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 and the first yaw position data of each yaw time period in the plurality of yaw time periods. 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] This disclosure generally relates to the field of wind power, and more specifically, to a method, apparatus and controller for identifying abnormal yaw positions of wind turbine generators. Background Technology

[0002] During the yaw process of a wind turbine generator, if the yaw position is abnormal (i.e., the collected yaw position data is abnormal), it will lead to the accumulation of yaw position errors, affecting the accuracy of yaw alignment with the wind or causing excessive cable twisting, thus affecting the safety of the wind turbine generator. Furthermore, frequent yaw position jumps or short-term holding of the yaw position will generate certain yaw errors, affecting the stability of yaw control. Most of these abnormal yaw position phenomena are caused by malfunctions in the yaw position sensor itself.

[0003] Currently, most wind turbine control systems only identify and protect against exceeding the upper and lower boundaries of yaw position, without addressing anomalies or providing protection for yaw position variations within the normal range. However, these anomalies can affect the stability of yaw control, the accuracy of wind control, and even the safe operation of the turbine. Summary of the Invention

[0004] One of the objectives of the exemplary embodiments disclosed herein is to provide a yaw position anomaly identification method capable of identifying yaw position anomalies of wind turbine generators.

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

[0006] According to embodiments of this disclosure, the step of determining whether the yaw position of a wind turbine generator is abnormal based on multiple yaw time periods and the first yaw position data of each of the multiple yaw time periods may include: filtering out at least one yaw time period that meets preset conditions from the multiple yaw time periods; in response to determining that there is a 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 periods and the yaw duration of each yaw time period is within a preset range, determining that the yaw position of the wind turbine generator has an abnormal step change.

[0007] According to embodiments of this disclosure, the step of selecting at least one skew time period that meets preset conditions from multiple skew time periods may include: sorting the multiple skew time periods according to the order of their respective start times; selecting from the sorted multiple skew time periods a skew time period in which the deviation between the end time of the previous skew time period and the start time of the next skew time period is within a preset time deviation, so as to obtain at least one skew time period.

[0008] According to embodiments of this disclosure, the step of determining that there is a 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 may 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 at least one yaw time period, and the yaw duration of each yaw time period in at least one yaw time period; in response to the ratio of the position difference to the corresponding yaw duration being greater than a first preset threshold, determining that there is a 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.

[0009] According to embodiments of this disclosure, the step of acquiring multiple first yaw position data within 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 within a predetermined yaw time period with the same yaw direction from the yaw position data.

[0010] According to embodiments of this disclosure, the step of filtering multiple first yaw position data with the same yaw direction and a predetermined yaw time period from yaw position data may include: filtering multiple segments of yaw position data where at least one of the yaw flag position of the wind turbine generator, the grease-adding status flag position of the wind turbine generator, and the unmooring flag position is set, or where the left yaw action field or the right yaw action field is continuously true; and removing the first n yaw position data from each segment to obtain multiple first yaw position data.

[0011] According to embodiments of this disclosure, the step of filtering multiple first yaw position data from yaw position data within a predetermined yaw time period with the same yaw direction may include: filtering multiple segments of yaw position data where the left yaw action field or the right yaw action field is continuously true and both the grease-adding status flag and the unmooring flag are reset; and removing the first n yaw position data from each segment to obtain multiple first yaw position data.

[0012] According to embodiments of this disclosure, the step of obtaining multiple yaw time periods in a predetermined yaw time period where the yaw position data remains unchanged based on multiple first yaw position change rates may include: performing a sliding window maximum value processing on the absolute values ​​of multiple yaw position change rates with a predetermined sliding window length and a predetermined sliding step size to obtain the outlines of multiple first yaw position change rates; filtering multiple first yaw position change rates and corresponding first yaw position data whose outlines are less than a second predetermined threshold; segmenting the first yaw position data based on whether the filtered first yaw position data is continuous to obtain multiple yaw segments where the yaw position data remains unchanged, and determining multiple yaw time periods corresponding to the multiple yaw segments based on the multiple yaw segments and a predetermined time interval.

