Yaw position abnormity identification method and device of wind generating set and controller

Abstract

The invention provides a yaw position anomaly identification method and device of a wind generating set and a controller. The yaw position abnormity identification method comprises the following steps: acquiring a plurality of pieces of first yaw position data in a preset 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 in the predetermined yaw time period is kept unchanged based on the plurality of first yaw position change rates; and 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, whether the yaw position of the wind generating set is abnormal or not is determined. According to the yaw position abnormity identification method disclosed by the embodiment of the invention, whether the yaw position of the wind generating set is abnormal or not can be judged.

Description

Technical Field

[0001] The present disclosure generally relates to the field of wind power, and more specifically, to a method, device and controller for identifying abnormal yaw position of a wind turbine generator set. Background Art

[0002] If the yaw position of the wind turbine is abnormal during the yaw process (i.e., the collected yaw position data is abnormal), on the one hand, it will lead to the accumulation of yaw position errors, affecting the yaw wind accuracy or excessive cable twisting, thus affecting the safety of the wind turbine; on the other hand, if the yaw position is frequently maintained for a long time, a certain yaw error will be generated, affecting the stability of yaw control. The above yaw position abnormality is mostly caused by the abnormality of the yaw position sensor itself (for example, it is generally caused by the cam of the yaw position sensor jumping teeth).

[0003] At present, most control systems of wind turbines only identify and protect the upper and lower boundaries of the yaw position, and do not do much abnormal identification and protection for the yaw position change rules within the normal range. However, these abnormalities may affect the stability of yaw control or the accuracy of wind control, and even the safe operation of the unit. Summary of the invention

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

[0005] According to a first aspect of the present disclosure, a method for identifying yaw position abnormality of a wind turbine generator set includes: obtaining multiple first yaw position data for predetermined yaw time periods with the same yaw direction; calculating multiple first yaw position change rates corresponding to the multiple first yaw position data; obtaining multiple yaw time periods in which the yaw position data in the predetermined yaw time period remains unchanged based on the multiple first yaw position change rates; determining whether the yaw position of the wind turbine generator set is abnormal based on 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.

[0006] According to an embodiment of the present disclosure, determining whether the yaw position of a wind turbine generator set is abnormal based on 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 may include: determining at least one yaw time period that meets 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 and the first yaw position data after the end time of each yaw time period in 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 set has a yaw position retention abnormality.

[0007] According to an embodiment of the present disclosure, the step of determining at least one yaw time period that meets a preset condition based on multiple yaw time periods may include: sorting the multiple yaw time periods according to the order of their respective start times; merging the yaw time periods whose deviations between the end time of the previous yaw time period and the start time of the next yaw time period are within a preset time deviation, so as to obtain 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 moment and the first yaw position data after the end moment of each yaw time period in at least one yaw time period may include: calculating the position difference between the first yaw position data before the start moment and the first yaw position data after the end moment of each yaw time period in 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 moment and the first yaw position data after the end moment of each yaw time period in at least one yaw time period.

[0009] According to an embodiment of the present disclosure, the step of acquiring multiple first yaw position data of a predetermined yaw time period with the same yaw direction may include: collecting yaw position data at predetermined time intervals; and filtering out multiple first yaw position data of a 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 filtering out multiple first yaw position data of a predetermined yaw time period with the same yaw direction from the yaw position data may include: filtering out multiple segments of yaw position data from the yaw position data in which at least one of the yaw flag position of the wind turbine, the greasing status flag and the unwinding flag of the wind turbine is set, and the left yaw action field or the right yaw action field is continuously true; removing the first n yaw position data in each segment to obtain multiple first yaw position data.

[0011] According to an embodiment of the present disclosure, the step of filtering out multiple first yaw position data of a predetermined yaw time period with the same yaw direction from the yaw position data may include: filtering out multiple segments of yaw position data from the yaw position data when the left yaw action field or the right yaw action field of the wind turbine generator set is continuously true and both the greasing status flag and the unwinding flag are reset; removing the first n yaw position data in each segment to obtain multiple first yaw position data.

