CN112183801A - Wind speed forecast correction method and device fusing wind power plant observation data - Google Patents
Wind speed forecast correction method and device fusing wind power plant observation data Download PDFInfo
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Abstract
The invention provides a wind speed forecast correction method and device fusing wind power plant observation data, comprising the following steps: determining a wind speed observation value of a node in a grid network corresponding to an area according to the wind speed observation value of a wind farm in the area; and correcting the wind speed forecast value of the node in the grid network corresponding to the area according to the wind speed observation value of the node in the grid network corresponding to the area. The technical scheme provided by the invention has the advantages that the correction process is more reasonable, objective and scientific, the availability of forecast data is maximized, scientific basis is provided for the prediction and scheduling of new energy power, and the labor cost and the resource waste are reduced to the greatest extent.
Description
Technical Field
The invention relates to the field of new energy wind power generation power prediction, in particular to a wind speed forecast correction method and device fusing wind power plant observation data.
Background
Wind power is typically developed on a large scale as new energy power generation, and large-scale grid-connected operation of the wind power brings great influence on safe, stable and economic operation of a power system. The output power of the wind power plant and the photovoltaic power plant is mainly determined by meteorological factors such as wind speed, wind direction and irradiance. In order to reflect the change process of an atmospheric system in the Prediction time, the new energy power generation power Prediction needs to adopt wind speed, wind direction and irradiance data of high-resolution Numerical Weather forecast (Numerical Weather forecast-NWP) close to a new energy power station as input quantities, and then a Prediction algorithm converts the meteorological element forecast of the NWP into the output power Prediction of a new energy station.
In nature, wind is affected by regions, terrains and climates and has high randomness and uncontrollable property, so that wind speed forecasting is an element with low forecasting accuracy in various meteorological elements, and inaccuracy of wind speed forecasting directly affects inaccuracy of output power of a wind power plant, so that important decisions in the processes of output optimization and power grid dispatching operation are affected. At present, research on correction after wind speed forecasting mainly focuses on business experience correction. Generally, forecasters in various regions can refer to forecast results in different modes to correct wind speed forecast results according to different geographic and climate conditions of the various regions. However, most of the correction is determined according to forecast areas, the resolution of the current business numerical weather forecast is 9km, the correction range is large, and the influence of tiny terrain on wind speed is often ignored in wind power plant forecast, wherein the meteorological data quantity in the power industry is large, the time interval is dense, the format is complex, more means and methods are needed for processing, the data quality is uneven, inspection and correction are needed from different space-time dimensions, the integration difficulty is high, and the wind speed correction data for wind power prediction with high construction quality, complete elements, consistent format and high space-time resolution are problems to be solved.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a method for correcting a wind speed forecast result of weather forecast to obtain a wind speed forecast result with higher final accuracy, and further improve the availability and applicability of the existing wind speed forecast product, thereby serving for the prediction and scheduling of the power generation power of new energy.
The purpose of the invention is realized by adopting the following technical scheme:
the invention provides a wind speed forecast correction method fusing wind power plant observation data, and the improvement is that the method comprises the following steps:
determining a wind speed observation value of a node in a grid network corresponding to an area according to the wind speed observation value of a wind farm in the area;
and correcting the wind speed forecast value of the node in the grid network corresponding to the area according to the wind speed observation value of the node in the grid network corresponding to the area.
Preferably, the determining the wind speed observed value of the node in the grid network corresponding to the area according to the wind speed observed value of the wind farm in the area includes:
acquiring a wind power plant which has a correlation relation with other wind power plants in an area in a grid adjacent to a node in a grid network corresponding to the area;
and determining the wind speed observation value of the node in the grid network corresponding to the area according to the wind speed observation value of the wind power plant which has a correlation relation with other wind power plants in the area in the grid adjacent to the node in the grid network corresponding to the area.
Further, the acquiring a wind farm in which a grid adjacent to a node in a grid network corresponding to the area has a correlation with other wind farms in the area includes:
if the similarity between various types of observation data of the ith wind farm in a grid range adjacent to the nodes in the grid network corresponding to the region and various types of observation data of the jth wind farm in other wind farms in the region is larger than a first similarity threshold corresponding to various types of observation data, a correlation exists between the ith wind farm and the jth wind farm, and the ith wind farm is obtained;
otherwise, the correlation does not exist between the ith wind power plant and the jth wind power plant, and the ith wind power plant is deleted;
wherein i ∈ (0, e); j ∈ (0, b); e is the total number of wind power plants in the grid range adjacent to the nodes in the grid network corresponding to the region; b is the total number of other wind power plants except the ith wind power plant in the region;
the categories of the observation data include: air temperature observation data, air pressure observation data, relative humidity observation data, wind speed observation data and precipitation observation data.
