CN114658611A - Method and device for detecting abnormality of main bearing of wind power generator - Google Patents
Method and device for detecting abnormality of main bearing of wind power generator Download PDFInfo
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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
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
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- F05B2260/80—Diagnostics
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/30—Control parameters, e.g. input parameters
- F05B2270/304—Spool rotational speed
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
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Abstract
Disclosed are a main bearing abnormality detection method and a main bearing abnormality detection device for a wind turbine, the main bearing abnormality detection method including: acquiring the rotating speed of the wind driven generator, and determining whether the change of the rotating speed of the wind driven generator is abnormal or not; responding to the abnormal change of the rotating speed of the wind driven generator, and determining whether the vibration frequency of the wind driven generator exceeds a preset threshold frequency; and outputting an alarm message indicating that the main bearing is abnormal in response to the vibration frequency of the wind driven generator exceeding a preset threshold frequency.
Description
Technical Field
The present disclosure relates generally to the field of wind power generation, and more particularly, to a method and an apparatus for detecting a main bearing abnormality of a wind power generator.
Background
With the progress of science and technology, electromechanical equipment of modern enterprises is comprehensively and rapidly developed in the direction of large-scale, continuous and automatic, and the objective requirements of reducing production cost, improving production efficiency, saving energy, tightening labor force, reducing waste products and improving product quality are met. In the industrial sector, whether various machines and equipment operate stably directly affects the economic benefit of enterprises, and some critical equipment even plays a role in determining the fate of the enterprises. Therefore, how to avoid equipment failure, especially catastrophic failure, has been a problem of great importance. Since the occurrence of an accident cannot be predicted for a long time, people have to take two countermeasures.
One strategy is to overhaul the equipment after it has been completely damaged. The method has great economic loss, and expensive maintenance cost is often needed for maintaining the equipment after the equipment is completely damaged, insufficient maintenance is often caused to cause catastrophic damage, the equipment needs to be replaced at a low rate, and casualties are caused at a high rate.
Another strategy is to periodically overhaul the equipment. This method is planned and preventive to some extent, but has the disadvantage of being very blind, and the stoppage of the machine causes a great economic loss if the plant is not out of order. This method often results in "over-maintenance", and the replacement parts have a residual value from their limits, thus resulting in high cost and poor economic results.
Therefore, the reasonable maintenance mode is predictable, namely the hidden danger can be monitored and forecasted in advance at the early stage of the equipment failure so as to take measures timely and reasonably. Therefore, equipment fault diagnosis technology comes into play.
For a wind driven generator, the working principle of the whole machine is as follows: wind energy is absorbed by the blades, and the blades generate rotating force by the wind energy to rotate the impeller; after the impeller rotates, the generator is driven to rotate through the main bearing of the generator, and therefore conversion from mechanical energy to electric energy is achieved. The main bearing of the wind driven generator belongs to a large-scale rotating part, and the main bearing is blocked along with the increase of the single machine capacity of the wind driven generator, so that the wind driven generator vibrates excessively during operation, the rotating speed shakes excessively, and even the unit safety is influenced, so that accidents are caused; therefore, safety detection of large components of wind turbine generator systems is also becoming increasingly important.
Fault diagnosis of current rotating components is generally accomplished through three approaches: vibration monitoring and fault diagnosis, acoustic emission method, iron spectrum analysis method.
Vibration monitoring and fault diagnosis: the method is characterized in that a strain gauge is arranged on a bearing to detect the vibration of the bearing, and the detected vibration parameters are analyzed and processed. Although the vibration signal can provide a large amount of failure information of the rotary machine, the signal configuration is complicated, and it is often difficult to clearly identify a failure, and thus a complicated signal processing technique is required. In addition, the bearing is generally a rotating structure, the bearing support is generally a closed structure, and it is inconvenient to install the strain gauge thereon. In the prior art, no relevant strain and vibration detection device is arranged on the main bearing of the generator.
An acoustic emission method: the principle of this method is that the elastic waves emitted from the acoustic emission source eventually propagate to the surface of the material, causing surface displacements that can be detected with acoustic emission sensors, which convert the mechanical vibrations of the material into electrical signals, which are then amplified, processed and recorded. The biggest drawbacks of acoustic emission signal processing are, above all, the variety of sources of Acoustic Emission (AE), the burstiness and uncertainty of the signal itself. Different AE source mechanisms may produce completely different AE signals. Secondly, the signals obtained by the AE sensor are more complex in terms of sound source, transmission medium, coupling signal.
