CN112561149A - Wind power prediction method based on power downscaling data fusion and statistical model - Google Patents
Wind power prediction method based on power downscaling data fusion and statistical model Download PDFInfo
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Abstract
The invention discloses a wind power prediction method based on power downscaling data fusion and a statistical model, which specifically comprises the following steps: step 1, performing power downscaling on historical reported wind speed of NWP through a regional climate mode RegCM to obtain a fine-scale wind speed; step 2, comparing with historical observation data, determining a credibility weight factor of historical wind speed data before and after power downscaling, and establishing a data fusion function model; step 3, obtaining historical wind speed according to the data fusion function model established in the step 2, and establishing an S-shaped curve-like model by combining historical wind power generation power data; and 4, obtaining a predicted wind speed according to the data fusion function model established in the step 2, and predicting future generating power by combining the S-shaped curve-like model established in the step 3.
Description
Technical Field
The invention belongs to the technical field of wind power, and particularly relates to a wind power prediction method based on power downscaling data fusion and a statistical model.
Background
With the rapid development of global economy, the demand for energy is also increasing. The development of renewable green energy has become a necessary direction of future human energy strategies. Wind energy is now considered a fundamental alternative to the combustion of fossil energy and is beginning to be widely used in our daily lives. However, the fluctuation of wind energy is large compared to fossil energy. When large-scale wind power is connected to a power grid, the safety scheduling of the power grid is greatly influenced. Therefore, wind power prediction is crucial to the reliability of the power system.
The current wind power prediction method can be divided into a physical method and a statistical method.
The principle of the physical method is to predict wind using numerical weather forecast (NWP) and physical information around the wind turbine. A method of generating a physical system of electricity generation, for example based on incremental self-organizing neural networks and exploratory data analysis techniques; or predicting meteorological parameters by using the NWP model and the artificial neural network. However, physical methods require a relatively deep knowledge of the meteorological knowledge and physical characteristics of the wind farm and the complexity of designing for a particular wind farm is very high. Therefore, most wind farms use statistical learning methods to predict wind power. According to historical data and NWP, a statistical learning method is adopted to predict wind power of a wind field, and a statistical model usually adopts online measurement, so that a better prediction method is provided for wind forecasting. Various prediction models applied to the field of wind power generation have advantages and disadvantages, and the prediction performance obtained by a single prediction model is often difficult to achieve the best. The combined prediction model is established by combining the advantages of a plurality of models, the prediction performance is improved, and the method is an important research direction for wind power prediction research.
At present, the hybrid methods can integrate the advantages of all methods and establish a more accurate prediction model. However, most of the current prediction methods rely on single NWP, and the grid resolution of meteorological information which can be provided currently in China is rough, so that certain uncertainty exists in the forecasted wind speed, and the uncertainty of wind power forecasting is increased. Therefore, the accuracy of wind speed forecasting is improved by combining the power downscaling technology and the data fusion technology, and the wind speed forecasting is coupled into a statistical model to find out a wind power forecasting method with a more credible forecasting result so as to better provide effective guidance for the safe dispatching of the power system.
Disclosure of Invention
The invention aims to provide a wind power prediction method based on power downscaling data fusion and a statistical model, and aims to solve the problems that the accuracy is low when the wind speed is predicted by numerical weather forecast NWP, the corresponding relation is inaccurate when the wind speed and the electric power corresponding relation are predicted by a coupling physics and statistical method at present.
In order to achieve the purpose, the invention provides the following technical scheme: the wind power prediction method based on the power downscaling data fusion and the statistical model specifically comprises the following steps:
step 1, performing power downscaling on historical reported wind speed of NWP through a regional climate mode RegCM to obtain a fine-scale wind speed;
step 2, comparing with historical observation data, determining a credibility weight factor of historical wind speed data before and after power downscaling, and establishing a data fusion function model;
step 3, obtaining historical wind speed according to the data fusion function model established in the step 2, and establishing an S-shaped curve-like model by combining historical wind power generation power data;
and 4, obtaining a predicted wind speed according to the data fusion function model established in the step 2, and predicting future power generation power by combining the S-shaped curve-like model established in the step 3.
Preferably, the step 1 specifically includes the following steps:
step 11, collecting a historical reported wind speed field, a gas temperature field, a gas pressure field and a relative humidity field output by numerical prediction NWP;
step 12, taking the NWP output field as an input field of a regional climate mode RegCM, operating the mode by using a supercomputer, and outputting a wind speed field with higher resolution;
and step 13, determining the position of the wind motor, interpolating the wind speed fields before and after the power downscaling by utilizing bilinear interpolation, and giving the wind speed values before and after the power downscaling at the wind outlet motor.