[0013] According to a second aspect of this disclosure, a computer-readable storage medium stores instructions or programs that, when executed by a processor, implement the above-described method for identifying abnormal yaw positions of a wind turbine generator.

[0014] According to a third aspect of this disclosure, a device for identifying abnormal yaw positions of a wind turbine generator set may include: a first acquisition unit for acquiring multiple first yaw position data over a predetermined yaw time period with the same yaw direction; a first calculation unit for calculating multiple first yaw position change rates corresponding to the multiple first yaw position data; a second acquisition unit for acquiring multiple yaw time periods in the predetermined yaw time period where the yaw position data remains unchanged based on the multiple first yaw position change rates; and an identification unit for determining 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 of the multiple yaw time periods.

[0015] According to a third aspect of this disclosure, a controller for a wind turbine generator set may include a processor and a computer-readable storage medium storing a program or instructions that, when executed by the processor, implement the aforementioned method for identifying abnormal yaw positions of the wind turbine generator set.

[0016] According to a fourth aspect of this disclosure, a wind turbine generator may include the aforementioned yaw position anomaly identification device or the aforementioned controller.

[0017] The yaw position identification method according to the embodiments of this disclosure can reduce yaw error and improve the stability of yaw control. Attached Figure Description

[0018] The above and other objects and features of exemplary embodiments of this disclosure will become clearer from the following description taken in conjunction with the accompanying drawings, which exemplarily illustrate the embodiments, wherein:

[0019] Figure 1 It is a graph reflecting the abnormal phenomenon of step changes in the yaw position of wind turbine generators;

[0020] Figure 2 This is a flowchart illustrating a yaw position anomaly identification method according to a first embodiment of the present disclosure;

[0021] Figure 3 This is a flowchart illustrating a yaw position anomaly identification method according to a second embodiment of the present disclosure;

[0022] Figure 4 This is a flowchart illustrating a yaw position anomaly identification method according to a third embodiment of the present disclosure;

[0023] Figure 5 It is a graph showing the step change of yaw position identified by the yaw position identification method according to an embodiment of the present disclosure;

[0024] Figure 6 This is a timing diagram showing the yaw rate change identified by the yaw position identification method according to an embodiment of the present disclosure;

[0025] Figure 7 This is a time-series diagram showing the yaw position change rate identified by the yaw position identification method according to an embodiment of the present disclosure;

[0026] Figure 8 It is a graph illustrating the status flag bits or fields according to embodiments of the present disclosure;

[0027] Figure 9 This is a block diagram illustrating a yaw position anomaly identification device according to an embodiment of the present disclosure. Detailed Implementation

[0028] The following detailed description is provided to aid in obtaining a full understanding of the methods, apparatus, and / or systems described herein. However, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein; equivalent substitutions or changes may be made, except for operations that must occur or be performed in a specific order. Furthermore, for clarity and conciseness, descriptions of content well-known in the art will be omitted or simplified.

[0029] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains upon understanding this disclosure. Unless expressly defined herein, terms (such as those defined in a general dictionary) shall be interpreted as having a meaning consistent with their meaning in the context of the relevant field and in this disclosure, and shall not be interpreted in an idealized or overly formalistic manner.

[0030] Unless otherwise specified, the same reference numerals generally refer to the same elements (e.g., components, steps, and methods). Reference numerals described in previous embodiments that reappear in later embodiments may be omitted. Furthermore, technical features described in different or the same embodiments can be combined in any way, as long as the combined embodiment or technical solution is complete and can solve the technical problem of this application or achieve the technical effects described or not described in this application but which can be determined based on the complete technical solution described above.

[0031] This disclosure obtains yaw position data over a predetermined time period under the same yaw direction, calculates the corresponding yaw position change rate, identifies multiple yaw segments (yaw position data collected at a predetermined sampling interval) whose yaw position remains unchanged within that time period, and identifies yaw position anomalies (e.g., abnormal step changes) based on these yaw segments and the corresponding yaw position data. The following will combine... Figures 1 to 9 Specific embodiments of this disclosure are described below.

[0032] Figure 1 It is a graph reflecting the abnormal phenomenon of step changes in the yaw position of wind turbine generators.

