CN109977436B - Turbulence intensity estimation method and device - Google Patents
Turbulence intensity estimation method and device Download PDFInfo
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Abstract
The application provides a turbulence intensity estimation method and device. The turbulence intensity estimation method comprises the following steps: acquiring historical data of the wind generating set at a preset sampling interval, and preprocessing the historical data; based on the positive correlation between the acceleration and the turbulence intensity of the wind generating set in the front-back direction of the engine room, a turbulence intensity estimation model is established by using the preprocessed data; the current turbulence intensity is estimated based on the established turbulence intensity estimation model. According to the turbulence intensity estimation method and the turbulence intensity estimation device, real-time and accurate estimation of the turbulence intensity can be realized, so that safe operation of the wind turbine generator set is ensured at any time, and the problem of shutdown of the wind turbine generator set under large yaw is avoided by reducing the load of the whole wind turbine generator set when the turbulence intensity is overlarge, and the availability of the wind turbine generator set is improved.
Description
Technical Field
The following description relates to the wind power field, and in particular, to a turbulence intensity estimation method and apparatus.
Background
Turbulence intensity is a standard for measuring the degree of pulsation of the air flow velocity, and is typically expressed in terms of the ratio of the root mean square of the pulsation velocity to the average wind speed, i.e., i=u '/U, where I is the turbulence intensity, U' is the root mean square of the pulsation velocity (i.e., the standard deviation of the wind speed), and U is the average wind speed.
In the wind power field, when the field wind speed is greatly changed (namely, the turbulence intensity) is greatly changed, the wind generating set is subjected to strong load impact, so that the turbulence intensity needs to be paid attention to at any time, and corresponding control measures are implemented when necessary to ensure the safety of the wind generating set. However, current methods of estimating turbulence intensity require mean and standard deviation of wind speed over 10 minutes, which makes it difficult to calculate turbulence intensity in real time. In addition, due to inaccuracy of wind speed measurement (the wind speed measured by the nacelle anemometer is greatly influenced by the wake of the wind turbine), excessive calculation amount and the like, the turbulence intensity cannot be accurately estimated when the wind turbine generator is in operation.
Disclosure of Invention
In order to solve the problems that the calculation of the turbulence intensity is difficult to implement and the accurate estimation of the turbulence intensity is difficult to realize, the invention provides a turbulence intensity estimation method and a turbulence intensity estimation device.
According to one aspect of the inventive concept, a turbulence intensity estimation method is provided. The turbulence intensity estimation method may include: acquiring historical data of the wind generating set at a preset sampling interval, and preprocessing the historical data, wherein the historical data at least comprises acceleration and turbulence intensity of the wind generating set in the front-back direction of a cabin; based on the positive correlation between the acceleration and the turbulence intensity of the wind generating set in the front-back direction of the engine room, a turbulence intensity estimation model is established by using the preprocessed data; the current turbulence intensity is estimated based on the established turbulence intensity estimation model.
Preferably, the step of preprocessing the history data may include: turbulence intensity in the historical data is classified according to the size, and the absolute acceleration value, the wind speed and the power value of the front-rear direction of the nacelle of the wind generating set corresponding to each class are respectively determined based on all turbulence intensity in each class.
Preferably, the step of establishing a turbulence intensity estimation model may comprise: and taking the absolute acceleration value, the power value and the wind speed of the wind generating set cabin in the front-back direction corresponding to each type as input, taking the type of turbulence intensity as output, and building a turbulence intensity estimation model through neural network training.
Preferably, the step of preprocessing the history data may include: dividing the turbulence intensity into a conventional turbulence working condition and a limit turbulence working condition; for each power value, under a normal turbulence working condition, obtaining an average value of absolute values of acceleration of the wind generating set in the front-rear direction of the nacelle of each specific wind speed as a first absolute value of acceleration; and obtaining an average value of absolute values of acceleration of the wind generating set in the front-rear direction of the nacelle of each specific wind speed as a second absolute value of acceleration under the extreme turbulence working condition for each power value.
Preferably, the step of establishing a turbulence intensity estimation model may comprise: for each power value, calculating a first function under a normal turbulence working condition by taking the wind speed as an independent variable, taking the absolute value of the first acceleration as a dependent variable, and calculating a second function under a limit turbulence working condition by taking the wind speed as the independent variable and taking the absolute value of the second acceleration as the dependent variable; and calculating a weighted average value between a first acceleration absolute value of the first function and a second acceleration absolute value of the second function at the wind speed at each wind speed according to the first function and the second function, and obtaining a corresponding relation between each wind speed and the calculated weighted average value of the acceleration absolute values as a turbulence intensity estimation model.
Preferably, the step of estimating the current turbulence intensity may comprise: determining a corresponding turbulence intensity estimation model according to the current power value; if the absolute value of the acceleration in the front-rear direction of the cabin of the current wind generating set is smaller than the weighted average value of the absolute values of the acceleration in the corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is a conventional turbulence working condition; and if the absolute value of the acceleration in the front-rear direction of the cabin of the current wind generating set is larger than the weighted average value of the absolute value of the acceleration in the corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is the limit turbulence working condition.
