CN117514637A - Resonance identification method of wind generating set, controller and wind generating set - Google Patents
Resonance identification method of wind generating set, controller and wind generating set Download PDFInfo
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/0276—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor controlling rotor speed, e.g. variable speed
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/0296—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor to prevent, counteract or reduce noise emissions
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
- F03D7/02—Controlling wind motors the wind motors having rotation axis substantially parallel to the air flow entering the rotor
- F03D7/04—Automatic control; Regulation
- F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
- F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
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Abstract
A resonance identification method of a wind generating set, a controller and the wind generating set are disclosed. The resonance identification method comprises the following steps: acquiring a cabin acceleration data sequence; dividing the cabin acceleration data sequence into a plurality of segments using a sliding window; determining an abnormal segment from the plurality of segments based on the statistics of the individual segments; and identifying a resonance interval of the wind generating set from the abnormal segment based on the cabin acceleration value in the abnormal segment, the generator rotating speed value corresponding to each sampling point and the statistical value of the differential sequence of the cabin acceleration value, wherein the resonance interval is a time interval when the wind generating set resonates. The resonance identification method, the controller and the wind generating set of the wind generating set can accurately identify the time interval of resonance of the wind generating set only by using the cabin acceleration and the generator rotating speed of the wind generating set, so that the method and the controller are easy to realize, the operation cost can be reduced, and the operation efficiency can be improved.
Description
Technical Field
The present disclosure relates generally to the field of wind power generation technology, and more particularly, to a resonance identification method of a wind power generator set, a controller, and a wind power generator set.
Background
Protection of vibration of the wind generating set in different working conditions and operation processes is an important strategy for safety control of the wind generating set. The generator can generate low-frequency vibration in the rotation process, and the vibration frequencies at different rotation speeds are different. In general, when the wind speed is small and the rotation speed is low, when the vibration frequency caused by the rotation speed of the generator is close to the natural frequency of the tower, resonance is easy to occur to the wind generating set, and the continuous resonance has the following adverse effects on the wind generating set: (1) The load of the wind generating set is increased and the fatigue degree is enhanced; (2) The service life of the wind generating set can be reduced, and even the safety of the wind generating set is affected; (3) The vibration protection of the wind generating set is stopped, and the generated energy is influenced; (4) When the wind generating set resonates, the wind generating set is often influenced by wind speed conditions, if the related wind conditions do not disappear in a short time, or the wind generating set is always under the wind conditions, the wind generating set can continuously resonate, frequent fault shutdown of the wind generating set is easily caused, and even the wind generating set cannot be started; (5) For the wind generating set of the same model, because the load design, the aerodynamic parameters of the set, the mechanical configuration and the like are completely consistent, but the wind condition conditions in a certain area (each fan position) are basically consistent, the resonance easily causes the situation that the load or vibration of the fans in the wind power plant is increased in batches, and the resonance is not limited to a single fan, so that the resonance problem of the wind generating set is invisible to the safe operation, the electric quantity loss and the like of the whole wind power plant.
Disclosure of Invention
Therefore, the embodiment of the disclosure provides a resonance identification method of a wind generating set, a controller and the wind generating set, which can effectively identify the resonance of the wind generating set under the working condition of small wind.
In one general aspect, there is provided a resonance identification method of a wind power generation set, the resonance identification method including: acquiring a cabin acceleration data sequence; dividing the cabin acceleration data sequence into a plurality of segments using a sliding window; determining an abnormal segment from the plurality of segments based on the statistics of the individual segments; and identifying a resonance interval of the wind generating set from the abnormal segment based on the cabin acceleration value in the abnormal segment, the generator rotating speed value corresponding to each sampling point and the statistical value of the differential sequence of the cabin acceleration value, wherein the resonance interval is a time interval when the wind generating set resonates.
Optionally, the step of obtaining the nacelle acceleration data sequence is performed in response to deriving a grid-tie rotational speed of the wind park based on the operational state of the wind park and the generator rotational speed estimate.
Optionally, the step of determining an abnormal segment from the plurality of segments based on the statistics of the respective segments comprises: and for each segment, determining any one segment as an abnormal segment in response to the statistical value of the any one segment being greater than a first preset threshold.
