CN111047076A - Photovoltaic short-term power forecasting method based on sunflower number 8 satellite cloud picture - Google Patents

Photovoltaic short-term power forecasting method based on sunflower number 8 satellite cloud picture Download PDF

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CN111047076A
CN111047076A CN201911149151.2A CN201911149151A CN111047076A CN 111047076 A CN111047076 A CN 111047076A CN 201911149151 A CN201911149151 A CN 201911149151A CN 111047076 A CN111047076 A CN 111047076A
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韩自奋
李晓纯
李扬
于娟
吕清泉
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Lanzhou Dafang Electronic Co ltd
State Grid Jiuquan Power Supply Co
State Grid Gansu Electric Power Co Ltd
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Abstract

The invention discloses a photovoltaic short-term power forecasting method based on a sunflower number 8 satellite cloud picture, which relates to the technical field of photovoltaic power generation and distribution and comprises the following steps of S1: calculating theoretical radiation of each power plant based on the geographical position and environment of each power plant; s2: calculating the total solar radiation on the inclined plane according to the theoretical radiation of each power plant; s3: collecting sunflower number 8 satellite cloud picture monitoring data every 1-5 days, and preprocessing the collected data; s4: carrying out cloud quantity inversion by utilizing the preprocessed data, establishing a neural network and a wavelet analysis prediction cloud quantity model, and predicting future cloud quantity; s5: calculating cloud cover coefficients according to the cloud amount obtained through prediction; s6: calculating to obtain effective total radiation according to the total solar radiation and the cloud shading coefficient; s7: the method has the advantages of simple method and high prediction accuracy.

