CN114021858A - Refined wind speed prediction method for power transmission line - Google Patents
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Abstract
The invention discloses a refined wind speed prediction method of a power transmission line, which comprises the following steps: performing wind speed downscaling calculation on forecast data of the conventional weather forecast system by using a CALMET module to obtain refined wind field forecast data with 1km horizontal resolution; acquiring monitoring data of the temperature, the air pressure, the humidity and the wind speed of a power tower point of a power transmission line; carrying out data fusion on wind speed forecast data with 1km horizontal resolution obtained by CALMET power downscaling and monitoring data of a power transmission line, establishing a wind speed correction forecast model based on a partial least square regression method, analyzing the difference between the near-ground wind speed and the observed wind speed of different correction forecast methods, and carrying out wind speed prediction of multivariate data fusion; and (3) carrying out detection on different sample quantity and numerical prediction fusion schemes on the wind speed correction forecasting model and the wind speed prediction, and condensing an optimization scheme aiming at typhoon situations of different power grids, thereby providing a finely-meshed near-ground wind speed correction forecasting field.
Description
Technical Field
The invention relates to the technical field of meteorological forecasting, in particular to a refined wind speed prediction method for a power transmission line.
Background
In the southeast coastal areas of Zhejiang, the typhoon of an important weather system in summer every year is one of the weather systems which cause the most accidents, the most loss and the widest range to the power grid of Zhejiang. In 2006, typhoon "sonmei" landed in south of Zhejiang, causing failure of 27 transmission lines and collapse of 98 iron towers; in 2013, the power transmission line in the Wenzhou area is greatly damaged by typhoon 'Feite' in autumn which logs in at the junction of Zhejiang Fujian in 10 months, and wind damage accidents such as wind deflection, disconnection and even tower collapse occur on more than 50 110kV and 220kV lines; typhoon No. 9 "brilliant" and typhoon No. 13 "sudiluo" caused windage accidents in many lines in wenzhou in 2015. Therefore, how to accurately and efficiently forecast the wind power has long-term significance on the normal operation of the power system. Forecasting of wind speed is always one of the difficulties in public weather forecasting. At present, the prediction method of wind speed mainly takes a numerical weather forecast method and a statistical method as main methods. The numerical weather forecasting method considers more dynamic processes, has long forecasting time efficiency, but reduces the forecasting precision along with the increase of the forecasting time efficiency. In the present stage, the WRF mode is influenced by factors such as imperfect physical parameterization scheme, inaccurate terrain, low resolution and the like, so that the wind speed forecasting result has larger relative observation difference.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a refined wind speed prediction method for a power transmission line, which is more accurate, fast and reliable in prediction, aiming at the above defects in the prior art.
The invention aims to complete the technical scheme that a refined wind speed prediction method of a power transmission line comprises the following steps:
1) performing wind speed downscaling calculation on forecast data of the conventional weather forecast system by using a CALMET module to obtain refined wind field forecast data with 1km horizontal resolution;
2) on the basis of the existing weather forecasting system, according to the actual demand of quasi-business forecasting, the monitoring data of the temperature, the air pressure, the humidity and the wind speed of the power pole and tower point of the power transmission line are obtained by comprehensively utilizing the data of the power transmission line in the aspects of on-line micrometeorology monitoring, automatic meteorological station observation and mode forecasting;
3) carrying out data fusion on wind speed forecast data with 1km horizontal resolution obtained by CALMET power downscaling and monitoring data of a power transmission line, establishing a wind speed correction forecast model based on a partial least square regression method, carrying out test comparison on local site prediction correction schemes, analyzing the difference between near-ground wind speed and observed wind speed of different prediction correction methods, and carrying out wind speed prediction of multivariate data fusion;
4) and (3) carrying out detection on different sample quantity and numerical prediction fusion schemes on the wind speed correction forecasting model and the wind speed prediction, and condensing an optimization scheme aiming at typhoon situations of different power grids, thereby providing a finely-meshed near-ground wind speed correction forecasting field.
Further, in step 3), the establishing of the wind speed correction forecasting model includes the following steps: firstly, taking historical mode forecast data as independent variables and observation data as dependent variables at the same time, establishing a model and acquiring parameters; then, the modeling parameters are brought into the forecasting time needing to be corrected, and the observed value of the wind speed, namely the so-called wind speed correction value, is calculated; modeling at the same time: 24 accumulated time prediction values are obtained in each prediction within 1-24 hours, a simultaneous prediction value and a corresponding observation value are adopted for modeling during modeling, and the prediction wind speed with the start time of t is obtained; firstly, selecting historical samples according to the modeling scheme, and calculating corresponding independent variable xiAnd a dependent variable y, and then obtaining a regression coefficient beta of each independent variable by using a partial least squares regression algorithmiFinally, the argument X of the time t of the start of the report is usediThe wind speed correction result Y at the future time t + Δ t is obtained.