Iron spectrum analysis: the method extracts a part of lubricating oil of the rolling machine as an oil sample, and deposits solid foreign matters contained in the oil sample flowing through the magnetic field on a glass sheet according to the size proportion by utilizing a high-gradient magnetic field so as to observe the shape, the size, the color and the material of foreign matter particles, thereby judging the type of equipment abrasion, predicting the running state of the machine and finding hidden dangers in time. However, ferrography is only suitable for laboratory environment and expensive equipment, and is not suitable for on-line diagnosis and wide application in industrial fields. And often need dismantle equipment and extract oil sample, cause economic loss because of shutting down.
In addition, the failure diagnosis of the rotating member can be performed by a scope detection method. Although the method is convenient to detect, the method has the defects that on one hand, the method needs halt detection, and on the other hand, the method needs extremely professional experience to diagnose whether the bearing inner ring is abnormal or not.
Disclosure of Invention
The embodiment of the disclosure provides a method and a device for detecting the abnormality of a main bearing of a wind driven generator based on a fan operation mechanism, bearing structure characteristics and data analysis, which can realize online operation detection and reduce the cost of a detection system.
In one general aspect, there is provided a main bearing abnormality detection method of a wind turbine, the main bearing abnormality detection method including: acquiring the rotating speed of the wind driven generator, and determining whether the change of the rotating speed of the wind driven generator is abnormal or not; responding to the abnormal change of the rotating speed of the wind driven generator, and determining whether the vibration frequency of the wind driven generator exceeds a preset threshold frequency; and outputting an alarm message indicating that the main bearing is abnormal in response to the vibration frequency of the wind driven generator exceeding a preset threshold frequency.
Optionally, the rotational speed of the wind turbine is obtained during a power generating operation of the wind turbine.
Optionally, the step of obtaining the rotation speed of the wind power generator comprises: and acquiring the fluctuation amplitude and the fluctuation period of the rotating speed of the wind driven generator.
Optionally, the step of determining whether the variation in the rotation speed of the wind turbine is abnormal includes: generating a sine function fitting the fluctuation of the rotating speed based on the fluctuation amplitude and the fluctuation period of the rotating speed of the wind driven generator; and determining whether the change of the rotating speed of the wind driven generator is abnormal or not based on the result of the sine function of the preset time and the acquired rotating speed of the wind driven generator.
Optionally, the step of obtaining the rotation speed of the wind power generator comprises: a first average of the rotational speed of the wind turbine is determined during a first time.
Optionally, the step of determining whether the variation of the rotation speed of the wind turbine is abnormal or not based on the result of the sine function of the preset time and the obtained rotation speed of the wind turbine includes: calculating a second average value of the rotating speed of the wind driven generator in the preset time period, and calculating a difference value between the second average value and the first average value; adding the result of the sine function of the preset time to the calculated difference; determining whether the added result is consistent with the real-time rotating speed of the wind driven generator at the preset time; and determining that the change of the rotating speed of the wind driven generator is abnormal in response to the addition result being consistent with the real-time rotating speed of the wind driven generator at the preset time.
Optionally, when the difference between the result of the addition and the real-time rotation speed of the wind turbine at the preset time is less than a predetermined threshold value, it is determined that the result of the addition is consistent with the real-time rotation speed of the wind turbine at the preset time.
Optionally, the step of determining whether the variation in the rotation speed of the wind turbine is abnormal includes: continuously detecting the fluctuation amplitude and the fluctuation period of the rotating speed of the wind driven generator obtained at different moments to determine whether the fluctuation amplitude and the fluctuation period are consistent; and determining that the change of the rotating speed of the wind driven generator is abnormal in response to the wave amplitude and the wave period being kept consistent.
Optionally, the step of determining whether the vibration frequency of the wind turbine exceeds a preset threshold frequency comprises: acquiring a vibration value of the wind driven generator; and calculating the number of zero crossings of the vibration value of the wind driven generator in unit time to serve as the vibration frequency of the wind driven generator.
Optionally, the step of determining whether the vibration frequency of the wind turbine exceeds a preset threshold frequency comprises: acquiring a vibration value of the wind driven generator; and performing fast Fourier transform on the obtained vibration value of the wind driven generator to obtain the vibration frequency of the wind driven generator.
In another general aspect, there is provided a main bearing abnormality detection apparatus of a wind turbine, the main bearing abnormality detection apparatus including: a rotational speed acquisition unit configured to acquire a rotational speed of the wind power generator and determine whether a variation in the rotational speed of the wind power generator is abnormal; a vibration frequency determination unit configured to determine whether a vibration frequency of the wind power generator exceeds a preset threshold frequency in response to an occurrence of an abnormality in a variation in a rotational speed of the wind power generator; an alarm unit configured to output an alarm message indicating abnormality of the main bearing in response to a vibration frequency of the wind turbine exceeding a preset threshold frequency.
