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CN111984905A - Wind turbine generator wind direction data filtering method based on fitting technology - Google Patents

Wind turbine generator wind direction data filtering method based on fitting technology Download PDF

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CN111984905A
CN111984905A CN202010690047.0A CN202010690047A CN111984905A CN 111984905 A CN111984905 A CN 111984905A CN 202010690047 A CN202010690047 A CN 202010690047A CN 111984905 A CN111984905 A CN 111984905A
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CN111984905B (en
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任娜
张博
陈思范
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MingYang Smart Energy Group Co Ltd
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Abstract

The invention discloses a wind turbine generator wind direction data filtering method based on a fitting technology, which comprises the following steps: 1) acquiring wind direction data measured at the current moment and k sampling data of k beats in front of the wind direction data; 2) fitting k sampling data of the front k beats by adopting a least square method to obtain a curve; 3) extrapolating by using the curve fitted in the step 2) to obtain the estimated value of the wind direction data of the (k + 1) th beat, namely the estimated value of the wind direction data at the current moment; 4) calculating the deviation between the wind direction data measured value and the estimated value at the current moment, and performing weighted fusion on the wind direction data measured value and the estimated value according to the deviation value to obtain a wind direction data calculation value of the (k + 1) th beat (at the current moment) as a filter value of the current measured wind direction; 5) and so on, the next cycle sampling period executes steps 1) to 4) in turn. According to the invention, the wind direction data is subjected to smooth filtering based on the fitting technology of the least square method, so that the measurement error of wind measuring equipment and the influence of interference are reduced, and the yaw wind effectiveness of the wind turbine generator is improved.