Preferably, the step of establishing a turbulence intensity estimation model may comprise: for each power value, the upper envelope of wind speed-acceleration is calculated as a turbulence intensity estimation model with the wind speed as the abscissa and the first acceleration absolute value as the ordinate.
Preferably, the step of estimating the current turbulence intensity may comprise: determining a corresponding turbulence intensity estimation model according to the current power value; if the absolute value of the acceleration of the current wind generating set in the front-rear direction of the cabin is positioned below the upper envelope line in the corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is a conventional turbulence working condition; if the absolute value of the acceleration of the current wind generating set cabin in the front-rear direction is located above the upper envelope line in the corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is the limit turbulence working condition.
Preferably, the step of establishing a turbulence intensity estimation model comprises: for each power value, the lower envelope of wind speed-acceleration is calculated as a turbulence intensity estimation model with wind speed as the abscissa and the second acceleration absolute value as the ordinate.
Preferably, the step of estimating the current turbulence intensity comprises: determining a corresponding turbulence intensity estimation model according to the current power value; if the absolute value of the acceleration of the current wind generating set in the front-rear direction of the cabin is positioned below a lower envelope line in a corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is a conventional turbulence working condition; if the absolute value of the acceleration of the current wind generating set cabin in the front-rear direction is located above the lower envelope line in the corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is the limit turbulence working condition.
According to another aspect of the inventive concept, a turbulence intensity estimating device is provided. The turbulence intensity estimation device may include: a preprocessing module configured to: acquiring historical data of the wind generating set at a preset sampling interval, and preprocessing the historical data, wherein the historical data at least comprises acceleration and turbulence intensity of the wind generating set in the front-back direction of a cabin; a model building module configured to: based on the positive correlation between the acceleration and the turbulence intensity of the wind generating set in the front-back direction of the engine room, a turbulence intensity estimation model is established by using the preprocessed data; and an estimation module configured to: the current turbulence intensity is estimated based on the established turbulence intensity estimation model.
According to another aspect of the inventive concept, there is provided a computer-readable storage medium storing program instructions that, when executed by a processor, cause the processor to perform the method as described above.
According to another aspect of the inventive concept, a computing device is provided. The computing device may include a processor; and a memory storing program instructions that, when executed by the processor, cause the processor to perform the method as described above.
According to the turbulence intensity estimation method and the turbulence intensity estimation device, a turbulence intensity estimation model can be established based on the positive correlation between the acceleration of the wind generating set cabin in the X direction and the turbulence intensity, so that the current turbulence intensity is estimated, the real-time and accurate estimation of the turbulence intensity is realized, the safe operation of the wind generating set is ensured at any time, the load of the whole wind generating set is reduced when the turbulence intensity is overlarge, the problems of inaccurate turbulence intensity estimation caused by inaccurate wind measurement and shutdown of the wind generating set under large yaw are avoided, and the availability of the wind generating set is improved.
Drawings
Exemplary embodiments of the present invention will be described in detail below with reference to the attached drawing figures, wherein,
FIG. 1a shows a graph of X-direction acceleration over time at different average wind speeds and turbulence intensities;
FIG. 1b shows a graph of X-direction acceleration as a function of wind speed for a wind park operating at a power level;
FIG. 2 is a flow chart illustrating a method of turbulence intensity estimation in accordance with an embodiment of the present invention;
FIG. 3 is a flow chart illustrating a method of turbulence intensity estimation in accordance with an embodiment of the present invention;
FIG. 4 shows a diagram of a turbulence estimation model built with the mean of the absolute value of the X-direction acceleration for a certain power value;
FIG. 5 shows a diagram of a turbulence estimation model built with an upper envelope of the absolute value of the X-direction acceleration for a certain power value;
FIG. 6 shows a diagram of a turbulence estimation model built with a lower envelope of absolute values of X-direction acceleration for a certain power value;
fig. 7 shows a block diagram of a turbulence intensity estimating device according to an exemplary embodiment of the invention.
Detailed Description
The invention is capable of various modifications and embodiments, it is to be understood that the invention is not limited to those embodiments, but includes all modifications, equivalents and alternatives falling within the spirit and scope of the invention. Furthermore, the terminology used in the exemplary embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the exemplary embodiments.
In various example embodiments of the invention, acceleration in the fore-aft direction of the wind turbine nacelle may be used interchangeably with acceleration in the X direction of the wind turbine nacelle (hereinafter simply referred to as X direction acceleration).
Fig. 1a shows a graph of X-direction acceleration over time at different average wind speeds and turbulence intensities. FIG. 1b shows a graph of X-direction acceleration as a function of wind speed for a wind park operating at a certain power level.
Fig. 1a and 1b show: as the wind speed increases, the absolute value of the X-direction acceleration increases gradually, and as the turbulence intensity increases, the X-direction acceleration increases accordingly, i.e., there is a positive correlation between the absolute value of the X-direction acceleration and the turbulence intensity. In addition, there is a positive correlation between the absolute value of the acceleration in the X direction and the power value (e.g., output power value) of the wind turbine generator system. Under the condition of the same wind generating set and the same wind speed and turbulence intensity, if the power values are different, the amplitude of the acceleration in the X direction is also different, and generally, the lower the power value is, the smaller the amplitude of the acceleration in the X direction is.