Optionally, the step of determining an abnormal segment from the plurality of segments based on the statistics of the respective segments further comprises: and executing merging operation on the determined abnormal fragments until the time interval of any two abnormal fragments is larger than the preset time length, wherein the merging operation refers to merging the front abnormal fragment and the rear abnormal fragment with the time interval smaller than the preset time length into one abnormal fragment.
Optionally, the first preset threshold value is determined based on a statistical value of a plurality of pre-acquired normal cabin acceleration data segments, wherein the normal cabin acceleration data segments refer to cabin acceleration data segments with cabin acceleration values smaller than a preset acceleration threshold value, and the length of the normal cabin acceleration data segments is equal to the length of the sliding window.
Optionally, the nacelle acceleration data sequence comprises a first nacelle acceleration data sequence and a second nacelle acceleration data sequence, wherein data in the first nacelle acceleration data sequence indicates nacelle acceleration in a first direction and data in the second nacelle acceleration data sequence indicates nacelle acceleration in a second direction, the second direction being perpendicular to the first direction.
Optionally, the resonance identification method further includes: and determining the maximum value in the first cabin acceleration data sequence and the second cabin acceleration data sequence, and determining the direction corresponding to the cabin acceleration data sequence where the maximum value is located as the main vibration direction.
Optionally, for a nacelle acceleration data sequence representing a main vibration direction, the steps of dividing the nacelle acceleration data sequence into a plurality of segments, determining an abnormal segment from the plurality of segments, and identifying a resonance interval of the wind turbine from the abnormal segment are performed.
Optionally, the step of identifying a resonance interval of the wind generating set from the abnormal segments comprises: determining an abnormal segment of the cabin acceleration value meeting the first acceleration condition as a first candidate segment; determining an interval meeting the generator rotating speed condition in the first candidate segment as a first candidate interval; determining a first candidate interval of which the cabin acceleration value meets a second acceleration condition as a second candidate interval; and determining a second candidate interval of which the statistical value of the differential sequence of the cabin acceleration values is larger than a second preset threshold value as a resonance interval of the wind generating set.
Optionally, the step of determining an abnormal segment of the nacelle acceleration value satisfying the first acceleration condition as a first candidate segment comprises: an anomaly segment comprising a predetermined number or more of nacelle acceleration values greater than a first acceleration threshold value is determined as a first candidate segment.
Optionally, the step of determining the interval satisfying the generator speed condition in the first candidate segment as the first candidate interval includes: and determining a section from the first sampling point to the ending sampling point of the first candidate segment as a first candidate section in response to the generator rotational speed corresponding to a plurality of consecutive sampling points in the first candidate segment being less than a first rotational speed threshold and the generator rotational speeds corresponding to all sampling points from the first sampling point to the ending sampling point of the first candidate segment being less than a second rotational speed threshold.
Optionally, the step of determining the first candidate interval, in which the nacelle acceleration value satisfies the second acceleration condition, as the second candidate interval comprises: and determining the first candidate section as a second candidate section in response to the cabin acceleration values in the predetermined section centered on the cabin acceleration maximum value being greater than the second acceleration threshold value.
Optionally, as the differential sequence, a result of subtracting the n+1th cabin acceleration value from the N-th cabin acceleration value in the second candidate interval, where n=1, …, N-1, N is the number of cabin acceleration values in the second candidate interval.
Optionally, the resonance identification method further includes: identifying abnormal data points with the cabin acceleration value larger than a preset acceleration threshold value from the cabin acceleration data sequence; for isolated outlier data points not belonging to the determined outlier segment, adding the isolated outlier data point to any one outlier segment in response to the interval between the isolated outlier data point and the any one outlier segment being less than or equal to a preset duration.
In another general aspect, there is provided a computer readable storage medium storing a computer program which, when executed by a processor, implements a method of resonance identification of a wind park as described above.
In another general aspect, there is provided a controller, comprising: a processor; and a memory storing a computer program which, when executed by the processor, implements the resonance identification method of the wind turbine generator set as described above.
In another general aspect, there is provided a wind power plant, characterized in that the wind power plant comprises a controller as described above.
According to the resonance identification method, the controller and the wind generating set of the wind generating set, disclosed by the embodiment of the invention, the risk of resonance of the wind generating set can be accurately identified before the wind generating set has serious faults by only using the cabin acceleration and the generator rotating speed of the wind generating set, so that a resonance early warning signal can be sent in advance to guide the on-site predictive maintenance of a wind farm.