Description

Wind turbine generator wind direction data filtering method based on fitting technology
Technical Field
The invention relates to the technical field of wind power generation, in particular to a wind turbine generator wind direction data filtering method based on a fitting technology.
Background
Currently, yaw control of a wind turbine generator is performed by taking a filter value for measuring wind direction deviation as a control reference value and determining whether the yaw control is performed by judging whether the filter value is larger than a corresponding threshold value, wherein the reference value comprises a filter value with a time constant of 10s, a filter value with a time constant of 30s and a filter value with a time constant of 60s, and values of the corresponding threshold values are different. Filtering processing according to the historical measurement value brings a certain time delay, and large errors are brought because no special processing is carried out on the abnormal value.
Disclosure of Invention
The invention aims to solve the time delay in the wind direction measurement filtering process of a wind turbine generator and avoid transient yaw actions (the yaw actions are considered as invalid yaw) caused by rapid data change of measurement information under the disturbance of noise, and provides a wind direction data filtering method of the wind turbine generator based on a fitting technology.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: a wind turbine generator wind direction data filtering method based on a fitting technology comprises the following steps:
1) data preparation
Acquiring wind direction data measured at the current moment and k sampling data of k beats in front of the wind direction data;
2) fitting of data
Fitting k sampling data of the front k beats by adopting a least square method to obtain a curve;
3) data extrapolation
Extrapolating by using the curve fitted in the step 2) to obtain the estimated value of the wind direction data of the (k + 1) th beat, namely the estimated value of the wind direction data at the current moment;
4) weighted fusion
Calculating the deviation between the wind direction data measured value and the estimated value at the current moment, and performing weighted fusion on the wind direction data measured value and the estimated value according to the deviation value to obtain a wind direction data calculated value of the (k + 1) th beat (at the current moment) as a filtering value of the current measured wind direction, so as to realize the filtering function;
5) and so on, the next cycle sampling period executes steps 1) to 4) in turn.
In the step 1), the wind direction Data measured at the current moment is Data (t), and k sampling Data of the previous k beats are Data (t-1), Data (t-2), … and Data (t-k);
in the step 2), k sampling Data (t-1), Data (t-2), … and Data (t-k) in the first k beats form a fitting Data sequence which is respectively
Figure BDA0002589019910000021
Obtaining a fitting curve by least square fitting
Figure BDA0002589019910000022
In order to fit the function values,
Figure BDA0002589019910000023
is a fitting function;
in step 3), the wind direction data estimated value of the (k + 1) th beat is obtained by using the fitted curve extrapolation
Figure BDA0002589019910000024
Namely the wind direction data estimated value at the current moment;
in step 4), the weighted fusion includes the following steps:
4.1) calculating the deviation e between the measured value and the estimated value of the wind direction data at the current moment:
Figure BDA0002589019910000025
Figure BDA0002589019910000026
the measured value data (t) of the wind direction data at the current moment is obtained;
4.2) according to the deviation value e, carrying out weighted fusion on the wind direction data measured value at the current moment and the estimated value to obtain a wind direction data calculated value at the current moment, wherein the principle is shown as the following formula:
Figure BDA0002589019910000027
wherein,
Figure BDA0002589019910000028
for the wind direction data measured value data (t) at the current moment,
Figure BDA0002589019910000029
the wind direction data estimated value, omega, of the k +1 th beat (the current moment) obtained by fitting and extrapolating measured value data of the previous k beats1、ω2As weighting factor, where1+ω2＝1，ω1≥0，ω2≥0；
4.3) selecting a weighting coefficient according to the deviation between the measured value and the estimated value of the wind direction data at the current moment
Figure BDA0002589019910000031
Determining, by using a segmentation method, the mathematical expression is as follows:
Figure BDA0002589019910000032
wherein, the kicking value of the deviation e of a and b is a boundary value, b > a > 0, f (| e |) is ω1Regarding the function relationship established by | e |, for f (| e |) function, a polynomial function distribution mode is adopted, and in order to simplify calculation, a trapezoidal distribution curve is adopted;
4.4) obtaining polynomial coefficients a after least squares polynomial fitting for a selected set of fitted data sequences0、a1、a2、…、amAnd obtaining corresponding fitting data as follows:
Figure BDA0002589019910000033
wherein, ajFor coefficients of polynomial fitting, m is the polynomial order, T (l) is the sampling time, i.e., T (l) is T × l, T is the sampling period, l is a count value from 1 to k, j is a count value from 0 to m,
Figure BDA0002589019910000034
fitting data calculated according to polynomial coefficients and sampling time;
the form after the expansion of the above formula (3) is as follows (4)
Figure BDA0002589019910000035
Then, calculating the data change rate A (l) of the sampling data segment at each moment:
Figure BDA0002589019910000036
where A (l) is the rate of change of data at time l,
Figure BDA0002589019910000041
is the sampled data at time i and,
Figure BDA0002589019910000042
sampling data at l-1 moment, removing the minimum and maximum end point values in the change rate, averaging the rest values, and averaging the initial value of the change rate
Figure BDA0002589019910000043
Namely:
Figure BDA0002589019910000044
at the next sampling time, the average data change rate a (p) at each time is calculated recursively while calculating the data change rate at each time according to equation (5):
Figure BDA0002589019910000045
wherein, p is more than k,
Figure BDA0002589019910000046
is the average rate of change of data at time p,
Figure BDA0002589019910000047
is the average data change rate at the time p-1, A (p) is the data change rate at the time p, A (p-k-1) is the data change rate at the time p-k-1;
according to practical application experience, a and b are defined as follows:
Figure BDA0002589019910000048
