurality of first yaw position change rates corresponding to the plurality of first yaw position data; a second obtaining unit configured to obtain, based on the plurality of first yaw position change rates, a plurality of yaw time periods in which the yaw position data remains unchanged in the predetermined yaw time period; and an identification unit configured to determine whether a yaw position of the wind turbine generator is abnormal according to the plurality of yaw time periods, the first yaw position data of each of the plurality of yaw time periods, and a yaw duration of each of the plurality of yaw time periods.

[0015] According to a fourth aspect of the present disclosure, a controller of a wind turbine generator includes a processor and a computer-readable storage medium, the computer-readable storage medium stores programs or instructions which, when executed by the processor, implement the above-mentioned yaw position anomaly identification method of the wind turbine generator.

[0016] According to a fifth aspect of the present disclosure, a wind turbine generator includes the above-mentioned yaw position anomaly identification device of the wind turbine generator or the above-mentioned controller.

[0017] The yaw position identification method according to the embodiments of the present disclosure can reduce yaw errors and improve the stability of yaw control.

[0018] The yaw position identification method according to the embodiments of the present disclosure can identify abnormal phenomena that the yaw position of the wind turbine generator remains unchanged for a long time. BRIEF DESCRIPTION OF DRAWINGS

[0019] The above and other objects and features of the exemplary embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings, which illustrate:

[0020] Figure 1 is a graph reflecting a yaw position keeping abnormal phenomenon of a wind turbine generator;

[0021] Figure 2 is a flowchart illustrating a yaw position abnormality recognition method according to a first embodiment of the present disclosure;

[0022] Figure 3 is a flowchart illustrating a yaw position abnormality recognition method according to a second embodiment of the present disclosure;

[0023] Figure 4 is a flowchart illustrating a yaw position abnormality recognition method according to a third embodiment of the present disclosure;

[0024] Figure 5 is a graph illustrating a yaw position keeping recognized by the yaw position recognition method according to the embodiments of the present disclosure;

[0025] Figure 6 is a graph illustrating a yaw rate change timing chart recognized by the yaw position recognition method according to the embodiments of the present disclosure;

[0026] Figure 7 is a graph illustrating a yaw position change rate timing chart recognized by the yaw position recognition method according to the embodiments of the present disclosure;

[0027] Figure 8 is a graph illustrating a state flag or field according to the embodiments of the present disclosure;

[0028] Figure 9 is a block diagram illustrating a yaw position abnormality recognition apparatus according to the embodiments of the present disclosure. DETAILED DESCRIPTION

[0029] The following detailed description is to help obtain a comprehensive understanding of the methods, apparatuses, and / or systems described herein. However, the order of the operations described herein is only an example, and is not limited to those set forth herein, but can be identically replaced or changed except for the operations that must occur or be performed in a specific order. Also, in order to be more clear and concise, the description of the contents well known in the art will be omitted or simplified.

[0030] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Terms, such as, for example, "technology", "science", "art" and the like, connoting no unusual skill will be interpreted according to their normal meanings in the context of their usage. Unless explicitly defined otherwise, terms such as those defined in commonly used dictionaries will be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the disclosure, and will not be interpreted ideally or overly formally.

[0031] Unless otherwise specified, the same reference numbers generally refer to the same elements (e.g., components, steps, and methods). The reference numbers described in the preceding embodiments, which reappear in the following embodiments, can be omitted. In addition, the technical features described in different or the same embodiments can be combined in any manner as long as the combined embodiments or technical solutions are complete and can solve the technical problems of the present application or achieve the technical effects described or not described in the present application but can be determined according to the complete technical solutions described above.

[0032] The present disclosure obtains yaw position data within a predetermined time period in the same yaw direction, calculates the corresponding yaw position change rate, determines a plurality of yaw segments (yaw position data collected at a predetermined sampling interval) in which the yaw position is constant within the time period, and identifies yaw position abnormalities (e.g., position maintenance abnormalities) based on these yaw segments, the yaw duration of each yaw segment, and the corresponding yaw position data. The following will be described in combination with Figures 1 to 9 Specific embodiments of the present disclosure are described.