Preferably, the step 2 specifically includes the following steps:
step 21, recording the historical reported wind speed of the non-downscaling single power generation fan as Vh1The corresponding credibility weighting factor is w1, and the historical return wind speed after the down-scaling is Vh2If the confidence weighting factor is w2, w1+ w2 is 1 (1);
step 22, recording the historical observation wind speed at a single power generation fan as VhAnd according to Vh1And Vh2And VhThe confidence weighting factor is determined based on the degree of similarity, and the degree of similarity is,
step 23, according to the similarity degree between the two historical reported wind speeds obtained in step 22 and the historical observed wind speed, the weighting factor w1 is,
solving a credibility weight factor by using a joint formula (1) and a formula (4);
and step 24, setting the wind speed after the two historical return wind speeds are fused as V, then,
V＝w1×Vh1+w2×Vh2(5)，
and (5) establishing a data fusion function model according to the formula (5) and fusing data of the future forecast wind speed before and after the downscaling.
Preferably, the step 3 specifically includes the following steps:
step 31, according to the property of the wind turbine, the output power at any moment and the wind speed at the corresponding moment are calculated in the following relation,
step 32, obtaining historical wind speed according to the data fusion function model established in the step 2, training a similar S-shaped curve by combining historical wind power generation power data, setting the similar S-shaped curve as,
step 33, fitting the S-like curve by using MATLAB software, obtaining a coefficient to be determined, obtaining a function expression of the S-like curve, substituting the function expression into the expression obtained in step 31, obtaining a P-V statistical prediction model,
preferably, the step 4 specifically includes the following steps:
step 41, carrying out power downscaling on the wind speed predicted by the NWP by using the regional climate mode RegCM, and fusing data before and after the power downscaling to obtain a predicted wind speed v in the future of the wind turbine;
and 42, bringing the predicted wind speed V into a P-V statistical prediction model to obtain the generated power P of the wind turbine at the future moment.
Preferably, in step 22, R1 and R2 represent the two historical reported wind speeds and the two historical records, respectivelyObserving the similarity degree of wind speed by history;
respectively represent two historical reported wind speeds, and
representing the average of observed wind speeds.
Preferably, in the step 31, p (V) is the output power of the wind turbine at the wind speed V, and S is the output power of the S-like curve model to be constructed; vCutting intoFor wind motor cut-in speed, VRated valueAt a rated speed, VCutting outFor cutting out the speed, and Pmax is the maximum output power of the wind turbine.
Preferably, in the step 32, e is a natural index, and a, b, c and d are undetermined coefficients; v is input wind speed, the input wind speed is between cut-in wind speed and rated wind speed of the wind turbine, and S is output power.
The wind power prediction method based on the power downscaling data fusion and the statistical model has the technical effects and advantages that:
1. when the wind speed before and after the power downscaling is subjected to data fusion, the downscaling obtains wind field data with finer resolution, the credibility weighting factor is larger, so that the wind speed after the data fusion is closer to the real wind speed, the influence of the original predicted wind speed is considered, and the wind speed at the wind field can be predicted better;
2. the constructed S-like curve function is the S-like curve function, and the function is obtained by MATLAB software fitting and better accords with the one-to-one correspondence relationship between the wind speed and the power than the S-like curve function;
3. the power downscaling data fusion and the statistical model are combined, uncertainty of numerical weather forecast wind speed is avoided, and meanwhile the corresponding relation between the wind speed and the generated power in the constructed prediction model is more accurate.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to fig. 1 in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. 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 power prediction method based on power downscaling data fusion and a statistical model, as shown in FIG. 1, and the method comprises the following steps:
step 1, carrying out power downscaling on historical reported wind speed of NWP through a regional climate mode RegCM to obtain wind speed with finer scale, and well simulating important climate events of east Asia seasonal wind regions, especially space-time evolution characteristics of east Asia seasonal rain zones through the application of the regional climate mode RegCM, so that the method has prediction capability on climate of China regions.
And 2, determining the credibility weight factors of the historical wind speed data before and after the power downscaling through comparison with the historical observation data, and establishing a data fusion function model.
And 3, obtaining historical wind speed according to the data fusion function model in the step 2, and establishing an S-shaped curve-like model by combining historical wind power generation power data.
And 4, obtaining a predicted wind speed according to the data fusion function model in the step 2, and predicting the future generated power by combining the S-shaped curve-like model established in the step 3.