y can be calculated according to the stepsk+1And obtaining a wind direction data calculation value of the (k + 1) th beat (at the current moment), and using the wind direction data calculation value as a wind direction data filtering value of the current moment, namely using the wind direction data filtering value as a current measured wind direction.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention adopts the design of sectional weight coefficients for effective information, available information and harmful information, the effective information is completely accepted during fusion, the available information is selected according to certain authority, and the harmful information is completely rejected.
2. The invention designs two kicking value zone boundary values, and judges which type of effective information, available information and harmful information the information belongs to by judging whether the data change rate at the current moment exceeds the kicking value zone boundary value.
3. The fitting data of the invention adopts historical measured values, thus ensuring the correction effect of the measured values of the system.
4. In order to avoid the harmful information from participating in data fitting, the harmful information is replaced by the current estimation value.
5. When the data changes rapidly, in order to avoid deviation from a real change track caused by continuously using estimated value fitting, the invention adopts the following mode: after rejecting harmful information 3 times in succession (i.e. omega)1Take 0 values 3 consecutive times), the 4 th time will not use the estimate instead, but still use the current measurements for data fitting.
Drawings
FIG. 1 is a logic flow diagram of the present invention.
FIG. 2 is a graph of a trapezoidal profile used in the present invention.
Detailed Description
The present invention will be further described with reference to the following specific examples.
As shown in fig. 1, the wind turbine generator direction data filtering method based on the fitting technique provided in this embodiment includes the following steps:
1) data preparation
Acquiring wind direction Data (t) measured at the current moment and k sampling Data (t-1), Data (t-2), … and Data (t-k) of the previous k beats.
2) Fitting of data
K sampling Data (t-1), Data (t-2), … and Data (t-k) in the first k beats form a fitting Data sequence which is respectively
Figure BDA0002589019910000051
Obtaining a fitting curve by least square fitting
Figure BDA0002589019910000052
Wherein,
Figure BDA0002589019910000053
in order to fit the function values,
Figure BDA0002589019910000054
is a fitting function.
3) Data extrapolation
Using fitted curve extrapolation to obtain the estimated value of the wind direction data of the (k + 1) th beat
Figure BDA0002589019910000055
I.e. the estimate of the wind direction data at the current time.
4) Weighted fusion
4.1) calculating the deviation e between the measured value and the estimated value of the wind direction data at the current moment:
Figure BDA0002589019910000061
Figure BDA0002589019910000062
the measured value data (t) of the wind direction data at the current moment is obtained;
4.2) according to the deviation value e, carrying out weighted fusion on the wind direction data measured value at the current moment and the estimated value to obtain a wind direction data calculated value at the current moment, wherein the principle is shown as the following formula:
Figure BDA0002589019910000063
wherein,
Figure BDA0002589019910000064
for the wind direction data measured value data (t) at the current moment,
Figure BDA0002589019910000065
the wind direction data estimated value, omega, of the k +1 th beat (the current moment) obtained by fitting and extrapolating measured value data of the previous k beats1、ω2As weighting factor, where1+ω2＝1，ω1≥0，ω2≥0；
4.3) selecting a weighting coefficient according to the deviation between the measured value and the estimated value of the wind direction data at the current moment
Figure BDA0002589019910000066
Determining, by using a segmentation method, the mathematical expression is as follows:
Figure BDA0002589019910000067
wherein a and b are kicking value with boundary value of the deviation e, b > a > 0, f (| e |) is ω1Regarding the functional relationship established by | e |, the invention adopts a mode of polynomial function distribution for f (| e |) function, and in order to simplify calculation, the invention adoptsA trapezoidal profile as shown in FIG. 2;
4.4) obtaining polynomial coefficients a after least squares polynomial fitting for a selected set of fitted data sequences0、a1、a2、…、amAnd obtaining corresponding fitting data as follows:
Figure BDA0002589019910000068
wherein, ajFor coefficients of polynomial fitting, m is the polynomial order, T (l) is the sampling time, i.e., T (l) is T × l, T is the sampling period, l is a count value from 1 to k, j is a count value from 0 to m,
Figure BDA0002589019910000071
fitting data calculated according to polynomial coefficients and sampling time.
The form after development of the above formula (3) is as follows (4):
Figure BDA0002589019910000072
then, calculating the data change rate A (l) of the sampling data segment at each moment:
Figure BDA0002589019910000073
where A (l) is the rate of change of data at time l,
Figure BDA0002589019910000074
is the sampled data at time i and,
Figure BDA0002589019910000075
sampling data at l-1 moment, removing the minimum and maximum end point values in the change rate, averaging the rest values, and averaging the initial value of the change rate
Figure BDA0002589019910000076
Namely:
Figure BDA0002589019910000077
at the next sampling time, the average data change rate at each time is calculated by recursion while the data change rate at each time is calculated according to the formula (5)
Figure BDA0002589019910000078
Figure BDA0002589019910000079
Wherein, p is more than k,
Figure BDA00025890199100000710
is the average rate of change of data at time p,
Figure BDA00025890199100000711
is the average data change rate at the time p-1, A (p) is the data change rate at the time p, A (p-k-1) is the data change rate at the time p-k-1;
according to practical application experience, a and b are defined as follows:
Figure BDA00025890199100000712
y can be calculated according to the stepsk+1And obtaining a wind direction data calculation value of the (k + 1) th beat (at the current moment), and using the wind direction data calculation value as a wind direction data filtering value of the current moment, namely using the wind direction data calculation value as a filtering value of the current measured wind direction, so that the filtering effect is realized.
5) And so on, the next cycle sampling period executes steps 1) to 4) in turn.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that the changes in the shape and principle of the present invention should be covered within the protection scope of the present invention.