[0033] Under normal circumstances, the yaw position (or yaw position data) of a wind turbine generator during yaw should change continuously. However, the yaw position may become abnormal due to malfunctions in the yaw position sensor or other components. For example, [the following may occur]. Figure 1 The anomalies shown (e.g., step changes) can be identified by analyzing the yaw position data and recognizing the abnormal features, thus determining whether the yaw position of the wind turbine is abnormal. Considering that most of these anomalies are caused by malfunctions in the yaw position sensor, it is possible to determine whether the yaw position sensor is malfunctioning.

[0034] Reference Figure 1 When the yaw position (or yaw position data) is abnormal, the yaw position can remain basically unchanged for a short period of time, as shown by curve G2. As the yaw progresses, the yaw position may suddenly change (as shown by curve G1). After the sudden change, the yaw position remains unchanged for a period of time (i.e., a short period of stability or a short period of stability). The phenomenon of sudden change → stability → sudden change → stability may occur.

[0035] The above-mentioned anomalies can be determined based on multiple deviation time periods and the deviation position (or deviation position data) of each deviation time period.

[0036] Figure 2 This is a flowchart illustrating a yaw position anomaly identification method according to a first embodiment of the present disclosure.

[0037] The yaw position anomaly identification method according to the first embodiment of the present disclosure may include steps S110, S120, S130 and S140.

[0038] In step S110, multiple first yaw position data for a predetermined yaw time period with the same yaw direction are acquired.

[0039] Obtaining yaw position data with the same yaw direction simplifies the identification process. If the yaw against the wind is short-lived and the yaw position changes significantly, the yaw direction may fluctuate. Identifying such anomalies requires time; yaw position data from manual, prolonged yaw in one direction or automatic yaw against the wind in one direction both meet the requirements. Yaw position data from conditions such as uncoiling, greasing, and prolonged yaw against the wind can be selected for identification and judgment. The predetermined yaw time period mentioned above can be a predetermined time period under the aforementioned conditions.

[0040] Yaw position data can be collected by a yaw position sensor. For example, yaw position data can be collected at predetermined time intervals (e.g., once every 7 seconds with a time interval of 6 seconds) to obtain multiple yaw position data (first yaw position data).

[0041] Specifically, the steps of acquiring multiple first yaw position data for 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 for a predetermined yaw time period with the same yaw direction from the yaw position data.

[0042] The steps for filtering multiple first yaw position data points with the same yaw direction for a predetermined yaw time period from the yaw position data include: filtering out multiple segments of yaw position data where the yaw flag (or yaw status flag) of the wind turbine generator is set, at least one of the grease-adding status flag and the unmooring flag of the wind turbine generator is set, or the left yaw action field or the right yaw action field is continuously true; removing the first n yaw position data points from each segment to obtain multiple first yaw position data points, where n can be selected as needed, and as an example, n can be 15.

[0043] Setting the yaw flag, grease application status flag, and unmooring flag can indicate that the yaw flag, grease application status flag, and unmooring flag change to their respective values.

[0044] In other words, multiple segments of yaw position data can be identified when the yaw action field is continuously true, the grease-adding status flag, and the unmooring flag are set. These multiple segments of yaw position data can refer to yaw position data with discontinuous index values ​​(or sampling numbers).

[0045] Additionally, the step of filtering multiple first yaw position data points with the same yaw direction for a predetermined yaw time period from the yaw position data may include: filtering multiple segments of yaw position data where the left yaw action field or the right yaw action field is continuously true and both the greasing status flag and the unmooring flag are reset; removing the first n yaw position data points from each segment to obtain the multiple first yaw position data points. The reset of the yaw flag, greasing status flag, and unmooring flag can indicate that the yaw flag, greasing status flag, and unmooring flag have become preset values ​​(e.g., zero).

[0046] A separate wind position flag can be set as needed to filter yaw position data with the same yaw direction over a long period of time.

[0047] As mentioned above, removing a predetermined number of yaw position data points from the beginning of each segment can improve the reliability of the filtering. Specifically, it can eliminate data instability factors such as state changes or signal delays.

[0048] In step S120, the multiple first yaw position change rates corresponding to the multiple first yaw position data are calculated.