Further, the similarity of the observed data between the x-th type observed data of the ith wind farm and the x-th type observed data of the jth wind farm is determined according to the following formula
In the formula (I), the compound is shown in the specification,
the x-th type observation data of the ith wind power plant at the ith historical moment;
the ith type of observation data of the jth wind power plant at the ith historical moment; w is axA second similarity threshold for the x-th class of observed data; d is the total number of historical time instants.
Further, determining the wind speed observation value of the node in the grid network corresponding to the area according to the wind speed observation value of the wind power plant having a correlation with other wind power plants in the area in the grid adjacent to the node in the grid network corresponding to the area includes:
determining a wind speed observed value V of a node in a grid network corresponding to the area according to the following formulaGZ：
In the formula etapThe weight occupied by the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region is obtained; vpThe wind speed observation value of the p-th wind power plant which has a correlation with other wind power plants in the area in the grid adjacent to the node in the grid network corresponding to the area is obtained; n is the total number of the wind power plants which are in a correlation relationship with other wind power plants in the region in grids adjacent to the nodes in the grid network corresponding to the region;
determining the weight eta occupied by the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region according to the following formulap：
In the formula, kpThe weighting proportion coefficient of the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region;
determining a weight proportion coefficient k of the p-th wind power plant which has a correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region according to the following formulap：
In the formula, LpAnd the distance between the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region and the nodes in the grid network corresponding to the region is obtained.
Preferably, the correcting the wind speed forecast value of the node in the grid network corresponding to the area according to the wind speed observation value of the node in the grid network corresponding to the area includes:
if the number of the wind power plants which are in correlation with other wind power plants in the area in the grids adjacent to the nodes in the grid network corresponding to the area is not less than 5, correcting the wind speed forecast value of the nodes in the grid network corresponding to the area to 0.5VY+0.5VGZ(ii) a Otherwise, correcting the wind speed forecast value of the node in the grid network corresponding to the area to be (1-0.1 · k) VY+0.1·k·VGZ；
Wherein, VYForecasting the wind speed of the nodes in the grid network corresponding to the area; vGZThe wind speed observed value of the node in the grid network corresponding to the area is obtained; and k is the number of the wind power plants which have correlation with other wind power plants in the region in the grids adjacent to the nodes in the grid network corresponding to the region.
Preferably, after correcting the wind speed forecast value of the node in the grid network corresponding to the area according to the wind speed observation value of the node in the grid network corresponding to the area, the method includes:
if the relative error between the wind speed forecast value of the node in the grid network corresponding to the area and the wind speed forecast value of the node in the grid network corresponding to the corrected area is less than 20%, the wind speed forecast value of the node in the grid network corresponding to the corrected area is issued; otherwise, the wind speed forecast value of the area corresponding to the nodes in the grid network before correction is issued.
Preferably, the relative error μ between the wind speed forecast value of the node in the grid network corresponding to the area and the wind speed forecast value of the node in the grid network corresponding to the corrected area is determined according to the following formula:
in the formula, VXForecasting the wind speed of the nodes in the grid network corresponding to the corrected area; vYAnd forecasting the wind speed of the nodes in the grid network corresponding to the area.
The invention provides a wind speed forecast correcting device for fusing observation data of a wind power plant, which is improved in that the device comprises:
a determination module: the grid network node observation value determination method comprises the steps of determining a wind speed observation value of a node in a grid network corresponding to an area according to a wind speed observation value of a wind farm in the area;
a correction module: and the wind speed forecast value of the node in the grid network corresponding to the area is corrected according to the wind speed observation value of the node in the grid network corresponding to the area.
Preferably, the determining module includes:
an acquisition unit: the method comprises the steps of acquiring a wind power plant which is in a correlative relation with other wind power plants in an area in a grid adjacent to nodes in a grid network corresponding to the area;
a determination unit: and the wind speed observation value of the node in the grid network corresponding to the area is determined according to the wind speed observation value of the wind power plant which has a correlation with other wind power plants in the area in the grid adjacent to the node in the grid network corresponding to the area.