Claims (2)

1. A wind turbine generator wind direction data filtering method based on a fitting technology is characterized by comprising the following steps:
1) data preparation
Acquiring wind direction data measured at the current moment and k sampling data of k beats in front of the wind direction data;
2) fitting of data
Fitting k sampling data of the front k beats by adopting a least square method to obtain a curve;
3) data extrapolation
Extrapolating by using the curve fitted in the step 2) to obtain the estimated value of the wind direction data of the (k + 1) th beat, namely the estimated value of the wind direction data at the current moment;
4) weighted fusion
Calculating the deviation between the wind direction data measured value and the estimated value at the current moment, and performing weighted fusion on the wind direction data measured value and the estimated value according to the deviation value to obtain a wind direction data calculation value of the (k + 1) th beat, wherein the wind direction data calculation value is used as a filtering value of the current measured wind direction to realize the filtering function;
5) and so on, the next cycle sampling period executes steps 1) to 4) in turn.
2. The wind turbine generator direction data filtering method based on the fitting technology as claimed in claim 1, wherein: in the step 1), the wind direction Data measured at the current moment is Data (t), and k sampling Data of the previous k beats are Data (t-1), Data (t-2), … and Data (t-k);
in the step 2), k sampling Data (t-1), Data (t-2), … and Data (t-k) in the first k beats form a fitting Data sequence which is respectively
Figure FDA0002589019900000011
Obtaining a fitting curve by least square fitting
Figure FDA0002589019900000012
Figure FDA0002589019900000013
In order to fit the function values,
Figure FDA0002589019900000014
is a fitting function;
in step 3), the wind direction data estimated value of the (k + 1) th beat is obtained by using the fitted curve extrapolation
Figure FDA0002589019900000015
Namely the wind direction data estimated value at the current moment;
in step 4), the weighted fusion includes the following steps:
4.1) calculating the deviation e between the measured value and the estimated value of the wind direction data at the current moment:
Figure FDA0002589019900000021
Figure FDA0002589019900000022
the measured value data (t) of the wind direction data at the current moment is obtained;
4.2) according to the deviation value e, carrying out weighted fusion on the wind direction data measured value at the current moment and the estimated value to obtain a wind direction data calculated value at the current moment, wherein the principle is shown as the following formula:
Figure FDA0002589019900000023
wherein,
Figure FDA0002589019900000024
for the wind direction data measured value data (t) at the current moment,
Figure FDA0002589019900000025
fitting extrapolated wind direction data of the (k + 1) th beat according to the measured value data of the first (k) beatsEstimate, ω1、ω2As weighting factor, where1+ω2＝1，ω1≥0，ω2≥0；
4.3) weighting factor ω1、ω2According to the deviation between the measured value and the estimated value of the wind direction data at the current moment
Figure FDA0002589019900000026
Determining, by using a segmentation method, the mathematical expression is as follows:
Figure FDA0002589019900000027
wherein a and b are kicking value with boundary value of the deviation e, b > a > 0, f (| e |) is ω1Regarding the function relationship established by | e |, for f (| e |) function, a polynomial function distribution mode is adopted, and in order to simplify calculation, a trapezoidal distribution curve is adopted;
4.4) obtaining polynomial coefficients a after least squares polynomial fitting for a selected set of fitted data sequences0、a1、a2、…、amAnd obtaining corresponding fitting data
Figure FDA0002589019900000028
The following formula:
Figure FDA0002589019900000029
wherein, ajFor coefficients of polynomial fitting, m is the polynomial order, T (l) is the sampling time, i.e., T (l) is T × l, T is the sampling period, l is a count value from 1 to k, j is a count value from 0 to m,
Figure FDA0002589019900000031
fitting data calculated according to polynomial coefficients and sampling time;
the form after the expansion of the above formula (3) is as follows (4)
Figure FDA0002589019900000032
Then, the data change rate a (l) of the sampling data segment at each time is calculated:
Figure FDA0002589019900000033
wherein A (l) is the data change rate at time l,
Figure FDA0002589019900000034
is the sampled data at time i and,
Figure FDA0002589019900000035
sampling data at l-1 moment, removing the minimum and maximum end point values in the change rate, and averaging the rest values to obtain the initial value of the average data change rate
Figure FDA0002589019900000036
Namely:
Figure FDA0002589019900000037
at the next sampling time, the average data change rate at each time is calculated by recursion while the data change rate at each time is calculated according to the formula (5)
Figure FDA0002589019900000038
Figure FDA0002589019900000039
Wherein p >k，
Figure FDA00025890199000000310
Is the average rate of change of data at time p,
Figure FDA00025890199000000311
is the average data change rate at the time p-1, A (p) is the data change rate at the time p, A (p-k-1) is the data change rate at the time p-k-1;
according to practical application experience, a and b are defined as follows:
Figure FDA00025890199000000312
y can be calculated according to the stepsk+1And obtaining a wind direction data calculation value of the (k + 1) th beat, and using the wind direction data calculation value as a wind direction data filtering value at the current moment, namely using the wind direction data calculation value as a filtering value of the current measured wind direction.
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