[0033] Figure 1 is a graph reflecting the yaw position maintenance abnormality phenomenon of the wind turbine generator.

[0034] Under normal circumstances, the yaw position (or yaw position data) during the yaw process of the wind turbine generator should be continuously changed, but the yaw position may be abnormal due to yaw position sensor abnormalities, etc., for example, the yaw position may be Figure 1 The yaw position maintenance phenomenon shown in FIG. 1 can be used to determine whether the yaw position of the wind turbine generator is abnormal by analyzing the yaw position data and identifying abnormal characteristics. Since such abnormalities are mostly caused by yaw position sensor abnormalities, it can be determined whether the yaw position sensor is abnormal based on this.

[0035] Referring to Figure 1 , Figure 1 The abscissa of FIG. 2 represents the sampling coefficient (or index value), the ordinate on the left represents the yaw position change rate, and the ordinate on the right represents the yaw position. When the yaw position (or yaw position data) is abnormal, the yaw position can remain essentially unchanged for a short period of time. If the yaw position remains unchanged as the yaw proceeds, an abnormal phenomenon occurs in which the yaw position remains for a long period of time (e.g., greater than 60 seconds).

[0036] The abnormal phenomenon can be determined according to a plurality of yaw time periods, a yaw duration of each of the plurality of yaw time periods, and a yaw position (or yaw position data).

[0037] Figure 2 is a flowchart illustrating a yaw position abnormality identification method according to a first embodiment of the present disclosure.

[0038] The yaw position abnormality identification method according to the first embodiment of the present disclosure can include steps S210, S220, S230, and S240.

[0039] In step S210, a plurality of first yaw position data of a predetermined yaw time period with the same yaw direction are obtained.

[0040] Obtaining yaw position data with the same yaw direction can simplify the identification process. If the yaw-to-wind state lasts for a short time and the yaw position changes greatly, the yaw direction may appear to be reciprocating. Identifying the above-mentioned abnormality requires a certain amount of time, and the yaw position data under the working conditions of untying, greasing, long-time wind yaw, etc. can be selected for identification and judgment. The above-mentioned predetermined yaw time period can be a predetermined time period of the above-mentioned working conditions.

[0041] The yaw position data can be collected by a yaw position sensor. As an example, the yaw position data can be collected at a predetermined time interval (e.g., once every seven seconds, with a time interval of 6 seconds), and accordingly, a plurality of yaw position data (first yaw position data) are obtained.

[0042] Specifically, the step of obtaining a plurality of first yaw position data of a predetermined yaw time period with the same yaw direction can include: collecting yaw position data at a predetermined time interval; and screening a plurality of first yaw position data of a predetermined yaw time period with the same yaw direction from the yaw position data.

[0043] The step of screening a plurality of first yaw position data of a predetermined yaw time period with the same yaw direction from the yaw position data includes:

[0044] The step of screening a plurality of first yaw position data of a predetermined yaw time period with the same yaw direction from the yaw position data includes: screening yaw position data of a plurality of segments in which at least one of a yaw flag (or a yaw state flag) of the wind turbine generator set is set, a greasing state flag and an untying flag of the wind turbine generator set are set, and a left yaw action field or a right yaw action field is continuously true from the yaw position data; and removing the first n yaw position data in each segment to obtain a plurality of first yaw position data. Here, n can be selected as needed. As an example, n can be 15.

[0045] The setting of the yaw flag bit, the grease state flag bit and the unmooring flag bit can indicate that the yaw flag bit, the grease state flag bit and the unmooring flag bit change to corresponding bit values.

[0046] That is, the yaw position data of multiple segments in a case where at least one of the yaw action field and the grease state flag bit and the unmooring flag bit is continuously set can be identified, where the yaw position data of multiple segments can refer to yaw position data with discontinuous index values (sample numbers).