In step 1 of the wind power prediction method based on the power downscaling data fusion and the statistical model, the power downscaling is performed on the historical return wind speed of the NWP through the regional climate mode RegCM to obtain a wind speed with finer scale, and the specific steps are as follows:
step 11, collecting a historical reported wind speed field, a gas temperature field, a gas pressure field and a relative humidity field output by numerical prediction NWP;
step 12, taking the NWP output field as an input field of a regional climate mode RegCM, operating the mode by using a supercomputer, and outputting a wind speed field with higher resolution;
and step 13, determining the position of the wind motor, interpolating the wind speed fields before and after the power downscaling by utilizing bilinear interpolation, and giving the wind speed values before and after the power downscaling at the wind outlet motor.
In step 2 of the wind power prediction method based on the power downscaling data fusion and statistical model, the credibility weight factors of the historical wind speed data before and after the power downscaling are determined through comparison with the historical observation data, and the specific steps of establishing the data fusion function model are as follows:
step 21, recording the historical reported wind speed of the non-downscaling single power generation fan as Vh1The corresponding credibility weighting factor is w1, and the historical return wind speed after the down-scaling is Vh2If the confidence weighting factor is w2, w1+ w2 is 1 (1).
Step 22, recording the historical observation wind speed at a single power generation fan as VhAccording to Vh1And Vh2And VhThe confidence weighting factor is determined based on the degree of similarity, and the degree of similarity is,
in formulae (2) and (3): r1 and R2 are the similarity degrees of the two historical return wind speeds and the historical observed wind speed respectively;
respectively represent two historical reported wind speeds, and
representing the average of observed wind speeds.
Step 23, two historical returns obtained according to step 22The similarity degree of the wind speed and the historical observed wind speed is determined, and the weighting factor w1 is determined according to the following formula, so that the weighting factor w1 is:
solving a credibility weight factor by the joint formula (1) and the formula (4);
and step 24, setting the wind speed after the two historical return wind speeds are fused as V, then,
V＝w1×Vh1+w2×Vh2(5)，
a data fusion function model is established according to the formula (5), and the method can be used for data fusion of future forecast wind speeds before and after downscaling, and the credibility weight of the downscaled wind speed field is relatively large due to the fact that the downscaled wind speed field scale is finer.
In step 3 of the wind power prediction method based on the power downscaling data fusion and statistical model, the historical wind speed is obtained according to the data fusion function model in step 2, and the specific steps of establishing the S-shaped curve-like model by combining the historical wind power generation power data are as follows:
step 31, in the wind power prediction of the wind power plant, according to the property of the wind turbine generator, the calculation relationship between the output power at any moment and the wind speed at the corresponding moment is,
in the formula (6), p (V) is the output power of the wind turbine at the wind speed V, and S is the output power of the S-like curve model to be constructed; vCutting intoIs the cut-in speed, V, of the wind turbineRated valueRated speed, V, of the wind turbineCutting outThe wind turbine is cut-out speed, Pmax is the maximum power output by the wind turbine, and the value of the numerical value is related to the property of the wind turbine and is a constant value.
Step 32, training a similar S-shaped curve by using the historical wind speed obtained by the data fusion function model established in the step 2 and combining the historical wind turbine generated power data, setting the similar S-shaped curve as,
in the formula (7), e is a natural index, a, b, c and d are undetermined coefficients, V is an input wind speed, the input wind speed is between the cut-in wind speed and the rated wind speed of the wind turbine, and S is output power.
Step 33, fitting the S-like curve by using MATLAB software, solving the coefficient to be determined, obtaining a function expression of the S-like curve, substituting the function expression into the formula (6), obtaining a P-V statistical prediction model,
the formula (8) is the P-V statistical prediction model.
In step 4 of the wind power prediction method based on the power downscaling data fusion and statistical model, the predicted wind speed is obtained according to the data fusion function model in step 2, and the specific steps of predicting the future generated power by combining the S-shaped curve-like model established in step 3 are as follows:
step 41, carrying out power downscaling on the wind speed predicted by the NWP by using the regional climate mode RegCM, and fusing data before and after the power downscaling to obtain a predicted wind speed v in the future of the wind turbine;
and step 42, substituting the obtained predicted wind speed v into the formula (8) to obtain the generated power P of the wind turbine at the future moment.
