CN116362382A - Short-term power prediction method and system based on icing state of wind power plant - Google Patents
Short-term power prediction method and system based on icing state of wind power plant Download PDFInfo
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Abstract
The invention discloses a short-term power prediction method and a system based on a wind farm icing state, which belong to the technical field of wind farm power prediction, and the method comprises the following steps: e text data of each fan of the wind power plant, wind tower data and historical ice coating thickness of the fans are obtained; comparing the actual output power of the fan with the initial predicted output power obtained by prediction calculation according to the wind tower data, and establishing a power-assisted prediction model; acquiring numerical meteorological data and geographic data of each fan, and correcting the numerical meteorological data; calculating an inertia coefficient, a retention coefficient and a freezing coefficient of the fan, and establishing an icing thickness calculation model; introducing fan icing conditions, calculating the association degree of the corrected meteorological data and the icing thickness by using a gray association degree method, correcting an icing thickness calculation model, and further calculating the icing thickness; inputting the icing thickness of the fan and the initial predicted output power into a power-assisted prediction model, and determining the final predicted output power; and reporting the final predicted output power.
Description
Technical Field
The invention belongs to the technical field of wind power plant power prediction, and particularly relates to a short-term power prediction method and system based on a wind power plant icing state.
Background
The wind farm is used for carrying out wind power prediction, and mainly aims at carrying out bidding on an electric power market and operation maintenance on the wind farm. As a grid-connected power supply, the wind power station needs to participate in market bidding before the day, so that dependence and demand degree on wind power prediction are larger and larger. For a wind farm, wind power prediction is beneficial to enterprises to reasonably arrange maintenance plans and improve the profitability of the enterprises. The aim of carrying out wind power prediction by a power grid operator is mainly to balance the power of the whole power grid, and ensure the safe and stable operation of the system. The wind farm is obligated to declare a power generation plan to the grid, and if the prediction error exceeds a certain range, the wind farm pays fine to the grid operator. Wind power prediction has become a national mandatory requirement according to related documents of the national energy agency.
The power prediction model on the market at present is constructed based on numerical weather forecast and historical operation data, and the condition that the prediction accuracy rate is reduced due to cliff-like drop in the ice-covered scene is not considered in the prediction of the output of a future fan, so that the power dispatching is affected, and economic loss is caused.
Disclosure of Invention
The invention provides a short-term power prediction lifting method and a short-term power prediction lifting system based on wind power plant icing state prediction, which are used for solving the technical problems that the existing prediction of the output of a future fan does not consider the condition that the prediction accuracy rate can be reduced in a cliff-like manner under an icing scene, so that the predicted fan power is inaccurate and the economic loss is caused by power scheduling.
First aspect
The invention provides a short-term power prediction method based on a wind power plant icing state, which comprises the following steps:
s101: e text data of each fan in the wind power plant, wind tower data reported by the wind power plant and historical ice coating thickness of the fan are obtained, wherein the E text data of the fan records actual output power;
s102: taking the historical icing state as an influence factor, and comparing the actual output power of the fan with the initial predicted output power obtained by prediction calculation according to the wind measuring tower data;
s103: according to the comparison result, a power-assisted prediction model taking initial predicted output power as input and actual output power as output is established;
s104: acquiring numerical meteorological data and geographic data of each fan, and correcting the numerical meteorological data to obtain corrected temperature, corrected humidity and corrected wind speed, wherein the numerical meteorological data comprises: meteorological temperature, meteorological humidity and meteorological wind speed, the geographic data of fan include: the altitude of the fan and the total altitude of the terrain where the fan is located;
s105: calculating the inertia coefficient, the retention coefficient and the freezing coefficient of the fan by combining the corrected temperature, the corrected humidity and the corrected wind speed;
s106: establishing an icing thickness calculation model according to the inertia coefficient, the retention coefficient and the freezing coefficient of the fan;
s107: introducing a fan icing condition, and entering S108 under the condition that the corrected temperature, the corrected humidity and the corrected wind speed meet the fan icing condition, otherwise entering S113;
s108: respectively calculating the correlation degree of the correction temperature, the correction temperature and the correction wind speed with the ice coating thickness by using a gray correlation degree method, and correcting the ice coating thickness calculation model;
s109: calculating the thickness of the ice coating by using the corrected ice coating thickness calculation model;
s110: calculating according to the wind measuring tower data to obtain initial predicted output power;
s111: inputting the icing thickness of the fan and the initial predicted output power into a power-assisted prediction model, and determining the final predicted output power;
s112: reporting the final predicted output power;
s113: and repeating S104-S112 at preset time intervals.