Optionally, the rotation speed acquisition unit is configured to acquire a rotation speed of the wind power generator during a power generation operation of the wind power generator.
Optionally, the rotational speed acquisition unit is configured to acquire a fluctuation amplitude and a cycle of the rotational speed of the wind turbine.
Optionally, the rotation speed obtaining unit is configured to: generating a sine function fitting the fluctuation of the rotating speed based on the fluctuation amplitude and the fluctuation period of the rotating speed of the wind driven generator; and determining whether the change of the rotating speed of the wind driven generator is abnormal or not based on the result of the sine function of the preset time and the acquired rotating speed of the wind driven generator.
Optionally, the rotational speed obtaining unit is configured to find a first average value of the rotational speed of the wind turbine during a first time.
Optionally, the rotation speed obtaining unit is configured to: calculating a second average value of the rotating speed of the wind driven generator in the preset time period, and calculating a difference value between the second average value and the first average value; adding the result of the sine function of the preset time to the calculated difference; determining whether the added result is consistent with the real-time rotating speed of the wind driven generator at the preset time; and determining that the change of the rotating speed of the wind driven generator is abnormal in response to the addition result being consistent with the real-time rotating speed of the wind driven generator at the preset time.
Optionally, the rotation speed obtaining unit is configured to: and when the difference between the addition result and the real-time rotating speed of the wind driven generator at the preset time is less than a preset threshold value, determining that the addition result is consistent with the real-time rotating speed of the wind driven generator at the preset time.
Optionally, the rotation speed obtaining unit is configured to: continuously detecting the fluctuation amplitude and the fluctuation period of the rotating speed of the wind driven generator obtained at different moments to determine whether the fluctuation amplitude and the fluctuation period are consistent; and determining that the change of the rotating speed of the wind driven generator is abnormal in response to the wave amplitude and the wave period being kept consistent.
Optionally, the vibration frequency determination unit is configured to: acquiring a vibration value of the wind driven generator; and calculating the number of zero crossings of the vibration value of the wind driven generator in unit time to serve as the vibration frequency of the wind driven generator.
Optionally, the vibration frequency determination unit is configured to: acquiring a vibration value of the wind driven generator; and performing fast Fourier transform on the obtained vibration value of the wind driven generator to obtain the vibration frequency of the wind driven generator.
In another general aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the method of detecting a main bearing abnormality of a wind turbine as described above.
In another general aspect, there is provided a controller, including: a processor; and a memory storing a computer program which, when executed by the processor, implements the method for detecting abnormality of the main bearing of the wind turbine generator as described above.
In another general aspect, there is provided a computer program product comprising a computer program which, when executed by a processor, implements a method of detecting a main bearing anomaly of a wind turbine generator as described above.
According to the main bearing abnormity detection method and the main bearing abnormity detection device of the wind driven generator, the operation condition of the wind driven generator is directly detected through data analysis, and online detection can be realized.
In addition, the method and the device for detecting the abnormality of the main bearing of the wind driven generator relate to few conditions, and the detection accuracy can be improved by combining the vibration value with the rotation speed change. The main bearing abnormity detection method and the main bearing abnormity detection device of the wind driven generator do not need to distinguish components of vibration values, and only need to detect the vibration frequency, so that the algorithm can be simplified. The method and the device for detecting the abnormality of the main bearing of the wind turbine generator detect the long-term fluctuation of the rotation speed value, and are not affected by the change of the wind speed.
Additional aspects and/or advantages of the present general inventive concept will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the general inventive concept.
Drawings
The above and other objects and features of the embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings illustrating embodiments, in which:
FIG. 1 is a graph of rotational speed of a wind turbine with main bearing anomalies;
FIG. 2 is a graph of the rotational speed of the wind turbine under normal conditions;
FIG. 3 is a vibration diagram of a wind turbine in which abnormality of a main bearing occurs;
FIG. 4 is a vibration diagram of a wind turbine under normal conditions;
FIG. 5 is a flowchart illustrating a main bearing abnormality detecting method of a wind turbine according to an embodiment of the present disclosure;
fig. 6 is a block diagram illustrating a main bearing abnormality detecting apparatus of a wind power generator according to an embodiment of the present disclosure;
fig. 7 is a block diagram illustrating a controller of a wind turbine 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, devices, and/or systems described herein. However, various changes, modifications, and equivalents of the methods, apparatus, and/or systems described herein will be apparent to those skilled in the art after reviewing 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 changed as will become apparent after understanding the disclosure of the present application, except to the extent that operations must occur in a particular order. Moreover, descriptions of features known in the art may be omitted for clarity and conciseness.