[0049] Multiple yaw position change rates can be calculated by the difference between two adjacent first yaw position data.

[0050] In step S130, multiple yaw time periods in a predetermined yaw time period are obtained based on multiple first yaw position change rates, where the yaw position data remains unchanged.

[0051] The yaw position data remaining unchanged means that the yaw position data remains basically unchanged. For example, if the rate of change of multiple first yaw position data within a certain yaw time period is within a preset range (e.g., 0.3 degrees), it can be determined that the yaw position data within that yaw time period remains basically unchanged.

[0052] The aforementioned yaw time period can be defined as a time period in which the rate of change of adjacent yaw position data is essentially zero (within a preset range). However, this is merely an example, and it can be further determined by combining relevant yaw position data. Based on this, multiple yaw time periods that meet the conditions can be identified.

[0053] In step S140, based on multiple yaw time periods and the first yaw position data of each yaw time period, it is determined whether the yaw position of the wind turbine is abnormal.

[0054] In cases where there are multiple yaw time periods with a zero rate of change in the first yaw position, the yaw of the wind turbine can be further analyzed by combining the first yaw position data of each yaw time period. For example, if the difference between the first yaw position data of a certain yaw time period and the yaw position data of the previous acquisition time is large, it can be determined that the yaw position of the wind turbine is abnormal.

[0055] Figure 3 This is a flowchart illustrating a yaw position anomaly identification method according to a second embodiment of the present disclosure.

[0056] The steps for determining whether the yaw position of a wind turbine is abnormal based on multiple yaw time periods and the first yaw position data of each of the multiple yaw time periods may include steps S141 and S142.

[0057] In step S141, at least one yaw time period that meets the preset conditions is selected from multiple yaw time periods.

[0058] In step S142, in response to the determination that there is a 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 is within a preset range, it is determined that there is an abnormal step change in the yaw position of the wind turbine generator.

[0059] In other words, the presence of an abnormal step change in the yaw position of a wind turbine can be determined by comparing the difference between the yaw position data of a single yaw time period and the yaw position data before the start of that single yaw time period (e.g., the previous yaw position data), as well as the duration of each yaw time period (i.e., yaw duration). As an example, at least two yaw time periods that meet preset conditions can be selected from multiple yaw time periods for judgment, thereby determining whether the wind turbine's yaw position exhibits an abnormal step change. When only one yaw time period is selected, the judgment can be based on the yaw position data before the start of that yaw time period (this yaw position data is from an unselected yaw time period) and the yaw position data after the end of that yaw time period (this yaw position data is from an unselected yaw time period).

[0060] For example, refer to Figure 1The start time of a single yaw time period can be determined, and then the rate of change of the yaw position data at the start time with the yaw position data at the previous time can be calculated. Similarly, the end time of a single yaw time period can be determined, and then the rate of change of the yaw position data at the end time with the yaw position data at the next time can be calculated. If both rates of change exceed a predetermined threshold, then it can be determined that there is an abnormal step change.

[0061] To further determine whether there are abnormal step changes in the yaw position of the wind turbine generator, multiple yaw time periods can be screened. 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 a preset time deviation; the yaw duration of each yaw time period is greater than a preset threshold.

[0062] Specifically, the step of selecting at least one skew time period that meets preset conditions from multiple skew time periods may include: sorting the multiple skew time periods according to the order of their respective start times; selecting from the sorted multiple skew time periods a skew time period in which the deviation between the end time of the previous skew time period and the start time of the next skew time period is within a preset time deviation, so as to obtain at least one skew time period.

[0063] As an example, from the sorted multiple yaw time periods, the yaw time periods whose 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 selected, and the yaw time periods with a yaw duration greater than a preset threshold can be further filtered out.

[0064] The change in the first yaw position data can be determined by the position difference between the first yaw position data before the start time and the first yaw position data after the end time of a certain yaw time period within at least one yaw time period, or by the position difference between the first yaw position data at the end time of a certain yaw time period before the start time and the first yaw position data at the start time of a certain yaw time period after the end time of that certain yaw time period.

