CN109751195B - Method and device for acquiring power curve of wind driven generator - Google Patents
Method and device for acquiring power curve of wind driven generator Download PDFInfo
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Abstract
The application discloses a method for acquiring a power curve of a wind driven generator, which comprises the steps of acquiring observation data of the wind driven generator during operation; the observation data comprises observation data of wind speed and observation data of output power; substituting the observation data into a state space equation of a pre-established wind turbine model; carrying out first-order linearization on the state space equation; and performing Kalman filtering on the linearized state space equation to generate de-noising data of the observation data so as to generate a power curve according to the de-noising data. According to the method, the actual observation data are taken as the basis, and on the basis of a state space equation established by a wind driven generator mechanism model, Kalman filtering is adopted to carry out optimal estimation on the observation data, so that the precision and the reliability can be effectively improved. The application also discloses a device for acquiring the power curve of the wind driven generator, and the device has the beneficial effects.
Description
Technical Field
The application relates to the technical field of wind power generation, in particular to a method and a device for acquiring a power curve of a wind power generator.
Background
With the rapid development and progress of wind power generation technology in recent years, wind power generation has played an important role in electric power systems in China. The power curve of the wind driven generator is an important characteristic of the wind driven generator, describes the relation between the output power of the wind driven generator and the wind speed, and is an important index for evaluating the performance and the power generation capacity of the wind driven generator set.
In the prior art, the obtaining mode of the power curve of the wind generating set is generally divided into two categories. One of the methods is to obtain a power curve by means of data fitting and the like on measured observation data of the wind generating set during operation by means of a mathematical statistic method. However, because the actual observation data includes a large amount of fault data, including fault points with high wind speed and zero power and turbulent flow points with excessive wind speed errors, the power curve error obtained by the mathematical statistics method is large; also, some common fitting functions are not fully applicable to wind turbines.
The other method is a mechanism model obtained according to the working principle of the wind driven generator:
wherein rho is the air density, A is the swept area of the wind wheel of the wind driven generator, V is the wind speed at the wind wheel, and Cp is the wind energy utilization coefficient. However, in practical applications, the parameter air density may vary with environmental factors such as temperature, air pressure, etc.; the wind energy utilization rate can be changed due to different blade tip speed ratios and different pitch angles; the wind speed at the actual measurement location is also different in magnitude and angle from the wind speed at the wind wheel. Therefore, these factors cause a large error.
Therefore, the technical problem to be solved by the technical personnel in the field is to be solved by adopting the method and the device for acquiring the power curve of the wind driven generator with high precision and high reliability.
Disclosure of Invention
The purpose of the application is to provide a method and a device for acquiring a power curve of a wind driven generator with high precision and high reliability.
In order to solve the above technical problem, the present application provides a method for obtaining a power curve of a wind turbine, including:
acquiring observation data of the wind driven generator during operation; the observation data comprises observation data of wind speed and observation data of output power;
substituting the observation data into a state space equation of a pre-established wind turbine model;
performing first-order linearization on the state space equation;
and performing Kalman filtering on the linearized state space equation to generate de-noising data of the observation data so as to generate the power curve according to the de-noising data.
Optionally, the acquiring the observation data of the wind turbine during operation includes:
and acquiring observation data of the wind driven generator during operation within a preset time period.
Optionally, the method further comprises:
and storing the de-noised data after the de-noised data of the observation data is generated.
Optionally, after the obtaining of the observation data of the wind turbine during operation and before the substituting the observation data into the state space equation of the pre-established wind turbine model, the method further includes:
and carrying out screening pretreatment on the observation data.
Optionally, the performing a screening pretreatment on the observation data includes:
dividing the observation data into a plurality of wind speeds born;
calculating an average wind speed of each wind speed Bin;
and filtering the observation data of which the error with the corresponding average wind speed exceeds a preset error threshold value in each wind speed Bin.