[0047] In addition, the step of screening multiple first yaw position data of a predetermined yaw time period with the same yaw direction from the yaw position data can include: screening the yaw position data of multiple segments in a case where the left yaw action field or the right yaw action field is continuously true and both the grease state flag bit and the unmooring flag bit are reset from the yaw position data; and removing the first n yaw position data in each segment to obtain the multiple first yaw position data. The reset of the yaw flag bit, the grease state flag bit and the unmooring flag bit can indicate that the yaw flag bit, the grease state flag bit and the unmooring flag bit change to zero.

[0048] The screening of the yaw position data of the same yaw direction for a long time according to the wind state flag bit can be set as needed.

[0049] As described above, removing the first n yaw position data in each segment can improve the screening reliability. Specifically, data instability factors such as state changes or signal delays can be removed.

[0050] In step S220, multiple first yaw position change rates corresponding to the multiple first yaw position data are calculated.

[0051] The multiple yaw position change rates can be calculated by the difference between two adjacent first yaw position data.

[0052] In step S230, multiple yaw time periods in which the yaw position data remains unchanged in a predetermined yaw time period are obtained based on the multiple first yaw position change rates.

[0053] The yaw position data remaining unchanged means that the yaw position data remains substantially unchanged, for example, the multiple first yaw position change rates in a certain yaw time period are all within a preset range (for example, 0.3 degrees), and it can be determined that the yaw position data in the yaw time period is substantially unchanged.

[0054] The time period in which the adjacent yaw position data change rates are substantially zero (within a preset range) can be determined as the above-mentioned yaw time period, but this is only an example, and the corresponding yaw position data can be further combined for determination. Accordingly, multiple yaw time periods that meet the conditions can be identified.

[0055] In step S240, it is determined whether the yaw position of the wind turbine generator is abnormal according to the plurality of yaw time periods, the first yaw position data of each of the plurality of yaw time periods, and the yaw duration of each of the plurality of yaw time periods.

[0056] The yaw of the wind turbine generator can be further determined to be abnormal in combination with the first yaw position data and the yaw duration of the yaw time period in the case that there are a plurality of yaw time periods in which the first yaw position change rate is zero. For example, the yaw position of the wind turbine generator can be determined to be abnormal when the difference between the first yaw position data of a yaw time period and the yaw position data at the time point one time point before the time point of the first yaw position data is small (for example, less than 2.5 degrees).

[0057] Figure 3 is a flow chart illustrating a yaw position abnormality identification method according to a second embodiment of the present disclosure.

[0058] The step of determining whether the yaw position of the wind turbine generator is abnormal according to the plurality of yaw time periods, the first yaw position data of each of the plurality of yaw time periods, and the yaw duration of each of the plurality of yaw time periods can include steps S241 and S242.

[0059] In step S241, at least one yaw time period that satisfies a preset condition is determined from the plurality of yaw time periods.

[0060] In step S242, in response to determining that there is no jump between the first yaw position data before the start time point and the first yaw position data after the end time point of each of the at least one yaw time period, and the yaw duration of each of the at least one yaw time period exceeds a first preset threshold, it is determined that the yaw position of the wind turbine generator has a yaw position maintaining abnormality.

[0061] That is, the yaw position of the wind turbine generator can be determined to have a yaw position maintaining abnormality by the size of the difference between the yaw position data of a single yaw time period and the yaw position data (for example, the previous yaw position data) before the start time point of the single yaw time period, and the duration of each of the yaw time periods (i.e., the yaw duration).

[0062] In order to further determine whether the yaw position of the wind turbine generator has a yaw position maintaining abnormality, the plurality of yaw time periods can be screened, and the screening condition (i.e., the preset condition) can include at least one of the following preset conditions: the time deviation between the end time point of the previous yaw time period and the start time point of the next yaw time period in the two adjacent yaw time periods is within a preset time deviation; and the yaw duration of each of the yaw time periods is greater than a first preset threshold.