[0012] According to an embodiment of the present disclosure, the step of acquiring multiple yaw time periods in which the yaw position data in a predetermined yaw time period remains unchanged based on multiple first yaw position change rates may include: performing 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 length to acquire contour lines of the multiple first yaw position change rates; screening multiple first yaw position change rates and corresponding first yaw position data whose contour lines are less than a third predetermined threshold; cutting the first yaw position data based on whether the screened first yaw position data is continuous to acquire multiple yaw segments in which the yaw position data remains unchanged, and determining multiple yaw time periods corresponding to the multiple yaw segments according to the multiple yaw segments and the predetermined time intervals.

[0013] According to a second aspect of the present disclosure, a computer-readable storage medium stores instructions or programs, and when the instructions or programs are executed by a processor, the above-mentioned method for identifying yaw position anomaly of a wind turbine generator set is implemented.

[0014] According to a third aspect of the present disclosure, a device for identifying abnormal yaw position of a wind turbine generator set includes: a first acquisition unit, which acquires multiple first yaw position data of a predetermined yaw time period with the same yaw direction; a first calculation unit, which calculates multiple first yaw position change rates corresponding to the multiple first yaw position data; a second acquisition unit, which acquires multiple yaw time periods in which the yaw position data in the predetermined yaw time period remain unchanged based on the multiple first yaw position change rates; and an identification unit, which determines whether the yaw position of the wind turbine generator set is abnormal based on the multiple yaw time periods and 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.

[0015] According to a fourth aspect of the present disclosure, a controller of a wind turbine generator set includes a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, the above-mentioned method for identifying yaw position abnormality of the wind turbine generator set is implemented.

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

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

[0018] The yaw position identification method according to the embodiment of the present disclosure can identify the abnormal phenomenon that the yaw position of the wind turbine generator set is maintained for a long time. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0020] Figure 1 It is a curve chart reflecting the abnormality of the yaw position maintenance of the wind turbine generator set;

[0021] Figure 2 is a flow chart showing a method for identifying yaw position abnormality according to a first embodiment of the present disclosure;

[0022] Figure 3 is a flow chart showing a method for identifying yaw position abnormality according to a second embodiment of the present disclosure;

[0023] Figure 4 is a flow chart showing a method for identifying yaw position abnormality according to a third embodiment of the present disclosure;

[0024] Figure 5 is a graph showing yaw position retention identified by a yaw position identification method according to an embodiment of the present disclosure;

[0025] Figure 6 is a timing diagram showing a yaw rate change identified by a yaw position identification method according to an embodiment of the present disclosure;

[0026] Figure 7 is a time series diagram showing a yaw position change rate identified by a yaw position identification method according to an embodiment of the present disclosure;

[0027] Figure 8 is a graph illustrating status flag bits or fields according to an embodiment of the present disclosure;

[0028] Fig. 9 is a block diagram showing a yaw position abnormality identification apparatus according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] The following detailed description is provided to help gain a comprehensive understanding of the methods, devices and / or systems described herein. However, the order of operations described herein is only an example and is not limited to those orders set forth herein, but may be equivalently replaced or changed except for operations that must occur or be performed in a specific order. In addition, for greater clarity and simplicity, the description of content 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 those generally understood by a person of ordinary skill in the art to which the present disclosure belongs after understanding the present disclosure. Unless explicitly defined as such herein, terms (such as those defined in a general dictionary) should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and should not be interpreted in an idealized or overly formal manner.

[0031] Unless otherwise specified, the same reference numerals generally refer to the same elements (e.g., components, steps, and methods). Reference numerals described in the previous embodiments may be omitted if they appear again in the subsequent embodiments. In addition, the technical features described in different or the same embodiments may 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 based on the above complete technical solutions.