Optionally, the screening pretreatment of the observation data further includes:
and after the observation data with the error of the wind speed born exceeding the preset error threshold value are filtered, the observation data with the wind speed not being zero and the output power being zero are filtered.
Optionally, the state observation of the state space equation comprises:
the air density, the differential speed of the measured wind speed and the contact wind speed of the blades, the included angle between the wind direction and the blades and the wind energy utilization rate are measured;
the observation data further includes observation data of the state observation.
Optionally, the first order linearizing the state space equation comprises:
and performing first-order Taylor expansion on the observation equation of the state space equation and then retaining a first-order term.
The application also provides a device for acquiring the power curve of the wind driven generator, which comprises:
a data acquisition module: the system is used for acquiring observation data of the wind driven generator during operation; the observation data comprises observation data of wind speed and observation data of output power;
a data fusion module: the system is used for substituting the observation data into a state space equation of a pre-established wind generator model;
a linearization processing module: for performing a first order linearization on the state space equation;
a Kalman filtering module: and the Kalman filtering is carried out on the linearized state space equation to generate de-noising data of the observation data so as to generate the power curve according to the de-noising data.
Optionally, the method further comprises:
a data storage module: the Kalman filtering module is used for generating de-noising data of the observation data, and then storing the de-noising data.
The method for acquiring the power curve of the wind turbine generator comprises the following steps: acquiring observation data of the wind driven generator during operation; the observation data comprises observation data of wind speed and observation data of output power; substituting the observation data into a state space equation of a pre-established wind turbine model; performing first-order linearization on the state space equation; and performing Kalman filtering on the linearized state space equation to generate de-noising data of the observation data so as to generate the power curve according to the de-noising data.
Therefore, compared with the prior art, the method for obtaining the power curve of the wind driven generator provided by the embodiment of the application takes a large amount of actual observation data as a basis, performs optimal estimation on the observation data by Kalman filtering on the basis of a state space equation established by a mechanism model of the wind driven generator, and obtains de-noising data with higher reliability of the observation data by utilizing the high precision of the Kalman filtering so as to generate the power curve of the wind driven generator. The device for acquiring the power curve of the wind driven generator can realize the method for acquiring the power curve of the wind driven generator and also has the beneficial effects.
Drawings
In order to more clearly illustrate the technical solutions in the prior art and the embodiments of the present application, the drawings that are needed to be used in the description of the prior art and the embodiments of the present application will be briefly described below. Of course, the following description of the drawings related to the embodiments of the present application is only a part of the embodiments of the present application, and it will be obvious to those skilled in the art that other drawings can be obtained from the provided drawings without any creative effort, and the obtained other drawings also belong to the protection scope of the present application.
FIG. 1 is a flow chart of a method for obtaining a power curve of a wind turbine provided in an embodiment of the present application;
fig. 2 is a block diagram of a structure of an apparatus for obtaining a power curve of a wind turbine according to an embodiment of the present disclosure.
Detailed Description
The core of the application is to provide a method and a device for acquiring the power curve of the wind driven generator with high precision and high reliability.
In order to more clearly and completely describe the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a flowchart of a method for obtaining a power curve of a wind turbine provided in an embodiment of the present application, which mainly includes the following steps:
step 1: acquiring observation data of the wind driven generator during operation; the observation data includes observation data of wind speed and observation data of output power.
Step 2: and substituting the observation data into a state space equation of a pre-established wind generator model.
Specifically, the state space equation is based on a wind turbine mechanism model
The established state space equation. Of course, the dimension of the variable matrix in the state space equation may be set according to the number of observation data acquired in step 1.
And step 3: the state space equation is linearized to the first order.
And 4, step 4: and performing Kalman filtering on the linearized state space equation to generate de-noising data of the observation data so as to generate a power curve according to the de-noising data.