The features described herein may be embodied in different forms and should not be construed as limited to the examples described herein. Rather, the examples described herein have been provided to illustrate only some of the many possible ways to implement the methods, devices, and/or systems described herein, which will be apparent after understanding the disclosure of the present application.
As used herein, the term "and/or" includes any one of the associated listed items and any combination of any two or more.
Although terms such as "first", "second", and "third" may be used herein to describe various elements, components, regions, layers or sections, these elements, components, regions, layers or sections should not be limited by these terms. Rather, these terms are only used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Thus, a first element, component, region, layer or section referred to in the examples described herein could also be referred to as a second element, component, region, layer or section without departing from the teachings of the examples.
In the specification, when an element (such as a layer, region or substrate) is described as being "on," "connected to" or "coupled to" another element, it can be directly on, connected to or coupled to the other element or one or more other elements may be present therebetween. In contrast, when an element is referred to as being "directly on," "directly connected to," or "directly coupled to" another element, there may be no intervening elements present.
The terminology used herein is for the purpose of describing various examples only and is not intended to be limiting of the disclosure. The singular is also intended to include the plural unless the context clearly indicates otherwise. The terms "comprises," "comprising," and "having" specify the presence of stated features, quantities, operations, elements, components, and/or combinations thereof, but do not preclude the presence or addition of one or more other features, quantities, operations, components, elements, and/or combinations thereof.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs after understanding the present disclosure. Unless explicitly defined as such herein, terms (such as those defined in general dictionaries) should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and should not be interpreted in an idealized or overly formal sense.
Further, in the description of the examples, when it is considered that detailed description of well-known related structures or functions will cause a vague explanation of the present disclosure, such detailed description will be omitted.
According to the main bearing abnormity detection method and the main bearing abnormity detection device of the wind driven generator disclosed by the embodiment of the disclosure, the running condition of the wind driven generator is detected by detecting the rotating speed fluctuation condition and the vibration frequency value of the wind driven generator. Specifically, the method for detecting the main bearing abnormality of the wind turbine generator and the main bearing abnormality detection device according to the embodiments of the present disclosure use the rotational speed and the vibration to jointly determine, and therefore, the accuracy of the detection can be ensured. The reason is that: vibration caused by other factors of the wind driven generator (such as yaw starting, stopping and abnormal torque change) can not cause continuous fluctuation of the rotating speed; the fluctuation of the rotating speed does not cause the high-frequency change of vibration. Because the bearing is of a ball structure, after the bearing has mechanical failure, certain intermittent resistance can be caused to the rotating speed of the wind driven generator, so that the rotating speed of an impeller of the wind driven generator is fluctuated to a certain extent; meanwhile, according to a mechanical formula F ═ ma, the high-frequency vibration of the wind driven generator can be caused due to unbalanced stress of the impeller in the rotating process and the vibration of the main bearing.
The principle of the main bearing abnormality detection method and the main bearing abnormality detection apparatus of the wind power generator according to the embodiment of the present disclosure is explained in detail below.
Fig. 1 is a graph of the rotational speed of a wind turbine generator in which abnormality of a main bearing occurs, and the abscissa represents a time value and the ordinate represents a rotational speed value. As can be seen from fig. 1, the generator rotational speed (i.e., the impeller rotational speed) fluctuates in a sinusoidal manner. The reason is that: after a main bearing of the generator is blocked or broken, the resistance borne by the impeller is small and large in the rotating process; the PID regulation has a certain hysteresis, the regulation frequency of which is not sufficient to correct such high-frequency rotational speed variations in a timely manner.
Fig. 2 is a graph of the rotational speed of the wind turbine in a normal condition, with the abscissa indicating the time value and the ordinate indicating the rotational speed value. As can be seen from fig. 2, when the main bearing is normal, the fluctuation of the rotating speed value of the wind power generator is small and only changes with the change of the wind speed because the impeller has no influence of the clearance resistance in the rotating process, and the fluctuation characteristic of the main bearing can make the rotating speed of the wind power generator tend to be stable under the regulation of PID control.
Fig. 3 is a vibration graph of a wind turbine generator in which abnormality of a main bearing occurs, and an abscissa indicates a time value and an ordinate indicates a vibration acceleration value. As can be seen from fig. 3, before time 0 (i.e. when the wind turbine is operating normally, not feathered), the vibration frequency of the wind turbine is high. The reason is that: due to the abnormality of the main bearing, the acting force is unbalanced in the rotating process, so that a certain exciting force is generated, and the exciting force acts on the wind driven generator to cause the vibration of the wind driven generator.