Further, the obtaining unit is configured to:
if the similarity between various types of observation data of the ith wind farm in a grid range adjacent to the nodes in the grid network corresponding to the region and various types of observation data of the jth wind farm in other wind farms in the region is larger than a first similarity threshold corresponding to various types of observation data, a correlation exists between the ith wind farm and the jth wind farm, and the ith wind farm is obtained;
otherwise, the correlation does not exist between the ith wind power plant and the jth wind power plant, and the ith wind power plant is deleted;
wherein i ∈ (0, e); j ∈ (0, b); e is the total number of wind power plants in the grid range adjacent to the nodes in the grid network corresponding to the region; b is the total number of other wind power plants except the ith wind power plant in the region;
the categories of the observation data include: air temperature observation data, air pressure observation data, relative humidity observation data, wind speed observation data and precipitation observation data.
Further, the similarity of the observed data between the x-th type observed data of the ith wind farm and the x-th type observed data of the jth wind farm is determined according to the following formula
In the formula (I), the compound is shown in the specification,
the x-th type observation data of the ith wind power plant at the ith historical moment;
the ith type of observation data of the jth wind power plant at the ith historical moment; w is axA second similarity threshold for the x-th class of observed data; d is the total number of historical time instants.
Further, the determining unit is configured to:
determining a wind speed observed value V of a node in a grid network corresponding to the area according to the following formulaGZ：
In the formula etapThe weight occupied by the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region is obtained; vpThe wind speed observation value of the p-th wind power plant which has a correlation with other wind power plants in the area in the grid adjacent to the node in the grid network corresponding to the area is obtained; n is the total number of the wind power plants which are in a correlation relationship with other wind power plants in the region in grids adjacent to the nodes in the grid network corresponding to the region;
determining the weight eta occupied by the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region according to the following formulap：
In the formula, kpThe weighting proportion coefficient of the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region;
determining a weight proportion coefficient k of the p-th wind power plant which has a correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region according to the following formulap：
In the formula, LpAnd the distance between the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region and the nodes in the grid network corresponding to the region is obtained.
Preferably, the modification module is configured to:
if the number of the wind power plants which are in correlation with other wind power plants in the area in the grids adjacent to the nodes in the grid network corresponding to the area is not less than 5, correcting the wind speed forecast value of the nodes in the grid network corresponding to the area to 0.5VY+0.5VGZ(ii) a Otherwise, correcting the wind speed forecast value of the node in the grid network corresponding to the area to be (1-0.1 · k) VY+0.1·k·VGZ；
Wherein, VYForecasting the wind speed of the nodes in the grid network corresponding to the area; vGZThe wind speed observed value of the node in the grid network corresponding to the area is obtained; and k is the number of the wind power plants which have correlation with other wind power plants in the region in the grids adjacent to the nodes in the grid network corresponding to the region.
Preferably, the apparatus further comprises:
the release module is used for releasing the corrected wind speed forecast value of the node in the grid network corresponding to the corrected region if the relative error between the wind speed forecast value of the node in the grid network corresponding to the region and the corrected wind speed forecast value of the node in the grid network corresponding to the region is less than 20%; otherwise, the wind speed forecast value of the area corresponding to the nodes in the grid network before correction is issued.
Preferably, the relative error μ between the wind speed forecast value of the node in the grid network corresponding to the area and the wind speed forecast value of the node in the grid network corresponding to the corrected area is determined according to the following formula:
in the formula, VXForecasting the wind speed of the nodes in the grid network corresponding to the corrected area; vYAnd forecasting the wind speed of the nodes in the grid network corresponding to the area.
Compared with the closest prior art, the invention has the following beneficial effects:
the control method and the control device provided by the invention determine the wind speed observed value of the node in the grid network corresponding to the area according to the wind speed observed value of the wind farm in the area; correcting the wind speed forecast value of the node in the grid network corresponding to the area according to the wind speed observation value of the node in the grid network corresponding to the area, and correcting the forecast wind speed by using the observed data of the wind power station aiming at the requirement of the new energy power forecast on a wind speed forecasting technology.
Drawings
FIG. 1 is a flow chart of a wind speed forecast correction method for fusing observed data of a wind power plant provided by the invention;
FIG. 2 is a structural diagram of a wind speed forecast correcting device for fusing observed data of a wind farm according to the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a wind speed forecast correction method fusing observed data of a wind power plant, which comprises the following steps of:
determining a wind speed observation value of a node in a grid network corresponding to an area according to the wind speed observation value of a wind farm in the area;
in a preferred embodiment of the present invention, the nodes in the grid network corresponding to the area include: the method is characterized in that the global ground is divided by a grid network with 2.5 degrees multiplied by 2.5 degrees (longitude and latitude), each grid is used as a node of the network, the geographical areas occupied by the network nodes obtained by the space sampling are different, but the positions of the network nodes are the actual geographical positions of the meteorological observation stations due to the regularly distributed nodes.