Thus, according to embodiments of the present invention, turbulence intensity can be estimated more accurately based on X-direction acceleration through multiple dimensions.
Fig. 2 is a flowchart illustrating a turbulence intensity estimation method according to an embodiment of the present invention.
Referring to fig. 2, a turbulence intensity estimation method according to an embodiment of the present invention may include: acquiring historical data of the wind generating set at a preset sampling interval, and preprocessing the historical data, wherein the historical data at least comprises acceleration and turbulence intensity of the wind generating set in the front-back direction of a cabin (step S110); based on the positive correlation between the acceleration and the turbulence intensity in the front-rear direction of the nacelle of the wind generating set, a turbulence intensity estimation model is established using the preprocessed data (step S130); the current turbulence intensity is estimated based on the established turbulence intensity estimation model (step S150).
The historical data of the wind turbine generator set acquired in step S110 may further include power values and wind speeds. Wherein the power value represents the value of the output power of the wind power plant.
The absolute value of the X-direction acceleration as described above with reference to fig. 1a and 1b has a positive correlation with the turbulence intensity. Thus, the turbulence intensity estimation method according to an exemplary embodiment of the present invention may use the preprocessed data to build a turbulence intensity estimation model using various methods based on a positive correlation between the acceleration and the turbulence intensity of the wind turbine generator system in the fore-and-aft direction.
According to an example embodiment of the invention, a neural network algorithm may be employed to build a turbulence intensity estimation model. In this case, in step S110, the turbulence intensities in the history data are classified by size, and the absolute acceleration value, the wind speed, and the power value in the front-rear direction of the nacelle of the wind turbine generator set corresponding to each class are determined based on all the turbulence intensities in each class, respectively.
Specifically, in step S110, the turbulence intensity in the history data is sorted from small to large, and then sequentially sorted. For example, when the turbulence intensities of the turbulence intensities in the [0, A ] section are classified into one type, the turbulence intensities of the turbulence intensities in the [ A, B ] section are classified into one type, wherein B > A, and the intervals of the [0, A ] section and the intervals of the (A, B ] section may be the same or different according to engineering practices.
After the preprocessing of the data is completed in step S110, in step S130, the absolute value of the acceleration, the power value and the wind speed in the front-rear direction of the nacelle of the wind turbine generator set corresponding to each class are taken as inputs, the class of the turbulence intensity is taken as output, and the turbulence intensity estimation model is built through the training of the neural network. Various types of neural networks, such as convolutional neural networks, deep neural networks, and the like, may be used according to example embodiments of the present invention.
Step S150 may estimate the current turbulence intensity based on a turbulence intensity estimation model established through neural network training. For example, the current X-direction acceleration absolute value, wind speed, and power value may be input to the turbulence intensity estimation model, and the turbulence intensity estimation model outputs the type of the current turbulence intensity, i.e., which section the current turbulence intensity is located in, so that it may be determined whether the current turbulence intensity is excessive. When the turbulence intensity estimated at step S150 exceeds a predetermined threshold, corresponding control measures (e.g., reducing the overall load) may be taken to ensure safe operation of the wind turbine.
In the above-described exemplary embodiment of establishing a turbulence intensity estimation model by means of a neural network, the accuracy of the estimated turbulence intensity is relatively high due to the relatively complex operation of the neural network method, whereas in engineering practice it may not be necessary to have a particularly high accuracy of the estimated turbulence intensity. Therefore, the number of intervals of the turbulence intensity classification can be reduced, and the power value can be divided into intervals, thereby reducing the amount of computation.
Hereinafter, a method of establishing a turbulence intensity estimation model and estimating turbulence intensity according to a weighted average of X-direction acceleration absolute values according to an exemplary embodiment of the present invention will be described with reference to fig. 3.
Step S310 in fig. 3 may correspond to step S110 in fig. 2, step S330 and step S350 in fig. 3 may be for step S130 in fig. 2, and step S370 in fig. 3 may be for step S150 in fig. 2.
In the pre-treatment of step S310, referring to fig. 3, the turbulence intensity is divided into a normal turbulence operation and a limit turbulence operation; for each power value, in a conventional turbulence condition, obtaining an average value of the absolute values of the acceleration in the X direction at each specific wind speed as a first absolute value of the acceleration (i.e., absolute value of the acceleration in the conventional turbulence condition); for each power value, in the extreme turbulence condition, an average value of the absolute values of the acceleration in the X direction at each specific wind speed is obtained as a second absolute value of the acceleration (i.e., absolute value of the acceleration in the extreme turbulence condition). Here, each power value may represent a single power value, or may represent a power value interval formed by a plurality of power values with similar working conditions.
In comparison with the above-described exemplary embodiment of the neural network, the number of the turbulence intensity dividing sections can be appropriately reduced at the time of preprocessing, and the most simplified case is divided into only two categories.
