CN114091240A - Method for researching long-range continuity of wind speed by using anemometer tower data - Google Patents
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Abstract
The invention discloses a method for researching long-range continuity of wind speed by using anemometer tower data, which comprises the following steps: carrying out data standardization processing and basic quality control on the raw data file of the anemometer tower data; carrying out data classification and extraction on anemometer tower data meeting research requirements; carrying out scale analysis by using a detrending fluctuation analysis method DFA; performing linear fitting and comparative analysis, and comprehensively acquiring the long-range continuity characteristics of the wind speed; the research aims at researching the long-range persistence characteristic of the wind speed change, and has important reference significance for disclosing the physical mechanism of the wind speed change, constructing a wind speed model and accurately predicting the wind speed.
Description
Technical Field
The invention relates to the technical field of meteorological data analysis. In particular to a method for researching the long-range continuity of wind speed by using the data of a wind measuring tower.
Background
Wind energy is an important renewable resource. Different from novel energy sources such as traditional fossil energy and solar energy, wind energy capture is greatly influenced by wind speed fluctuation, and the wind energy capture has obvious instability and has great influence on researches such as power output of a fan, wind energy utilization efficiency and power grid stability. Due to the comprehensive influence of different space-time scale physical processes of the earth surface, local topography and other factors, the wind speed of the atmospheric boundary layer shows obvious characteristics of non-stationarity, intermittence, long-range continuity and the like. The deep research on the change characteristics of the wind speed has important significance for constructing a wind speed model, accurately predicting the wind speed and reducing or preventing damage to a fan, a building and other objects.
The long-range continuity of the wind speed (also referred to as long-range power-law correlation and long-range memory) means that the autocorrelation function of the wind speed sequence slowly decays in the form of power-law from C(s) to s-γConversion to the frequency domain, i.e. the power spectral function S (f) is in the form of a power law scale law S (f) f-βSlowly decaying. Because the autocorrelation function slowly decays in a power law form, time domain correlation still exists between the fluctuation of the wind speed at longer time intervals, the wind speed at the past moment can continuously influence the wind speed at the current moment and the next moment, namely the wind speed has memory (at the moment, 0)<β<1, alpha satisfies 0.50<α<1.0). Peng et al proposed a Detrended Fluctuation Analysis (DFA) method to obtain a scale index α ═ β +1)/2, which allows the long-range memorability of time series to be studied conveniently. The method can effectively overcome the influence of sequence nonlinearity and non-stationarity, and is widely applied to the estimation of the scale index of the long-range wind speed persistence.
The scale analysis can represent the basic dynamic characteristics of the wind speed change, study the wind speed scale behavior, better understand the physical mechanism behind the wind speed change and contribute to the evaluation and management of wind energy. In recent years, scholars in different foreign fields carry out continuous and deep research on scale behaviors based on wind speed sequences of different locations and different resolutions. Govindan et al indicate that the non-stationary high frequency wind velocity data (2Hz) exhibits a significant long range correlation with a DFA scale index of about 1.1. The hourly average wind speed data from 28 meteorological stations in north dakota, usa was continuously studied by kavassier et al and found to resemble brownian noise on a small scale with a scale index of about 1.40 and a scale index of about 0.7 on a large scale, exhibiting long-range memory. Kocak et al studied the hourly average wind speed data from 20 meteorological stations in the northwest of Turkey, indicating that the scale curve has a buckling phenomenon, and the scale index size is independent of geographical factors such as distance to the ocean and observation height. De Oliveira et al studied the hourly maximum and average wind speed sequences for 4 meteorological stations in bubon, bernanceau, northeastern brazil, indicating that these sequences have a close long-range power-law correlation and exhibit two different scale intervals. Telesca et al indicate that a 10 minute time series of average wind speeds for 6 meteorological stations in the mountainous region of Switzerland, there are two different scale intervals, and the scale features are independent of station altitude.