Fig. 4 is a vibration graph of a wind turbine under normal conditions, with the abscissa representing a time value and the ordinate representing a vibration acceleration value. As can be seen from fig. 4, the vibration frequency of the wind power generator is significantly lower than the vibration frequency of the wind power generator shown in fig. 3. Through analysis of a bearing vibration mechanism, the vibration frequency value of the wind driven generator is about 40-50 Hz after a main bearing of the wind driven generator fails, and the vibration frequency of the wind driven generator shown in FIG. 4 is only a few Hz.
A main bearing abnormality detection method and a main bearing abnormality detection apparatus of a wind turbine according to an embodiment of the present disclosure will be described in detail below with reference to fig. 5 and 6.
Fig. 5 is a flowchart illustrating a main bearing abnormality detection method of a wind power generator according to an embodiment of the present disclosure. The main bearing abnormality detection method of the wind power generator according to the embodiment of the present disclosure may be performed in a main controller or other dedicated processor of the wind power generator.
Referring to fig. 5, in step S501, the rotation speed of the wind power generator is acquired, and it is determined whether an abnormality occurs in a variation in the rotation speed of the wind power generator. Here, the real-time rotation speed of the wind turbine may be obtained through various methods, which are not limited by the present disclosure. Further, in step S501, the rotation speed of the wind turbine may be acquired during the wind turbine generating operation. This is because, during the shutdown and pitch-retracting process of the wind turbine, the vibration applied to the wind turbine becomes large, and the vibration curve having a low frequency but a high amplitude is obtained after the vibration due to the bearing abnormality is superimposed. For this reason, the main bearing abnormality detection method of the wind power generator according to the embodiment of the present disclosure may be performed by a main controller or a dedicated processor of the wind power generator during the power generation operation of the wind power generator.
Specifically, in step S501, the fluctuation width and period of the rotation speed of the wind turbine may be acquired. Then, a sine function fitting the fluctuation of the rotation speed can be generated based on the fluctuation amplitude and the period of the rotation speed of the wind driven generator
Wherein A represents the fluctuation amplitude, the fluctuation period T is 2 pi/omega,
indicating the value of the offset angle.
Here, it should be noted that, since the rotation speed of the wind turbine varies with the variation of the wind speed, if a sine function fitting is used, the calculation result also needs to vary with the decrease of the rotation speed. For example, as shown in fig. 1, the rotation speed as a whole tends to decrease, and only a slight fluctuation does not change. This further illustrates that the fluctuation of the rotational speed is independent of the change of the wind speed after the abnormality of the main bearing. Therefore, in order to effectively determine whether or not an abnormality occurs in the variation of the rotation speed of the wind turbine, in step S501, an average value of the rotation speed of the wind turbine during the first time period (hereinafter referred to as a first average value) may be found. Here, the first time may be set by those skilled in the art according to actual needs, for example, the first time may be more than 200 ms.
After generating the sine function fitting the fluctuation of the rotation speed, it may be determined whether the variation of the rotation speed of the wind power generator is abnormal based on the result of the sine function for a preset time and the acquired rotation speed of the wind power generator. Specifically, an average value (hereinafter, referred to as a second average value) of the rotation speeds of the wind turbine during a preset time may be first obtained, and a difference between the second average value and the first average value may be calculated. Here, the preset time may be set by those skilled in the art according to actual needs as long as the length of the preset time is not greater than 1/4 of the period of the sine function. For example, the preset time may be 100ms or 200ms, but is not limited thereto. And then substituting the preset time into the generated sine function fitting the fluctuation of the rotating speed to obtain a result of the sine function of the preset time, and adding the result of the sine function of the preset time and the calculated difference value between the second average value and the first average value. Next, it may be determined whether the result of the addition is consistent with the real-time rotation speed of the wind turbine at the preset time. For example, when the difference between the result of the addition and the real-time rotation speed of the wind turbine at the preset time is less than a predetermined threshold value, it may be determined that the result of the addition coincides with the real-time rotation speed of the wind turbine at the preset time. And determining that the variation of the rotating speed of the wind driven generator is abnormal in response to the addition result being consistent with the real-time rotating speed of the wind driven generator at the preset time. Here, the predetermined threshold may be a value within a range of 0.05 to 0.1rpm, but is not limited thereto, and a person skilled in the art may set the predetermined threshold to other values according to actual needs.
On the other hand, in step S501, the fluctuation width and the period of the rotation speed of the wind turbine acquired at different times may be continuously detected to determine whether the fluctuation width and the period are kept consistent. In response to the fluctuation amplitude and the period remaining consistent, it may be determined that the variation in the rotational speed of the wind turbine is abnormal.
