CN113850440A - Wind speed prediction method using MCP based on average wind speed correction - Google Patents

Wind speed prediction method using MCP based on average wind speed correction Download PDF

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CN113850440A
CN113850440A CN202111161794.6A CN202111161794A CN113850440A CN 113850440 A CN113850440 A CN 113850440A CN 202111161794 A CN202111161794 A CN 202111161794A CN 113850440 A CN113850440 A CN 113850440A
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陈广宇
姜婷婷
潘航平
端和平
刘华清
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Zhejiang Windey Co Ltd
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Abstract

The invention discloses a wind speed prediction method by using MCP corrected based on average wind speed, which solves the problem that the evaluation result uncertainty is large due to the fact that the processing of wind measurement data by the MCP method in the prior art cannot give consideration to generated energy and the average wind speed, and comprises the following steps: s1: processing the used wind speed data; s2: the complete wind measurement data of the target station is obtained by conjecture through a piecewise linear fitting method and is used as a wind speed reference time sequence V1(ii) a S3: the complete wind measurement data of the target station is obtained by utilizing a Weibull fitting method as a wind frequency reference time sequence V2(ii) a S4: according to the wind speed reference time sequence V1And a wind frequency reference time series V2And correcting the average wind speed to obtain a final predicted wind speed time sequence V. The method for scientifically and accurately acquiring complete wind measurement data is provided for wind resource assessment of distributed projects, the uncertainty of traditional assessment is reduced, and the method for evaluating wind resources of distributed projects is improvedThe precision is evaluated, the calculation process is simple, and the method is suitable for daily business use.

Description

Wind speed prediction method using MCP based on average wind speed correction
Technical Field
The invention relates to the technical field of processing of anemometry data in wind resource assessment, in particular to a wind speed prediction method by using MCP based on average wind speed correction.
Background
Wind resource assessment is a core link of wind power plant development and plays an important role in wind power plant benefits and success or failure of wind power plant investment. With the gradual reduction of the wind power grid price in China, the development of wind power plants gradually turns to southeast mountainous areas with low wind speed and complex terrain, and the profit and loss of the wind power plants almost touch the balance point, so that more rigorous requirements on the accuracy of wind resource evaluation are provided. For some wind power projects, the development period is short, and the long-term (at least one year) wind measurement data support is not provided, so that the investigation and evaluation of wind power resources in a field area are the first problems faced by the projects.
At present, wind resources are evaluated mainly by predicting wind speed, and the Chinese patent office 2021, 4 and 16 discloses an invention named as a wind speed prediction method for wind power plant power prediction, wherein the publication number of the invention is CN112668807A, and the invention comprises the steps of firstly, randomly extracting wind speed measurement data and wind speed prediction data in a plurality of time periods; calculating wind speed prediction deviation data in each time period, and cleaning the data; thirdly, normalization processing; fourthly, detecting outliers; fifthly, fitting a wind speed prediction deviation curve to obtain a basic model of the wind speed prediction deviation value; sixthly, obtaining a prediction deviation data set; constructing a training set of the extreme learning machine; constructing two extreme learning machine models, and training to obtain a deviation correction model of the wind speed prediction deviation value; and ninthly, obtaining a final predicted value of the wind speed through a basic model of the wind speed prediction deviation value and a deviation correction model of the wind speed prediction deviation value. The wind speed prediction method based on the deviation correction technology can be used for predicting the wind speed by combining the existing wind speed prediction method, further improves the wind speed prediction precision, has obvious effect and is convenient to popularize. But the process is more complicated.