In China, Feng et al researches the daily average wind speed sequence of 4 meteorological observation stations in China and indicates that long-range power law correlation generally exists; li et al studied the atmospheric turbulence wind speed vertical component observed in Huaihe river test (HUBEX), indicating that the non-stationarity of wind speed affects its scaling behavior, which can cause the bending phenomenon of the scaling curve; the grandbin et al researches hourly wind speed time series of a certain wind field in northeast China, and indicates that wind speed fluctuation has long-range correlation and presents obvious multi-fractal characteristics; wang et al studied the 10-minute average wind speed sequence of 10 meteorological sites in Yunnan province, where the turning point of the found wind speed scale interval is on a time scale of 24 hours, and suggested that when applying the DFA method, the length of the studied time sequence should exceed 1 day to avoid the scale index from being abnormal; zeng et al studied the time series of high frequency wind speeds (1Hz) observed in the campus of Tianjin university, indicated that the scaling behavior of high frequency wind speeds is more complex, and the DFA scale index is greater than 1.0, and suggested that the time length of the sample series should reach 20 minutes to better study the multi-scale and multi-typing characteristics of high frequency wind speeds. Yuan-Yong et al studied 6 sets of time series of wind speeds observed at a fixed altitude in a wind tower, indicating that there is significant self-similarity and long-range positive correlation between them.
However, much of the research has been based on ground meteorological stations, or fixed-height observation points of the external field of the atmosphere. The wind speed time series observed at different heights of the same observation point and the analysis of internal structural features thereof are rarely researched, and particularly, the quantitative depiction of the long-range continuous features of the wind speeds at different heights is lacked; meanwhile, the influence of different resolutions of wind speed on the DFA statistical result is less researched.
Disclosure of Invention
Therefore, the technical problem to be solved by the invention is to provide a method for researching the long-range continuity of the wind speed by using the anemometer tower data, by means of the anemometer tower observation data, the long-range continuity of the wind speed sequences at different geographic positions is researched, the scale behaviors of the wind speed sequences at different vertical heights of the same place are contrastively analyzed, the influence of the data resolution on the wind speed scale behaviors is also researched, and the near-ground wind speed long-range continuity characteristics are contrastively analyzed more comprehensively compared with the existing research results.
In order to solve the technical problems, the invention provides the following technical scheme:
a method for researching long-range continuity of wind speed by using anemometer tower data comprises the following steps:
(1) carrying out data standardization processing and basic quality control on the raw data file of the anemometer tower data;
(2) carrying out data classification and extraction on anemometer tower data meeting research requirements;
(3) carrying out scale analysis by using a detrending fluctuation analysis method DFA;
(4) and performing linear fitting and comparative analysis, and comprehensively obtaining the long-range continuity characteristics of the wind speed.
The method for researching the long-range continuity of the wind speed by using the anemometer tower data comprises the following steps of (1):
(1-1) the wind measuring tower data original data file comprises wind speed data of all height layers of an observation time T of a certain A wind measuring tower; standardizing the collected original data, re-extracting and organizing all the existing data of the current time to obtain a new data file, wherein the new data file comprises a time sequence of all the data of the current time observed at a certain observation height H of the anemometer tower A;
(1-2) performing basic quality control on the data subjected to the standardized processing, and eliminating obvious error data in the data so as to avoid influencing a final result; the quality control comprises the following steps: checking a limit value, checking an extreme value, checking internal consistency, checking space-time consistency and checking manual intervention;
and (1-3) reserving the data after the quality control inspection and carrying out subsequent processing.
In the method for researching long-range wind speed continuity by using anemometer tower data, in the step (2), the extracted data comprises wind speed and other physical quantities obtained based on wind speed data statistics, and the other physical quantities obtained based on the wind speed data statistics comprise wind shear indexes based on the wind speed, different altitudes based on the wind speed, geographical positions far from and near a coastline and different climate areas based on the wind speed.
The method for researching the long-range continuity of the wind speed by using the anemometer tower data comprises the following steps of (2): the research requirements of the anemometer tower data are as follows: data were observed at least 85% of the time per year and the data length was at least 5 years in duration.