With continued reference to FIG. 5, in step S502, in response to an abnormality in the change in the rotational speed of the wind turbine, it is determined whether the vibration frequency of the wind turbine exceeds a preset threshold frequency (e.g., without limitation, 40 Hz). On the other hand, if there is no abnormality in the change in the rotation speed of the wind turbine, it indicates that there is no abnormality in the main bearing, and thus the main bearing abnormality detection method of the wind turbine according to the embodiment of the present disclosure may be exited.
Specifically, in step S502, a vibration value of the wind turbine is first acquired. Here, the vibration value (including the vibration frequency and/or the vibration amplitude) of the nacelle of the wind turbine may be obtained by various methods, which are not limited by the present disclosure. Then, the number of times the vibration value of the wind turbine crosses zero per unit time (for example, but not limited to, 1 second) may be obtained as the vibration frequency of the wind turbine. For example, for two adjacent sampling points, if the vibration value of the first sampling point is greater than 0 and the vibration value of the second sampling point is less than 0, or the vibration value of the first sampling point is less than 0 and the vibration value of the second sampling point is greater than 0, the number of zero crossings is increased by 1. The vibration values of all adjacent sampling points in unit time are compared, and all zero-crossing times are counted, so that the zero-crossing times of the vibration values of the wind driven generator in unit time (such as but not limited to 1 second) can be obtained. Alternatively, a Fast Fourier Transform (FFT) may be performed on the obtained vibration values (vibration value sequence) of the wind turbine to obtain the vibration frequency of the wind turbine.
Thereafter, in step S503, in response to the vibration frequency of the wind turbine exceeding the preset threshold frequency, an alarm message indicating abnormality of the main bearing is output. For example, an alarm message indicating abnormality of the main bearing may be output in the form of sound, light, electricity, vibration, or the like. Alternatively, a warning message indicating a main bearing abnormality may be output to a control center of the wind farm or to a site outside the wind farm for handling the main bearing abnormality. On the other hand, if the vibration frequency of the wind turbine does not exceed the preset threshold frequency, it indicates that there is no main bearing abnormality, and thus the main bearing abnormality detection method of the wind turbine according to the embodiment of the present disclosure may be exited.
As described above, according to the method for detecting abnormality of the main bearing of the wind turbine generator of the present disclosure, it is not necessary to distinguish components of the vibration value, and it is only necessary to detect the magnitude of the frequency value, so that the abnormality detection algorithm can be simplified. On the other hand, the method for detecting the abnormality of the main bearing of the wind driven generator is based on the common judgment of the rotating speed and the vibration, so that the detection precision can be ensured, the running condition of the wind driven generator is directly detected through data analysis, and the online detection can be realized. In addition, the method for detecting the abnormality of the main bearing of the wind driven generator has few related conditions, can improve the detection accuracy by combining the vibration value with the rotation speed change, and is not influenced by the wind speed change because the rotation speed value is detected to pulsate for a long time.
Fig. 6 is a block diagram illustrating a main bearing abnormality detecting apparatus of a wind turbine according to an embodiment of the present disclosure. The main bearing abnormality detection apparatus of the wind power generator according to the embodiments of the present disclosure may be provided in a main controller or other dedicated processor of the wind power generator, or may be implemented as a dedicated apparatus in the wind power generator.
Referring to fig. 6, a main bearing abnormality detecting apparatus 600 of a wind turbine may include a rotational speed obtaining unit 610, a vibration frequency determining unit 620, and an alarm unit 630. The rotation speed obtaining unit 610 may obtain the rotation speed of the wind power generator and determine whether a variation in the rotation speed of the wind power generator is abnormal. The vibration frequency determination unit 620 may determine whether the vibration frequency of the wind turbine exceeds a preset threshold frequency in response to the occurrence of an abnormality in the variation of the rotational speed of the wind turbine. The alarm unit 630 may output an alarm message indicating abnormality of the main bearing in response to the vibration frequency of the wind turbine exceeding a preset threshold frequency.
Specifically, the rotational speed acquisition unit 610 may acquire the rotational speed of the wind turbine during the wind turbine generating operation. As described above, in acquiring the rotational speed of the wind turbine, the rotational speed acquisition unit 610 may acquire the fluctuation amplitude and the period of the rotational speed of the wind turbine. Then, the rotational speed acquisition unit 610 may generate a sine function fitting the fluctuation of the rotational speed based on the fluctuation amplitude and cycle of the rotational speed of the wind turbine, and determine whether the variation of the rotational speed of the wind turbine is abnormal based on the result of the sine function for a preset time and the acquired rotational speed of the wind turbine.