[0063] As an example, the yaw time period in which the deviation between the end time of the previous yaw time period and the start time of the subsequent yaw time period is within the preset time deviation can be further selected from the sorted plurality of yaw time periods, and the yaw time period in which the yaw duration is greater than the first preset threshold is further screened out.

[0064] In addition, the yaw time period satisfying the condition can be obtained in a screening and merging manner. Specifically, the step of determining at least one yaw time period satisfying the preset condition from the plurality of yaw time periods can include: sorting the plurality of yaw time periods according to the chronological order of the respective start times; and merging the yaw time period in which the deviation between the end time of the previous yaw time period and the start time of the subsequent yaw time period is within the preset time deviation, to obtain at least one yaw time period.

[0065] The retention of the first yaw position data can be determined by the position difference between the first yaw position data before the start time of a certain yaw time period in the obtained at least one yaw time period and the first yaw position data after the end time.

[0066] Here, the first yaw position data before the start time of a certain yaw time period refers to the first yaw position data (the yaw position data collected 6 seconds before the start time) before the start time of the yaw time period, and the first yaw position data after the end time of a certain yaw time period refers to the first yaw position data (the yaw position data collected 6 seconds after the end time) after the end time of the yaw time period.

[0067] Specifically, the step of determining that the first yaw position data before the start time and the first yaw position data after the end time of each yaw time period in the at least one yaw time period do not jump can include: calculating the position difference between the first yaw position data before the start time and the first yaw position data after the end time of each yaw time period in the at least one yaw time period; and in response to the position difference being less than or equal to a second preset threshold, determining that the first yaw position data before the start time and the first yaw position data after the end time of each yaw time period in the at least one yaw time period do not jump (i.e., the position is retained).

[0068] Taking the normal yaw rate of 0.3 degrees per second as an example, the yaw position data is collected every 7 seconds, assuming that the first yaw position data is 0 degrees, the second yaw position data is 0.3 degrees, the third yaw position data is 0.6 degrees, and so on. If the yaw position data remains unchanged from the fourth yaw position data, until the eighth data yaw position data, that is, the yaw position data remains unchanged for 28 seconds, the first yaw position data before the start time of the yaw time period is 0.6 degrees, and the first yaw position data after the end time of the yaw time period is still 0.6 degrees. The position difference between the two first yaw position data and the yaw time length can be used to determine whether there is a yaw position keeping abnormality, thereby improving the recognition accuracy.

[0069] In specific implementation, the yaw time period with the yaw position change rate meeting the requirement can be screened by a sliding window maximum value method.

[0070] As an example, the step of obtaining a plurality of yaw time periods in which the yaw position data remains unchanged in the predetermined yaw time period based on a plurality of first yaw position change rates can include: performing a sliding window maximum value processing on the absolute values of the plurality of yaw position change rates with a predetermined sliding window length and a predetermined sliding step, to obtain an envelope line of the plurality of first yaw position change rates; screening a plurality of first yaw position change rates and the corresponding first yaw position data whose envelope line is less than a second predetermined threshold; cutting the first yaw position data according to whether the screened first yaw position data is continuous, to obtain a plurality of yaw segments in which the yaw position data remains unchanged, and determining a plurality of yaw time periods corresponding to the plurality of yaw segments according to the plurality of yaw segments and a predetermined time interval.

[0071] In addition, the sliding window method is only used as an example, and in specific implementation, the yaw time period with the yaw position change rate meeting the requirement can also be screened by a yaw position change rate marking binning method (i.e., classification according to the size of the yaw position change rate).

[0072] In addition, an abnormal segment result table can be output, and the result table can include the number of abnormalities (one segment is one abnormality), the start time and the end time of the abnormal segment, the mean value of the yaw position and the mean value of the yaw position change rate in the abnormal segment.

[0073] Figure 4 is a flowchart showing a yaw position abnormality recognition method according to a third embodiment of the present disclosure.