According to an example embodiment of the invention, a turbulence intensity of less than or equal to 0.13 is assumed to be a normal turbulence condition and a turbulence intensity of greater than 0.13 is assumed to be a limit turbulence condition, and thus, there may be multiple sets of data for the normal turbulence condition and the limit turbulence condition. From the sets of data for the conventional turbulence regime, an average value of the absolute values of acceleration in the X-direction (i.e., the first absolute value of acceleration) at each wind speed is obtained. In other words, when the history data of the wind turbine generator system is acquired at predetermined sampling intervals, the average value of the absolute value of the acceleration in the X direction may be acquired by a method of moving average. For example, the average value of the absolute values of the X-direction acceleration in the plurality of groups of data of the conventional turbulence working condition can be calculated when the average wind speed is 13m/s, which is equivalent to obtaining more accurate absolute values of the X-direction acceleration for the average wind speed of 13 m/s. As such, each different wind speed in the historical data may be correlated with an average value of X-direction acceleration absolute values (i.e., a first acceleration absolute value) for each power value under conventional turbulence conditions; and under extreme turbulence conditions, for each power value, each different wind speed in the historical data is correlated to an average value of the absolute values of the X-direction acceleration (i.e., the second absolute value of acceleration).
In step S330, for each power value, a first function under the normal turbulence operation is calculated with the wind speed as an independent variable, with the average value of the X-direction acceleration absolute values of the normal turbulence operation as a dependent variable (i.e., with the first acceleration absolute value) and a second function under the limit turbulence operation is calculated with the wind speed as an independent variable, with the average value of the front-rear direction acceleration absolute values of the wind turbine nacelle under the limit turbulence operation as a dependent variable (i.e., with the second acceleration absolute value).
Step S330 is described in detail taking the calculation of the first function under normal turbulence conditions as an example. Specifically, for each power value, some points (v, m) where the data amount is continuously concentrated are selected as feature points under the normal turbulence condition, where v is the wind speed and m is the average value of the X-direction acceleration absolute values (i.e., the first acceleration absolute value) obtained for the wind speed v in step S310. And then, carrying out fitting calculation on the selected characteristic points to obtain the corresponding relation between each wind speed and the average value of the absolute values of the acceleration in the X direction (namely, the absolute value of the first acceleration), namely, the first function under the conventional turbulence working condition. Alternatively, according to an exemplary embodiment of the present invention, the piecewise function of the absolute value of the wind speed and the acceleration in the X direction may be obtained by linearly interpolating the feature points, i.e., the first function is in the form of a piecewise function. However, the inventive concept is not so limited and various forms of the first function may be obtained according to various fitting methods.
The second function under extreme turbulence conditions is calculated similarly to the above description, and the same description thereof is omitted here for the sake of brevity.
In step S350, a weighted average of the first function between the first acceleration absolute value of the wind speed and the second acceleration absolute value of the second function between the wind speed is calculated at each wind speed according to the first function and the second function, and a correspondence between each wind speed and the weighted average of the calculated acceleration absolute values is obtained as a turbulence intensity estimation model.
For example, in an alternative example embodiment where the first and second functions are piecewise functions, a weighted average of the ordinate of the two piecewise functions may be calculated. However, the inventive concept is not limited thereto, and for example, the mean of the ordinate of the two piecewise functions may also be directly determined.
Fig. 4 shows a diagram of a turbulence estimation model built with the mean value of the absolute value of the acceleration in the X-direction for a certain power value.
Referring to fig. 4, the abscissa is the wind speed c, and the ordinate is the mean value b of the absolute values of acceleration calculated by averaging.
In step S370, the turbulence intensity may be estimated using the established turbulence intensity estimation model, specifically, a corresponding turbulence intensity estimation model is determined according to the current power value; if at the current wind speed, the current X-direction acceleration absolute value is less than the ordinate in the corresponding turbulence intensity estimation model (i.e. the weighted average of the acceleration absolute values in the turbulence intensity estimation model), then the current turbulence intensity is a conventional turbulence regime; if at the current wind speed, the current X-direction acceleration absolute value is greater than the ordinate in the corresponding turbulence intensity estimation model (i.e., the weighted average of the acceleration absolute values in the turbulence intensity estimation model), then the current turbulence intensity is the limit turbulence regime.
If the current power value is the same as or similar to the power value of FIG. 4, then FIG. 4 may be determined as a turbulence intensity estimation model corresponding to the current power value. Referring to fig. 4, the diagonally shaded portion is the turbulence intensity for a conventional turbulence regime, and the unlabeled portion is the turbulence intensity for an extreme turbulence regime. For example, if the current wind speed is c 1 And the absolute value of the current X-direction acceleration is smaller than b 0 The current turbulence intensity can be determined to be the conventional turbulence working condition; if the current wind speed is c 1 And the absolute value of the current X-direction acceleration is greater than or equal to b 0 The current turbulence intensity may be determined to be the extreme turbulence regime.
When the current turbulence intensity is determined to be the limit turbulence working condition, corresponding control measures (for example, reducing the load of the whole wind turbine) can be adopted to ensure the safe operation of the wind turbine generator.
When it is determined that the current turbulence intensity is the normal turbulence condition, step S310 may be returned, and the history data is extracted and the subsequent steps are performed within a predetermined period of time before the next moment.
The establishment of the turbulence intensity estimation model may be further simplified on the basis of the turbulence intensity estimation method described with reference to fig. 3 and 4.