In the method for researching the long-range continuity of the wind speed by using the anemometer tower data, in the step (2), an observation place is selected by using a control variable method, and the selected data are classified according to the difference of the resolution of the data; and simultaneously, the resolution of the data is selected, and the data are classified according to different observation places and used for subsequent comparative analysis.
The method for researching the long-range continuity of the wind speed by using the anemometer tower data comprises the following steps in step (3):
(3-1) the data classified and extracted in the step (2) is an original sequence which is expressed as { xi}; 1,2, …, N, pitch-flat sequence to original sequence
To perform tirednessAnd adding to obtain a profile sequence:
wherein: { xiThe original sequence is used as the sequence;
is the average of the original sequence; { x'i-is a pitch-plateau sequence; { Y (j) } is the profile sequence; n is a positive integer greater than or equal to 1;
(3-2) window division: decomposing the profile sequence (Y (j)) into N with equal time length s and non-overlapping with each othersN/s windows; then, carrying out window division again from the tail part of the sequence in the reverse direction to obtain N which are not overlapped with each othersA window; to obtain 2NsEach window is numbered as v;
(3-3) in each window v, fitting the profile sequence { Y (j) } by using a k-order polynomial to obtain a fitted trend sequence
The fitted trend sequence was then subtracted from the profile sequence Y (j) } in all windows
The detrended sequence Y is obtaineds(j)}：
(3-4) for the detrending sequence, calculating the variance function in each window
(3-5) mixing 2NsAfter the variance functions calculated by the windows are accumulated, the square opening is carried out to obtain a fluctuation function F(s) to be calculated:
(3-6) original sequence { xiThe long-range correlation, the power law scaling relationship between the function f(s) and the window s should be satisfied: f(s) to sα。
The method for researching the long-range continuity of the wind speed by using the anemometer tower data comprises the following steps of (4):
(4-1) performing linear fitting on lg (F (s)) and window size lg(s) in a Log-Log plot (Log-Log plot) to obtain a scale index alpha;
(4-2) displaying the DFA analysis results of the data obtained under the same classification standard in the same logarithmic coordinate system, and carrying out comparative analysis on the influence of different variables on the long-range continuity characteristics of the wind speed;
and (4-3) comprehensively acquiring the long-range continuity characteristics of the near-ground wind speed through comparative analysis.
The method for researching the long-range continuity of the wind speed by using the anemometer tower data comprises the following steps of (4-1):
if alpha is 0.5, the original time sequence is white noise sequence which is irrelevant; if alpha is greater than 0.5, the sequence is positively correlated in a long range, and the larger the alpha value is, the stronger the correlation is; if α <0.5, it indicates that the sequence is long-range anti-correlated, with larger values followed more easily by smaller values.
The technical scheme of the invention achieves the following beneficial technical effects:
based on wind speed data of the wind measuring tower provided by a professional observation network of wind energy resources on land in China, the long-range persistence characteristics of wind speed time sequences with different resolutions observed at different heights of 103 wind measuring towers are researched by using a Detrended Fluctuation Analysis (DFA) method. The result shows that the wind speed time sequences at different heights observed by the same anemometer tower have consistent scaling behaviors and are irrelevant to the time resolution of data; the long-range continuity of the wind speed has good consistency between the vertical upper layer and the vertical lower layer of the anemometer tower. By utilizing the universality, the quality of the anemometer tower data can be effectively checked, and abnormal problems in the data can be found; for the average wind speed sequence of 6 hours, the DFA index (alpha) value range of the wind speed observed by 103 wind measuring towers is basically between 0.55 and 0.91, the long-range continuity is strong, and the regional characteristics are not obvious; ③ for the 10 minute average wind speed sequence, the DFA scale index curve has a kink, bounded by the 24 hour scale, presenting two distinct independent scale intervals: on the larger time scale, the scale index α has a value of 0.80, whereas on the smaller time scale, α has a value of about 1.38. The research aims at researching the long-range persistence characteristic of the wind speed change, and has important reference significance for disclosing the physical mechanism of the wind speed change, constructing a wind speed model and accurately predicting the wind speed.