[0032] The present invention obtains yaw position data within a predetermined time period under the same yaw direction, calculates the corresponding yaw position change rate, determines multiple yaw segments (yaw position data collected at a predetermined sampling interval) in which the yaw position remains unchanged within the time period, and identifies yaw position anomalies (for example, position retention anomalies) based on these yaw segments, the yaw duration of each yaw segment, and the corresponding yaw position data. Figures 1 to 9 Specific embodiments of the present disclosure are described.

[0033] Figure 1 It is a curve graph reflecting the abnormal phenomenon of yaw position maintenance of wind turbine generator set.

[0034] Under normal circumstances, the yaw position (or yaw position data) of a wind turbine during yaw should change continuously. However, the yaw position may be abnormal due to abnormalities such as the yaw position sensor. For example, Figure 1 The yaw position holding phenomenon shown can be used to determine whether the yaw position of the wind turbine is abnormal by analyzing the yaw position data and identifying abnormal features. Considering that most of these abnormalities are caused by abnormal yaw position sensors, it is possible to determine whether the yaw position sensor is abnormal.

[0035] Reference Figure 1 , Figure 1 The horizontal axis represents the sampling coefficient (or index value), the left vertical axis represents the yaw position change rate, and the right vertical axis represents the yaw position. When the yaw position (or yaw position data) is abnormal, the yaw position can remain basically unchanged in a short time. As the yaw progresses, if the yaw position remains unchanged, the abnormal phenomenon that the yaw position remains for a long time (for example, more than 60 seconds) occurs.

[0036] The above abnormal phenomenon can be determined according to a plurality of yaw time periods, a yaw duration of each yaw time period in 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 may include step S210 , step S220 , step S230 , and step S240 .

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

[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 go back and forth. It takes a certain amount of time to identify the above abnormality. The yaw position data under conditions such as unwinding, greasing, and long-term yaw-to-wind can be selected for identification and judgment. The above-mentioned predetermined yaw time period can be a predetermined time period for the above-mentioned working conditions.

[0041] The yaw position data may be collected by a yaw position sensor. As an example, the yaw position data may be collected at predetermined time intervals (eg, once every seven seconds, with a time interval of 6 seconds), thereby obtaining a plurality of yaw position data (first yaw position data).

[0042] Specifically, the step of acquiring a plurality of first yaw position data of a predetermined yaw time period with the same yaw direction may include: collecting yaw position data at predetermined time intervals; and filtering out 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 selecting a plurality of first yaw position data of a predetermined yaw time period with the same yaw direction from the yaw position data comprises:

[0044] Filter out multiple segments of yaw position data from the yaw position data when the yaw flag (or yaw state flag) of the wind turbine generator set is set, at least one of the grease state flag and the unwinding flag of the wind turbine generator set is set, and the left yaw action field or the right yaw action field is continuously true; remove the first n yaw position data in each segment to obtain multiple first yaw position data. Here n can be selected as needed, and as an example, n can be 15.

[0045] The setting of the yaw flag bit, the grease state flag bit and the cable release flag bit may indicate that the yaw flag bit, the grease state flag bit and the cable release flag bit are changed to corresponding bit values.

[0046] That is to say, it is possible to identify multiple segments of yaw position data when the yaw action field is continuously true and at least one of the grease status flag and the cable release flag is set. The multiple segments of yaw position data here may refer to yaw position data with discontinuous index values ​​(sampling sequence numbers).

[0047] In addition, the step of selecting a plurality of first yaw position data of a predetermined yaw time period with the same yaw direction from the yaw position data may include: selecting a plurality of segments of yaw position data from the yaw position data when the left yaw action field or the right yaw action field is continuously true and both the grease state flag and the cable release flag are reset; removing the first n yaw position data in each segment to obtain the plurality of first yaw position data. The resetting of the yaw flag, the grease state flag and the cable release flag may indicate that the yaw flag, the grease state flag and the cable release flag become zero.

[0048] A separate wind status flag can be set as needed to filter the yaw position data in the same yaw direction for a long time.

[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 eliminated.