Description

Photovoltaic short-term power forecasting method based on sunflower number 8 satellite cloud picture
Technical Field
The invention relates to the technical field of photovoltaic power generation and distribution, in particular to a photovoltaic short-term power forecasting method based on a sunflower number 8 satellite cloud picture.
Background
Solar photovoltaic power generation is a power generation form for directly converting solar radiation energy into electric energy by utilizing the photovoltaic effect of a solar cell, at the present stage, the popularization and application of solar energy increasingly present an unprecedented world trend, and the solar industry becomes one of new energy industries which are briskly raised globally. The development and utilization of clean, safe and environment-friendly solar energy become a social choice for relieving the increasingly severe energy shortage and a strong force for treating severe environmental pollution in the human society.
The stable operation of the power grid needs to keep a certain balance between the supply and demand parties, namely, the starting and the stopping of generating sets such as thermal power, hydropower and the like are arranged in advance according to the consumption change of a user, so that the total power supplied is adjusted correspondingly, the photovoltaic power generation is greatly influenced by weather and cannot be freely controlled like the thermal power and the hydropower, so that the output power generated by the photovoltaic power station has the characteristics of violent change, intermittence and the like, therefore, the photovoltaic power station is merged into the power grid to have great influence on the balance of the power grid, the output power of the photovoltaic power station is effectively predicted, the output power of the photovoltaic power station is brought into a power generation plan of the power grid and participates in real-time scheduling, the automatic control of the power generation operation can be realized, and the multi.
However, the difference between a short-term power prediction curve and an actual curve in the conventional photovoltaic prediction system is large, the prediction deviation is large, and particularly, the error is large and the accuracy is low in a rainy day or a sudden change process of weather.
Disclosure of Invention
The invention aims to: the invention provides a photovoltaic short-term power forecasting method based on a sunflower number 8 satellite cloud picture, and aims to solve the problems that the difference between a short-term power forecasting curve and an actual curve in a photovoltaic forecasting system is large and the forecasting accuracy is low.
The invention specifically adopts the following technical scheme for realizing the purpose:
a photovoltaic short-term power forecasting method based on a sunflower No. 8 satellite cloud picture comprises the following steps:
s1: calculating theoretical radiation of each power plant based on the geographical position and environment of each power plant;
s2: calculating the total solar radiation on the inclined plane according to the theoretical radiation of each power plant;
s3: collecting satellite monitoring data every 1-5 days, and preprocessing the collected data;
s4: carrying out cloud quantity inversion by utilizing the preprocessed data, establishing a neural network and a wavelet analysis prediction cloud quantity model, and predicting future cloud quantity;
s5: calculating cloud cover coefficients according to the cloud amount obtained through prediction;
s6: calculating to obtain effective total radiation according to the total solar radiation and the cloud shading coefficient;
s7: the effective total radiation is converted into power, and the predicted power is obtained.
Further, the geographical locations and environments of the power plants in S1 include: the longitude and latitude, the altitude, the solar declination angle, the solar hour angle, the atmosphere transparency and the atmosphere quality of each power plant.
Further, the theoretical radiation includes:
horizontal plane direct solar radiation intensity:
Ebh＝E0×Tb×sinh
wherein E0Indicating the intensity of solar radiation, T, at any moment of the upper boundary of the atmospherebThe direct radiation atmospheric transparency coefficient is represented, and h represents the solar altitude;
horizontal plane solar scattered radiation intensity:
Figure BDA0002283054350000021
wherein k is2A parameter representing the atmospheric turbidity degree, m (z, h) represents the altitude z, and the atmospheric pressure is corrected when the solar altitude h is higher than the altitude z;
total solar radiation intensity in the horizontal plane:
Eth＝Ebh+Edh。
further, the total solar radiation on the inclined plane in S2 is:
Et＝Ebh×Rb+Edh×Rd+Eth×Rρ
wherein R isbAs a direct radiation correction factor, RdAs a scattered radiation correction factor, RρIs a ground reflection correction factor.
Further, the preprocessing the acquired data in S3 includes: time zone conversion, geometric correction, radiation correction and clipping.
Further, the S4 specifically includes:
s4.1: carrying out cloud quantity inversion by using the wave band for detecting cloud quantity to form cloud quantity data, and checking an inversion result;
s4.2: performing quality control on the acquired error data, interpolating the cloud data time resolution from 10 minutes to 15 minutes, and performing nearest point interpolation processing on the missing data;
s4.3: and (4) predicting the short-term cloud cover by utilizing a neural network and wavelet analysis prediction cloud cover model.
Further, the cloud coverage coefficient in S5 includes:
direct radiation cloud coverage coefficient:
α＝(300-Y)/100
wherein Y is the cloud amount with the time resolution of 15 minutes;
scattering radiation cloud coverage coefficient:
β＝1-2α。
further, the effective total radiation in S6 is:
E＝Etbh×α+Etdh×β
wherein E istbhIs direct radiation on an inclined plane, EtdhIs the scattered radiation on the inclined plane.
Further, in S7, the effective total radiation is converted into power, and the calculation formula is:
Figure BDA0002283054350000031