The scale characteristics and the spatial distribution characteristics of the wind field are researched by utilizing the wind speed data at different heights obtained by observation of the wind measuring tower, so that the complexity of the space structure of the wind field of the atmospheric boundary layer can be more clearly revealed; on the other hand, the method is helpful for understanding the continuous and intermittent characteristics of the wind speed, and has important referential significance for the statistical analysis and modeling simulation of the wind energy resources.
The wind measuring tower data is utilized to focus on the internal structural characteristics of the wind speed time sequence at different heights of the atmospheric boundary layer: on one hand, compared with the wind speed of 2m or 10m close to the ground observed by a traditional ground meteorological station, the wind speed of different heights can be observed by the wind measuring tower, and the vertical change structural characteristics of the atmospheric wind field can be more clearly shown. On the other hand, compared with the approximately instantaneous vertical wind speed profile obtained by sounding observation, the wind measuring tower has the advantage of continuous observation, and a wind speed change sequence for a long time can be obtained.
Drawings
FIG. 1 shows the spatial distribution of 400 anemometer towers across the country;
FIG. 2 is a flow chart of the present application (only wind speed data at different resolutions and wind speed data at different positions at the same location are taken as examples);
FIG. 3 is a time series of average wind speeds of the anemometer tower for 6 hours and 10 minutes; (a) a white noise sequence, (b) a 6 hour average wind speed sequence, (c) a 10 minute average wind speed sequence;
FIG. 4 is a long-range persistence index characteristic of anemometry tower wind speed at different time scales for wind speeds of different resolutions;
FIG. 5 is a 6 hour by 6 hour average wind speed variation time series observed at different altitudes by the anemometer tower;
FIG. 6 DFA analysis results of wind speed sequences at different heights of the anemometer tower;
FIG. 76 shows the spatial distribution of the long-range persistence index of the average wind speed over 76 hours.
Detailed Description
First, research data
The data is from a Chinese land wind energy resource professional observation network formally built in 2009. As shown in fig. 1, the distribution of 400 wind towers in the country is mainly concentrated in the north and south east coast where wind energy resources are abundant, and the distribution of wind towers is sparse in wide middle and south regions. The wind measuring instruments are all ZQZ-TF wind speed and direction sensors produced by radio scientific research institute of Jiangsu province.
The data used in this study were 10 minute-by-minute average wind speed and 6 hour average wind speed data (four times a day: 02, 08, 14, 20). All wind speed data are subjected to system quality control, and the method comprises the specific steps of threshold value inspection, extreme value inspection, internal consistency inspection, space-time consistency inspection, manual intervention inspection and the like. The 103 anemometers with longer-time observation data in the period of 2009-2018 (as shown in fig. 7 below) are mainly selected to eliminate the influence of too short sequence on the analysis result.
Method for researching long-range continuity of wind speed by using anemometer tower data
The research method of this embodiment is shown in the flow chart of fig. 2, and the detailed steps and processes are described as follows:
1. data normalization and quality control
1.1 original data file of anemometer tower data, generally named according to the anemometer tower number and observation time. Therefore, each data file only contains the wind speed data of all height layers of a certain A anemometer tower at the time T of one observation. And (3) carrying out standardization processing on the collected original data, namely re-extracting and organizing all the existing time data to obtain a data file which comprises a time sequence of all the time observation data at a certain observation height H of the anemometer tower A.
1.2, performing basic quality control on the data which is processed in a standardized way, and eliminating obvious error data in the data so as to avoid influencing a final result. The quality control links in each step comprise: checking a limit value, checking an extreme value, checking internal consistency, checking space-time consistency and checking manual intervention. And (4) leaving the data which passes the quality control check and performing subsequent processing.