According to the resonance identification method, the controller and the wind generating set of the wind generating set, which are disclosed by the embodiment of the invention, the time interval of resonance of the wind generating set can be identified under the working condition of small wind, so that a complete machine system can be effectively protected, the fatigue of the set is reduced, the service life of the set is prolonged, the basis can be provided for optimizing the grid-connected rotating speed, and the design of a fan is guided. In addition, the time interval of resonance of the wind generating set can be identified under the working condition of small wind, so that abnormal vibration in the processes of generating operation, power limiting operation and yaw of the wind generating set can be effectively avoided, and the method has important significance for guaranteeing operation safety of batch fans.
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The foregoing and other objects and features of embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings in which the embodiments are shown, in which:
FIG. 1 is a flow chart illustrating a method of resonance identification of a wind turbine generator set according to an embodiment of the present disclosure;
FIG. 2 is a diagram illustrating an example of abnormal data points identified based on a preset acceleration threshold;
FIG. 3 is a diagram showing an example of an abnormal fragment determined by a sliding window standard deviation detection method
FIG. 4 is a flowchart illustrating a method of identifying resonance intervals of a wind turbine from anomalous fragments in accordance with an embodiment of the disclosure;
fig. 5A to 5E are diagrams showing examples of resonance section identification results visually demonstrated
Fig. 6 is a block diagram illustrating a controller according to an embodiment of the present disclosure.
Detailed Description
The following detailed description is provided to assist the reader in obtaining a thorough understanding of the methods, apparatus, and/or systems described herein. However, various changes, modifications, and equivalents of the methods, apparatuses, and/or systems described herein will be apparent after an understanding of the disclosure of the present application. For example, the order of operations described herein is merely an example and is not limited to those set forth herein, but may be altered as will be apparent after an understanding of the disclosure of the present application, except for operations that must occur in a particular order. Furthermore, descriptions of features known in the art may be omitted for clarity and conciseness.
At present, vibration protection of a wind generating set mainly judges abnormality from the angles of amplitude and duration through the cabin acceleration characteristics of a vibration sensor, and takes an over-limit fault of the cabin acceleration as an example, and fault shutdown is triggered as long as the vibration amplitude exceeds a certain threshold value. From the viewpoints of unit load and service life, even if the resonance amplitude does not reach the threshold value, the unit load can be increased and the service life of the unit can be possibly shortened when the unit is in a resonance state for a long time; from the early warning point of view, an effective method for identifying abnormal states from the operation data of the cabin acceleration sensor is lacking.
When the wind generating set is started and connected, the grid connection rotating speed is low in order to avoid impact on the set and a power grid, and the set resonance can be caused during grid connection. In the case of resonance of the machine set itself, if the effect of the sudden loading of the electromagnetic torque by the generator during grid connection is added, further increases in vibration of the machine set may result.
In addition, the wind generating set can also operate in a power-limiting and rotating speed-limiting mode in the power generation process. In these modes, the wind speed is high or slightly high, but the generator rotation speed is low, and the resonance of the machine set is likely to occur. On the other hand, when the wind generating set is stopped, the wind generating set must also go through the process of rotating speed from high to low, and resonance occurs in a certain set in the process. On the other hand, during yaw, in which the wind direction of the wind turbine is changed, a similar problem may occur, namely that the vibration of the wind turbine increases during yaw operation and is at the same time just within the resonance speed.
Therefore, the disclosure provides a resonance identification method of a wind generating set suitable for a small wind working condition, a controller and the wind generating set. A resonance recognition method of a wind power generation set, a controller, and a wind power generation set according to embodiments of the present disclosure are described in detail below with reference to fig. 1 to 6.
FIG. 1 is a flow chart illustrating a method of resonance identification of a wind turbine generator set according to an embodiment of the present disclosure. The resonance identification method can be executed by a main controller of the wind generating set, and can also be executed by any special controller in the wind generating set, so long as the special controller can acquire cabin acceleration data and generator rotating speed data of the wind generating set and identify a small wind working condition according to the generator rotating speed. The above-described resonance identification method may identify an abnormal section from the cabin acceleration data sequence using a sliding window standard deviation detection method, and then identify a time zone (i.e., resonance zone) in which resonance occurs from the abnormal section using a time sequence thresholding method.