[0074] The yaw position abnormality recognition method according to the third embodiment of the present disclosure can include steps S410, S420, S430, S440, S450, S460, S470, S480, S490, and S4100.

[0075] In step S410, the yaw position data is collected every 7 seconds, and the yaw position data can be collected by the yaw position sensor.

[0076] In step S420, the segments under specific working conditions are screened out from the collected data as single yaw segments. The specific working conditions here can be the same direction of wind for a long time, or the greasing working condition or the unmooring working condition.

[0077] In step S430, data preprocessing is performed on each screened yaw segment to remove the data of the first 100 seconds in each continuous segment, so that the data unstable factors such as state change or signal delay can be eliminated.

[0078] In step S440, the yaw position change rate corresponding to each piece of data (yaw position change rate) in each continuous segment is calculated.

[0079] In step S450, the start position and the end position of each position-kept data point or segment (or, the index value at the start time and the index value at the end time can also be obtained) are obtained.

[0080] In step S460, the start time, the end time, the duration, the segment point number, the yaw position corresponding to the data before the start time, and the yaw position corresponding to the data after the end time of each segment are calculated.

[0081] In step S470, screening is performed according to whether the yaw position jumps, whether the yaw duration meets the requirement, and whether the time interval meets the requirement, for example, according to the position difference between the yaw position corresponding to the data before the start time of the yaw segment and the yaw position corresponding to the data after the end time being less than a predetermined threshold value, the yaw duration of the yaw segment reaching a predetermined threshold value, and the time interval between the adjacent two yaw segments being within a predetermined time interval.

[0082] In step S480, the yaw position data of the adjacent two yaw time periods can be combined when the time interval is within a preset time range (for example, 30 seconds).

[0083] The predetermined threshold value (for example, the first predetermined threshold value) and the like of the present disclosure, the preset threshold value, and the predetermined time interval can be empirical values.

[0084] In step S490, the combined yaw segment is taken as a new segment, and the start time, the end time, the duration (i.e., the yaw duration), the segment point number, the yaw position corresponding to the data before the start time, and the yaw position corresponding to the data after the end time of the new segment are calculated, and the yaw duration identification and the yaw position keeping identification are performed, which can be specifically as described above, and will not be described here.

[0085] In step S4100, an abnormal segment result table is output. The result table can reflect the number of abnormalities (one segment corresponds to one abnormality), the start time and end time of the abnormal segment, the average of the yaw position within the abnormal segment, and / or the average of the yaw position change rate.

[0086] Figure 5 is a curve graph showing the yaw position identified by the yaw position identification method according to the embodiment of the present disclosure; Figure 6 is a time sequence graph showing the yaw rate change identified by the yaw position identification method according to the embodiment of the present disclosure; Figure 7 is a time sequence graph showing the yaw position change rate identified by the yaw position identification method according to the embodiment of the present disclosure; Figure 8 is a curve graph showing the state flag bit or field according to the embodiment of the present disclosure.

[0087] Referring to Figure 5 , the yaw position shows a long-time keeping abnormality, and a long-time keeping abnormality from about 00:22 to about 00:24 can be identified. Referring to Figure 6 , the yaw rate is basically kept unchanged (about 0 degrees). Referring to Figure 7 , the yaw position change rate is kept as zero for a long time. Referring to Figure 8 , Figure 8 , the curve G1 reflects the unmooring or greasing state, 15 represents the unmooring flag position bit, 16 represents the greasing flag position bit, the curve G2 reflects the yaw state, the yaw flag bit is 1 indicating yaw, and the yaw flag bit is 0 indicating no yaw.

[0088] Figure 9 is a block diagram of a yaw position abnormality identification device according to the embodiment of the present disclosure.

[0089] The yaw position abnormality identification device 600 according to the embodiment of the present disclosure can include a first acquisition unit 610, a first calculation unit 620, a second acquisition unit 630, and an identification unit 640.