In particular, Kalman filtering is a commonly used filtering algorithm in an automatic control system, which can perform an optimal estimation on the system through a successive filtering process. According to the method and the device, observation data obtained through measurement and a mechanism model of the wind driven generator are combined through a state space equation, so that high-precision and high-reliability denoising data are obtained through Kalman filtering, and further a power curve of the wind driven generator is obtained. According to the Kalman filtering algorithm theory, after the state space equation corresponding to the wind driven generator mechanism model needs first-order linearization, filtering processing is carried out, and therefore optimal estimation is carried out to obtain de-noising data of observation data.
Therefore, the method for acquiring the power curve of the wind driven generator provided by the embodiment of the application takes a large amount of actual observation data as a basis, adopts Kalman filtering to perform optimal estimation on the observation data on the basis of a state space equation established by a mechanism model of the wind driven generator, and acquires de-noising data with higher reliability of the observation data by utilizing the high precision of the Kalman filtering so as to generate the power curve of the wind driven generator.
The method for obtaining the power curve of the wind turbine provided by the application is based on the embodiment as follows:
as a preferred embodiment, the acquiring of the observation data of the wind turbine during operation comprises:
and acquiring observation data of the wind driven generator in operation within a preset time period.
Specifically, when the observation data of the wind driven generator during operation is obtained each time, the observation data in a fixed time period can be obtained in a rolling manner in a time domain, so that the latest data in the latest time period can be obtained, the historical data information is comprehensively utilized, and the influence caused by uncertain wind speed and wind direction is overcome. The preset duration of the time period can be selected and set by a person skilled in the art, and the embodiment of the present application is not limited thereto.
As a preferred embodiment, further comprising:
and after the de-noising data of the observation data are generated, storing the de-noising data.
Specifically, after the data processing is completed, the obtained denoised data of the observation data may be stored, and may be specifically saved in a specified file.
As a preferred embodiment, after obtaining the observation data of the wind turbine during operation and before substituting the observation data into the state space equation of the pre-established wind turbine model, the method further includes:
and (4) carrying out screening pretreatment on the observation data.
As described above, in the observation data measured by various types of sensors, there is always a large amount of bad data including fault points and turbulence points. Therefore, in order to further improve the accuracy and reliability, before the Kalman filtering is performed by substituting the observation data into the state space equation, the observation data needs to be preprocessed so as to filter out the fault points, turbulence points, and the like.
As a preferred embodiment, the screening pretreatment of the observation data comprises:
dividing the observation data into a plurality of wind speeds bian;
calculating the average wind speed of each wind speed Bin;
and filtering the observation data of which the error with the corresponding average wind speed exceeds a preset error threshold value in each wind speed Bin.
Specifically, by adopting the method of wind speed bian, the points with overlarge wind speed errors in the observed data, namely turbulent flow points, can be filtered. In a wind speed Bin, the wind speed values of all observation points should not differ greatly from the average wind speed of the wind speed Bin, and if the error exceeds a preset error threshold value, the observation point is a turbulence point. Of course, the preset error threshold may be selected and set by a person skilled in the art according to an actual application, and the embodiment of the present application does not limit this.
As a preferred embodiment, the pre-processing of the observation data by screening further comprises:
after the observation data with the error between each wind speed Bin and the corresponding average wind speed exceeding a preset error threshold value are filtered, the observation data with the wind speed not being zero and the output power being zero are filtered.
Specifically, in the measured observation data, when the output power of the wind driven generator is zero, two situations are included, one is that the wind speed is zero, and the other is that the wind speed is not zero. Obviously, the latter belongs to the observation of the point of failure. Of course, considering factors such as mechanical friction, a reasonable preset wind speed threshold value may also be set by those skilled in the art, and the observation data that the wind speed is greater than the preset wind speed threshold value and the output power is zero is taken as the observation data of the fault point and filtered out.
As a preferred embodiment, the state observations of the state space equation comprise:
the air density, the differential speed of the measured wind speed and the contact wind speed of the blades, the included angle between the wind direction and the blades and the wind energy utilization rate are measured;
the observation data also includes observation data of a state observation.