1.3 the quality control links are common data anomaly identification methods, and the time-space consistency check is taken as an example for explanation. The anemometer tower data can be checked for spatial vertical consistency, which is different from the ground observation data. And when the element difference between two adjacent observation heights is larger than a given threshold value, the data is considered to be suspicious. Unless under very few special weather conditions (such as frontal transit), the meteorological elements are unlikely to change drastically within a limited spatio-temporal range. For example, the difference in wind speed observed at heights of 60m and 10m should be less than 8 m/s.
2. Data classification extraction
2.1 the method of data classification extraction is not limited to the two mentioned in the flow chart, but can also include other physical quantities derived based on the wind speed data, such as based on the wind shear index, based on different geographical locations (altitude, distance from the shoreline), based on belonging to different climate areas, etc. Two classification methods in the flow chart are exemplified herein.
2.2 selecting the anemometer tower data meeting the research requirement, wherein observation data is required to be available at least 85% of the time each year, and the data length is at least 5 years continuously, so that the sufficient data length is maintained, and the requirement of subsequent processing and analysis is met.
2.3 by using a control variable method, for example, an observation place (longitude, latitude, observation height and the like) is selected, and data is classified according to the difference of the resolution (6 hours, 10 minutes and the like) of the data and used for subsequent comparative analysis.
2.4 Using controlled variables, such as the resolution of the selected data (6 hours, 10 minutes, etc.), the data are sorted according to the location of observation (longitude, latitude, altitude of observation) for subsequent comparative analysis.
3. Scale analysis Using DFA method
3.1 for a given original sequence xi}; i 1,2, …, N, which is first ordered from the flat sequence
And accumulating to obtain a profile sequence:
the reason for obtaining the profile is that the influence caused by experimental errors can be reduced through one-time integration, and meanwhile, the trend hidden in the time sequence can be preliminarily judged.
3.2 the sequence of profiles Y (j) is then decomposed into N of equal time length s, which do not overlap each othersN/s windows. Considering that the window size is not necessarily evenly divisible by the data length, in order to fully utilize the data left at the tail of the profile sequence and not taken into account during windowing, it is necessary to divide the window from the tail of the sequence again, and obtain N's that do not overlap each othersA window. Thus obtaining 2NsEach window is numbered v.
3.3 within each window v, we fit the profile sequence with a polynomial of order k to obtain
(Detrend fitting using a polynomial of order k, known as the DFA method of order k, typically with k taken to 2) and then subtracting the fitted trend sequence from the profile sequence { Y (j) } in all windows
The detrended sequence Y is obtaineds(j)}：
3.4 Next for the detrending sequence, a variance function is calculated in each window
3.5 finally, 2NsAfter the variance functions calculated by the windows are accumulated, the square opening is carried out to obtain the fluctuation function F(s) to be calculated:
in the DFA method, taking time windows of different sizes, the ripple function f(s) increases with increasing window size s. If the original sequence { xiThe long-range correlation, the power law scaling relationship between the function f(s) and the window s should be satisfied:
F(s)～sα。
4. comprehensive acquisition of long-range continuity characteristics of wind speed
4.1 the scaling index α is obtained by linear fitting of F(s) and window size s in a Log-Log plot (Log-Log plot). If alpha is 0.5, the original time sequence is white noise sequence which is irrelevant; if alpha is greater than 0.5, the sequence is positively correlated in a long range, and the larger the alpha value is, the stronger the correlation is; if α <0.5, it indicates that the sequence is long-range anti-correlated, with larger values followed more easily by smaller values.
And 4.2, displaying the data DFA analysis results obtained under the same classification standard in the same logarithmic coordinate system, and comparing and analyzing the influence of different variables (such as data resolution, observation height and the like) on the long-range continuous characteristics of the wind speed.
4.3, comprehensively acquiring the long-range continuity characteristics of the near-ground wind speed through the comparative analysis.