Referring to fig. 1, in step S101, a nacelle acceleration data sequence is acquired. Here, the cabin acceleration data sequence may be acquired by various existing methods, such as, but not limited to, by a cabin acceleration sensor. The cabin acceleration data sequence is used as a time sequence, and the duration of the time sequence can be arbitrarily set by a person skilled in the art according to actual needs, for example, but not limited to, 1 day, 12 hours, and the like. Alternatively, the acquired cabin acceleration data sequence may be preprocessed, e.g., by differencing missing values, replacing outliers, etc.
Alternatively, step S101 may be performed when the grid-connected rotational speed of the wind turbine generator set is estimated based on the operation state of the wind turbine generator set and the rotational speed of the generator. This means that when the grid-connected rotational speed of the wind turbine generator system cannot be estimated (or the estimated grid-connected rotational speed is a null value), the resonance recognition method of the wind turbine generator system according to the embodiment of the present disclosure is not performed. The reason is that when the grid-connected rotational speed of the wind turbine generator system cannot be estimated (or the estimated grid-connected rotational speed is a null value), the resonance section obtained by the above-described resonance identification method is ambiguous and cannot be used as a reasonable identification result.
In step S102, the nacelle acceleration data sequence is divided into a plurality of segments using a sliding window. According to embodiments of the present disclosure, the sampling period of the cabin acceleration data sequence may be on the order of seconds, while the length of the sliding window may be on the order of minutes, however, the present disclosure does not make any restrictions on the sampling period of the cabin acceleration data sequence and the length of the sliding window.
In step S103, an abnormal segment is determined from the plurality of segments based on the statistical value of each segment. Specifically, for each segment, any one segment is determined to be an abnormal segment in response to the statistical value of the any one segment being greater than a first preset threshold. Here, the statistical value may be a standard deviation of the cabin acceleration data in the segment, but the present disclosure is not limited thereto, and for example, the statistical value may be an expectation or variance, or the like. The first preset threshold may be determined based on statistics (e.g., without limitation, standard deviation) of a plurality of normal cabin acceleration data segments acquired in advance. The normal cabin acceleration data segment refers to a cabin acceleration data segment with a cabin acceleration value smaller than a preset acceleration threshold value, and the length of the normal cabin acceleration data segment is equal to the length of the sliding window.
According to embodiments of the present disclosure, to avoid overlapping of the abnormal segments and an excessive number of abnormal segments, a merging operation may be performed on the determined abnormal segments until a time interval of any two abnormal segments is greater than a preset duration (e.g., without limitation, 10 minutes). The merging operation is to merge the two front and rear abnormal segments with the time interval smaller than the preset time length into one abnormal segment.
Alternatively, outlier data points having a cabin acceleration value greater than a preset acceleration threshold may also be identified from the cabin acceleration data sequence. For example, if the cabin acceleration value exceeds a preset threshold range (l, h), the cabin acceleration value may be identified as a outlier. Here, (l, h) may be, for example (-0.04 m/s) 2 ，0.04m/s 2 ) But the present disclosure is not limited thereto. Then, for isolated outlier data points not belonging to the outlier segment determined in step S103, if the interval between the isolated outlier data point and any one outlier segment is less than or equal to a preset period of time (e.g., without limitation, 10 minutes), the isolated outlier data point may be added to any one outlier segment described above. However, if the interval between the isolated outlier data point and any outlier segment is greater than the predetermined period of time, the isolated outlier data point will not be usedAny abnormal fragments were added. Thus, in the subsequent processing, the abnormal data points to which no abnormal fragment is added will be ignored. The step of identifying outlier data points may be performed before step S102, and the step of adding isolated outlier data points to the outlier segment may be performed in step S103. However, the present disclosure is not limited thereto, and the above-described steps of identifying outlier data points and adding isolated outlier data points to the outlier segment may also be performed together in step S103.