[0065] Here, the first yaw position data before the start time of a certain yaw time period refers to the first yaw position data before the start time of that yaw time period (yaw position data collected 6 seconds before the start time), and the first yaw position data after the end time of a certain yaw time period refers to the first yaw position data after the end time of that yaw time period (yaw position data collected 6 seconds after the end time).

[0066] Specifically, the step of determining that there is a 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 may 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 at least one yaw time period, and the yaw duration of each yaw time period in at least one yaw time period; and, in response to the ratio of the position difference to the corresponding yaw duration being greater than a first preset threshold, determining that there is a 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.

[0067] Taking a normal yaw rate of 0.3 degrees / second as an example, data is collected every 7 seconds. Assume 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 an abnormal step change occurs starting from the third yaw position data, and the yaw position data remains unchanged until the eighth data point, that is, the yaw position data is maintained for 35 seconds, the first yaw position data at the beginning of this yaw time period is 0.3 degrees, and the first yaw position data after the end of this yaw time period is 2.4 degrees. The ratio of the position difference between these two first yaw position data points to the yaw duration can be used to determine whether there is a sudden change. In other words, the longer the yaw duration, the larger the difference between the two. Determining whether there is an abnormal step change by using the ratio of the position difference to the yaw duration can improve the recognition accuracy.

[0068] In practice, the yaw time period that meets the requirements can be selected by using a sliding window to take the maximum value.

[0069] As an example, the step of obtaining multiple yaw time periods in a predetermined yaw time period where the yaw position data remains unchanged based on multiple first yaw position change rates may include: performing a sliding window maximum value processing on the absolute values ​​of multiple yaw position change rates with a predetermined sliding window length and a predetermined sliding step size to obtain the outlines of multiple first yaw position change rates; filtering multiple first yaw position change rates and corresponding first yaw position data whose outlines are less than a second predetermined threshold; segmenting the first yaw position data based on whether the filtered first yaw position data is continuous to obtain multiple yaw segments where the yaw position data remains unchanged, and determining multiple yaw time periods corresponding to the multiple yaw segments based on the multiple yaw segments and a predetermined time interval.

[0070] In addition, the sliding window method is only used as an example. In actual implementation, the yaw position change rate marking and compartment method (i.e., classifying according to the magnitude of the yaw position change rate) can also be used to filter the yaw time period that meets the requirements.

[0071] Although not shown, multiple yaw time periods and corresponding yaw position data can be merged. For example, segments with time intervals within 5 minutes (the segments and yaw time periods can be converted by a predetermined time interval) can be merged into a complete new segment, thereby integrating ladder segments or adjacent ladder segments.

[0072] Additionally, an abnormal segment result table can be output, which can include the number of abnormalities (one segment is one abnormality), the start and end times of the abnormal segment, the mean yaw position and the mean yaw position change rate within the abnormal segment.

[0073] Figure 4 This is a flowchart illustrating a yaw position anomaly identification method according to a third embodiment of the present disclosure.

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

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

[0076] In step S420, segments under specific operating conditions are selected from the collected data as single yaw segments. The specific operating condition here can be a long-term wind in the same direction.

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

[0078] In step S440, the yaw position change rate corresponding to each data point (yaw position change rate) within each consecutive segment is calculated.

[0079] In step S450, the start and end positions of the data points or segments held at each position are obtained (i.e., the start time and end time, or these two positions can be obtained by index value).

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

[0081] In step S470, filtering is performed based on whether the yaw position changes abruptly, whether the yaw duration meets the requirements, and whether the time interval meets the requirements. For example, filtering is performed based on the following criteria: the 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 of the yaw segment is greater 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. Additionally, yaw time segments with the same yaw duration and the same time interval between two adjacent yaw time segments can also be filtered out.

[0082] The predetermined threshold, preset threshold, predetermined time interval, etc. disclosed herein can all be empirical values.

[0083] In step S480, each yaw segment is merged.

[0084] In step S490, the start time, end time, duration (i.e., yaw duration), number of segments, 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.

[0085] In step S4100, an abnormal segment result table is output. This 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 mean yaw position and / or the mean yaw position change rate within the abnormal segment.