And correcting the wind speed forecast value of the node in the grid network corresponding to the area according to the wind speed observation value of the node in the grid network corresponding to the area.
Specifically, the determining the wind speed observed value of the node in the grid network corresponding to the area according to the wind speed observed value of the wind farm in the area includes:
acquiring a wind power plant which has a correlation relation with other wind power plants in an area in a grid adjacent to a node in a grid network corresponding to the area;
and determining the wind speed observation value of the node in the grid network corresponding to the area according to the wind speed observation value of the wind power plant which has a correlation relation with other wind power plants in the area in the grid adjacent to the node in the grid network corresponding to the area.
Further, the acquiring a wind farm in which a grid adjacent to a node in a grid network corresponding to the area has a correlation with other wind farms in the area includes:
if the similarity between various types of observation data of the ith wind farm in a grid range adjacent to the nodes in the grid network corresponding to the region and various types of observation data of the jth wind farm in other wind farms in the region is larger than a first similarity threshold corresponding to various types of observation data, a correlation exists between the ith wind farm and the jth wind farm, and the ith wind farm is obtained;
otherwise, the correlation does not exist between the ith wind power plant and the jth wind power plant, and the ith wind power plant is deleted;
wherein i ∈ (0, e); j ∈ (0, b); e is the total number of wind power plants in the grid range adjacent to the nodes in the grid network corresponding to the region; b is the total number of other wind power plants except the ith wind power plant in the region;
if one observation data of the ith wind power plant and the jth wind power plant at the same time is missing, the similarity between the observation data of the ith wind power plant and the observation data of the jth wind power plant is considered to be 0;
the categories of the observation data include: air temperature observation data, air pressure observation data, relative humidity observation data, wind speed observation data and precipitation observation data.
In the optimal embodiment of the invention, forecast data and observation data in a relatively continuous and complete time period of at least more than 5 years are selected, the correctness, the real-time property, the repeatability, the file priority and the like of the selected data are checked, the repeated data and abnormal data are subjected to primary elimination and correction processing, the data are subjected to primary quality control, and finally complete and accurate forecast and observation data are obtained.
After the integrity of the time and space data is checked, the data needs to be further checked from 4 aspects of space-time error check, climate limit value check, station extreme value check, internal consistency check and the like.
Further, the similarity of the observed data between the x-th type observed data of the ith wind farm and the x-th type observed data of the jth wind farm is determined according to the following formula
In the formula (I), the compound is shown in the specification,
the x-th type observation data of the ith wind power plant at the ith historical moment;
the ith type of observation data of the jth wind power plant at the ith historical moment; w is axA second similarity threshold for the x-th class of observed data; d is the total number of historical moments;
in the preferred embodiment of the present invention, the first similarity threshold and the second similarity threshold between the x-th type observed data of the ith wind farm and the x-th type observed data of the jth wind farm are neither too small nor too large in terms of selection; thus preserving statistically significant strong similarity data while removing some weak similarity data (usually due to noise or measurement errors);
because, if the threshold value is selected too large, much data with reference value can be filtered out; if the threshold selection is too small, the data at the selection may contain too much redundant information, making further calculations difficult.
Further, determining the wind speed observation value of the node in the grid network corresponding to the area according to the wind speed observation value of the wind power plant having a correlation with other wind power plants in the area in the grid adjacent to the node in the grid network corresponding to the area includes:
determining a wind speed observed value V of a node in a grid network corresponding to the area according to the following formulaGZ：
In the formula etapThe weight occupied by the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region is obtained; vpThe wind speed observation value of the p-th wind power plant which has a correlation with other wind power plants in the area in the grid adjacent to the node in the grid network corresponding to the area is obtained; n is the total number of the wind power plants which are in a correlation relationship with other wind power plants in the region in grids adjacent to the nodes in the grid network corresponding to the region;
determining the weight eta occupied by the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region according to the following formulap：
In the formula, kpThe weighting proportion coefficient of the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region;
determining a weight proportion coefficient k of the p-th wind power plant which has a correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region according to the following formulap：
In the formula, LpAnd the distance between the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region and the nodes in the grid network corresponding to the region is obtained.