Fig. 2 is a diagram showing an example of abnormal data points identified based on a preset acceleration threshold, and fig. 3 is a diagram showing an example of abnormal segments determined by a sliding window standard deviation detection method. In FIG. 2, the horizontal axis represents the time of day value, the vertical axis represents the acceleration amplitude, and the preset acceleration threshold may be + -0.04m/s 2 In fig. 3, the horizontal axis represents the time value, the vertical axis represents the acceleration amplitude, the length of the sliding window is 10 minutes, and the first preset threshold value is 0.190416.
In step S104, a resonance section of the wind turbine generator set is identified from the abnormal segment based on the nacelle acceleration value in the abnormal segment, the generator rotation speed value corresponding to each sampling point, and the statistical value of the differential sequence of the nacelle acceleration values. Here, the resonance interval refers to a time interval during which the wind turbine generator system resonates. Step S104 will be described in detail later.
According to an embodiment of the present disclosure, the cabin acceleration data sequence may comprise a first cabin acceleration data sequence, the data in the first cabin acceleration data sequence being indicative of cabin acceleration in a first direction, and a second cabin acceleration data sequence, the data in the second cabin acceleration data sequence being indicative of cabin acceleration in a second direction, the second direction being perpendicular to the first direction. For example, the first direction may be a horizontal direction and the second direction may be a vertical direction. Accordingly, the resonance recognition method of the wind generating set according to the embodiment of the present disclosure may further include: and determining the maximum value in the first cabin acceleration data sequence and the second cabin acceleration data sequence, and determining the direction corresponding to the cabin acceleration data sequence where the maximum value is located as the main vibration direction. The above-described step of determining the main vibration direction may be performed after step S101 and before step S102, and thus, steps S102, S103, and S104 may be performed only for the nacelle acceleration data sequence indicating the main vibration direction. However, the present disclosure is not limited thereto, and for example, the above-described step of determining the main vibration direction may also be performed after step S103 and before step S104.
FIG. 4 is a flowchart illustrating a method of identifying resonance intervals of a wind turbine from anomalous fragments in accordance with an embodiment of the disclosure.
Referring to fig. 4, in step S401, an abnormal segment whose cabin acceleration value satisfies a first acceleration condition is determined as a first candidate segment. Specifically, an abnormal segment including a predetermined number or more of nacelle acceleration values greater than a first acceleration threshold value may be determined as a first candidate segment. For example, for an abnormal segment, if c (e.g., without limitation, 10) or more nacelle acceleration values are greater than a first acceleration threshold b (e.g., without limitation, 0.04 m/s) 2 ) The anomalous fragment may be determined to be a first candidate fragment. By determining candidate segments, the search interval of the resonant segment identification rule can be narrowed.
In step S402, a section satisfying the generator rotation speed condition in the first candidate segment is determined as a first candidate section. Specifically, if the generator rotational speed corresponding to a plurality of consecutive sampling points in the first candidate segment is less than the first rotational speed threshold, and the generator rotational speeds corresponding to all sampling points from the first sampling point to the end sampling point of the first candidate segment among the plurality of sampling points are less than the second rotational speed threshold, the interval from the first sampling point to the end sampling point of the first candidate segment may be determined as the first candidate interval. For example, for a first candidate interval, the generator speed corresponding to a number (e.g., without limitation, 5) of sampling points from the starting position is less than a first speed threshold d1 (e.g., without limitation, 5 r/min), and the generator speed corresponding to all sampling points within the interval is less than a second speed threshold d2 (e.g., without limitation, 6 r/min). According to an embodiment of the present disclosure, the first rotational speed threshold d1 may be equal to the second rotational speed threshold d2. In this case, step S202 may be simplified as: if the generator rotation speeds corresponding to all sampling points from the specific sampling point of the first candidate segment to the ending sampling point of the first candidate segment are smaller than the first rotation speed threshold d1 or the second rotation speed threshold d2, the interval from the specific sampling point to the ending sampling point can be determined as the first candidate interval. By determining the candidate interval, the corresponding non-small wind working condition when the rotation speed of the generator is high can be eliminated.
In step S403, a first candidate section in which the cabin acceleration value satisfies the second acceleration condition is determined as a second candidate section. Specifically, if the cabin acceleration values in the predetermined section centered on the cabin acceleration maximum value among the first candidate sections are each greater than the second acceleration threshold value, the first candidate section may be determined as the second candidate section. For example, if the cabin acceleration values in the intervals around the cabin acceleration maximum value (e.g., without limitation, 20 seconds) in a certain first candidate interval are both greater than the second acceleration threshold e (e.g., without limitation, 0.03 m/s) 2 ) The first candidate interval may be determined to be a second candidate interval.