Further, the rotation speed obtaining unit 610 may obtain a first average value of the rotation speed of the wind turbine during the first time. Subsequently, the rotation speed obtaining unit 610 may obtain a second average value of the rotation speed of the wind turbine during a preset time period, and calculate a difference value between the second average value and the first average value; adding the result of the sine function of the preset time to the calculated difference; determining whether the added result is consistent with the real-time rotating speed of the wind driven generator within preset time; and determining that the change of the rotating speed of the wind driven generator is abnormal in response to the addition result being consistent with the real-time rotating speed of the wind driven generator at the preset time. Here, when the difference between the result of the addition and the real-time rotation speed of the wind turbine at the preset time is less than the predetermined threshold value, the rotation speed acquisition unit 610 may determine that the result of the addition coincides with the real-time rotation speed of the wind turbine at the preset time. On the other hand, the rotation speed acquisition unit 610 may continuously detect the fluctuation amplitude and the period of the rotation speed of the wind turbine acquired at different times to determine whether the fluctuation amplitude and the period are consistent; and determining that the variation of the rotation speed of the wind turbine is abnormal in response to the fluctuation amplitude and the period being kept consistent.
The vibration frequency determination unit 620 may acquire a vibration value of the wind turbine and find the number of times of zero crossing of the vibration value of the wind turbine per unit time as the vibration frequency of the wind turbine. Alternatively, the vibration frequency determination unit 620 may acquire a vibration value of the wind turbine and perform a fast fourier transform on the acquired vibration value of the wind turbine to acquire a vibration frequency of the wind turbine.
Fig. 7 is a block diagram illustrating a controller of a wind turbine according to an embodiment of the present disclosure.
Referring to fig. 7, a controller 700 of a wind power generator according to an embodiment of the present disclosure may be, but is not limited to, a main controller of the wind power generator. For example, the controller 700 of the wind power generator according to an embodiment of the present disclosure may be a controller of a wind farm, or a dedicated controller provided in the wind power generator. The controller 700 of the wind power generator according to the embodiment of the present disclosure may include a processor 710 and a memory 720. The processor 710 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 720 stores computer programs to be executed by the processor 710. Memory 720 includes high speed random access memory and/or non-volatile computer-readable storage media. The main bearing abnormality detection method of the wind turbine described above may be implemented when the processor 710 executes a computer program stored in the memory 720.
Alternatively, the controller 700 may communicate with other components in the wind turbine in a wired/wireless communication manner, and may also communicate with other devices in the wind farm in a wired/wireless communication manner. Further, the controller 700 may communicate with a device external to the wind farm in a wired/wireless communication manner.
The main bearing abnormality detection method of a wind turbine according to an 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 a processor, the method for detecting abnormality of the main bearing of the wind turbine generator as described above may be implemented. Examples of computer-readable storage media 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, non-volatile 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 memory, Hard Disk Drive (HDD), solid-state disk drive (SSD), card-type memory (such as a multimedia card, a Secure Digital (SD) card or an extreme digital (XD) card), tape, a floppy disk, a magneto-optical data storage device, an optical data storage device, a hard disk, a magnetic tape, a magneto-optical data storage device, a hard disk, a magnetic tape, a magnetic data storage device, a magnetic tape, a magnetic data storage device, a magnetic tape, a magnetic data storage device, a magnetic tape, a magnetic data storage device, a magnetic tape, a magnetic data storage device, a magnetic tape, a magnetic data storage device, a magnetic disk, a magnetic data storage device, a magnetic disk, A solid state disk, and any other device configured to store and provide a computer program and any associated data, data files, and data structures to a processor or computer in a non-transitory manner such that the processor or computer can execute the computer program. 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 fashion by one or more processors or computers.
On the other hand, the main bearing abnormality detection method of a wind turbine according to an embodiment of the present disclosure may be implemented as a computer program product comprising a computer program which, when executed by a processor, implements the main bearing abnormality detection method of a wind turbine as described above.
According to the main bearing abnormity detection method and the main bearing abnormity detection device of the wind driven generator disclosed by the embodiment of the disclosure, the operation condition of the wind driven generator is directly detected through data analysis, and online detection can be realized. In addition, the method and the device for detecting the abnormality of the main bearing of the wind driven generator relate to few conditions, and the detection accuracy can be improved by combining the vibration value with the rotation speed change. The main bearing abnormity detection method and the main bearing abnormity detection device of the wind driven generator do not need to distinguish components of vibration values, and only need to detect the vibration frequency, so that the algorithm can be simplified. The method and the device for detecting the abnormality of the main bearing of the wind power generator detect the long-term pulsation of the rotating speed value, so the method and the device are not influenced by the change of the wind speed.