According to an example embodiment of the invention, the turbulence intensity estimation model may be built from an upper envelope of the absolute value of the X-direction acceleration and a lower envelope of the absolute value of the X-direction acceleration.
In an example embodiment in which the turbulence intensity estimation model is built from the upper envelope of the X-direction acceleration absolute values, compared to the method described with reference to fig. 3, the average value of the X-direction acceleration absolute values (i.e., the first acceleration absolute value) at each specific wind speed is obtained for each power value only in the conventional turbulence operation when the step of preprocessing is performed. That is, in the exemplary embodiment in which the turbulence intensity estimation model is built from the upper envelope of the X-direction acceleration absolute value, the turbulence intensity model can be built using only the historical data of the conventional turbulence conditions.
Then, at the step of establishing the turbulence intensity estimation model, for each power value, an upper envelope of wind speed-acceleration is calculated as the turbulence intensity estimation model with wind speed as an abscissa and an average value of X-direction acceleration absolute values of a conventional turbulence operation as an ordinate (i.e., with a first acceleration absolute value as an ordinate). According to the inventive concept, the upper envelope may be calculated according to various methods, which are not particularly limited herein.
Fig. 5 shows a diagram of a turbulence estimation model built with an upper envelope of the absolute value of the acceleration in the X-direction for a certain power value.
In fig. 5, the abscissa represents the wind speed c, the ordinate represents the absolute value of acceleration b (e.g., the first absolute value of acceleration), and the broken line and the thin solid line are the preprocessed first history data and the preprocessed second history data of the normal turbulence operation acquired at the first sampling interval and the second sampling interval, respectively; the thick solid line is the turbulence intensity estimation model established.
The step of estimating the current turbulence intensity is described with reference to fig. 5. If the current power value is the same as or similar to the power value of FIG. 5, then FIG. 5 may be determined as a turbulence intensity estimation model corresponding to the current power value.
If at the current wind speed, the current X-direction acceleration absolute value is located below the upper envelope of FIG. 5, the current turbulence intensity is the conventional turbulence condition; if at the current wind speed, the current X-direction acceleration absolute value is above the upper envelope of FIG. 5, then the current turbulence intensity is the limit turbulence regime.
In an example embodiment in which the turbulence intensity estimation model is built from the lower envelope of the X-direction acceleration absolute values, compared to the method described with reference to fig. 3, the average value of the X-direction acceleration absolute values (i.e., the second acceleration absolute value) at each specific wind speed is obtained for each power value only in the extreme turbulence condition when the step of preprocessing is performed. In an example embodiment where the turbulence intensity estimation model is built from the lower envelope of the absolute value of the X-direction acceleration, the turbulence intensity model may be built using only historical data of the extreme turbulence regime.
Then, in the step of establishing the turbulence intensity estimation model, for each power value, a lower envelope of wind speed-acceleration is calculated as the turbulence intensity estimation model with the wind speed as an abscissa and an average value of acceleration absolute values in the front-rear direction of the nacelle of the wind turbine generator set in the extreme turbulence condition as an ordinate (i.e., with the second acceleration absolute value as an ordinate). According to the inventive concept, the lower envelope may be calculated according to various methods, which are not particularly limited herein.
Fig. 6 shows a diagram of a turbulence estimation model built with a lower envelope of X-direction acceleration absolute values for a certain power value.
In fig. 6, the abscissa represents the wind speed c, the ordinate represents the absolute value of acceleration b (e.g., the absolute value of the second acceleration), and the broken line and the thin solid line are the first history data after preprocessing and the second history data after preprocessing, respectively, of the limit turbulence condition acquired at the first sampling interval and the second sampling interval; the thick solid line is the turbulence intensity estimation model established.
The step of estimating the current turbulence intensity is described with reference to fig. 6. If the current power value is the same as or similar to the power value of FIG. 6, then FIG. 6 may be determined as a turbulence intensity estimation model corresponding to the current power value.
If at the current wind speed, the current X-direction acceleration absolute value is located below the lower envelope of FIG. 6, the current turbulence intensity is the conventional turbulence condition; if at the current wind speed, the current X-direction acceleration absolute value is above the lower envelope of FIG. 6, then the current turbulence intensity is the limit turbulence regime.
When the current turbulence intensity is determined to be the limit turbulence working condition, corresponding control measures (for example, reducing the load of the whole wind turbine) can be adopted to ensure the safe operation of the wind turbine generator.
When the current turbulence intensity is determined to be the normal turbulence working condition, a preprocessing step can be returned, and the historical data is extracted and the subsequent steps are carried out within a preset time period before the next moment.
By the method, the actual time and accurate estimation of the turbulence intensity can be realized, so that the safe operation of the wind generating set is ensured.
Fig. 7 shows a block diagram of a turbulence intensity estimating device according to an exemplary embodiment of the invention.
Referring to fig. 7, a turbulence intensity estimation device 700 according to an example embodiment may include a preprocessing module 710, a model building module 730, and an estimation module 750.
The preprocessing module 710 is configured to: and acquiring historical data of the wind generating set at preset sampling intervals, and preprocessing the historical data, wherein the historical data at least comprises acceleration and turbulence intensity of the wind generating set in the front-back direction of a cabin. In particular, the preprocessing module 710 may perform the preprocessing operations described above in operation with fig. 2 to 6, and a detailed description thereof is omitted herein for brevity.