In step S404, a second candidate interval, in which the statistical value (e.g., without limitation, the standard deviation) of the differential sequence of nacelle acceleration values is greater than a second preset threshold value (e.g., without limitation, 0.01), is determined as a resonance interval of the wind turbine. Specifically, the (n+1) th cabin acceleration value and the (N) th cabin acceleration value in the second candidate interval may be subtracted, where n=1, …, N-1, N is the number of cabin acceleration values in the second candidate interval as a differential sequence of cabin acceleration values in the second candidate interval.
Compared with the prior art that high-frequency data analysis (millisecond-level data) is used, the method and the device realize storage of second-level data by merging candidate intervals, analyze resonance based on the second-level data, and save computing resources. In addition, the method and the device further identify resonance after acquiring the abnormal segments by combining the sliding window time sequence statistical distribution characteristics, so that the identification accuracy is greatly improved, and the expansion of the analysis resonance based on second-order data is realized.
According to embodiments of the present disclosure, the number of identified resonance intervals may also be counted and the identified resonance intervals and/or the number of resonance intervals may be visually presented.
According to the resonance identification method of the wind generating set, only the cabin acceleration and the generator rotating speed of the wind generating set are used, so that the time interval of resonance of the wind generating set can be accurately identified, the implementation is easy, the operation cost can be reduced, and the operation efficiency is improved. On the other hand, according to the resonance identification method of the wind generating set, which is disclosed by the embodiment of the invention, before the wind generating set has serious faults, the risk of resonance of the wind generating set can be accurately identified, so that a resonance early warning signal is sent in advance, and the on-site predictive maintenance of a wind farm is guided.
In addition, according to the resonance identification method of the wind generating set, the time interval of resonance of the wind generating set can be identified under the working condition of small wind, so that a complete machine system can be effectively protected, fatigue of the set is reduced, the service life of the set is prolonged, and basis can be provided for optimization of grid-connected rotation speed. On the other hand, the time interval of resonance of the wind generating set can be identified under the working condition of small wind, so that abnormal vibration of the wind generating set in the processes of generating operation, power limiting operation and yaw can be effectively avoided.
Fig. 5A to 5E are diagrams showing an example of a resonance section identification result visually demonstrated.
Referring to fig. 5A to 5E, the horizontal axis represents a time value and the vertical axis represents the magnitude of the corresponding physical quantity. Fig. 5A shows a variation of the generator rotational speed. In particular, the upper straight line represents the grid-connected rotational speed of the wind power plant. Fig. 5B shows a state change process of the wind power plant from start to stop. Fig. 5C shows a change curve of the nacelle acceleration in the non-main vibration direction, and fig. 5D shows a change curve of the nacelle acceleration in the main vibration direction. In fig. 5C and 5D, the leftmost vertical line indicates the start time position of the resonance section, the rightmost curve indicates the end time position of the resonance section, and the middle curve indicates the time position of the nacelle acceleration maximum in the main vibration direction. FIG. 5E shows a wind speed variation curve, three bold dashed lines from top to bottom indicating wind speed values of 1.75m/s,1.46m/s and 0.93m/s, respectively. It can be seen that the resonance identification method of the wind generating set according to the embodiment of the present disclosure is applicable to the low wind condition.
Fig. 6 is a block diagram illustrating a controller according to an embodiment of the present disclosure. The controller can be realized as a main controller of the wind generating set, and can also be realized as any special controller in the wind generating set, so long as the special controller can acquire cabin acceleration data and generator rotating speed data of the wind generating set and identify a small wind working condition according to the generator rotating speed.
Referring to fig. 6, a controller 600 according to an embodiment of the present disclosure includes a processor 610 and a memory 620. The processor 610 may include, but is not limited to, a Central Processing Unit (CPU), a Digital Signal Processor (DSP), a microcomputer, a Field Programmable Gate Array (FPGA), a system on a chip (SoC), a microprocessor, an Application Specific Integrated Circuit (ASIC), and the like. The memory 620 may store computer programs to be executed by the processor 610. Memory 620 may include high-speed random access memory and/or non-volatile computer-readable storage media. When the processor 610 executes the computer program stored in the memory 620, the above-described resonance identification method of the wind turbine generator set may be implemented.