[0090] The first acquisition unit 610 can acquire a plurality of first yaw position data of a predetermined yaw time period with the same yaw direction.

[0091] As an example, the first acquisition unit 610 can collect yaw position data at predetermined time intervals, and screen multiple first yaw position data of a predetermined yaw time period with the same yaw direction from the yaw position data. Specifically, the first acquisition unit 610 can screen yaw position data of multiple segments with the yaw flag position bit of the wind turbine generator set, at least one of the greasing state flag bit and the unmooring flag bit of the wind turbine generator set being set, and the left yaw action field or the right yaw action field being continuously true from the yaw position data; remove the first n yaw position data in each segment to obtain the multiple first yaw position data.

[0092] In addition, the first acquisition unit 610 can also screen yaw position data of multiple segments with the yaw flag position bit of the wind turbine generator set, the left yaw action field or the right yaw action field being continuously true, and both the greasing state flag bit and the unmooring flag bit being reset from the yaw position data.

[0093] The first calculation unit 620 can calculate multiple first yaw position change rates corresponding to the multiple first yaw position data.

[0094] The second acquisition unit 630 can acquire multiple yaw time periods with the yaw position data remaining unchanged in the predetermined yaw time period based on the multiple first yaw position change rates.

[0095] The second acquisition unit 630 can perform a sliding window maximum value processing on the absolute values of the multiple yaw position change rates with a predetermined sliding window length and a predetermined sliding step, to obtain an envelope line of the multiple first yaw position change rates; screen multiple first yaw position change rates and the corresponding first yaw position data with the envelope line being less than a second predetermined threshold; cut the first yaw position data according to whether the screened first yaw position data is continuous, to obtain multiple yaw segments with the yaw position data remaining unchanged, and determine multiple yaw time periods corresponding to the multiple yaw segments according to the multiple yaw segments and the predetermined time intervals.

[0096] The identification unit 640 can determine whether the yaw position of the wind turbine generator is abnormal according to the multiple yaw time periods, the first yaw position data of each yaw time period in the multiple yaw time periods, and the yaw duration of each yaw time period.

[0097] For example, the identification unit 640 can determine at least one yaw time period satisfying a preset condition based on the multiple yaw time periods; in response to determining that there is no jump between the first yaw position data before the start time of each yaw time period in the at least one yaw time period and the first yaw position data after the end time, and the yaw duration of each yaw time period exceeds a first preset threshold, determine that the yaw position of the wind turbine generator has a yaw position retention abnormality.

[0098] The identification unit 640 can also make a hold judgment across multiple yaw time periods.

[0099] The identification unit 640 can calculate a position difference value of the first yaw position data before the start time and the first yaw position data after the end time of each of the at least one yaw time period; and determine that there is no jump of the first yaw position data before the start time and the first yaw position data after the end time of each of the at least one yaw time period in response to the position difference value being less than or equal to a second preset threshold value.

[0100] It should be understood that each unit or module in the control device according to the exemplary embodiments of the present disclosure can be implemented by hardware components and / or software components. A person skilled in the art can implement each unit according to the processing performed by the defined unit, for example, using a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a software algorithm, etc.

[0101] Each operation of the above-described steps can be written as a software program or instruction, and thus the control method according to the exemplary embodiments of the present disclosure can be implemented via software, and the computer-readable storage medium of the exemplary embodiments of the present disclosure can store a computer program that, when executed by a processor, implements the yaw position abnormality identification method according to the above-described exemplary embodiments.

[0102] According to various embodiments of the present disclosure, a device (e.g., a module or its function) or a method can be implemented by a program or an instruction stored in a computer-readable storage medium. Where the instruction is executed by a processor, the processor can perform a function corresponding to the instruction or execute a method corresponding to the instruction. At least a part of a module can be implemented by the processor (e.g., executed). At least a part of a programming module can include a module, a program, a routine, an instruction set, and a process for performing at least one function. In one example, the instruction or software includes machine code made by a compiler, which is directly executed by one or more processors or computers. In another example, the instruction or software includes a higher level code that is executed by one or more processors or computers using an interpreter. The instruction or software can be written in any programming language based on the block diagrams and the flowcharts and the corresponding descriptions in the specification.