Specifically, according to the foregoing, the air density, the wind speed, and the wind energy utilization rate are all variable parameters, and are not fixed, and in order to eliminate errors caused by these factors on the result, in the embodiment of the present application, the air density, the differential speed between the measured wind speed and the blade contact wind speed, the included angle between the wind direction and the blade, and the wind energy utilization rate are all used as state observed quantities, so that in the Kalman filtering process, these parameters are also optimally estimated, and thus, the denoising data of the output power of the wind turbine in the corresponding environment is obtained according to the optimal estimation values of these parameters.
The output power after the wind driven generator is expanded and the state equation of the state space equation of the output power are as follows:
X(k+1)＝X(k)+w(k)；
wherein y (k) is the output power of the wind turbine; v (k) is blade contact wind speed; state observed quantity x (k) ═ ρ (k) cos θ (k) Cp(k)ΔV(k)]TRho (k) is air density, theta (k) is angle between wind direction and blade, Cp(k) In order to obtain the wind energy utilization rate, delta V (k) is the differential speed of the measured wind speed and the contact wind speed of the blades; w (k) ═ wρ(k) wθ(k) wC(k) wv(k)]TFor dynamic errors, wρ(k) Dynamic error of air density, wθ(k) Dynamic error of wind direction angle, wC(k) Dynamic error of wind energy utilization, wv(k) Is the dynamic error of the wind speed.
The observation equation from which the state space equation can be derived is:
wherein N is a matrix parameter in the observation equation, and may be specifically determined by the number of acquired observation data.
Since the air density, the angle between the wind and the blades, the wind energy utilization rate and the differential speed between the measured wind speed and the blade contact wind speed are all almost unchanged in the time domain ranging from the moment k-N +1 to the moment k, they can be respectively measured by the measured values rho (k), theta (k), C at the moment kp(k) Δ v (k), the observation equation can be simplified as:
as a preferred embodiment, the first order linearization of the state space equation comprises:
and (4) performing first-order Taylor expansion on the observation equation of the state space equation and then retaining a first-order term.
Specifically, for the state space equation in which the air density, the differential speed between the measured wind speed and the blade contact wind speed, the included angle between the wind direction and the blade, and the wind energy utilization rate are used as the state observed quantity, linearization can be performed through first-order taylor expansion, and the first-order partial derivative, namely the observation matrix, after first-order linearization is obtained is as follows:
thus, a Kalman filtering process can be performed, and the state prediction value X (k | k-1) at the time k and the covariance matrix P (k | k-1) thereof are calculated:
X(k|k-1)＝X(k-1|k-1)+ω(k)；
P(k|k-1)＝Φ(k)P(k-1|k-1)ΦT(k)+Q；
wherein X (k-1| k-1) is the state optimal estimation value at the k-1 moment; p (k-1| k-1) is a covariance matrix of X (k-1| k-1); phi (k) is a unit array; q is the variance of ω (k).
Then, a Kalman gain matrix k (k) may be calculated:
K(k)＝P(k|k-1)HT(k)(H(k)P(k|k-1)HT(k)+R)；
wherein R is the variance of v (k).
From this, the optimal state estimation value x (k) at time k and the covariance matrix p (k) thereof can be calculated:
X(k)＝X(k|k-1)+K(y(k)-y(k|k-1))；
P(k)＝(In-K(k)H(k))P(k|k-1)；
where y (k | k-1) is the predicted output value at time k.
Thus, one calculation in the Kalman filtering process is completed. And circularly processing the processes until all data are processed.
According to the above contents, the air density, the differential speed of the measured wind speed and the blade contact wind speed, the included angle between the wind direction and the blade and the wind energy utilization rate are used as state observed quantities, and the Kalman filtering theory is utilized to carry out optimal estimation, so that the more accurate denoising data of the output power is obtained, meanwhile, the soft measurement of the air density, the wind direction, the wind speed and the like is realized, and a new fan operation monitoring means is provided.