Alternatively, the controller 600 may communicate with other various components in the wind park in a wired or wireless communication manner, and may also communicate with other devices in the wind park (e.g., a master controller of the wind park) in a wired or wireless communication manner. In addition, the controller 600 may communicate with devices external to the wind farm in a wired or wireless communication.
The resonance recognition method of the wind generating set according to the embodiment of the present disclosure may be written as a computer program and stored on a computer-readable storage medium. When the computer program is executed by the processor, the resonance identification method of the wind generating set can be realized. Examples of the computer readable storage medium include: read-only memory (ROM), random-access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), dynamic random-access memory (DRAM), static random-access memory (SRAM), flash memory, nonvolatile memory, CD-ROM, CD-R, CD + R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD + R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, blu-ray or optical disk storage, hard Disk Drives (HDD), solid-state disks (SSD), card-type memories (such as multimedia cards, secure Digital (SD) cards or ultra-fast digital (XD) cards), magnetic tapes, floppy disks, magneto-optical data storage devices, hard disks, solid-state disks, and any other devices configured to store computer programs and any associated data, data files and data structures in a non-transitory manner and to provide the above computer programs and any associated data, data files and data structures to a processor or computer to enable the processor or computer to execute the programs. In one example, the computer program and any associated data, data files, and data structures are distributed across networked computer systems such that the computer program and any associated data, data files, and data structures are stored, accessed, and executed in a distributed manner by one or more processors or computers.
According to the resonance identification method, the controller and the wind generating set of the wind generating set, disclosed by the embodiment of the invention, the risk of resonance of the wind generating set can be accurately identified before the wind generating set has serious faults by only using the cabin acceleration and the generator rotating speed of the wind generating set, so that a resonance early warning signal can be sent in advance to guide the on-site predictive maintenance of a wind farm.
According to the resonance identification method, the controller and the wind generating set of the wind generating set, which are disclosed by the embodiment of the invention, the time interval of resonance of the wind generating set can be identified under the working condition of small wind, so that a complete machine system can be effectively protected, the fatigue of the set is reduced, the service life of the set is prolonged, the basis can be provided for optimizing the grid-connected rotating speed, and the design of a fan is guided. In addition, the time interval of resonance of the wind generating set can be identified under the working condition of small wind, so that abnormal vibration in the processes of generating operation, power limiting operation and yaw of the wind generating set can be effectively avoided, and the method has important significance for guaranteeing operation safety of batch fans.
Although a few embodiments of the present disclosure have been shown and described, it would be appreciated by those skilled in the art that changes may be made in these embodiments without departing from the principles and spirit of the disclosure, the scope of which is defined in the claims and their equivalents.
Claims (17)
1. A method of resonance identification of a wind turbine generator set, the method comprising:
acquiring a cabin acceleration data sequence;
dividing the cabin acceleration data sequence into a plurality of segments using a sliding window;
determining an abnormal segment from the plurality of segments based on the statistics of the individual segments;
and identifying a resonance interval of the wind generating set from the abnormal segment based on the cabin acceleration value in the abnormal segment, the generator rotating speed value corresponding to each sampling point and the statistical value of the differential sequence of the cabin acceleration value, wherein the resonance interval is a time interval when the wind generating set resonates.
2. The resonance identification method as set forth in claim 1, wherein the step of obtaining the nacelle acceleration data sequence is performed in response to deriving a grid-tie rotational speed of the wind turbine based on the operational state of the wind turbine and the generator rotational speed estimate.
3. The resonance identification method as set forth in claim 1, wherein the step of determining an abnormal segment from the plurality of segments based on the statistical value of each segment includes:
and for each segment, determining any one segment as an abnormal segment in response to the statistical value of the any one segment being greater than a first preset threshold.
4. The resonance identification method as set forth in claim 3, wherein the step of determining an abnormal segment from the plurality of segments based on the statistical value of each segment further comprises:
performing a merging operation on the determined abnormal segments until the time interval of any two abnormal segments is greater than a preset duration,
the merging operation is to merge the front and rear abnormal segments with the time interval smaller than the preset time length into one abnormal segment.