[0103] The computer readable storage medium includes a non-transitory computer readable storage medium, for example, can include a magnetic medium such as a floppy disk and a magnetic tape, an optical medium (including a compact disc (CD) ROM and a DVD ROM), a magneto-optical medium such as a floptical disk, a hardware device designed to store and execute program commands such as ROM, RAM, and a flash memory, designed to store and execute program commands. The program commands include language codes executable by a computer using an interpreter and machine language codes generated by a compiler. The above-mentioned hardware device can be implemented by one or more software modules for performing the operations of the various embodiments of the present disclosure.

[0104] The module or the programming module of the present disclosure can include at least one of the aforementioned components with some components omitted or other components added. The operations of the module, the programming module, or other components can be executed sequentially, in parallel, cyclically, or heuristically. In addition, some operations can be executed in different orders, can be omitted, or can be extended with other operations.

[0105] The computer readable storage medium and / or the yaw position abnormality recognition device of the exemplary embodiments of the present disclosure can be part of a controller (for example, a main controller) of a wind turbine generator system.

[0106] The controller according to the exemplary embodiments of the present disclosure can include a processor and a computer readable storage medium, wherein the computer readable storage medium stores a computer program or instructions, when the computer program or instructions are executed by the processor, the yaw position abnormality recognition method as described in the above exemplary embodiments is implemented.

[0107] The wind turbine generator system of the embodiments of the present disclosure can include the computer readable storage medium, the yaw position abnormality recognition device, or the controller as described above.

[0108] The yaw position abnormality recognition method and the yaw position abnormality recognition device according to the embodiments of the present disclosure can judge the yaw position abnormality of the wind turbine generator system.

[0109] The yaw position recognition method and the yaw position abnormality recognition device according to the embodiments of the present disclosure can reduce the yaw error and improve the stability of the yaw control.

[0110] Although some exemplary embodiments of the present disclosure have been shown and described, those skilled in the art should understand that modifications can be made to the embodiments without departing from the principles and spirit of the present disclosure, for example, technical features of different embodiments can be combined.

Claims

1. A yaw position abnormality identification method for a wind turbine generator system, characterized by, The method comprises: obtaining a plurality of first yaw position data of a predetermined yaw time period in the same yaw direction; calculating a plurality of first yaw position change rates corresponding to the plurality of first yaw position data; obtaining a plurality of yaw time periods in which the yaw position data remains unchanged in the predetermined yaw time period based on the plurality of first yaw position change rates; determining whether the yaw position of the wind turbine generator is abnormal according to the plurality of yaw time periods, the first yaw position data of each yaw time period in the plurality of yaw time periods, and the yaw duration of each yaw time period.

2. The yaw position abnormality identification method of a wind power plant according to claim 1, characterized in that, The step of determining whether the yaw position of the wind turbine generator is abnormal according to the plurality of yaw time periods, the first yaw position data of each yaw time period in the plurality of yaw time periods, and the yaw duration of each yaw time period comprises: determining at least one yaw time period in the plurality of yaw time periods that meets a preset condition; in response to determining that there is no jump between the first yaw position data before the start time and the first yaw position data after the end time of each yaw time period in the at least one yaw time period, and the yaw duration of each yaw time period exceeds a first preset threshold, determining that the yaw position of the wind turbine generator has a yaw position retention abnormality.

3. The yaw position abnormality identification method of a wind power generator system according to claim 2, characterized by, The step of determining at least one yaw time period in the plurality of yaw time periods that meets a preset condition comprises: sorting the plurality of yaw time periods in the order of their respective start times; merging the yaw time periods whose end time of the previous yaw time period and the start time of the next yaw time period deviate within a preset time deviation to obtain the at least one yaw time period.