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

[0051] A plurality of yaw position change rates may be obtained by calculating the difference between two adjacent first yaw position data.

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

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

[0054] The time period in which the change rate of adjacent yaw position data is substantially zero (within a preset range) can be determined as the above yaw time period, but this is only an example and can also be further determined in combination with the corresponding yaw position data. Accordingly, multiple yaw time periods that meet the conditions can be identified.

[0055] In step S240, whether the yaw position of the wind turbine generator set is abnormal is determined 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.

[0056] When there are multiple yaw time periods in which the first yaw position change rate is zero, the first yaw position data and the yaw duration of the yaw time period may be further combined to determine whether the yaw of the wind turbine generator set is abnormal. For example, when the difference between the first yaw position data of a yaw time period and the yaw position data at a moment before the moment of the first yaw position data is small (for example, less than 2.5 degrees), it may be determined that the yaw position of the wind turbine generator set is abnormal.

[0057] Figure 3 is a flowchart 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 set is abnormal may include step S241 and step S242 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.

[0059] In step S241, at least one yaw time period satisfying a preset condition among a plurality of yaw time periods is determined.

[0060] In step S242, 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 at least one yaw time period and the first yaw position data after the end time period, and the yaw duration of each yaw time period exceeds a first preset threshold, it is determined that there is a yaw position maintenance abnormality in the yaw position of the wind turbine generator set.

[0061] That is to say, it is possible to determine whether there is any yaw position abnormality in the yaw position of the wind turbine generator set by the size of the difference between the yaw position data of a single yaw time period and the yaw position data before the start time of the single yaw time period (for example, the previous yaw position data) and the duration of each yaw time period (that is, the yaw duration).

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

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

[0064] In addition, the yaw time periods that meet the conditions may be obtained by first screening and then merging. Specifically, the step of determining at least one yaw time period that meets the preset conditions among the multiple yaw time periods may include: sorting the multiple yaw time periods in the order of their respective start times; merging the yaw time periods whose deviations between the end time of the previous yaw time period and the start time of the next yaw time period are within the preset time deviation, so as to obtain at least one yaw time period.

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

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

[0067] Specifically, the step of determining that there is no jump between the first yaw position data before the start moment of each yaw time period in at least one yaw time period and the first yaw position data after the end moment may include: calculating the position difference between the first yaw position data before the start moment of each yaw time period in at least one yaw time period and the first yaw position data after the end moment; 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 moment of each yaw time period in at least one yaw time period and the first yaw position data after the end moment (i.e., position maintenance).

[0068] Taking the normal yaw rate of 0.3 degrees / second as an example, it is collected once every 7 seconds. Assume 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 is maintained abnormally from the fourth yaw position data, the yaw position data remains unchanged until the eighth data, that is, the yaw position data is maintained 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. It is also possible to determine whether there is a yaw position maintenance abnormality through the ratio of the position difference between the two first yaw position data and the yaw duration, thereby improving the recognition accuracy.

[0069] In specific implementation, the yaw time period in which the yaw position change rate meets the requirements can be screened by taking the maximum value through a sliding window.

[0070] As an example, the step of obtaining a plurality of yaw time periods in which the yaw position data in a predetermined yaw time period remains unchanged based on a plurality of first yaw position change rates may include: performing 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 length to obtain contour lines of the plurality of first yaw position change rates; screening a plurality of first yaw position change rates and corresponding first yaw position data whose contour lines are less than a second predetermined threshold; cutting the first yaw position data based on 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 intervals.

[0071] In addition, the sliding window method is only used as an example. In specific implementation, the yaw position change rate labeling binning method (i.e., classification according to the size of the yaw position change rate) can also be used to select the yaw time period in which the yaw position change rate meets the requirements.

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

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

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

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

[0076] In step S420, segments under specific working conditions are selected from the collected data as single yaw segments. The specific working condition here can be facing the wind in the same direction for a long time, or can be a greasing working condition or a cable unwinding working condition.