The method is characterized in that a wind measuring tower or other wind measuring equipment (serving as a target station) is erected in a site, wind is measured on the spot for a plurality of months to obtain measured data, synchronous function mapping from a reference station to the target station is established by using a Correlation model in combination with synchronous wind condition data of the ground meteorological station data or medium-scale data (serving as a reference station) in the site range, and complete wind measuring data of the target station is obtained by conjecturing the long-term wind condition data of the reference station for wind resource evaluation. However, the wind speed predicted by the conventional MCP method cannot give consideration to the generated energy and the average wind speed, and compared with actual measurement of a wind measuring tower, the wind speed predicted by the conventional MCP method is often in a situation that the average wind speed error is small but the energy density is distorted or the energy density is close to the actual situation but the average wind speed has a large error, so that the uncertainty of the generated energy is increased, and the wind resource evaluation and investment decision are influenced.
Disclosure of Invention
The invention aims to solve the problem that the uncertainty of an evaluation result is large due to the fact that the processing of wind measurement data by a prediction related measurement method in the prior art cannot take into account the generated energy and the average wind speed, and provides a wind speed prediction method by using an MCP based on average wind speed correction.
In order to achieve the purpose, the invention adopts the following technical scheme: a wind speed prediction method using MCP corrected based on average wind speed, comprising the steps of:
s1: processing the used wind speed data;
s2: the complete wind measurement data of the target station is obtained by conjecture through a piecewise linear fitting method and is used as a wind speed reference time sequence V1；
S3: the complete wind measurement data of the target station is obtained by utilizing a Weibull fitting method as a wind frequency reference time sequence V2；
S4: according to the wind speed reference time sequence V1And a wind frequency reference time series V2And correcting the average wind speed to obtain a final predicted wind speed time sequence V.
The data used is first processed to remove some distorted, invalid data. The piecewise linear fitting method is a common data processing method in the prior art, is verified by more than 400 anemometer tower data samples in the early stage, statistically proves that the accuracy of the conjecture of the average wind speed by the piecewise linear fitting method is high, and the average wind speed of the anemometer data of the target station can be well restored by the piecewise linear fitting method. The Weibull fitting method is used in the prior art, and is verified by more than 400 anemometer tower data samples in the earlier stage of the invention, so that the statistical significance proves that the estimation accuracy of the selected Weibull fitting method on the wind frequency is higher, and the Weibull fitting method can be used for well restoring the wind frequency of the target station wind measurement data. The method selects a Weibull fitting method with higher wind frequency estimation precision to obtain a wind frequency reference sequence, is used for restoring the wind frequency attribute of target station wind measurement data, corrects the average wind speed of the wind frequency reference sequence to be consistent with a wind speed reference sequence (obtained by a piecewise linear fitting method with higher wind speed estimation precision), is used for restoring the average wind speed attribute of the target station wind measurement data, and obtains a final prediction result after correcting a time sequence. The wind speed is predicted by using the prediction correlation measurement method based on average wind speed correction, and the final prediction result has high precision, practical theory and simple calculation process.
Preferably, the wind speed data includes anemometry data and long-term data, wherein the anemometry data is used as the wind speed of the target station, and the long-term data is used as the wind speed of the reference station. And combining the long-term data with the wind measurement data, and calculating to obtain a final predicted wind speed time sequence V.
Preferably, in step S2, the specific step of obtaining complete anemometry data of the target station by using a piecewise linear fitting method includes:
s2.1: forming two-dimensional wind speed scatter points by taking the wind speed of a target station at a synchronous time interval as a vertical coordinate and the wind speed of a reference station as a horizontal coordinate;
s2.2: dividing the scattered points into a plurality of equidistant intervals of wind speed based on the reference wind speed, wherein the predicted wind speed value of each interval is as follows:
Figure BDA0003290457020000041
wherein:
Figure BDA0003290457020000042
Figure BDA0003290457020000043
in the formula (I), the compound is shown in the specification,
Figure BDA0003290457020000044
for interval-predicted wind speed values, h, obtained by means of piecewise linear fittingiIs the middle point of the ith equidistant interval,
Figure BDA0003290457020000045
the average target wind speed of the ith equidistant interval is obtained;
s2.3: taking one point in each interval, and connecting all the points to obtain a piecewise linear fitting curve;
s2.4: calculating a wind speed reference time sequence:
Figure BDA0003290457020000046
where n is the prediction period length.