4. The yaw position abnormality identification method of a wind power plant according to claim 3, characterized in that, The step of determining that there is no jump between the first yaw position data before the start time and the first yaw position data after the end time of each yaw time period in the at least one yaw time period comprises: calculating the position difference between the first yaw position data before the start time and the first yaw position data after the end time of each yaw time period in the at least one yaw time period; in response to the position difference being less than or equal to a second preset threshold, determining that there is no jump between the first yaw position data before the start time and the first yaw position data after the end time of each yaw time period in the at least one yaw time period.

5. The yaw position abnormality identification method of a wind power generating unit according to any one of claims 1 to 4, characterized by, The step of obtaining a plurality of first yaw position data of a predetermined yaw time period in the same yaw direction comprises: collecting yaw position data at a predetermined time interval; screening a plurality of first yaw position data of the predetermined yaw time period in the same yaw direction from the yaw position data.

6. The yaw position abnormality identification method of a wind power plant according to claim 5, characterized in that, The step of screening a plurality of first yaw position data of a predetermined yaw time period in the same yaw direction from the yaw position data comprises: screening yaw position data of a plurality of segments in which at least one of the yaw flag position bit of the wind turbine generator, the fatting state flag bit and the uncabling flag bit of the wind turbine generator, the left yaw action field or the right yaw action field is continuously true from the yaw position data; removing the first n yaw position data in each segment to obtain the plurality of first yaw position data.

7. The yaw position abnormality identification method of a wind power plant according to claim 5, characterized by, The step of screening, from the yaw position data, a plurality of first yaw position data of the predetermined yaw time period in the same yaw direction comprises: Screening, from the yaw position data, a plurality of segment yaw position data in which the left yaw action field or the right yaw action field of the wind turbine generator is continuously true and both the grease adding state flag bit and the uncabling flag bit are reset; Removing the first n yaw position data in each segment to obtain the plurality of first yaw position data.

8. The yaw position abnormality identification method of a wind power plant according to claim 5, characterized by, The step of obtaining, based on the plurality of first yaw position change rates, a plurality of yaw time periods in which the yaw position data remains unchanged in the predetermined yaw time period comprises: Performing a sliding window maximum value processing on the absolute values of the plurality of yaw position change rates with a predetermined sliding window length and a predetermined sliding step to obtain an envelope line of the plurality of first yaw position change rates; Screening the plurality of first yaw position change rates and the corresponding first yaw position data whose envelope line is less than a third predetermined threshold value; Cutting the first yaw position data according to whether the screened first yaw position data is continuous to obtain a plurality of yaw segments in which the yaw position data remains unchanged, and determining a plurality of yaw time periods corresponding to the plurality of yaw segments according to the plurality of yaw segments and the predetermined time interval.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores instructions or programs which, when executed by a processor, implement the yaw position anomaly identification method of the wind turbine generator according to any one of claims 1 to 8.

10. A yaw position abnormality identification device of a wind turbine generator system, characterized by, Comprise: A first obtaining unit that obtains a plurality of first yaw position data of a predetermined yaw time period in the same yaw direction; A first calculating unit that calculates a plurality of first yaw position change rates corresponding to the plurality of first yaw position data; A second obtaining unit that obtains, based on the plurality of first yaw position change rates, a plurality of yaw time periods in which the yaw position data remains unchanged in the predetermined yaw time period; An identification unit that determines whether the yaw position of the wind turbine generator is abnormal according to the plurality of yaw time periods, the first yaw position data of each yaw time period in the plurality of yaw time periods, and the yaw duration of each yaw time period.

11. A controller for a wind power plant, characterized in that Comprise a processor and a computer readable storage medium storing programs or instructions which, when executed by the processor, implement the yaw position anomaly identification method of the wind turbine generator according to any one of claims 1 to 8.

12. A wind power unit, characterized in that Comprise the yaw position anomaly identification device of the wind turbine generator according to claim 10 or the controller according to claim 11.

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