The following describes an apparatus for obtaining a power curve of a wind turbine provided in an embodiment of the present application.
Referring to fig. 2, fig. 2 is a block diagram illustrating a structure of an apparatus for obtaining a power curve of a wind turbine provided in the present application; the device comprises a data acquisition module 1, a data fusion module 2, a linearization processing module 3 and a Kalman filtering module 4;
the data acquisition module 1 is used for acquiring observation data of the wind driven generator during operation; the observation data comprises observation data of wind speed and observation data of output power;
the data fusion module 2 is used for substituting the observation data into a state space equation of a pre-established wind turbine model;
the linearization processing module 3 is used for carrying out first-order linearization on the state space equation;
the Kalman filtering module 4 is used for Kalman filtering the linearized state space equation to generate de-noising data of the observation data so as to generate a power curve according to the de-noising data.
Therefore, the device for acquiring the power curve of the wind driven generator provided by the application is based on a large amount of actual observation data, based on a state space equation established by a mechanism model of the wind driven generator, the Kalman filtering is adopted to carry out optimal estimation on the observation data, the influence caused by indirect measurement and noise is reduced, and the high precision of the Kalman filtering is utilized to acquire the de-noising data with higher reliability of the observation data so as to generate the power curve of the wind driven generator.
The device for obtaining the power curve of the wind turbine provided by the application is based on the above embodiment:
as a preferred embodiment, the data obtaining module 1 is specifically configured to:
and acquiring observation data of the wind driven generator in operation within a preset time period.
As a preferred embodiment, the system further comprises a data storage module:
the data storage module is used for storing the denoised data after the Kalman filtering module 4 generates the denoised data of the observation data.
As a preferred embodiment, the system further comprises a data preprocessing module:
the data preprocessing module is used for screening and preprocessing the observation data acquired by the data acquisition module 1 after the data acquisition module 1 acquires the observation data of the wind driven generator in operation and before the data fusion module 2 substitutes the observation data into a state space equation of a pre-established wind driven generator model.
As a preferred embodiment, the data preprocessing module is specifically configured to:
dividing the observation data into a plurality of wind speeds bian; calculating the average wind speed of each wind speed Bin; and filtering the observation data of which the error with the corresponding average wind speed exceeds a preset error threshold value in each wind speed Bin.
As a preferred embodiment, the data preprocessing module is further configured to:
after the observation data with the error between each wind speed Bin and the corresponding average wind speed exceeding a preset error threshold value are filtered, the observation data with the wind speed not being zero and the output power being zero are filtered.
As a preferred embodiment, the state observations of the state space equation comprise:
the air density, the differential speed of the measured wind speed and the contact wind speed of the blades, the included angle between the wind direction and the blades and the wind energy utilization rate are measured;
the observation data also includes observation data of a state observation.
As a preferred embodiment, the linearization processing module 3 is specifically configured to:
and (4) performing first-order Taylor expansion on the observation equation of the state space equation and then retaining a first-order term.
The specific implementation of the apparatus for obtaining a power curve of a wind turbine provided in the present application and the method for obtaining a power curve of a wind turbine described above may be referred to correspondingly, and will not be described herein again.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
It is further noted that, throughout this document, relational terms such as "first" and "second" are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. The term "comprising", without further limitation, means that the element so defined is not excluded from the group consisting of additional identical elements in the process, method, article, or apparatus that comprises the element.