And reducing the average wind speed of the target station wind measurement data by using a piecewise linear fitting method.
Preferably, in step S2.3, a point is taken in each interval, the abscissa of the point is the midpoint of the abscissa of the interval, and the ordinate of the point is the average value of the target wind speeds in the interval.
Preferably, in step S3, the specific step of estimating complete anemometry data of the target station by using the weibull fitting method includes:
s3.1: establishing a power function model:
Figure BDA0003290457020000051
in the formula, the coefficients α and β are derived from the weibull distribution of the target wind speed and the reference wind speed at the synchronization period:
Figure BDA0003290457020000052
Figure BDA0003290457020000053
in the formula (I), the compound is shown in the specification,
Figure BDA0003290457020000054
for the wind speed value, k, of the predicted time interval obtained by the Weibull fitting methodyIs the shape parameter, k, of the Weibull distribution of the wind speed at the target stationxFor the shape parameter of the Weibull distribution of wind speeds at the reference station, λyIs a proportional parameter, lambda, of the Weibull distribution of the wind speed at the target stationxIs a proportional parameter of the Weibull distribution of the wind speed of the reference station;
s3.2: calculating a wind frequency reference time sequence:
Figure BDA0003290457020000055
where n is the prediction period length.
The energy density of the target station wind measurement data with high precision is obtained by using a Weibull fitting method, and the precision of generated energy calculation is improved.
Preferably, in step S4, the wind speed reference time series V is used1And a wind frequency reference time series V2The method for correcting the average wind speed comprises the following steps: time series V based on wind speed1Average wind speed and wind frequency reference time series V2As a coefficient to correct the time series V2And obtaining a final predicted wind speed time series V.
Preferably, in step S4, the specific wind speed correction calculation formula is:
V＝ζ·V2
wherein
Figure BDA0003290457020000061
In the formula:
Figure BDA0003290457020000062
is a wind speed reference time sequence V1Is determined by the average value of (a) of (b),
Figure BDA0003290457020000063
is a wind speed reference time sequence V2Average value of (a). Therefore, complete anemometry data are obtained, and the obtained result is high in precision.
Therefore, the invention has the following beneficial effects: the method can give consideration to the generated energy and the average wind speed in the prediction of related measurement methods, provides a scientific and accurate method for acquiring complete wind measurement data for wind resource assessment of distributed projects, reduces the uncertainty of traditional assessment, improves the assessment accuracy, has simple calculation process and popular and easy-to-understand principle, and is suitable for daily business use.
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FIG. 1 is a flow chart of the operation of the method of the present invention.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
in the embodiment shown in fig. 1, a wind speed prediction method using MCP based on average wind speed correction can be seen, which operates as follows:
the first step is as follows: processing the wind speed data
First, wind speed data including anemometry data (as target station wind speed) and long-term data (as reference station data) is processed, distorted and invalid data is removed, and the following steps are continued using the processed data.
The second step is that: the complete wind measurement data of the target station is obtained by conjecture through a piecewise linear fitting method and is used as a wind speed reference time sequence V1
Through more than 400 anemometer tower data samples, the estimation accuracy of the average wind speed is high by selecting a piecewise linear fitting method, and the average wind speed of the target station anemometer data can be well restored by using the piecewise linear fitting method. The method comprises the following specific steps:
forming two-dimensional wind speed scatter points by taking the wind speed of a target station at a synchronous time interval as a vertical coordinate and the wind speed of a reference station as a horizontal coordinate; then dividing the scattered points into a plurality of equidistant intervals of wind speed based on the reference wind speed: and (3) taking a point in each interval, wherein the abscissa of the point is the middle point of the abscissa of the interval, and the ordinate of the point is the average value of the target wind speed in the interval, and connecting all the points to obtain a piecewise linear fitting curve.