The model building module 730 is configured to: based on the positive correlation between the acceleration and the turbulence intensity of the wind generating set in the front-back direction of the engine room, a turbulence intensity estimation model is established by using the preprocessed data. Specifically, the model building module 730 may perform the operations of building the turbulence intensity estimation model as described above in operation of fig. 2 to 6, and a detailed description thereof is omitted herein for brevity.
The estimation module 750 may be configured to: the current turbulence intensity is estimated based on the established turbulence intensity estimation model. Specifically, the model building module 750 may perform the operations of estimating the current turbulence intensity as described above in operation of fig. 2 to 6, and a detailed description thereof is omitted herein for brevity.
Although the example embodiment of fig. 7 only describes that the turbulence intensity estimation device 700 may include a preprocessing module 710, a model building module 730, and an estimation module 750, the inventive concept is not so limited. For example, the turbulence intensity estimation device 700 may further include a control module (not shown). If the turbulence intensity estimated at the estimation module 750 exceeds a certain threshold (i.e., the current turbulence intensity is the limit turbulence condition), the control module employs corresponding control measures (e.g., reducing the overall load) to ensure safe operation of the wind turbine.
According to the turbulence intensity estimation method and the turbulence intensity estimation device, a turbulence intensity estimation model can be established based on the positive correlation between the X-direction acceleration and the turbulence intensity of the wind generating set so as to estimate the current turbulence intensity, and the real-time and accurate estimation of the turbulence intensity is realized, so that the safe operation of the wind generating set is ensured at any time, the whole machine load is reduced when the turbulence intensity is overlarge, the shutdown problem of the wind generating set under large yaw is avoided, and the availability of the wind generating set is improved.
According to an exemplary embodiment of the inventive concept, the steps of the turbulence intensity estimation method and the modules in the turbulence intensity estimation device described in fig. 1a to 7 may be written as a program or software. The program or software may be written in any programming language based on the block diagrams and flowcharts shown in the figures and the corresponding descriptions in the specification. In one example, the program or software may include machine code that is directly executed by one or more processors or computers, such as machine code generated by a compiler. In another example, the program or software includes higher level code that is executed by one or more processors or computers using an interpreter. The program or software may be recorded, stored, or fixed in one or more non-transitory computer-readable storage media. In one example, the program or software or one or more non-transitory computer readable storage media may be distributed on a computer system.
According to an example embodiment of the inventive concepts, the steps of the turbulence intensity estimation method and the modules in the turbulence intensity estimation device described in fig. 1a to 7 may be implemented on a computing device comprising a processor and a memory. The memory stores program instructions for controlling the processor to carry out the operations of the various units as described above.
Although specific exemplary embodiments of the present invention have been described in detail above with reference to fig. 1a to 7, the present invention may be modified in various forms without departing from the spirit and scope of the inventive concept. The exemplary embodiments described herein are to be considered in all respects only as illustrative and not restrictive. The descriptions of features or aspects in each of the example embodiments will be considered to apply to similar features or aspects in other example embodiments. Suitable results may be achieved if the described techniques are performed in a different order and/or if components in the described systems, architectures, or apparatus are combined in a different manner and/or are replaced or supplemented by other components or their equivalents. Therefore, the scope of the present disclosure is defined not by the detailed description but by the claims and their equivalents, and all changes within the scope of the claims and their equivalents are to be construed as being included in the present disclosure.
Claims (10)
1. A method of turbulence intensity estimation, comprising:
acquiring historical data of the wind generating set at a preset sampling interval, and preprocessing the historical data, wherein the historical data at least comprises acceleration and turbulence intensity of the wind generating set in the front-back direction of a cabin;
based on the positive correlation between the acceleration and the turbulence intensity of the wind generating set in the front-back direction of the engine room, a turbulence intensity estimation model is established by using the preprocessed data;
based on the established turbulence intensity estimation model, the current turbulence intensity is estimated,
the step of preprocessing the historical data comprises the following steps: classifying turbulence intensities in the historical data according to the magnitude, and respectively determining an absolute acceleration value, a wind speed and a power value in the front-rear direction of the nacelle of the wind generating set corresponding to each class based on all turbulence intensities in each class, wherein the power value represents the value of the output power of the wind generating set, and
wherein the step of establishing a turbulence intensity estimation model comprises: for each power value, calculating a first function under a normal turbulence working condition by taking the wind speed as an independent variable, taking the absolute value of the first acceleration as a dependent variable, and calculating a second function under a limit turbulence working condition by taking the wind speed as the independent variable and taking the absolute value of the second acceleration as the dependent variable; calculating a weighted average of a first acceleration absolute value of the first function at the wind speed and a second acceleration absolute value of the second function at the wind speed at each wind speed according to the first function and the second function, and obtaining a corresponding relation between each wind speed and the calculated weighted average of the acceleration absolute values as a turbulence intensity estimation model, wherein the step of preprocessing historical data comprises the following steps of: dividing the turbulence intensity into a conventional turbulence working condition and a limit turbulence working condition, obtaining an average value of absolute values of accelerations of the wind turbine generator system in the front-rear direction of the nacelle of each specific wind speed as a first absolute value of acceleration in the conventional turbulence working condition, and obtaining an average value of absolute values of accelerations of the wind turbine generator system in the front-rear direction of the nacelle of each specific wind speed as a second absolute value of acceleration in the limit turbulence working condition;