Third, results and analysis
3.1 different resolution wind speed sequence features
As in fig. 3, a time series of average wind speeds of the anemometer tower by 6 hours and 10 minutes is given. It can be seen that the wind speed sequences for both frequencies show distinct structural features compared to the white noise in fig. 3 (a). The wind speed values in the white noise sequence are randomly and uniformly distributed on the whole time axis, and the real wind speed observation sequence shows stronger cluster characteristics, namely the large values and the small values of the wind speed are more prone to be concentrated. Meanwhile, comparing fig. 3(b) and fig. 3(c), it can be seen that although the wind speed time series of two different resolutions both show cluster characteristics, in the 10-minute average wind speed series with the higher resolution, a structure with a larger time scale is shown, and in the time with a small scale, the wind speed is more continuous, and a larger wind speed is more likely to occur behind the larger wind speed, and a smaller wind speed is more likely to occur behind the smaller wind speed.
3.2 Long-range continuous comparative analysis of wind speeds with different resolutions
In 3.1, qualitative differences of wind speed sequences with different resolutions are given through comparison, and how to quantify the internal structure differences of the wind speeds is worthy of further intensive study. As in fig. 4, the structural feature differences of wind speed at different resolutions on different time scales are quantified using the DFA method. It can be seen that for the anemometer tower wind speed data with 6 hours resolution, the scale index is approximately 0.80 in the whole scale interval, and the good long-range persistence is shown. For the anemometer tower wind speed data with 10 min resolution, the daily scale s is equal to sxTwo distinct scale intervals can be found, bounded by 24 hours, i.e. the scale index has an inflection point: on a small scale, the scale index α is 1.38, approximating brownian noise; on a large scale, the scale index of the data is the same as that of the 6-hour resolution data, namely alpha is 0.8, and the data shows better long-range persistence; and the existing researchThe results were identical. The scale index has a corner phenomenon, which is often caused by the fact that the correlation of the sequence on different time scales changes. The small-scale behavior can be understood as the movement of small vortexes in the atmospheric turbulence, and the movement property is similar to the molecular movement because the time and space scales of the small vortexes are small; whereas large scale motion (i.e. the motion of large vortices) shows some long range dependence. The size scale is controlled by different leading factors, so that the DFA curve is bent. After shuffling the wind speed sequences of two resolutions, the same scale index as the white noise sequence can be obtained, i.e. α is 0.5.
3.3 wind speed sequence characteristics at different altitudes
As shown in fig. 5, the anemometer tower observed average wind speed data 6 hours by 6 hours at different heights. It can be seen that the change rule of the wind speed at different heights is approximately consistent. At any moment, the wind speed is gradually increased along with the gradually increased observation height. At the moment of extremely low wind speed, the difference value of the wind speeds at different heights is small; and at the moment of the maximum wind speed, the difference of the wind speeds at different heights is larger. It can also be seen that the time series of the average wind speeds observed at any altitude have obvious daily cyclic variation characteristics, i.e. the average value of the wind speeds in the daytime is larger, and the average value of the wind speeds at night is smaller, and is consistent with the existing observation facts and statistical results.
3.4 Long-range continuity comparative analysis of wind speeds at different heights
FIG. 6 compares the long-range persistence characteristics of observed wind speeds at different altitudes. It can be seen from the figure that the piecewise scaling behavior obtained in the previous figure 4 does not change with the observed altitude (from 10m to 100m), and the long-range continuity of the wind speed is well consistent between the upper and lower vertical layers of the wind tower. By utilizing the universality, the quality of the anemometer tower data can be effectively checked, and the abnormal problem in the data can be found. For example, if the observation instrument at a certain height is damaged, the wind speed sequence of the observation instrument is obviously wrong, which inevitably affects the long-range continuity, and the DFA curve of the observation instrument shows different scale characteristics from the observation data of other height layers. It should be noted that, since the long-range persistence indexes of wind speeds with different resolutions do not change with the change of the observation altitude, only the DFA curve result of the average wind speed in 10 minutes is given here, and the rest is not repeated.