The technical solutions provided by the present application are described in detail above. The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
Claims (10)
1. A method of obtaining a power curve for a wind turbine, comprising:
acquiring observation data of the wind driven generator during operation; the observation data comprises observation data of wind speed and observation data of output power;
substituting the observation data into a state space equation of a pre-established wind turbine model;
performing first-order linearization on the state space equation;
performing Kalman filtering on the linearized state space equation to generate de-noising data of the observation data so as to generate the power curve according to the de-noising data;
wherein the state space equation of the wind turbine model is as follows:
X(k+1)＝X(k)+w(k)；
wherein y (k) is the output power of the wind turbine; v (k) is blade contact wind speed; state observed quantity x (k) ═ ρ (k) cos θ (k) Cp(k) ΔV(k)]TRho (k) is air density, theta (k) is angle between wind direction and blade, Cp(k) In order to obtain the wind energy utilization rate, delta V (k) is the differential speed of the measured wind speed and the contact wind speed of the blades; w (k) ═ wρ(k) wθ(k) wC(k) wv(k)]TFor dynamic errors, wρ(k) Dynamic error of air density, wθ(k) Dynamic error of wind direction angle, wC(k) For the utilization of wind energyDynamic error of (w)v(k) Is the dynamic error of the wind speed.
2. The method of claim 1, wherein the obtaining of observation data of the wind turbine during operation comprises:
and acquiring observation data of the wind driven generator during operation within a preset time period.
3. The acquisition method according to claim 1, further comprising:
and storing the de-noised data after the de-noised data of the observation data is generated.
4. The method of obtaining as claimed in claim 1, wherein after said obtaining observation data while the wind turbine is operating and before said substituting said observation data into the state space equations of the pre-established wind turbine model, further comprising:
and carrying out screening pretreatment on the observation data.
5. The method according to claim 4, wherein the pre-processing of the observation data by screening comprises:
dividing the observation data into a plurality of wind speeds born;
calculating an average wind speed of each wind speed Bin;
and filtering the observation data of which the error with the corresponding average wind speed exceeds a preset error threshold value in each wind speed Bin.
6. The method of claim 5, wherein the pre-processing of the observation data by the filter further comprises:
and after the observation data with the error of the wind speed born exceeding the preset error threshold value are filtered, the observation data with the wind speed not being zero and the output power being zero are filtered.
7. The acquisition method according to any one of claims 1 to 6, wherein the state observation of the state space equation comprises:
the air density, the differential speed of the measured wind speed and the contact wind speed of the blades, the included angle between the wind direction and the blades and the wind energy utilization rate are measured;
the observation data further includes observation data of the state observation.
8. The method of claim 7, wherein the first order linearizing the state space equation comprises:
and performing first-order Taylor expansion on the observation equation of the state space equation and then retaining a first-order term.
9. An apparatus for obtaining a power curve of a wind turbine, comprising:
a data acquisition module: the system is used for acquiring observation data of the wind driven generator during operation; the observation data comprises observation data of wind speed and observation data of output power;
a data fusion module: the system is used for substituting the observation data into a state space equation of a pre-established wind generator model;
a linearization processing module: for performing a first order linearization on the state space equation;
a Kalman filtering module: the Kalman filtering is carried out on the linearized state space equation to generate de-noising data of the observation data, so that the power curve is generated according to the de-noising data;
wherein the state space equation of the wind turbine model is as follows:
X(k+1)＝X(k)+w(k)；
wherein y (k) is the output power of the wind turbine; v (k) isBlade contact wind speed; state observed quantity x (k) ═ ρ (k) cos θ (k) Cp(k) ΔV(k)]TRho (k) is air density, theta (k) is angle between wind direction and blade, Cp(k) In order to obtain the wind energy utilization rate, delta V (k) is the differential speed of the measured wind speed and the contact wind speed of the blades; w (k) ═ wρ(k) wθ(k) wC(k) wv(k)]TFor dynamic errors, wρ(k) Dynamic error of air density, wθ(k) Dynamic error of wind direction angle, wC(k) Dynamic error of wind energy utilization, wv(k) Is the dynamic error of the wind speed.
10. The acquisition device according to claim 9, further comprising:
a data storage module: the Kalman filtering module is used for generating de-noising data of the observation data, and then storing the de-noising data.
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