Further, in step 3), the wind speed prediction specifically includes:
the method for forecasting the shortages by utilizing the multi-element shortages is characterized in that the shortages are forecasted by utilizing the following equation:
H(ti)＝H(ti-1)+fT[HNWP(ti)-HNWP(ti-1)] (1)
wherein f isTThe coefficient considering the influence of the cloud amount forecast deviation is obtained by the following equation:
fT＝1+cNCERR (2)
wherein C isERRIs the cloud amount prediction error, cNIs an empirical coefficient, cNTaking the mixture between 0.5 and 0.7; for temperature prediction, fTIs an important influence factor, the wind speed forecast has small influence on the cloud cover, and the influence factors of the humidity and the temperature are considered in the wind speed correction process, so that the wind speed error caused by the cloud cover forecast deviation is not considered, namely f is takenT＝1；
And finally, fusing the extrapolation wind speed forecast and the numerical mode forecast by adopting a 'blending' idea, wherein the fusion mode is as follows:
H(t)＝wt(t)×HDZ(t)+(1-wt(t))×HNWP(t) (3)
wherein HDZ(t) and HNWP(t) correcting forecast and numerical forecast wind speed at time t, wt (t) is weight at time t, and the values are as follows
Where α and β are the weights of the numerical predictions at t 0 and t 6, respectively, and γ represents the slope of wt (t) in the middle of the fusion period, according to equation (4), the predictions of h (t) for the first two hours are based mainly on extrapolated predictions, while at the rear end of the predictions the specific gravity of the numerical predictions increases with time, and in addition, in order to smooth the change of wt (t) from t +2 to t +4 hours, the value of γ is set to be approximately equal to 1.
The invention has the beneficial technical effects that: the invention effectively combines effective wind speed fine research technical methods in the previous research, reasonably optimizes the timeliness difference between monitoring data, forecast data and actual application, aims to serve safe and reliable operation of a power grid, improve the disaster prevention and reduction capability of the power grid, combines the actual operation condition of the power grid, and provides professional, rapid, reliable and effective guidance suggestions for power transmission and transformation equipment of the power grid.
Drawings
FIG. 1 is a block flow diagram of a refined wind speed prediction method according to the present invention;
FIG. 2 is a schematic diagram of a partial least squares regression modeling flow;
FIG. 3 is a basic framework diagram of a wind speed correction service;
FIG. 4 is a schematic diagram of a modeling solution technical route;
fig. 5 is a schematic diagram of the ratio of numerical prediction to fusion prediction.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly understood by those skilled in the art, the present invention is further described with reference to the accompanying drawings and examples.
As shown in fig. 1 to 5, the method for predicting the refined wind speed of the power transmission line according to the present invention includes the following steps:
1) and performing wind speed reduction scale calculation on forecast data of the conventional weather forecast system by using a CALMET module to obtain refined wind field forecast data with the horizontal resolution of 1 km. The CALMET module (California weather/photochemical mode) is mainly used for processing a high-resolution wind field diagnosis mode of meteorological data, and has been widely applied to the power downscaling research of wind fields. According to the topographic data with the resolution of 90m and the CALMET module, the wind field forecasting result of the coarse grid can be downscaled to the resolution of 1km, so that a more refined and more accurate wind field forecasting and analyzing product is provided.
2) On the basis of the existing weather forecasting system, according to the actual demand of the quasi-business forecasting, the monitoring data of the temperature, the air pressure, the humidity and the wind speed of the power pole and tower point of the power transmission line are obtained by comprehensively utilizing the data of the power transmission line in the aspects of on-line micrometeorology monitoring, automatic meteorological station observation and mode forecasting.
3) Carrying out data fusion on wind speed forecast data with 1km horizontal resolution obtained by CALMET power downscaling and monitoring data of a power transmission line, establishing a wind speed correction forecast model based on a partial least square regression method, carrying out test comparison on local site prediction correction schemes, analyzing the difference between near-ground wind speed and observed wind speed of different prediction correction methods, and carrying out wind speed prediction of multivariate data fusion; the partial least squares regression can better solve the problems of multiple correlations among independent variables, sample number and the like which cannot be solved by the conventional ordinary multiple regression, and can realize the comprehensive application of various data analysis methods.