The predicted wind speed values for each interval are as follows:
Figure BDA0003290457020000071
wherein:
Figure BDA0003290457020000072
Figure BDA0003290457020000073
in the formula (I), the compound is shown in the specification,
Figure BDA0003290457020000074
for interval-predicted wind speed values, h, obtained by means of piecewise linear fittingiIs the middle point of the ith equidistant interval,
Figure BDA0003290457020000075
the average target wind speed of the ith equidistant interval is obtained;
calculating a wind speed reference time sequence according to the obtained wind speed value of the prediction time period:
Figure BDA0003290457020000076
where n is the prediction period length.
The third step: the complete wind measurement data of the target station is obtained by utilizing a Weibull fitting method as a wind frequency reference time sequence V2
Through more than 400 wind measuring tower data samples, the estimation accuracy of the wind frequency is high by selecting a Weibull fitting method, the energy density of the wind measuring data of the target station can be well restored by using the Weibull fitting method, and the accuracy of the generated energy calculation is improved. The method comprises the following specific steps:
establishing a power function model:
Figure BDA0003290457020000081
in the formula, the coefficients α and β are derived from the weibull distribution of the target wind speed and the reference wind speed at the synchronization period:
Figure BDA0003290457020000082
Figure BDA0003290457020000083
in the formula (I), the compound is shown in the specification,
Figure BDA0003290457020000084
for the wind speed value, k, of the predicted time interval obtained by the Weibull fitting methodyIs the shape parameter, k, of the Weibull distribution of the wind speed at the target stationxFor the shape parameter of the Weibull distribution of wind speeds at the reference station, λyIs a proportional parameter, lambda, of the Weibull distribution of the wind speed at the target stationxWeibull distribution of wind speed for reference stationThe ratio parameter of (a);
calculating a wind frequency reference time sequence according to the obtained wind speed value of the prediction time period:
Figure BDA0003290457020000085
where n is the prediction period length.
The fourth step: using the obtained wind speed reference time series V1And a wind frequency reference time series V2Calculating the final predicted wind speed time series V
Time series V based on wind speed1Average wind speed and wind frequency reference time series V2As a coefficient to correct the time series V2：
Figure BDA0003290457020000086
In the formula:
Figure BDA0003290457020000087
is a wind speed reference time sequence V1Is determined by the average value of (a) of (b),
Figure BDA0003290457020000088
is a wind speed reference time sequence V2Average value of (a).
Final predicted wind speed time series V:
V＝ζ·V2。
the technical conception of the invention is as follows: a Weibull fitting method with high wind frequency presumption precision is selected to obtain a wind frequency reference sequence, the wind frequency reference sequence is used for restoring the wind frequency attribute of target station wind measurement data, then the average wind speed of the wind frequency reference sequence is corrected to be consistent with a wind speed reference sequence (obtained by a piecewise linear fitting method with high wind speed presumption precision) and used for restoring the average wind speed attribute of the target station wind measurement data, and the corrected time sequence is a final prediction result. The wind speed is predicted by the MCP method based on average wind speed correction, a scientific and accurate method for acquiring complete wind measurement data is provided for wind resource assessment of distributed projects, the uncertainty of traditional assessment is greatly reduced, the assessment accuracy is improved, the calculation process is simple, the principle is popular and easy to understand, and the method is suitable for daily business use.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.

Claims (7)

1. A wind speed prediction method using MCP corrected based on average wind speed, comprising the steps of:
s1: processing the used wind speed data;
s2: the complete wind measurement data of the target station is obtained by conjecture through a piecewise linear fitting method and is used as a wind speed reference time sequence V1；
S3: the complete wind measurement data of the target station is obtained by utilizing a Weibull fitting method as a wind frequency reference time sequence V2；
S4: according to the wind speed reference time sequence V1And a wind frequency reference time series V2And correcting the average wind speed, and calculating to obtain a final predicted wind speed time sequence V.