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

[0087] Reference Figure 5 The yaw position changes exhibit a stepped pattern, with four abnormal stepped changes identified between approximately 22:41 and 22:52 on the 23rd. (Refer to...) Figure 6 The yaw rate remained essentially constant (around 0.3 degrees). (Refer to...) Figure 7 The rate of change of yaw position changes abruptly within a short period, but the overall rate of change of yaw position is essentially zero. (Refer to...) Figure 8 The unmooring flag is set to position 15, 16 represents adding grease, yaw status flag is 1 indicating yaw, and yaw flag is 0 indicating no yaw.

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

[0089] A yaw position anomaly identification device 700 according to an embodiment of the present disclosure may include a first acquisition unit 710, a first calculation unit 720, a second acquisition unit 730, and an identification unit 740.

[0090] The first acquisition unit 710 can acquire multiple first yaw position data for a predetermined yaw time period with the same yaw direction.

[0091] As an example, the first acquisition unit 710 can collect yaw position data at predetermined time intervals and filter out multiple first yaw position data within a predetermined yaw time period with the same yaw direction from the yaw position data. Specifically, the first acquisition unit 710 can filter out multiple segments of yaw position data from the yaw position data where at least one of the yaw flag position, the grease-adding status flag, and the unmooring flag of the wind turbine generator is set, or where the left yaw action field or the right yaw action field is consecutively true; and remove the first n yaw position data from each segment to obtain the multiple first yaw position data.

[0092] In addition, the first acquisition unit 710 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 are continuously true and both the grease-adding status flag and the unmooring flag are reset.

[0093] The first calculation unit 720 can calculate multiple first yaw position change rates corresponding to multiple first yaw position data.

[0094] The second acquisition unit 730 can acquire multiple yaw time periods in a predetermined yaw time period where the yaw position data remains unchanged, based on multiple first yaw position change rates. The second acquisition unit can perform a sliding window maximum value operation on the absolute values ​​of the multiple yaw position change rates using a predetermined sliding window length and a predetermined sliding step size to obtain the outlines of the multiple first yaw position change rates; filter out multiple first yaw position change rates and corresponding first yaw position data whose outlines are less than a second predetermined threshold; segment the first yaw position data based on whether the filtered first yaw position data is continuous to obtain multiple yaw segments where the yaw position data remains unchanged; and determine multiple yaw time periods corresponding to the multiple yaw segments based on the multiple yaw segments and a predetermined time interval.

[0095] The identification unit 740 can determine whether the yaw position of the wind turbine is abnormal based on multiple yaw time periods and the first yaw position data of each of the multiple yaw time periods.

[0096] For example, the identification unit 740 can filter out at least one yaw time period that meets preset conditions from multiple yaw time periods; in response to determining that there is a 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 is within the preset range, it is determined that there is an abnormal step change in the yaw position of the wind turbine generator.

[0097] The identification unit 740 can also make transition judgments across multiple yaw time periods. In addition, the identification unit 740 can also merge data from multiple yaw segments to form a new yaw segment.

[0098] It should be understood that the various units or modules in the control apparatus according to the exemplary embodiments of this disclosure may be implemented as hardware components and / or software components. Those skilled in the art can implement the various units, for example, using field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), software algorithms, etc., according to the processes performed by the defined various units.

[0099] Each of the above steps can be programmed as a software program or instruction. Therefore, the control method according to the exemplary embodiments of this disclosure can be implemented via software. The computer-readable storage medium of the exemplary embodiments of this disclosure can store a computer program that, when executed by a processor, implements the yaw position anomaly identification method as described in the above exemplary embodiments.

[0100] According to various embodiments of this disclosure, apparatus (e.g., modules or their functions) or methods can be implemented by programs or instructions stored in a computer-readable storage medium. When such instructions are executed by a processor, the processor can perform a function corresponding to the instruction or perform a method corresponding to the instruction. At least a portion of a module can be implemented (e.g., executed) by a processor. At least a portion of a programmed module can include modules, programs, routines, instruction sets, and procedures for performing at least one function. In one example, the instructions or software include machine code (such as machine code generated by a compiler) that is directly executed by one or more processors or computers. In another example, the instructions or software include higher-level code that is executed by one or more processors or computers using an interpreter. Instructions or software can be written using any programming language based on the block diagrams and flowcharts shown in the accompanying drawings and the corresponding description in the specification.