4) And (3) carrying out detection on different sample quantity and numerical prediction fusion schemes on the wind speed correction forecasting model and the wind speed prediction, and condensing an optimization scheme aiming at typhoon situations of different power grids, thereby providing a finely-meshed near-ground wind speed correction forecasting field.
Partial least squares regression is known as a second generation statistical regression technique, and is a multivariate statistical method. First in the chemical field, was first proposed in 1983 by wood and abacno et al. Due to the convenience and the excellent prediction capability of the application and the comprehensive application of various data analysis methods, the method is widely applied to various fields such as finance, chemistry, biology and the like in recent years. In the aspect of meteorological problem research, the judo and the like are applied to estimate the precipitation distribution in the Chinese area; songjinjie et al propose a statistical prediction method of tropical cyclone strength based on the method; the evaluation and the inspection result show that good prediction effect is obtained.
The algorithm theory is a mathematical optimization technology, can realize multi-dependent variable to multi-independent variable modeling statistically, and can better solve the problems that the conventional ordinary multiple regression cannot solve, such as the problem of multiple correlation among independent variables in wind speed determination, the problem of occasional missing of historical observation and the like in practical application. FIG. 2 is a flow of partial least squares regression modeling. Unlike conventional modeling schemes, partial least squares regression first proceeds fromExtracting a first effective component t from an independent variable vector X1It is not only the linear combination of independent variables, but also the data variation in the maximum carrying, and the dependent variable vector Y is assumed to be BPLS1t1+eYIf the equation does not reach satisfactory precision through cross validity check, the X and Y are used for being t1Interpreted residual information eXAnd eYAnd (3) carrying out the 2 nd round component extraction until the satisfactory precision is achieved, and finally reducing the regression equation Y-B about the original variablePLSX。
In the step 3), the establishing of the wind speed correction forecasting model comprises the following steps: firstly, taking historical mode forecast data as independent variables and observation data as dependent variables at the same time, establishing a model and acquiring parameters; then, the modeling parameters are brought into the forecasting time needing to be corrected, and the observed value of the wind speed, namely the so-called wind speed correction value, is calculated; modeling at the same time: 24 accumulated time prediction values are obtained in each prediction within 1-24 hours, a simultaneous prediction value and a corresponding observation value are adopted for modeling during modeling, and the prediction wind speed with the start time of t is obtained; firstly, selecting historical samples according to the modeling scheme, and calculating corresponding independent variable xiAnd a dependent variable y, and then obtaining a regression coefficient beta of each independent variable by using a partial least squares regression algorithmiFinally, the argument X of the time t of the start of the report is usediThe wind speed correction result Y at the future time t + Δ t is obtained.
In the step 3), the wind speed prediction specifically comprises the following steps:
the method for forecasting the shortages by utilizing the multi-element shortages is characterized in that the shortages are forecasted by utilizing the following equation:
H(ti)＝H(ti-1)+fT[HNWP(ti)-HNWP(ti-1)] (1)
wherein f isTThe coefficient considering the influence of the cloud amount forecast deviation is obtained by the following equation:
fT＝1+cNCERR (2)
wherein C isERRIs the cloud amount prediction error, cNIs an empirical coefficient, cNTaking 0.5-0Between 7; for temperature prediction, fTIs an important influence factor, the wind speed forecast has small influence on the cloud cover, and the influence factors of the humidity and the temperature are considered in the wind speed correction process, so that the wind speed error caused by the cloud cover forecast deviation is not considered, namely f is takenT＝1；
And finally, fusing the extrapolation wind speed forecast and the numerical mode forecast by adopting a 'blending' idea, wherein the fusion mode is as follows:
H(t)＝wt(t)×HDZ(t)+(1-wt(t))×HNWP(t) (3)
wherein HDZ(t) and HNWP(t) correcting forecast and numerical forecast wind speed at time t, wt (t) is weight at time t, and the values are as follows
Where α and β are the weights of the numerical predictions at t 0 and t 6, respectively, and γ represents the slope of wt (t) in the middle of the fusion period, according to equation (4), the predictions of h (t) for the first two hours are based mainly on extrapolated predictions, while at the rear end of the predictions the specific gravity of the numerical predictions increases with time, and in addition, in order to smooth the change of wt (t) from t +2 to t +4 hours, the value of γ is set to be approximately equal to 1.