In the optimal embodiment of the invention, the smaller the distance between the p-th wind farm which has correlation with other wind farms in the region in the grid adjacent to the node in the grid network corresponding to the region and the node in the grid network corresponding to the region, the larger the corresponding weight proportion coefficient, and if the p-th wind farm which has correlation with other wind farms in the region in the grid adjacent to the node in the grid network corresponding to the region coincides with the node in the grid network corresponding to the region, the corresponding weight proportion coefficient is 1.
Specifically, the correcting the wind speed forecast value of the node in the grid network corresponding to the area according to the wind speed observation value of the node in the grid network corresponding to the area includes:
if the number of the wind power plants which are in correlation with other wind power plants in the area in the grids adjacent to the nodes in the grid network corresponding to the area is not less than 5, correcting the wind speed forecast value of the nodes in the grid network corresponding to the area to 0.5VY+0.5VGZ(ii) a Otherwise, correcting the wind speed forecast value of the node in the grid network corresponding to the area to be (1-0.1 · k) VY+0.1·k·VGZ；
Wherein, VYForecasting the wind speed of the nodes in the grid network corresponding to the area; vGZThe wind speed observed value of the node in the grid network corresponding to the area is obtained; and k is the number of the wind power plants which have correlation with other wind power plants in the region in the grids adjacent to the nodes in the grid network corresponding to the region.
Specifically, after correcting the wind speed forecast value of the node in the grid network corresponding to the area according to the wind speed observation value of the node in the grid network corresponding to the area, the method includes:
if the relative error between the wind speed forecast value of the node in the grid network corresponding to the area and the wind speed forecast value of the node in the grid network corresponding to the corrected area is less than 20%, the wind speed forecast value of the node in the grid network corresponding to the corrected area is issued; otherwise, the wind speed forecast value of the area corresponding to the nodes in the grid network before correction is issued.
Specifically, the relative error μ between the wind speed forecast value of the node in the grid network corresponding to the area and the wind speed forecast value of the node in the grid network corresponding to the corrected area is determined according to the following formula:
in the formula, VXForecasting the wind speed of the nodes in the grid network corresponding to the corrected area; vYAnd forecasting the wind speed of the nodes in the grid network corresponding to the area.
In the optimal embodiment of the invention, the wind speed forecast errors under different climatic conditions in the area have characteristic differences, so when the wind speed forecast values of the nodes in the grid network corresponding to the area are corrected,
first, the climate type at the node in the grid network corresponding to the area should be confirmed;
secondly, selecting various kinds of observation data at historical moments corresponding to the same climate types as those of the nodes in the grid network corresponding to the region to determine the wind power plants which are in the related relation with other wind power plants in the region in the grids adjacent to the nodes in the grid network corresponding to the region;
thirdly, determining wind speed observation data of the nodes in the grid network corresponding to the region according to the wind speed observation value of the wind power plant which has a correlation relation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region;
and finally, correcting the wind speed forecast data of the nodes in the grid network corresponding to the area by using the wind speed observation data of the nodes in the grid network corresponding to the area.
Different climatic conditions in the area can be divided into a strong wind period, a weak wind period and a moderate wind period according to the wind period; according to the weather types, the weather can be divided into continuous sunny weather, cold tide over-climate weather and heavy rainfall weather; the method can be divided into complex terrains and flat terrains according to terrains; therefore, there are 18 species in total, 2 × 3.
In the optimal embodiment of the invention, the technical scheme of the invention can be used for correcting the historical forecast data of the nodes in the grid network corresponding to the area, thereby enhancing the reliability of the historical forecast data of the nodes in the grid network corresponding to the area.
The invention also provides a wind speed forecast correcting device for fusing observed data of a wind power plant, as shown in fig. 2, the device comprises:
a determination module: the grid network node observation value determination method comprises the steps of determining a wind speed observation value of a node in a grid network corresponding to an area according to a wind speed observation value of a wind farm in the area;
a correction module: and the wind speed forecast value of the node in the grid network corresponding to the area is corrected according to the wind speed observation value of the node in the grid network corresponding to the area.
Specifically, the determining module includes:
an acquisition unit: the method comprises the steps of acquiring a wind power plant which is in a correlative relation with other wind power plants in an area in a grid adjacent to nodes in a grid network corresponding to the area;
a determination unit: and the wind speed observation value of the node in the grid network corresponding to the area is determined according to the wind speed observation value of the wind power plant which has a correlation with other wind power plants in the area in the grid adjacent to the node in the grid network corresponding to the area.