The method mainly solves the defects that the accuracy is low when the wind speed is predicted by the numerical weather forecast NWP, the corresponding relation is not accurate when the corresponding relation between the wind speed and the electric power is predicted by a coupling physics and statistical method and the like at present, provides the wind power prediction method based on the power downscaling data fusion and the statistical model, can more accurately predict the wind speed at the wind turbine, and enables the wind speed and the electric power to be more accurately predicted by the coupling physics and statistical method.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (8)
1. The wind power prediction method based on the power downscaling data fusion and the statistical model is characterized by comprising the following steps:
step 1, performing power downscaling on historical reported wind speed of NWP through a regional climate mode RegCM to obtain a fine-scale wind speed;
step 2, comparing with historical observation data, determining a credibility weight factor of historical wind speed data before and after power downscaling, and establishing a data fusion function model;
step 3, obtaining historical wind speed according to the data fusion function model established in the step 2, and establishing an S-shaped curve-like model by combining historical wind power generation power data;
and 4, obtaining a predicted wind speed according to the data fusion function model established in the step 2, and predicting future power generation power by combining the S-shaped curve-like model established in the step 3.
2. The wind power prediction method based on the power downscaling data fusion and the statistical model according to claim 1, wherein the step 1 specifically includes the following steps:
step 11, collecting a historical reported wind speed field, a gas temperature field, a gas pressure field and a relative humidity field output by numerical prediction NWP;
step 12, taking the NWP output field as an input field of a regional climate mode RegCM, operating the mode by using a supercomputer, and outputting a wind speed field with higher resolution;
and step 13, determining the position of the wind motor, interpolating the wind speed fields before and after the power downscaling by utilizing bilinear interpolation, and giving the wind speed values before and after the power downscaling at the wind outlet motor.
3. The wind power prediction method based on the power downscaling data fusion and the statistical model according to claim 1, wherein the step 2 specifically includes the following steps:
step 21, recording the historical reported wind speed of the non-downscaling single power generation fan as Vh1The corresponding credibility weighting factor is w1, and the historical return wind speed after the down-scaling is Vh2If the confidence weighting factor is w2, w1+ w2 is 1 (1);
step 22, recording the historical observation wind speed at a single power generation fan as VhAnd according to Vh1And Vh2And VhThe confidence weighting factor is determined based on the degree of similarity, and the degree of similarity is,
step 23, according to the similarity degree between the two historical reported wind speeds obtained in step 22 and the historical observed wind speed, the weighting factor w1 is,
solving a credibility weight factor by using a joint formula (1) and a formula (4);
and step 24, setting the wind speed after the two historical return wind speeds are fused as V, then,
V＝w1×Vh1+w2×Vh2 (5)，
and (5) establishing a data fusion function model according to the formula (5) and fusing data of the future forecast wind speed before and after the downscaling.
4. The wind power prediction method based on the power downscaling data fusion and the statistical model according to claim 1, wherein the step 3 specifically includes the following steps:
step 31, according to the property of the wind turbine, the output power at any moment and the wind speed at the corresponding moment are calculated in the following relation,
step 32, obtaining historical wind speed according to the data fusion function model established in the step 2, training a similar S-shaped curve by combining historical wind power generation power data, setting the similar S-shaped curve as,
step 33, fitting the S-like curve by using MATLAB software, obtaining a coefficient to be determined, obtaining a function expression of the S-like curve, substituting the function expression into the expression obtained in step 31, obtaining a P-V statistical prediction model,
5. the wind power prediction method based on the power downscaling data fusion and the statistical model according to claim 1, wherein the step 4 specifically includes the following steps:
step 41, carrying out power downscaling on the wind speed predicted by the NWP by using the regional climate mode RegCM, and fusing data before and after the power downscaling to obtain a predicted wind speed v in the future of the wind turbine;
and 42, bringing the predicted wind speed V into a P-V statistical prediction model to obtain the generated power P of the wind turbine at the future moment.
6. The wind power prediction method based on the power downscaling data fusion and statistical model according to claim 3, characterized in that: in step 22, R1 and R2 are the similarity between the two historical return wind speeds and the historical observed wind speed respectively;
7. The wind power prediction method based on the power downscaling data fusion and statistical model according to claim 4, characterized in that: in the step 31, p (V) is the output power of the wind turbine at the wind speed V, and S is the output power of the S-like curve model to be constructed;
Vcutting intoFor wind motor cut-in speed, VRated valueAt a rated speed, VCutting outFor cutting out the speed, and Pmax is the maximum output power of the wind turbine.
8. The wind power prediction method based on the power downscaling data fusion and statistical model according to claim 4, characterized in that: in the step 32, e is a natural index, and a, b, c and d are undetermined coefficients;
v is input wind speed, the input wind speed is between cut-in wind speed and rated wind speed of the wind turbine, and S is output power.
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