Example (b):
according to the research content of the project, the technical route is applied to No. 4 typhoon 'Saigy' in 2020 to carry out research. The typhoon is generated in the eastern face of the Japanese Lvsong island at 8 months and 1 days in 2020, the strength is gradually strengthened towards the northwest and the east, the typhoon is logged in the coastal region of Leqing City in Zhejiang province at the near peak strength of 30 minutes before and after 3 minutes in the early morning at 8 months and 4 days in 8 months, the maximum wind power near the center reaches 13 grades when logging in, and the strength is gradually weakened to be tropical storm and then moves to the sea surface in the yellow sea after longitudinally penetrating through Zhejiang province and Jiangsu provinces. The typhoon is influenced by the characteristics of small circulation, concentrated energy, slow moving speed after landing and the like of 'black lattice ratio', and the direct economy caused by the typhoon is as high as 104.6 billion yuan. Meanwhile, the power grid accidents such as line breakage, windage yaw and the like are caused in Zhejiang 34 times in total (the accident records within 3 hours of the same accident point are classified into 1 time), wherein the accidents with serious line breakage and pole and tower damage are 6 times, and the method is one of typhoon weather processes which have the most serious influence on a power system in recent years. The blackland is at typhoon level when logging in, and accidents caused by the blackland are mainly located in a temperate environment and are intensively distributed on two sides of a typhoon moving path.
Since the impact of broken wires and damage on the grid is much higher than other accident types, for this reason the test will be started below for the above two types of accidents, the remaining accident points being used as an assessment. Table 1 shows the nearest meteorological site to the accident site, wherein the sites are less than 2km apart from the K8507 site by more than 10km from the accident site, and basically represent the monitoring data of the accident site. The time of accidents is mainly concentrated within 6 hours before and after typhoon landing. When an accident occurs, the maximum wind speed of all stations is more than 30m/s, and exceeds the design wind speed of the power transmission line. The time change trends of the 10-minute average wind speed, the 2-minute average wind speed and the gust wind speed are similar. The K3266 stands near a typhoon moving path, and can detect that suspected typhoon eyes pass through from 02 to 04, and the wind speed change shows a trend of weakening and strengthening. The forecast wind speed after the CALMET downscaling scale is reduced, but the change is not obvious. The forecast wind speed is corrected after the automatic station monitoring is introduced, the whole wind speed is reduced by about 3m/s, and the wind speed correcting effect is obvious. The purple line graph is the forecast wind speed after fusion, and is partially optimized relative to the PLS corrected wind speed. Therefore, the wind speed forecasting and monitoring trend is closer and closer after a set of processes of scale reduction, correction and fusion schemes. Table 2 shows the quantitative statistical results, the initial WRF forecasted wind speed absolute error is 5.54m/s, and the forecasted wind speed absolute error is reduced to 2.95m/s after the scheme is finally fused. The root mean square error and the relative error have similar statistical conclusions.
TABLE 1 selected automatic weather station and distance Difference
TABLE 2 prediction error of each recipe before parameterization recipe
The analysis shows that the PLS scheme has obvious correction effect on the wind speed, but the influence of the number of samples on the forecast result is not considered. Previous research results show that the number of samples has a significant influence on the forecast results. The following is therefore intended to initiate a sensitivity experiment on the effect of the sample number on the wind speed correction. The influence of the number of samples on the statistical error is not linearly changed, and when the number of samples is only 3 to 4, all the statistical errors are larger; when the number of samples gradually increases to 5 to 6, the statistical error gradually decreases, which is obviously reflected in the change of MRE, and when the number of samples further increases to more than 7, the performance of the statistical error begins to show an unstable trend, and the total error is larger than that when the number of samples is only 5 to 6. Table 3 shows the quantitative results of the influence of the number of samples on the wind speed forecasting effect, and the results of 6 sites have been averaged to show the overall effect. It can be seen that when the number of samples is 5, all 3 statistical errors are minimal, i.e.: when the statistical correction is carried out on the wind speed forecast by using the calamet forecast result 5 hours ahead, the error of the obtained wind speed correction forecast relative to the monitoring result is minimum.
TABLE 3 quantitative results of sample number on wind speed correction improvement effect
The wind speed correction result is applied to other 28 accident points, in order to embody the universality of the prediction scheme, the optimized scheme is applied to all stations with monitoring data within a range of 3km around each accident point, the prediction absolute error is mainly concentrated between 2.5 and 3.5m/s when windage yaw accidents and other types of accidents occur, the prediction absolute error of the accident point influenced by foreign matters is slightly higher and is mainly distributed at 4 m/s. After the scheme is optimized, the wind deflection and the forecast absolute errors of other types of accidents are integrally reduced to be near 2.5m/s, and the forecast absolute errors of two foreign matter accident points are reduced to be below 3.5 m/s. Therefore, the optimized scheme not only improves serious accident types such as broken wire damage and the like, but also has obvious positive effects on accidents with small influence on windage yaw and the like. Meanwhile, compared with other accident type absolute error box body diagrams such as windage yaw and the like, the error abnormal value is obviously reduced. Therefore, the improved wind speed forecasting scheme has remarkable improvement on the forecasting of the power grid accident point as a whole.