Specifically, the obtaining unit is configured to:
if the similarity between various types of observation data of the ith wind farm in a grid range adjacent to the nodes in the grid network corresponding to the region and various types of observation data of the jth wind farm in other wind farms in the region is larger than a first similarity threshold corresponding to various types of observation data, a correlation exists between the ith wind farm and the jth wind farm, and the ith wind farm is obtained;
otherwise, the correlation does not exist between the ith wind power plant and the jth wind power plant, and the ith wind power plant is deleted;
wherein i ∈ (0, e); j ∈ (0, b); e is the total number of wind power plants in the grid range adjacent to the nodes in the grid network corresponding to the region; b is the total number of other wind power plants except the ith wind power plant in the region;
the categories of the observation data include: air temperature observation data, air pressure observation data, relative humidity observation data, wind speed observation data and precipitation observation data.
Further, the similarity of the observed data between the x-th type observed data of the ith wind farm and the x-th type observed data of the jth wind farm is determined according to the following formula
In the formula (I), the compound is shown in the specification,
the x-th type observation data of the ith wind power plant at the ith historical moment;
the ith type of observation data of the jth wind power plant at the ith historical moment; w is axA second similarity threshold for the x-th class of observed data; d is the total number of historical time instants.
Further, the determining unit is configured to:
determining a wind speed observed value V of a node in a grid network corresponding to the area according to the following formulaGZ：
In the formula etapThe weight occupied by the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region is obtained; vpThe wind speed observation value of the p-th wind power plant which has a correlation with other wind power plants in the area in the grid adjacent to the node in the grid network corresponding to the area is obtained; n is the total number of the wind power plants which are in a correlation relationship with other wind power plants in the region in grids adjacent to the nodes in the grid network corresponding to the region;
determining the weight eta occupied by the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region according to the following formulap：
In the formula, kpThe weighting proportion coefficient of the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region;
determining a weight proportion coefficient k of the p-th wind power plant which has a correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region according to the following formulap：
In the formula, LpAnd the distance between the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region and the nodes in the grid network corresponding to the region is obtained.
Specifically, the correction module is configured to:
if the number of the wind power plants which are in correlation with other wind power plants in the area in the grids adjacent to the nodes in the grid network corresponding to the area is not less than 5, correcting the wind speed forecast value of the nodes in the grid network corresponding to the area to 0.5VY+0.5VGZ(ii) a Otherwise, correcting the wind speed forecast value of the node in the grid network corresponding to the area to be (1-0.1 · k) VY+0.1·k·VGZ；
Wherein, VYForecasting the wind speed of the nodes in the grid network corresponding to the area; vGZThe wind speed observed value of the node in the grid network corresponding to the area is obtained; and k is the number of the wind power plants which have correlation with other wind power plants in the region in the grids adjacent to the nodes in the grid network corresponding to the region.
Specifically, the apparatus further comprises:
the release module is used for releasing the corrected wind speed forecast value of the node in the grid network corresponding to the corrected region if the relative error between the wind speed forecast value of the node in the grid network corresponding to the region and the corrected wind speed forecast value of the node in the grid network corresponding to the region is less than 20%; otherwise, the wind speed forecast value of the area corresponding to the nodes in the grid network before correction is issued.
Specifically, the relative error μ between the wind speed forecast value of the node in the grid network corresponding to the area and the wind speed forecast value of the node in the grid network corresponding to the corrected area is determined according to the following formula:
in the formula, VXForecasting the wind speed of the nodes in the grid network corresponding to the corrected area; vYAnd forecasting the wind speed of the nodes in the grid network corresponding to the area.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (16)
1. A wind speed forecast correction method fusing observed data of a wind power plant is characterized by comprising the following steps:
determining a wind speed observation value of a node in a grid network corresponding to an area according to the wind speed observation value of a wind farm in the area;
and correcting the wind speed forecast value of the node in the grid network corresponding to the area according to the wind speed observation value of the node in the grid network corresponding to the area.
2. The method of claim 1, wherein determining the wind speed observations of the nodes in the grid network corresponding to the region from the wind speed observations of the wind farm in the region comprises:
acquiring a wind power plant which has a correlation relation with other wind power plants in an area in a grid adjacent to a node in a grid network corresponding to the area;
and determining the wind speed observation value of the node in the grid network corresponding to the area according to the wind speed observation value of the wind power plant which has a correlation relation with other wind power plants in the area in the grid adjacent to the node in the grid network corresponding to the area.