[0077] In step S430, data preprocessing is performed on each screened yaw segment to remove the first 100 seconds of data in each continuous segment, thereby eliminating data instability factors such as state changes or signal delays.

[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 the data point or segment held at each position are obtained (or the start index value and the end index value may also be obtained).

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

[0081] In step S470, screening is performed based on whether the yaw position jumps, whether the yaw duration meets the requirements, and whether the time interval meets the requirements. For example, screening is performed based on whether the position difference between the yaw position corresponding to the previous data at the start time of the yaw segment and the yaw position corresponding to the next data at the end time is less than a predetermined threshold, the yaw duration of the yaw segment reaches a predetermined threshold, and the time interval between two adjacent yaw segments is within a predetermined time interval.

[0082] In step S480, the yaw position data of two adjacent yaw time periods whose time interval is within a preset time range (eg, 30 seconds) may be merged.

[0083] The predetermined threshold (for example, the first predetermined threshold, etc.), the preset threshold, the predetermined time interval, etc. of the present disclosure may all be empirical values.

[0084] In step S490, the merged yaw segment is taken as a new segment, and the start time, end time, duration (i.e., yaw duration), segment points, yaw position corresponding to the data before the start time, and yaw position corresponding to the data after the end time of the new segment are calculated, and yaw duration identification and yaw position retention identification are performed. The details are as described above and will not be repeated here.

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

[0086] Figure 5 is a graph showing yaw position retention identified by a yaw position identification method according to an embodiment of the present disclosure; Figure 6 is a timing diagram showing a yaw rate change identified by a yaw position identification method according to an embodiment of the present disclosure; Figure 7 is a time series diagram showing a yaw position change rate identified by a yaw position identification method according to an embodiment of the present disclosure; Figure 8 is a graph illustrating status flag bits or fields according to an embodiment of the present disclosure.

[0087] Reference Figure 5 , the yaw position shows a long-term abnormality, and the long-term abnormality from about 00:22 to about 00:24 can be identified. Figure 6 , the yaw rate remains basically unchanged (around 0 degrees). Figure 7 , the yaw position change rate remains zero for a long time. Figure 8 , Figure 8 The curve G1 in the figure reflects the state of unwinding or greasing, 15 represents the position of the unwinding mark, 16 represents the position of the greasing mark, and the curve G2 reflects the yaw state. The yaw mark of 1 represents yaw, and the yaw mark of 0 represents no yaw.

[0088] Fig. 9 is a block diagram showing a yaw position abnormality identification apparatus according to an embodiment of the present disclosure.

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

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

[0091] As an example, the first acquisition unit 610 may collect yaw position data at a predetermined time interval, and filter out a plurality of 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 may filter out a plurality of segments of yaw position data from the yaw position data when at least one of the yaw flag position of the wind turbine generator set, the grease state flag of the wind turbine generator set, and the unwinding flag is set, and the left yaw action field or the right yaw action field is continuously true; remove the first n yaw position data in each segment to obtain the plurality of first yaw position data.

[0092] In addition, the first acquisition unit 610 can also filter out multiple segments of yaw position data from the yaw position data when the yaw flag position, left yaw action field or right yaw action field of the wind turbine generator set is continuously true and both the greasing status flag and the unwinding flag are reset.

[0093] The first calculation unit 620 may calculate a plurality of first yaw position change rates corresponding to the plurality of first yaw position data.

[0094] The second acquiring unit 630 may acquire a plurality of yaw time periods in which the yaw position data in the predetermined yaw time period remains unchanged based on the plurality of first yaw position change rates.

[0095] The second acquisition unit 630 may perform 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 length to obtain contour lines of multiple first yaw position change rates; screen multiple first yaw position change rates and corresponding first yaw position data whose contour lines are less than a second predetermined threshold; cut the first yaw position data based on whether the screened first yaw position data is continuous to obtain multiple yaw segments in which the yaw position data remains 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 may determine whether the yaw position of the wind turbine generator set 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 that meets the preset conditions based on multiple yaw time periods; 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 at least one yaw time period and the yaw duration of each yaw time period exceeds a first preset threshold, it is determined that there is a yaw position maintenance abnormality in the yaw position of the wind turbine generator set.