the method comprises the following steps of obtaining a capacity of a unit, obtaining a weak radiation reduction coefficient, obtaining b a temperature reduction coefficient, obtaining c an efficiency coefficient when the inverter unit is started or stopped, and obtaining K an inverter conversion efficiency constant value.
The invention has the following beneficial effects:
1. according to the method, theoretical radiation of each power plant is calculated through the geographic position and the environment of each power plant, astronomical radiation is corrected, the cloud cover extracted from satellite monitoring data is used for radiation forecasting, the cloud cover downscaling error of a numerical mode is changed, the forecasting accuracy is improved, and in order to improve power forecasting in rainy days, a wavelet function is used as a weight function of a neural network, so that the forecasted short-term cloud cover is more accurate, and the forecasting accuracy is further improved.
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FIG. 1 is a schematic flow diagram of the process of the present invention.
Detailed Description
For a better understanding of the present invention by those skilled in the art, the present invention will be described in further detail below with reference to the accompanying drawings and the following examples.
Example 1
As shown in fig. 1, the present embodiment provides a photovoltaic short-term power forecasting method based on a sunflower number 8 satellite cloud, which includes the following steps:
s1: based on the geographical location and environment of each power plant, including: calculating theoretical radiation of each power plant according to longitude and latitude, altitude, solar declination angle, solar hour angle, atmosphere transparency and atmosphere quality of each power plant;
the calculation formula of the declination angle of the sun is as follows:
Figure BDA0002283054350000032
n is the accumulated days arranged according to the day sequence;
the calculation formula of the solar time angle is as follows:
Figure BDA0002283054350000033
t is Beijing time, psi is local longitude;
the calculation formula of the solar altitude angle is as follows:
Figure BDA0002283054350000034
Figure BDA0002283054350000035
is the geographic latitude;
the calculation formula of the solar radiation intensity at any moment of the upper boundary of the atmosphere is as follows:
Figure BDA0002283054350000041
n is the cumulative days in the order of days, EscIs the sun constant, Esc＝1367w/m2；
When the solar altitude h is more than or equal to 30 degrees, the atmospheric mass m (h) is 1/sinh;
when the solar altitude h is less than 30 degrees, the atmospheric mass m (h) is [1229+ (614 × sinh)2]1/2-614×sinh；
For the area with larger altitude, the atmospheric pressure is corrected, namely:
Figure BDA0002283054350000042
wherein z is the altitude of the observation site, p (z) is the atmospheric pressure of the observation site,
Figure BDA0002283054350000043
the atmospheric pressure correction coefficient;
the direct radiation atmospheric transparency coefficient is as follows: t isb＝0.56×(e-0.56m(z,h)+e-0.096m(z,h))×k1，k1The value range is 0.8-0.9;
the atmospheric transparency coefficient of the scattered radiation is as follows: t isa＝0.2710-0.2939×Tb；
The theoretical radiation comprises:
horizontal plane direct solar radiation intensity:
Ebh＝E0×Tb×sinh
wherein E0Indicating the intensity of solar radiation, T, at any moment of the upper boundary of the atmospherebThe direct radiation atmospheric transparency coefficient is represented, and h represents the solar altitude;
horizontal plane solar scattered radiation intensity:
Figure BDA0002283054350000044
wherein k is2A parameter representing the degree of atmospheric turbidity, k being 0.60. ltoreq. k when the atmosphere is turbid2K is not more than 0.70, and k is not less than 0.710 when the atmospheric quality is normal2K is not more than 0.80, and k is not less than 0.810 when the atmospheric quality is better2The altitude z is less than or equal to 0.90, m (z, h) represents the altitude z, and the atmospheric pressure is corrected when the solar altitude h is higher;
total solar radiation intensity in the horizontal plane:
Eth＝Ebh+Edh；
s2: the total solar radiation on the inclined plane is calculated according to the theoretical radiation of each power plant, namely:
Et＝Ebh×Rb+Edh×Rd+Eth×Rρ
wherein R isbIn order to be a direct radiation correction factor,
Figure BDA0002283054350000051
Rdas a scattered radiation correction factor, Rd＝(1+cosβ)/2；
RρAs a ground reflection correction factor, Rd＝ρ(1-cosβ)/2；
Figure BDA0002283054350000052
β is the inclination angle of the inclined plane, delta is the declination angle of the sun,
Figure BDA0002283054350000053
the sun time angle is, rho is the ground reflectivity, and is 0.2 under the normal condition, and rho is 0.7 during snow accumulation;
upper middle solar time angle
Figure BDA0002283054350000054
Wherein t issWhen the sun is present;
declination angle of the sun
Figure BDA0002283054350000055
Wherein n is a date number;
s3: collecting sunflower number 8 satellite cloud picture monitoring data every 3 days, and preprocessing the collected data, wherein the method comprises the following steps: time zone conversion, geometric correction, radiation correction and clipping;
s4: carrying out cloud quantity inversion by utilizing the preprocessed data, establishing a neural network and wavelet analysis prediction cloud quantity model, and predicting future cloud quantity, wherein the method specifically comprises the following steps:
s4.1: carrying out cloud quantity inversion by using the wave band for detecting cloud quantity to form cloud quantity data, and checking an inversion result;
s4.2: performing quality control on the acquired error data, interpolating the cloud data time resolution from 10 minutes to 15 minutes, and performing nearest point interpolation processing on the missing data;
s4.3: predicting short-term cloud cover by using a neural network and wavelet analysis prediction cloud cover model;
in the function space L2(R) (or more broadly, Hilbert space), a mother wavelet function (also called basic wavelet function) Ψ (x) is selected so as to satisfy the allowance condition:
Figure BDA0002283054350000056
in the formula
Figure BDA0002283054350000057
A Fourier transform of Ψ (x);
performing expansion and translation transformation on Ψ (x) to obtain a wavelet basis function system { Ψa,b(x)}
Figure BDA0002283054350000058
For any f (x) epsilon L2(R) its continuous wavelet transform is defined as:
Figure BDA0002283054350000061
the inversion formula is:
Figure BDA0002283054350000062
in practical applications, especially in computer implementation, the continuous wavelet and its transform are often discretized, and binary discretization is usually adopted, i.e. let a be 2m,b＝k2mThen, then
Figure BDA0002283054350000063
The dyadic wavelet must be an allowed wavelet and an orthogonal wavelet basis. Consider a continuous, square-integrable function f (x) e L2(R) at resolution 2mApproximation of fm(x) The theory of multi-resolution analysis shows that:
Figure BDA0002283054350000064
phi (x) is a scale function, and phi (x) is obtained by performing stretching and translation transformation on the scale functionmk(x)：
Figure BDA0002283054350000065
Figure BDA0002283054350000066
Function f (x) is at 2mAnd 2m-1Difference in information at resolution (i.e. detail) Dmf (x), obtainable by decomposing the function f (x) on a wavelet orthogonal basis, thereby defining a full and orthogonal multiresolution description, namely the wavelet description:
Figure BDA0002283054350000067
Figure BDA0002283054350000068
Ψmk(x) Is a dyadic wavelet defined by the formula (5), then f (x) is at 2m-1The approximation at resolution is:
Figure BDA0002283054350000069
mallat and states that for any one function f (x) e L2(R) can be expanded over a set of orthogonal wavelet bases:
Figure BDA0002283054350000071
the formula (11) is a wavelet decomposition of a square integrable function, provides a theoretical framework for designing a wavelet neural network, and the theory can be popularized to a multidimensional situation, for example, two dimensions are taken as an example, if a two-dimensional scale function is defined
Figure BDA0002283054350000072
Then
Figure BDA0002283054350000073
Then there are:
Figure BDA0002283054350000074
the same principle is as follows:
Figure BDA0002283054350000075
Figure BDA0002283054350000076
in general, a wavelet neural network model includes an input layer, an output layer, and a hidden layer, where the hidden layer includes two kinds of nodes: a wavelet base node (Ψ node) and a scale function node (Φ node);
1. layered multi-resolution learning: network output f (x) at 2LApproximation at resolution (lowest resolution):
Figure BDA0002283054350000077
f (x) at 2L-1Approximation at resolution:
Figure BDA0002283054350000078
first term in the formula (18)
Figure BDA0002283054350000079
Denotes f (x) is in 2LThe approximation in resolution, calculated in equation (17), is the coefficient aLkThe second term in the formula (18) is the same as in the formula (17)
Figure BDA00022830543500000710
Showing added detail.
Considering again that f (x) is at 2L-2，2L-3… approximation at resolution, there are:
Figure BDA0002283054350000081
Figure BDA0002283054350000082
Figure BDA0002283054350000083
Figure BDA0002283054350000084
the above equation is the learning algorithm of the wavelet neural network;
2. and (3) network coefficient calculation:
the following can be rewritten for formula (19):
Figure BDA0002283054350000085
ciis the network weight coefficient, θiThe method is an activation function (a scale function or a wavelet function), and if a wavelet neural network has n nodes and m training data, the method comprises the following steps:
Figure BDA0002283054350000086
i.e. f ═ Ac (21)
The least squares solution of equation (20) is:
c＝((ATA)-1AT)f
＝A+f (22)
A+a pseudo-inverse matrix called A, and
Figure BDA0002283054350000091
if sample xiUniform distribution, thetai(i ═ 1, 2.., n) is an orthogonal group, then aTA is an n × n identity matrix, andc＝ATf (24)；
3. wavelet neural network learning: after selecting a proper wavelet function and a proper scale function, training a phi node on the coarsest scale L until the network converges, determining an approximation error (in the embodiment, an existing error calculation method is adopted) and adding a proper psi node to reduce the approximation error in order to make the network converge, and finally optimizing the network, using a new sample to check the network and removing the psi node with a small weight until a performance criterion is met.
The computational complexity of the wavelet neural network training is between O (N) and O (N)2) N is the number of learning samples, and if the learning samples are uniformly distributed, the calculation complexity is O (N); if the learning samples are non-uniformly distributed, the computational complexity is O (N)2)。
The network output is a predicted short-term cloud cover;
s5: calculating cloud coverage coefficients according to the cloud amount obtained by prediction, wherein the cloud coverage coefficients comprise:
direct radiation cloud coverage coefficient:
α＝(300-Y)/100
wherein Y is the cloud amount with the time resolution of 15 minutes;
scattering radiation cloud coverage coefficient:
β＝1-2α；
s6: and calculating to obtain effective total radiation according to the total solar radiation and the cloud shading coefficient:
E＝Etbh×α+Etdh×β
wherein E istbhIs direct radiation on an inclined plane, EtdhIs the scattered radiation on the inclined plane;
s7: converting the effective total radiation into power to obtain predicted power, wherein the calculation formula is as follows:
Figure BDA0002283054350000092
the method comprises the following steps of obtaining a capacity of a unit, obtaining a weak radiation reduction coefficient, obtaining b a temperature reduction coefficient, obtaining c an efficiency coefficient when the inverter unit is started or stopped, and obtaining K an inverter conversion efficiency constant value.
The above description is only a preferred embodiment of the present invention, and not intended to limit the present invention, the scope of the present invention is defined by the appended claims, and all structural changes that can be made by using the contents of the description and the drawings of the present invention are intended to be embraced therein.