2. The method as claimed in claim 1, wherein the wind speed data includes anemometric data and long-term data in step S1, wherein the anemometric data is used as a target station wind speed and the long-term data is used as a reference station wind speed.
3. A wind speed prediction method using MCP based on mean wind speed correction according to claim 1 or 2, wherein in step S2, the specific step of using the piecewise linear fitting method to obtain complete anemometric data of the target station is:
s2.1: forming two-dimensional wind speed scatter points by taking the wind speed of a target station at a synchronous time interval as a vertical coordinate and the wind speed of a reference station as a horizontal coordinate;
s2.2: dividing the scattered points into a plurality of equidistant intervals of wind speed based on the reference wind speed, wherein the predicted wind speed value of each interval is as follows:
Figure FDA0003290457010000011
wherein:
Figure FDA0003290457010000021
Figure FDA0003290457010000022
in the formula (I), the compound is shown in the specification,
Figure FDA0003290457010000023
for interval-predicted wind speed values, h, obtained by means of piecewise linear fittingiIs the middle point of the ith equidistant interval,
Figure FDA0003290457010000024
the average target wind speed of the ith equidistant interval is obtained;
s2.3: taking one point in each interval, and connecting all the points to obtain a piecewise linear fitting curve;
s2.4: calculating a wind speed reference time sequence:
Figure FDA0003290457010000025
where n is the prediction period length.
4. A method for wind speed prediction using MCP based on mean wind speed correction according to claim 1, wherein in step S2.3, a point is taken in each interval, the abscissa of the point is the midpoint of the abscissa of the interval, and the ordinate is the mean value of the target wind speed in the interval.
5. A wind speed prediction method using MCP based on mean wind speed correction according to claim 1 or 2, wherein in step S3, the specific step of using the weibull fitting method to obtain complete anemometric data of the target station is:
s3.1: establishing a power function model:
Figure FDA0003290457010000026
in the formula, the coefficients α and β are derived from the weibull distribution of the target wind speed and the reference wind speed at the synchronization period:
Figure FDA0003290457010000027
Figure FDA0003290457010000028
in the formula (I), the compound is shown in the specification,
Figure FDA0003290457010000029
for the wind speed value, k, of the predicted time interval obtained by the Weibull fitting methodyIs the shape parameter, k, of the Weibull distribution of the wind speed at the target stationxFor the shape parameter of the Weibull distribution of wind speeds at the reference station, λyIs a proportional parameter, lambda, of the Weibull distribution of the wind speed at the target stationxIs a proportional parameter of the Weibull distribution of the wind speed of the reference station;
s3.2: calculating a wind frequency reference time sequence:
Figure FDA0003290457010000031
where n is the prediction period length.
6. The method as claimed in claim 1, wherein the wind speed prediction method using MCP corrected based on mean wind speed is performed in step S4 according to a wind speed reference time series V1And a wind frequency reference time series V2The method for correcting the average wind speed comprises the following steps: time series V based on wind speed1Average wind speed and wind frequency reference time series V2As a coefficient to correct the time series V2And obtaining a final predicted wind speed time series V.
7. A wind speed prediction method using MCP based on mean wind speed correction according to claim 1 or 6, wherein in step S4, the specific wind speed correction calculation formula is:
V＝ζ·V2
wherein
Figure FDA0003290457010000032
In the formula:
Figure FDA0003290457010000033
is a wind speed reference time sequence V1Is determined by the average value of (a) of (b),
Figure FDA0003290457010000034
is a wind speed reference time sequence V2Average value of (a).
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CN116609552A (en) * 2023-07-18 2023-08-18 江西省气象探测中心 Wind speed measurement uncertainty assessment method, system, storage medium and device
CN116609552B (en) * 2023-07-18 2023-10-20 江西省气象探测中心 Wind speed measurement uncertainty assessment method, system, storage medium and device

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