[0098] The identification unit 640 may also perform the holding judgment across multiple yaw time periods.

[0099] The identification unit 640 can calculate 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 at least one yaw time period; in response to the position difference being less than or equal to a second preset threshold, it is determined 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 at least one yaw time period.

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

[0101] Each operation of the above steps can be written as a software program or instruction. Therefore, the control method according to the exemplary embodiment of the present disclosure can be implemented via software. The computer-readable storage medium of the exemplary embodiment of the present disclosure can store a computer program. When the computer program is executed by the processor, the yaw position abnormality identification method as described in the above exemplary embodiment is implemented.

[0102] According to various embodiments of the present disclosure, a device (e.g., a module or their functions) or a method may be implemented by a program or instruction stored in a computer-readable storage medium. When the instruction is executed by a processor, the processor may perform a function corresponding to the instruction or perform a method corresponding to the instruction. At least a portion of a module may be implemented (e.g., executed) by a processor. At least a portion of a programming module may include a module, a program, a routine, an instruction set, and a process for performing at least one function. In one example, an instruction or software includes a machine code (such as a machine code generated by a compiler) directly executed by one or more processors or computers. In another example, an instruction or software includes a higher level code executed by one or more processors or computers using an interpreter. Instructions or software may be written in any programming language based on the block diagrams and flow charts shown in the accompanying drawings and the corresponding descriptions in the specification.

[0103] Computer-readable storage media include non-transitory computer-readable storage media, for example, magnetic media such as floppy disks and tapes, optical media (including compact disks (CD) ROMs and DVD ROMs), magneto-optical media such as floppy disks, hardware devices such as ROMs, RAMs, and flash memories 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 devices 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 programming module of the present disclosure may include at least one of the aforementioned components when some components are omitted or other components are added. The operation of the module, programming module or other components may be performed sequentially, in parallel, in a loop or in a tentative manner. In addition, some operations may be performed in different orders, may be omitted or extended with other operations.

[0105] The computer-readable storage medium and / or the yaw position abnormality identification device of the exemplary embodiments of the present disclosure may be a part of a controller (eg, a main controller) of a wind turbine generator set.

[0106] According to an exemplary embodiment of the present disclosure, a controller may include: a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program or instructions, and when the computer program or instructions are executed by the processor, the yaw position abnormality identification method as described in the above exemplary embodiment is implemented.

[0107] The wind turbine generator set according to the embodiment of the present disclosure may include the computer-readable storage medium, the yaw position abnormality identification device or the controller as described above.

[0108] According to the yaw position abnormality identification method and yaw position abnormality identification device of the embodiments of the present disclosure, it is possible to determine whether the yaw position of a wind turbine generator set is abnormal.

[0109] The yaw position identification method and the yaw position abnormality identification 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, it will be understood by those skilled in the art that modifications may be made to these embodiments without departing from the principles and spirit of the present disclosure, the scope of which is defined by the claims and their equivalents; for example, the technical features of different embodiments may be combined.

Claims

1. A method for identifying abnormal yaw position of a wind turbine generator set, characterized in that: include: Acquire a plurality of first yaw position data in 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; Acquire, based on the plurality of first yaw position change rates, a plurality of yaw time periods in which the yaw position data in the predetermined yaw time period remains unchanged; Whether the yaw position of the wind turbine generator set is abnormal is determined 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.

2. The method for identifying abnormal yaw position of a wind turbine generator set according to claim 1, characterized in that: Determining whether the yaw position of the wind turbine generator set 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 includes: Determining at least one yaw time period among the multiple 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 of each yaw time period in the at least one yaw time period and the first yaw position data after the end time period, and the yaw duration of each yaw time period exceeds a first preset threshold, it is determined that there is a yaw position maintenance abnormality in the yaw position of the wind turbine generator set.