alternatively, the step of establishing a turbulence intensity estimation model comprises: for each power value, calculating an upper envelope of wind speed-acceleration as a turbulence intensity estimation model by taking wind speed as an abscissa and taking a first acceleration absolute value as an ordinate, wherein the step of preprocessing historical data comprises the following steps of: dividing the turbulence intensity into a conventional turbulence working condition and a limit turbulence working condition, obtaining an average value of absolute values of accelerations of the wind turbine generator system in the front-rear direction of the nacelle of each specific wind speed as a first absolute value of acceleration in the conventional turbulence working condition, and obtaining an average value of absolute values of accelerations of the wind turbine generator system in the front-rear direction of the nacelle of each specific wind speed as a second absolute value of acceleration in the limit turbulence working condition;
alternatively, the step of establishing a turbulence intensity estimation model comprises: for each power value, calculating a lower envelope of wind speed-acceleration as a turbulence intensity estimation model by taking wind speed as an abscissa and taking a second acceleration absolute value as an ordinate, wherein the step of preprocessing the historical data comprises the following steps of: the turbulence intensity is divided into a normal turbulence condition in which an average value of absolute values of accelerations in the front-rear direction of the nacelle of the wind turbine generator at each specific wind speed is obtained as a first absolute value of acceleration, and a limit turbulence condition in which an average value of absolute values of accelerations in the front-rear direction of the nacelle of the wind turbine generator at each specific wind speed is obtained as a second absolute value of acceleration.
2. The turbulence intensity estimation method according to claim 1, wherein the step of estimating the current turbulence intensity when a correspondence of each wind speed to a weighted average of calculated absolute values of acceleration is taken as the turbulence intensity estimation model comprises:
determining a corresponding turbulence intensity estimation model according to the current power value;
if the absolute value of the acceleration in the front-rear direction of the cabin of the current wind generating set is smaller than the weighted average value of the absolute values of the acceleration in the corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is a conventional turbulence working condition;
and if the absolute value of the acceleration in the front-rear direction of the cabin of the current wind generating set is larger than the weighted average value of the absolute value of the acceleration in the corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is the limit turbulence working condition.
3. The turbulence intensity estimation method as recited in claim 1, wherein the step of estimating the current turbulence intensity when an upper envelope of wind speed-acceleration is used as the turbulence intensity estimation model includes:
determining a corresponding turbulence intensity estimation model according to the current power value;
if the absolute value of the acceleration of the current wind generating set in the front-rear direction of the cabin is positioned below the upper envelope line in the corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is a conventional turbulence working condition;
if the absolute value of the acceleration of the current wind generating set cabin in the front-rear direction is located above the upper envelope line in the corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is the limit turbulence working condition.
4. The turbulence intensity estimation method as recited in claim 1, wherein the step of estimating the current turbulence intensity when a lower envelope of wind speed-acceleration is used as the turbulence intensity estimation model includes:
determining a corresponding turbulence intensity estimation model according to the current power value;
if the absolute value of the acceleration of the current wind generating set in the front-rear direction of the cabin is positioned below a lower envelope line in a corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is a conventional turbulence working condition;
if the absolute value of the acceleration of the current wind generating set cabin in the front-rear direction is located above the lower envelope line in the corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is the limit turbulence working condition.
5. A turbulence intensity estimating apparatus, comprising:
a preprocessing module configured to: acquiring historical data of the wind generating set at a preset sampling interval, and preprocessing the historical data, wherein the historical data at least comprises acceleration and turbulence intensity of the wind generating set in the front-back direction of a cabin;
a model building module configured to: based on the positive correlation between the acceleration and the turbulence intensity of the wind generating set in the front-back direction of the engine room, a turbulence intensity estimation model is established by using the preprocessed data;
an estimation module configured to: based on the established turbulence intensity estimation model, the current turbulence intensity is estimated,
wherein the preprocessing module is further configured to: classifying turbulence intensities in the historical data according to the magnitude, and respectively determining an absolute acceleration value, a wind speed and a power value in the front-rear direction of the nacelle of the wind generating set corresponding to each class based on all turbulence intensities in each class, wherein the power value represents the value of the output power of the wind generating set, and