3.5 spatial distribution characteristics of long-range continuity of wind speed of anemometer tower
Fig. 7 shows the long-range persistence index spatial distribution of the average wind speed for 6 hours at a height of 70m for 103 wind towers. It can be seen that the DFA index value ranges from 0.55 to 0.91 (satisfying 0.50< α <1.0), both of which show strong long-range persistence and are not obvious in regional characteristics. Generally speaking, the scale indexes of anemometry towers which are close to each other are also more similar in size due to the similarity of local environments. The relationship between the scale index and the distance between the anemometer tower and the coastline and the altitude of the anemometer tower is not obvious, and is identical with the existing foreign research results.
Fourth, conclusion and discussion
The atmospheric boundary layer wind speed field is always in a turbulent flow state, is a fluid with high Reynolds number and presents space-time multi-scale structural characteristics. Compared with scalar fields such as a temperature field and a humidity field, the wind field is more susceptible to local circulation, small-scale terrain and other factors, and complexity is further represented. The scale characteristics and the spatial distribution characteristics of the wind field are researched by utilizing the wind speed data at different heights obtained by observation of the wind measuring tower, so that the complexity of the space structure of the wind field of the atmospheric boundary layer can be more clearly revealed; on the other hand, the method is helpful for understanding the continuous and intermittent characteristics of the wind speed, and has important referential significance for the statistical analysis and modeling simulation of the wind energy resources.
Based on the data of the wind measuring tower, the evaluation of the wind energy resources by using the observation data of the wind measuring tower is one of 3 common wind energy resource evaluation means at present. The existing research based on anemometer tower wind speed data focuses on wind energy resource evaluation, wind speed change trend research, wind shear index statistical analysis and other aspects, and the anemometer tower data is less utilized to focus on the internal structure characteristics of wind speed time sequences at different heights of an atmospheric boundary layer. On one hand, compared with the wind speed of 2m or 10m close to the ground observed by a traditional ground meteorological station, the wind speed of different heights can be observed by the wind measuring tower, and the vertical change structural characteristics of the atmospheric wind field can be more clearly shown. On the other hand, compared with the approximately instantaneous vertical wind speed profile obtained by sounding observation, the wind measuring tower has the advantage of continuous observation, and a wind speed change sequence for a long time can be obtained. The characteristics and advantages of the anemometer tower for observing the wind speed of the atmospheric boundary layer support the research content, and the research result of the former people on the long-range continuity characteristic of the wind speed is enriched and expanded.
Govindan et al have been in research to show that it is desirable to analyze wind speed observations at more locations in order to verify the universality of the wind speed scale index. Based on data obtained by a China onshore wind energy resource professional observation network, the long-range continuity of wind speed sequences of 103 wind measuring towers at different vertical heights and different time resolutions is researched, the space-time range of the long-range continuity research of the wind speeds is greatly expanded, results identical with those of the previous research are obtained, and new conclusions that wind speed scaling behaviors are influenced by the time resolutions and scaling characteristics of the wind speeds at the same place are unrelated to the vertical observation heights are given. The DFA method is only one method for studying the long-range persistence of the wind speed sequence, and then the MF-DFA (Multi fractional rectified Fluctuation analysis) method can be utilized to expand the formula
And the value range of the medium k is used for continuously carrying out deep research on the multi-scale and multi-parting structural characteristics of the wind speed data of the anemometer tower.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications are possible which remain within the scope of the appended claims.
Claims (8)
1. A method for researching long-range continuity of wind speed by using anemometer tower data is characterized by comprising the following steps:
(1) carrying out data standardization processing and basic quality control on the raw data file of the anemometer tower data;
(2) carrying out data classification and extraction on anemometer tower data meeting research requirements;
(3) carrying out scale analysis by using a detrending fluctuation analysis method DFA;
(4) and performing linear fitting and comparative analysis, and comprehensively obtaining the long-range continuity characteristics of the wind speed.