3. The method of claim 2, wherein obtaining a wind farm for which there is a correlation with other wind farms in the area in a grid adjacent to a node in the grid network corresponding to the area comprises:
if the similarity between various types of observation data of the ith wind farm in a grid range adjacent to the nodes in the grid network corresponding to the region and various types of observation data of the jth wind farm in other wind farms in the region is larger than a first similarity threshold corresponding to various types of observation data, a correlation exists between the ith wind farm and the jth wind farm, and the ith wind farm is obtained;
otherwise, the correlation does not exist between the ith wind power plant and the jth wind power plant, and the ith wind power plant is deleted;
wherein i ∈ (0, e); j ∈ (0, b); e is the total number of wind power plants in the grid range adjacent to the nodes in the grid network corresponding to the region; b is the total number of other wind power plants except the ith wind power plant in the region;
the categories of the observation data include: air temperature observation data, air pressure observation data, relative humidity observation data, wind speed observation data and precipitation observation data.
4. The method of claim 3, wherein the similarity of observed data between class x observed data for the ith wind farm and class x observed data for the jth wind farm is determined as follows
In the formula (I), the compound is shown in the specification,
the x-th type observation data of the ith wind power plant at the ith historical moment;
the ith type of observation data of the jth wind power plant at the ith historical moment; w is axA second similarity threshold for the x-th class of observed data; d is the total number of historical time instants.
5. The method of claim 2, wherein determining the wind speed observations for the nodes in the grid network corresponding to the region based on the wind speed observations for the wind farm that has a correlation with other wind farms in the region in a grid adjacent to the nodes in the grid network corresponding to the region comprises:
determining a wind speed observed value V of a node in a grid network corresponding to the area according to the following formulaGZ：
In the formula etapThe weight occupied by the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region is obtained; vpThe wind speed observation value of the p-th wind power plant which has a correlation with other wind power plants in the area in the grid adjacent to the node in the grid network corresponding to the area is obtained; n is the total number of the wind power plants which are in a correlation relationship with other wind power plants in the region in grids adjacent to the nodes in the grid network corresponding to the region;
determining the weight eta occupied by the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region according to the following formulap：
In the formula, kpThe weighting proportion coefficient of the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region;
determining a weight proportion coefficient k of the p-th wind power plant which has a correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region according to the following formulap：
In the formula, LpAnd the distance between the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region and the nodes in the grid network corresponding to the region is obtained.
6. The method of claim 1, wherein the modifying the wind speed forecast values for the nodes in the area-specific grid network based on the wind speed observations for the nodes in the area-specific grid network comprises:
if the number of the wind power plants which are in correlation with other wind power plants in the area in the grids adjacent to the nodes in the grid network corresponding to the area is not less than 5, correcting the wind speed forecast value of the nodes in the grid network corresponding to the area to 0.5VY+0.5VGZ(ii) a Otherwise, correcting the wind speed forecast value of the node in the grid network corresponding to the area to be (1-0.1 · k) VY+0.1·k·VGZ；
Wherein, VYForecasting the wind speed of the nodes in the grid network corresponding to the area; vGZThe wind speed observed value of the node in the grid network corresponding to the area is obtained; and k is the number of the wind power plants which have correlation with other wind power plants in the region in the grids adjacent to the nodes in the grid network corresponding to the region.
7. The method of claim 1, wherein after correcting the wind speed forecasted values for the nodes in the area-corresponding grid network based on the wind speed observations for the nodes in the area-corresponding grid network, comprising:
if the relative error between the wind speed forecast value of the node in the grid network corresponding to the area and the wind speed forecast value of the node in the grid network corresponding to the corrected area is less than 20%, the wind speed forecast value of the node in the grid network corresponding to the corrected area is issued; otherwise, the wind speed forecast value of the area corresponding to the nodes in the grid network before correction is issued.
8. The method of claim 1, wherein the relative error μ between the wind speed forecast values for the nodes in the grid network for the region corresponding to the corrected region is determined as follows:
in the formula, VXForecasting the wind speed of the nodes in the grid network corresponding to the corrected area; vYAnd forecasting the wind speed of the nodes in the grid network corresponding to the area.
9. A wind speed forecast correction device fusing observed data of a wind power plant is characterized by comprising the following components:
a determination module: the grid network node observation value determination method comprises the steps of determining a wind speed observation value of a node in a grid network corresponding to an area according to a wind speed observation value of a wind farm in the area;
a correction module: and the wind speed forecast value of the node in the grid network corresponding to the area is corrected according to the wind speed observation value of the node in the grid network corresponding to the area.