5. A resonance identification method as claimed in claim 3, wherein the first preset threshold value is determined based on statistics of a plurality of pre-acquired normal cabin acceleration data segments, wherein a normal cabin acceleration data segment refers to a cabin acceleration data segment having a cabin acceleration value smaller than a preset acceleration threshold value, and the length of the normal cabin acceleration data segment is equal to the length of the sliding window.
6. The resonance identification method as set forth in claim 1, wherein the cabin acceleration data sequence comprises a first cabin acceleration data sequence and a second cabin acceleration data sequence, wherein data in the first cabin acceleration data sequence is indicative of cabin acceleration in a first direction and data in the second cabin acceleration data sequence is indicative of cabin acceleration in a second direction, the second direction being perpendicular to the first direction.
7. The resonance identification method as set forth in claim 6, wherein the resonance identification method further includes:
and determining the maximum value in the first cabin acceleration data sequence and the second cabin acceleration data sequence, and determining the direction corresponding to the cabin acceleration data sequence where the maximum value is located as the main vibration direction.
8. The resonance identification method as set forth in claim 7, wherein the steps of dividing the nacelle acceleration data sequence into a plurality of segments, determining an abnormal segment from the plurality of segments, and identifying a resonance interval of the wind turbine from the abnormal segment are performed for the nacelle acceleration data sequence representing the main vibration direction.
9. The resonance identification method as set forth in claim 1, wherein the step of identifying a resonance interval of the wind turbine from the abnormal segments includes:
determining an abnormal segment of the cabin acceleration value meeting the first acceleration condition as a first candidate segment;
determining an interval meeting the generator rotating speed condition in the first candidate segment as a first candidate interval;
determining a first candidate interval of which the cabin acceleration value meets a second acceleration condition as a second candidate interval;
and determining a second candidate interval of which the statistical value of the differential sequence of the cabin acceleration values is larger than a second preset threshold value as a resonance interval of the wind generating set.
10. The resonance identification method as set forth in claim 9, wherein the step of determining an abnormal segment of the nacelle acceleration value satisfying the first acceleration condition as a first candidate segment includes:
an anomaly segment comprising a predetermined number or more of nacelle acceleration values greater than a first acceleration threshold value is determined as a first candidate segment.
11. The resonance identification method as set forth in claim 9, wherein the step of determining an interval satisfying the generator rotation speed condition in the first candidate segment as the first candidate interval includes:
and determining a section from the first sampling point to the ending sampling point of the first candidate segment as a first candidate section in response to the generator rotational speed corresponding to a plurality of consecutive sampling points in the first candidate segment being less than a first rotational speed threshold and the generator rotational speeds corresponding to all sampling points from the first sampling point to the ending sampling point of the first candidate segment being less than a second rotational speed threshold.
12. The resonance identification method as set forth in claim 9, wherein the step of determining a first candidate interval in which the cabin acceleration value satisfies the second acceleration condition as the second candidate interval includes:
and determining the first candidate section as a second candidate section in response to the cabin acceleration values in the predetermined section centered on the cabin acceleration maximum value being greater than the second acceleration threshold value.
13. The resonance identification method as set forth in claim 9, wherein the result of subtracting the n+1th cabin acceleration value from the N-th cabin acceleration value in the second candidate interval is the differential sequence, wherein N = 1, …, N-1, N is the number of cabin acceleration values in the second candidate interval.
14. The resonance identification method as set forth in claim 1, wherein the resonance identification method further includes:
identifying abnormal data points with the cabin acceleration value larger than a preset acceleration threshold value from the cabin acceleration data sequence;
for isolated outlier data points not belonging to the determined outlier segment, adding the isolated outlier data point to any one outlier segment in response to the interval between the isolated outlier data point and the any one outlier segment being less than or equal to a preset duration.
15. A computer readable storage medium storing a computer program, characterized in that the method of resonance identification of a wind park according to any one of claims 1-14 is implemented when the computer program is executed by a processor.
16. A controller, the controller comprising:
a processor; and
memory storing a computer program which, when executed by a processor, implements a method of resonance identification of a wind park according to any one of claims 1-14.
17. A wind power plant, characterized in that the wind power plant comprises a controller according to claim 16.
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