wherein the model building module is further configured to: for each power value, calculating a first function under a normal turbulence working condition by taking the wind speed as an independent variable and the absolute value of the first acceleration as a dependent variable, and calculating a second function under a limit turbulence working condition by taking the wind speed as an independent variable and the absolute value of the second acceleration as a dependent variable; calculating, at each wind speed, a weighted average of the first function between a first absolute value of acceleration of the wind speed and a second absolute value of acceleration of the second function between the wind speeds, based on the first function and the second function, obtaining a correspondence of each wind speed to the weighted average of the calculated absolute values of acceleration as a turbulence intensity estimation model, wherein the preprocessing module is further configured to: dividing the turbulence intensity into a conventional turbulence working condition and a limit turbulence working condition, obtaining an average value of absolute values of accelerations of the wind turbine generator system in the front-rear direction of the nacelle of each specific wind speed as a first absolute value of acceleration in the conventional turbulence working condition, and obtaining an average value of absolute values of accelerations of the wind turbine generator system in the front-rear direction of the nacelle of each specific wind speed as a second absolute value of acceleration in the limit turbulence working condition;
alternatively, the model building module is further configured to: for each power value, calculating an upper envelope of wind speed-acceleration as a turbulence intensity estimation model with wind speed as an abscissa and a first acceleration absolute value as an ordinate, wherein the preprocessing module is further configured to: dividing the turbulence intensity into a conventional turbulence working condition and a limit turbulence working condition, obtaining an average value of absolute values of accelerations of the wind turbine generator system in the front-rear direction of the nacelle of each specific wind speed as a first absolute value of acceleration in the conventional turbulence working condition, and obtaining an average value of absolute values of accelerations of the wind turbine generator system in the front-rear direction of the nacelle of each specific wind speed as a second absolute value of acceleration in the limit turbulence working condition;
alternatively, the model building module is further configured to: for each power value, calculating a lower envelope of wind speed-acceleration as a turbulence intensity estimation model with wind speed as abscissa and a second absolute value of acceleration as ordinate, wherein the preprocessing module is further configured to: the turbulence intensity is divided into a normal turbulence condition in which an average value of absolute values of accelerations in the front-rear direction of the nacelle of the wind turbine generator at each specific wind speed is obtained as a first absolute value of acceleration, and a limit turbulence condition in which an average value of absolute values of accelerations in the front-rear direction of the nacelle of the wind turbine generator at each specific wind speed is obtained as a second absolute value of acceleration.
6. The turbulence intensity estimation device of claim 5, wherein when a correspondence of each wind speed to a weighted average of calculated absolute values of acceleration is taken as a turbulence intensity estimation model, the estimation module is further configured to:
determining a corresponding turbulence intensity estimation model according to the current power value;
if the absolute value of the acceleration in the front-rear direction of the cabin of the current wind generating set is smaller than the weighted average value of the absolute values of the acceleration in the corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is a conventional turbulence working condition;
and if the absolute value of the acceleration in the front-rear direction of the cabin of the current wind generating set is larger than the weighted average value of the absolute value of the acceleration in the corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is the limit turbulence working condition.
7. The turbulence intensity estimation device of claim 5, wherein when an upper envelope of wind speed-acceleration is used as the turbulence intensity estimation model, the estimation module is further configured to:
determining a corresponding turbulence intensity estimation model according to the current power value;
if the absolute value of the acceleration of the current wind generating set in the front-rear direction of the cabin is positioned below the upper envelope line in the corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is a conventional turbulence working condition;
if the absolute value of the acceleration of the current wind generating set cabin in the front-rear direction is located above the upper envelope line in the corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is the limit turbulence working condition.
8. The turbulence intensity estimation device of claim 5, wherein when taking the lower envelope of wind speed-acceleration as the turbulence intensity estimation model, the estimation module is further configured to:
determining a corresponding turbulence intensity estimation model according to the current power value;
if the absolute value of the acceleration of the current wind generating set in the front-rear direction of the cabin is positioned below a lower envelope line in a corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is a conventional turbulence working condition;
if the absolute value of the acceleration of the current wind generating set cabin in the front-rear direction is located above the lower envelope line in the corresponding turbulence intensity estimation model at the current wind speed, the current turbulence intensity is the limit turbulence working condition.
9. A computer readable storage medium storing program instructions that when executed by a processor cause the processor to perform the method of any one of claims 1 to 4.
10. A computing device, comprising:
a processor;
a memory storing program instructions that when executed by a processor cause the processor to perform the method of any one of claims 1 to 4.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104457782A (en) * | 2013-09-24 | 2015-03-25 | 霍尼韦尔国际公司 | System and method for processing and displaying wake turbulence |
| CN107392397A (en) * | 2017-08-25 | 2017-11-24 | 广东工业大学 | A kind of short-term wind speed forecasting method, apparatus and system |
| CN107408194A (en) * | 2015-02-05 | 2017-11-28 | 雅玛西有限公司 | The method and system of turbulent flow data is obtained and presented via the communicator on aircraft |
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| CN103244349B (en) * | 2013-04-24 | 2015-04-01 | 北京金风科创风电设备有限公司 | Fan tower vibration suppression system and control system for improving fan cut-out wind speed |
| CN106812658B (en) * | 2015-11-27 | 2019-09-06 | 中国船舶重工集团海装风电股份有限公司 | A kind of control method and device of wind power generating set |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104457782A (en) * | 2013-09-24 | 2015-03-25 | 霍尼韦尔国际公司 | System and method for processing and displaying wake turbulence |
| CN107408194A (en) * | 2015-02-05 | 2017-11-28 | 雅玛西有限公司 | The method and system of turbulent flow data is obtained and presented via the communicator on aircraft |
| CN107392397A (en) * | 2017-08-25 | 2017-11-24 | 广东工业大学 | A kind of short-term wind speed forecasting method, apparatus and system |
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