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 (13)
1. A method for detecting abnormality of a main bearing of a wind turbine generator, comprising:
acquiring the rotating speed of the wind driven generator, and determining whether the change of the rotating speed of the wind driven generator is abnormal or not;
responding to the abnormal change of the rotating speed of the wind driven generator, and determining whether the vibration frequency of the wind driven generator exceeds a preset threshold frequency;
and outputting an alarm message indicating that the main bearing is abnormal in response to the vibration frequency of the wind driven generator exceeding a preset threshold frequency.
2. The main bearing abnormality detecting method according to claim 1, wherein the rotational speed of the wind power generator is acquired during a power generating operation of the wind power generator.
3. The main bearing abnormality detecting method according to claim 1, wherein the step of obtaining the rotational speed of the wind power generator includes: and acquiring the fluctuation amplitude and the fluctuation period of the rotating speed of the wind driven generator.
4. The main bearing abnormality detecting method according to claim 3, wherein the step of determining whether the variation in the rotational speed of the wind turbine is abnormal includes:
generating a sine function fitting the fluctuation of the rotating speed based on the fluctuation amplitude and the fluctuation period of the rotating speed of the wind driven generator;
and determining whether the change of the rotating speed of the wind driven generator is abnormal or not based on the result of the sine function of the preset time and the acquired rotating speed of the wind driven generator.
5. The main bearing anomaly detection method of claim 4, wherein said step of deriving a speed of rotation of said wind turbine includes: a first average of the rotational speed of the wind turbine is determined during a first time.
6. The main bearing abnormality detecting method according to claim 5, wherein the step of determining whether the variation in the rotational speed of the wind power generator is abnormal or not based on the result of the sine function for the preset time and the acquired rotational speed of the wind power generator includes:
calculating a second average value of the rotating speed of the wind driven generator during the preset time, and calculating a difference value between the second average value and the first average value;
adding the result of the sine function of the preset time to the calculated difference;
determining whether the added result is consistent with the real-time rotating speed of the wind driven generator at the preset time;
and determining that the change of the rotating speed of the wind driven generator is abnormal in response to the addition result being consistent with the real-time rotating speed of the wind driven generator at the preset time.
7. The main bearing abnormality detecting method according to claim 6, characterized in that it is determined that the result of addition coincides with the real-time rotation speed of the wind power generator at the preset time when the difference between the result of addition and the real-time rotation speed of the wind power generator at the preset time is less than a predetermined threshold value.
8. The main bearing abnormality detecting method according to claim 3, wherein the step of determining whether the variation in the rotational speed of the wind turbine is abnormal includes:
continuously detecting the fluctuation amplitude and the fluctuation period of the rotating speed of the wind driven generator obtained at different moments to determine whether the fluctuation amplitude and the fluctuation period are consistent;
and determining that the change of the rotating speed of the wind driven generator is abnormal in response to the wave amplitude and the wave period being kept consistent.
9. The main bearing abnormality detecting method according to claim 1, wherein the step of determining whether the vibration frequency of the wind power generator exceeds a preset threshold frequency includes:
acquiring a vibration value of the wind driven generator;
and calculating the number of zero crossings of the vibration value of the wind driven generator in unit time to serve as the vibration frequency of the wind driven generator.
10. The main bearing abnormality detecting method according to claim 1, wherein the step of determining whether the vibration frequency of the wind power generator exceeds a preset threshold frequency includes:
acquiring a vibration value of the wind driven generator;
and performing fast Fourier transform on the obtained vibration value of the wind driven generator to obtain the vibration frequency of the wind driven generator.
11. A main bearing abnormality detection device for a wind turbine generator, comprising:
a rotational speed acquisition unit configured to acquire a rotational speed of the wind power generator and determine whether a variation in the rotational speed of the wind power generator is abnormal;
a vibration frequency determination unit configured to determine whether a vibration frequency of the wind power generator exceeds a preset threshold frequency in response to an occurrence of an abnormality in a variation in a rotational speed of the wind power generator;
an alarm unit configured to output an alarm message indicating abnormality of the main bearing in response to a vibration frequency of the wind turbine exceeding a preset threshold frequency.
12. A computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements a main bearing abnormality detection method of a wind power generator according to any one of claims 1 to 10.
13. A controller, characterized in that the controller comprises:
a processor; and
a memory storing a computer program which, when executed by the processor, implements the method of detecting a main bearing abnormality of a wind turbine according to any one of claims 1 to 10.
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