3. The method for identifying abnormal yaw position of a wind turbine generator set according to claim 2, characterized in that: The step of determining at least one yaw time period among the plurality of yaw time periods that meets a preset condition comprises: Sorting the multiple yaw time periods according to the order of their respective start times; The yaw time periods whose deviations between the end time of the previous yaw time period and the start time of the next yaw time period are within a preset time deviation are merged to obtain the at least one yaw time period.

4. The method for identifying abnormal yaw position of a wind turbine generator set 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 of each yaw time period in the at least one yaw time period and the first yaw position data after the end time period comprises: Calculate a 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, it is determined 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 method for identifying abnormal yaw position of a wind turbine generator set according to any one of claims 1 to 4, characterized in that: The step of acquiring a plurality of first yaw position data in a predetermined yaw time period with the same yaw direction comprises: collecting yaw position data at predetermined time intervals; A plurality of first yaw position data of the predetermined yaw time period with the same yaw direction are selected from the yaw position data.

6. The method for identifying abnormal yaw position of a wind turbine generator set according to claim 5, characterized in that: The step of selecting a plurality of first yaw position data of the predetermined yaw time period having the same yaw direction from the yaw position data comprises: Filtering out from the yaw position data a plurality of segments of yaw position data in which a yaw flag of the wind turbine generator set, at least one of a grease state flag and a cable release flag of the wind turbine generator set is set, and a left yaw action field or a right yaw action field is continuously true; The first n yaw position data in each segment are removed to obtain the plurality of first yaw position data.

7. The method for identifying abnormal yaw position of a wind turbine generator set according to claim 5, characterized in that: The step of selecting a plurality of first yaw position data of the predetermined yaw time period with the same yaw direction from the yaw position data comprises: Filter out multiple segments of yaw position data from the yaw position data when the left yaw action field or the right yaw action field of the wind turbine generator set is continuously true and both the grease adding state flag and the cable unwinding flag are reset; The first n yaw position data in each segment are removed to obtain the plurality of first yaw position data.

8. The method for identifying abnormal yaw position of a wind turbine generator set according to claim 5, characterized in that: The step of acquiring a plurality of yaw time periods in which the yaw position data in the predetermined yaw time period remains unchanged based on the plurality of first yaw position change rates 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 length to obtain outer contours of the plurality of first yaw position change rates; Screening a plurality of first yaw position change rates and corresponding first yaw position data whose outer contours are less than a third predetermined threshold; The first yaw position data is cut based on whether the filtered first yaw position data is continuous to obtain a plurality of yaw segments in which the yaw position data remains unchanged, and a plurality of yaw time periods corresponding to the plurality of yaw segments are determined according to the plurality of yaw segments and the predetermined time intervals.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions or programs, and when the instructions or programs are executed by a processor, the method for identifying yaw position anomaly of a wind turbine generator set according to any one of claims 1 to 8 is implemented.

10. A device for identifying abnormal yaw position of a wind turbine generator set, characterized in that: include: A first acquisition unit, acquiring a plurality of first yaw position data in a predetermined yaw time period with the same yaw direction; A first calculation unit calculates a plurality of first yaw position change rates corresponding to the plurality of first yaw position data; a second acquiring unit, acquiring, based on the plurality of first yaw position change rates, a plurality of yaw time periods in which the yaw position data in the predetermined yaw time period remains unchanged; The identification unit determines whether the yaw position of the wind turbine generator set 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.

11. A controller for a wind turbine generator set, characterized in that: The method comprises a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores a program or an instruction, and when the program or the instruction is executed by the processor, the method for identifying the abnormal yaw position of a wind turbine generator set according to any one of claims 1 to 8 is implemented.

12. A wind turbine generator set, characterized in that: It comprises the device for identifying abnormal yaw position of a wind turbine generator set according to claim 10 or the controller according to claim 11.

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