10. The apparatus of claim 9, wherein the determining module comprises:
an acquisition unit: the method comprises the steps of acquiring a wind power plant which is in a correlative relation with other wind power plants in an area in a grid adjacent to nodes in a grid network corresponding to the area;
a determination unit: and the wind speed observation value of the node in the grid network corresponding to the area is determined according to the wind speed observation value of the wind power plant which has a correlation with other wind power plants in the area in the grid adjacent to the node in the grid network corresponding to the area.
11. The apparatus of claim 10, wherein the obtaining unit is to:
if the similarity between various types of observation data of the ith wind farm in a grid range adjacent to the nodes in the grid network corresponding to the region and various types of observation data of the jth wind farm in other wind farms in the region is larger than a first similarity threshold corresponding to various types of observation data, a correlation exists between the ith wind farm and the jth wind farm, and the ith wind farm is obtained;
otherwise, the correlation does not exist between the ith wind power plant and the jth wind power plant, and the ith wind power plant is deleted;
wherein i ∈ (0, e); j ∈ (0, b); e is the total number of wind power plants in the grid range adjacent to the nodes in the grid network corresponding to the region; b is the total number of other wind power plants except the ith wind power plant in the region;
the categories of the observation data include: air temperature observation data, air pressure observation data, relative humidity observation data, wind speed observation data and precipitation observation data.
12. The apparatus of claim 11, wherein the similarity of observed data between class x observed data for the ith wind farm and class x observed data for the jth wind farm is determined as follows
In the formula (I), the compound is shown in the specification,
the x-th type observation data of the ith wind power plant at the ith historical moment;
the ith type of observation data of the jth wind power plant at the ith historical moment; w is axA second similarity threshold for the x-th class of observed data; d is the total number of historical time instants.
13. The apparatus of claim 10, wherein the determination unit is to:
determining a wind speed observed value V of a node in a grid network corresponding to the area according to the following formulaGZ：
In the formula etapThe weight occupied by the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region is obtained; vpIs a regionCorresponding to a wind speed observation value of a p-th wind power plant which has a correlation with other wind power plants in the area in grids adjacent to the nodes in the grid network; n is the total number of the wind power plants which are in a correlation relationship with other wind power plants in the region in grids adjacent to the nodes in the grid network corresponding to the region;
determining the weight eta occupied by the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region according to the following formulap：
In the formula, kpThe weighting proportion coefficient of the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region;
determining a weight proportion coefficient k of the p-th wind power plant which has a correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region according to the following formulap：
In the formula, LpAnd the distance between the p-th wind power plant which has correlation with other wind power plants in the region in the grid adjacent to the nodes in the grid network corresponding to the region and the nodes in the grid network corresponding to the region is obtained.
14. The apparatus of claim 9, wherein the modification module is to:
if the number of the wind power plants which are in correlation with other wind power plants in the area in the grids adjacent to the nodes in the grid network corresponding to the area is not less than 5, correcting the wind speed forecast value of the nodes in the grid network corresponding to the area to 0.5VY+0.5VGZ(ii) a Otherwise, forecasting the wind speed of the node in the grid network corresponding to the areaCorrected to (1-0.1. k) VY+0.1·k·VGZ；
Wherein, VYForecasting the wind speed of the nodes in the grid network corresponding to the area; vGZThe wind speed observed value of the node in the grid network corresponding to the area is obtained; and k is the number of the wind power plants which have correlation with other wind power plants in the region in the grids adjacent to the nodes in the grid network corresponding to the region.
15. The apparatus of claim 9, wherein the apparatus further comprises:
the release module is used for releasing the corrected wind speed forecast value of the node in the grid network corresponding to the corrected region if the relative error between the wind speed forecast value of the node in the grid network corresponding to the region and the corrected wind speed forecast value of the node in the grid network corresponding to the region is less than 20%; otherwise, the wind speed forecast value of the area corresponding to the nodes in the grid network before correction is issued.
16. The apparatus of claim 9, wherein the relative error μ between the wind speed forecast values for the nodes in the grid network for the region corresponding to the corrected region is determined as follows:
in the formula, VXForecasting the wind speed of the nodes in the grid network corresponding to the corrected area; vYAnd forecasting the wind speed of the nodes in the grid network corresponding to the area.
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