Claims (9)

1. A photovoltaic short-term power forecasting method based on a sunflower No. 8 satellite cloud picture is characterized by comprising the following steps:
s1: calculating theoretical radiation of each power plant based on the geographical position and environment of each power plant;
s2: calculating the total solar radiation on the inclined plane according to the theoretical radiation of each power plant;
s3: collecting sunflower number 8 satellite cloud picture monitoring data every 1-5 days, and preprocessing the collected data;
s4: carrying out cloud quantity inversion by utilizing the preprocessed data, establishing a neural network and a wavelet analysis prediction cloud quantity model, and predicting future cloud quantity;
s5: calculating cloud cover coefficients according to the cloud amount obtained through prediction;
s6: calculating to obtain effective total radiation according to the total solar radiation and the cloud shading coefficient;
s7: and converting the effective total radiation into power to obtain predicted power.
2. The photovoltaic short-term power forecasting method based on the sunflower satellite cloud 8 as claimed in claim 1, wherein the geographical locations and environments of each power plant in S1 include: the longitude and latitude, the altitude, the solar declination angle, the solar hour angle, the atmosphere transparency and the atmosphere quality of each power plant.
3. The photovoltaic short-term power forecasting method based on the sunflower satellite cloud figure 8 is characterized in that the theoretical radiation comprises the following steps:
horizontal plane direct solar radiation intensity:
Ebh＝E0×Tb×sinh
wherein E0Indicating the intensity of solar radiation, T, at any moment of the upper boundary of the atmospherebThe direct radiation atmospheric transparency coefficient is represented, and h represents the solar altitude;
horizontal plane solar scattered radiation intensity:
Figure FDA0002283054340000011
wherein k is2A parameter representing the atmospheric turbidity degree, m (z, h) represents the altitude z, and the atmospheric pressure is corrected when the solar altitude h is higher than the altitude z;
total solar radiation intensity in the horizontal plane:
Eth＝Ebh+Edh。
4. the photovoltaic short-term power forecasting method based on the sunflower satellite cloud 8 as claimed in claim 3, wherein the total solar radiation on the inclined plane in S2 is:
Et＝Ebh×Rb+Edh×Rd+Eth×Rρ
wherein R isbAs a direct radiation correction factor, RdAs a scattered radiation correction factor, RρIs a ground reflection correction factor.
5. The photovoltaic short-term power forecasting method based on the sunflower satellite cloud 8 as claimed in claim 1, wherein the preprocessing of the collected data in S3 includes: time zone conversion, geometric correction, radiation correction and clipping.
6. The photovoltaic short-term power forecasting method based on the sunflower satellite cloud figure No. 8 as claimed in claim 1, wherein the S4 is specifically:
s4.1: carrying out cloud quantity inversion by using the wave band for detecting cloud quantity to form cloud quantity data, and checking an inversion result;
s4.2: performing quality control on the acquired error data, interpolating the cloud data time resolution from 10 minutes to 15 minutes, and performing nearest point interpolation processing on the missing data;
s4.3: and (4) predicting the short-term cloud cover by utilizing a neural network and wavelet analysis prediction cloud cover model.
7. The photovoltaic short-term power forecasting method based on the sunflower satellite cloud image No. 8 as claimed in claim 6, wherein the cloud shading coefficient in S5 includes:
direct radiation cloud coverage coefficient:
α＝(300-Y)/100
wherein Y is the cloud amount with the time resolution of 15 minutes;
scattering radiation cloud coverage coefficient:
β＝1-2α。
8. the method for photovoltaic short-term power forecasting based on the sunflower satellite cloud figure 8 as claimed in claim 7, wherein the effective total radiation in S6 is:
E＝Etbh×α+Etdh×β
wherein E istbhIs direct radiation on an inclined plane, EtdhIs the scattered radiation on the inclined plane.
9. The photovoltaic short-term power forecasting method based on the sunflower satellite cloud 8 as claimed in claim 8, wherein the effective total radiation is converted into power in S7, and the calculation formula is:
Figure FDA0002283054340000021
the method comprises the following steps of obtaining a capacity of a unit, obtaining a weak radiation reduction coefficient, obtaining b a temperature reduction coefficient, obtaining c an efficiency coefficient when the inverter unit is started or stopped, and obtaining K an inverter conversion efficiency constant value.
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Cited By (3)

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CN112508255A (en) * 2020-12-01 2021-03-16 北京科技大学 Photovoltaic output ultra-short-term prediction method and system based on multi-source heterogeneous data
CN113504582A (en) * 2021-06-24 2021-10-15 中国气象局公共气象服务中心(国家预警信息发布中心) Photovoltaic power station solar radiation short-term forecasting method based on satellite radiation product
CN115423200A (en) * 2022-09-16 2022-12-02 南通沃太新能源有限公司 Method for predicting photovoltaic power by complementing solar irradiation in off-line state

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