The specific embodiments described herein are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (3)
1. A refined wind speed prediction method of a power transmission line is characterized by comprising the following steps: the method comprises the following steps:
1) performing wind speed downscaling calculation on forecast data of the conventional weather forecast system by using a CALMET module to obtain refined wind field forecast data with 1km horizontal resolution;
2) on the basis of the existing weather forecasting system, according to the actual demand of quasi-business forecasting, the monitoring data of the temperature, the air pressure, the humidity and the wind speed of the power pole and tower point of the power transmission line are obtained by comprehensively utilizing the data of the power transmission line in the aspects of on-line micrometeorology monitoring, automatic meteorological station observation and mode forecasting;
3) carrying out data fusion on wind speed forecast data with 1km horizontal resolution obtained by CALMET power downscaling and monitoring data of a power transmission line, establishing a wind speed correction forecast model based on a partial least square regression method, carrying out test comparison on local site prediction correction schemes, analyzing the difference between near-ground wind speed and observed wind speed of different prediction correction methods, and carrying out wind speed prediction of multivariate data fusion;
4) and (3) carrying out detection on different sample quantity and numerical prediction fusion schemes on the wind speed correction forecasting model and the wind speed prediction, and condensing an optimization scheme aiming at typhoon situations of different power grids, thereby providing a finely-meshed near-ground wind speed correction forecasting field.
2. The refined wind speed prediction method of the power transmission line according to claim 1, characterized in that: in step 3), the establishment of the wind speed correction forecasting model comprises the following steps: firstly, taking historical mode forecast data as independent variables and observation data as dependent variables at the same time, establishing a model and acquiring parameters; then, the modeling parameters are brought into the forecasting time needing to be corrected, and the observed value of the wind speed, namely the so-called wind speed correction value, is calculated; modeling at the same time: 24 accumulated time prediction values are obtained in each prediction within 1-24 hours, a simultaneous prediction value and a corresponding observation value are adopted for modeling during modeling, and the prediction wind speed with the start time of t is obtained; firstly, selecting historical samples according to the modeling scheme, and calculating corresponding independent variable xiAnd a dependent variable y, and then obtaining a regression coefficient beta of each independent variable by using a partial least squares regression algorithmiFinally, the argument X of the time t of the start of the report is usediThe wind speed correction result Y at the future time t + Δ t is obtained.
3. The refined wind speed prediction method of the power transmission line according to claim 1 or 2, characterized in that: in step 3), the wind speed prediction specifically comprises the following steps:
the method for forecasting the shortages by utilizing the multi-element shortages is characterized in that the shortages are forecasted by utilizing the following equation:
H(ti)＝H(ti-1)+fT[HNWP(ti)-HNWP(ti-1)] (1)
wherein f isTThe coefficient considering the influence of the cloud amount forecast deviation is obtained by the following equation:
fT＝1+cNCERR (2)
wherein C isERRIs the cloud amount prediction error, cNIs an empirical coefficient, cNTaking the mixture between 0.5 and 0.7; for temperature prediction, fTIs an important influencing factor, andfor wind speed forecast, the influence of cloud cover is small, and the influence factors of humidity and temperature are considered in the process of wind speed correction, so that the wind speed error caused by the cloud cover forecast deviation is not considered, namely f is takenT＝1；
And finally, fusing the extrapolation wind speed forecast and the numerical mode forecast by adopting a 'blending' idea, wherein the fusion mode is as follows:
H(t)＝wt(t)×HDZ(t)+(1-wt(t))×HNWP(t) (3)
wherein HDZ(t) and HNWP(t) correcting forecast and numerical forecast wind speed at time t, wt (t) is weight at time t, and the values are as follows
Where α and β are the weights of the numerical predictions at t 0 and t 6, respectively, and γ represents the slope of wt (t) in the middle of the fusion period, according to equation (4), the predictions of h (t) for the first two hours are based mainly on extrapolated predictions, while at the rear end of the predictions the specific gravity of the numerical predictions increases with time, and in addition, in order to smooth the change of wt (t) from t +2 to t +4 hours, the value of γ is set to be approximately equal to 1.
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| CN115793104A (en) * | 2022-10-26 | 2023-03-14 | 国网山东省电力公司济南供电公司 | Method and device for conjecturing call height and wind speed of power grid tower |
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