[0101] Computer-readable storage media include non-transitory computer-readable storage media, such as magnetic media like floppy disks and magnetic tapes, optical media (including optical disc (CD) ROMs and DVD ROMs), magneto-optical media like flexible optical discs, hardware devices such as ROMs and RAMs designed for storing and executing program instructions, and flash memory. The program instructions include language code executable by a computer using an interpreter and machine language code generated by a compiler. The aforementioned hardware devices can be implemented by one or more software modules for performing the operations of the various embodiments of this disclosure.

[0102] The modules or programming modules disclosed herein may include at least one of the aforementioned components, with some components omitted or others added. The operations of the modules, programming modules, or other components may be executed sequentially, in parallel, cyclically, or probingly. Furthermore, some operations may be executed in a different order, may be omitted, or may be extended with other operations.

[0103] The computer-readable storage medium and / or yaw position anomaly identification device of the exemplary embodiments of this disclosure may be part of the controller (e.g., the main controller) of a wind turbine generator set.

[0104] A controller according to an exemplary embodiment of the present disclosure may include a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program or instructions that, when executed by the processor, implement the yaw position anomaly identification method as described in the exemplary embodiment above.

[0105] The wind turbine generator set of the embodiments of this disclosure may include the computer-readable storage medium, yaw position anomaly detection device or controller as described above.

[0106] The yaw position anomaly identification method and yaw position anomaly identification device according to the embodiments of the present disclosure can determine that the yaw position of a wind turbine generator is abnormal.

[0107] The yaw position identification method and yaw position anomaly identification device according to embodiments of the present disclosure can reduce yaw error and improve the stability of yaw control.

[0108] While some exemplary embodiments of this disclosure have been shown and described, those skilled in the art will understand that modifications may be made to these embodiments without departing from the principles and spirit of this disclosure as defined by the claims and their equivalents. For example, technical features of different embodiments may 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 and the first yaw position data of each of the plurality of yaw time periods.

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 and the first yaw position data of each of the plurality of yaw time periods comprises: selecting at least one yaw time period that meets a preset condition from the plurality of yaw time periods; in response to determining that there is a jump between the first yaw position data before the start time of each of the at least one yaw time period and the first yaw position data after the end time and the yaw duration of each of the at least one yaw time period is within a preset range, determining that the yaw position of the wind turbine generator has a step change abnormality.

3. The yaw position abnormality identification method of a wind power generator system according to claim 2, characterized by, The step of selecting at least one yaw time period that meets a preset condition from the plurality of yaw time periods comprises: sorting the plurality of yaw time periods in the order of the start time of each of the plurality of yaw time periods; selecting a yaw time period from the sorted plurality of yaw time periods, in which the deviation between the end time of a previous yaw time period and the start time of a subsequent yaw time period is 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 a jump between the first yaw position data before the start time of each of the at least one yaw time period and the first yaw position data after the end time comprises: calculating the position difference between the first yaw position data before the start time of each of the at least one yaw time period and the first yaw position data after the end time and the yaw duration of each of the at least one yaw time period; in response to the ratio of the position difference to the corresponding yaw duration being greater than a first preset threshold, determining that there is a jump between the first yaw position data before the start time of each of the at least one yaw time period and the first yaw position data after the end time.

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; selecting 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 selecting a plurality of first yaw position data of the predetermined yaw time period in the same yaw direction from the yaw position data comprises: selecting 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 grease 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 the plurality of first yaw position data of the predetermined yaw time period with the same yaw direction from the yaw position data comprises: Screening the yaw position data of the plurality of segments from the yaw position data in the case that the yaw flag position bit, the left yaw action field or the right yaw action field of the wind turbine generator set are continuously true and both the greasing 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 the 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 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 the outer contour 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 outer contour line is less than a second predetermined threshold value; 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 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 abnormality identification method of the wind turbine generator set 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 with 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 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; An identification unit that determines whether the yaw position of the wind turbine generator set is abnormal according to the plurality of yaw time periods and the first yaw position data of each yaw time period in the plurality of yaw time periods.

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

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

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