2. The method for studying long-range wind speed continuity by using anemometer tower data as claimed in claim 1, wherein in step (1):
(1-1) the wind measuring tower data original data file comprises wind speed data of all height layers of an observation time T of a certain A wind measuring tower; standardizing the collected original data, re-extracting and organizing all the existing data of the current time to obtain a new data file, wherein the new data file comprises a time sequence of all the data of the current time observed at a certain observation height H of the anemometer tower A;
(1-2) performing basic quality control on the data subjected to the standardized processing, and eliminating obvious error data in the data so as to avoid influencing a final result; the quality control comprises the following steps: checking a limit value, checking an extreme value, checking internal consistency, checking space-time consistency and checking manual intervention;
and (1-3) reserving the data after the quality control inspection and carrying out subsequent processing.
3. The method for researching long-range wind speed persistence by using anemometer tower data as claimed in claim 1, wherein in the step (2), the extracted data includes wind speed and other physical quantities statistically obtained based on the wind speed data, and the other physical quantities statistically obtained based on the wind speed data include wind shear index based on wind speed, different altitudes based on wind speed, geographical positions far from and near to a coastline, and different climate zones based on wind speed.
4. The method for studying long-range wind speed continuity by using anemometer tower data as claimed in claim 1, wherein in step (2): the research requirements of the anemometer tower data are as follows: data were observed at least 85% of the time per year and the data length was at least 5 years in duration.
5. The method for studying the long-range persistence of wind speed by using anemometer tower data as claimed in claim 1, wherein in the step (2), the observation site is selected by using a control variable method, and the selected data is classified according to the difference of the resolution of the data; and simultaneously, the resolution of the data is selected, and the data are classified according to different observation places and used for subsequent comparative analysis.
6. The method for studying the long-range continuity of the wind speed by using the anemometer tower data as claimed in claim 1, wherein the step (3) comprises the following steps:
(3-1) the data classified and extracted in the step (2) is an original sequence which is expressed as { xi}; 1,2, …, N, pitch-flat sequence to original sequence
xiAnd accumulating to obtain a profile sequence:
wherein: { xiThe original sequence is used as the sequence;
is the average of the original sequence; { xi ′-is a pitch-plateau sequence; { Y (j) } is the profile sequence; n is a positive integer greater than or equal to 1;
(3-2) window division: decomposing the profile sequence (Y (j)) into N with equal time length s and non-overlapping with each othersN/s windows; then, window division is performed again from the tail part of the sequence in the reverse direction, and the same is performedObtaining N which are not overlapped with each othersA window; to obtain 2NsEach window is numbered as v;
(3-3) in each window v, fitting the profile sequence { Y (j) } by using a k-order polynomial to obtain a fitted trend sequence
The fitted trend sequence was then subtracted from the profile sequence Y (j) } in all windows
The detrended sequence Y is obtaineds(j)}：
(3-4) for the detrending sequence, calculating the variance function F in each windows2(v)：
(3-5) mixing 2NsAfter the variance functions calculated by the windows are accumulated, the square opening is carried out to obtain a fluctuation function F(s) to be calculated:
(3-6) original sequence { xiThe long-range correlation, the power law scaling relationship between the function f(s) and the window s should be satisfied: f(s) to sα。
7. The method for studying the long-range continuity of wind speed by using anemometer tower data as claimed in claim 6, wherein in the step (4):
(4-1) performing linear fitting on lg (F (s)) and window size lg(s) in a Log-Log plot (Log-Log plot) to obtain a scale index alpha;
(4-2) displaying the DFA analysis results of the data obtained under the same classification standard in the same logarithmic coordinate system, and carrying out comparative analysis on the influence of different variables on the long-range continuity characteristics of the wind speed;
and (4-3) comprehensively acquiring the long-range continuity characteristics of the near-ground wind speed through comparative analysis.
8. The method for studying long-range wind speed continuity by using anemometer tower data as claimed in claim 7, wherein in the step (4-1):
if alpha is 0.5, the original time sequence is white noise sequence which is irrelevant; if alpha is greater than 0.5, the sequence is positively correlated in a long range, and the larger the alpha value is, the stronger the correlation is; if α <0.5, it indicates that the sequence is long-range anti-correlated, with larger values followed more easily by smaller values.
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