Second aspect
The invention provides a short-term power prediction system based on a wind power plant icing state, which comprises the following components:
the acquisition module is used for acquiring E text data of each fan in the wind power plant, wind tower data reported by the wind power plant and historical icing thickness of the fans, wherein the E text data of the fans records actual output power;
the comparison module is used for comparing the actual output power of the fan with the initial predicted output power obtained by prediction calculation according to the wind measuring tower data by taking the historical icing state as an influence factor;
the first building module is used for building a power-assisted prediction model taking predicted output power as input and actual output power as output according to the comparison result;
the correction module is used for acquiring numerical meteorological data and geographic data of each fan, correcting the numerical meteorological data to obtain corrected temperature, corrected humidity and corrected wind speed, wherein the numerical meteorological data comprises: meteorological temperature, meteorological humidity and meteorological wind speed, the geographic data of fan include: the altitude of the fan and the total altitude of the terrain where the fan is located;
the first calculation module is used for calculating the inertia coefficient, the retention coefficient and the freezing coefficient of the fan by combining the corrected temperature, the corrected humidity and the corrected wind speed;
the second building module is used for building an icing thickness calculation model according to the inertia coefficient, the retention coefficient and the freezing coefficient of the fan;
the introducing module is used for introducing the fan icing condition, and entering S108 when the corrected temperature, the corrected humidity and the corrected wind speed meet the fan icing condition, or else entering S113;
the correction module is used for respectively calculating the correlation degree of the correction temperature, the correction temperature and the correction wind speed with the ice coating thickness by using a gray correlation degree method and correcting the ice coating thickness calculation model;
the second calculation module is used for calculating the thickness of the ice coating by using the corrected ice coating thickness calculation model;
the prediction module is used for calculating initial predicted output power according to the wind tower data;
the determining module is used for inputting the icing thickness of the fan and the initial predicted output power into the power-assisted prediction model and determining the final predicted output power;
the output module is used for reporting the final predicted output power;
and the repeating module is used for repeating the steps S104-S112 at preset time intervals.
Compared with the prior art, the invention has at least the following beneficial technical effects:
according to the invention, influence factor historical icing thickness is introduced, a power-assisted prediction model taking initial predicted output power as input and actual output power as output is established, then acquired numerical meteorological data are corrected according to geographic data of each fan, influence of the icing thickness of the fan caused by the geographic position of the fan is reduced, then the correlation degree between the corrected data and the icing thickness is calculated by using a gray correlation degree method, the differential numerical meteorological data are adjusted, the prediction accuracy of the icing thickness of the fan is improved, the actual output power of the fan is obtained according to the predicted icing thickness, the influence of icing on the initial predicted output power is reduced, the prediction accuracy of the actual output power of the fan is improved, the power scheduling accuracy is improved, and economic loss is avoided.
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The above features, technical features, advantages and implementation of the present invention will be further described in the following description of preferred embodiments with reference to the accompanying drawings in a clear and easily understood manner.
FIG. 1 is a flow chart of a short-term power prediction method based on a wind farm icing state;
fig. 2 is a schematic structural diagram of a short-term power prediction system based on a icing state of a wind farm.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will explain the specific embodiments of the present invention with reference to the accompanying drawings. It is evident that the drawings in the following description are only examples of the invention, from which other drawings and other embodiments can be obtained by a person skilled in the art without inventive effort.
For simplicity of the drawing, only the parts relevant to the invention are schematically shown in each drawing, and they do not represent the actual structure thereof as a product. Additionally, in order to simplify the drawing for ease of understanding, components having the same structure or function in some of the drawings are shown schematically with only one of them, or only one of them is labeled. Herein, "a" means not only "only this one" but also "more than one" case.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
In this context, it should be noted that the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected, unless explicitly stated or limited otherwise; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In addition, in the description of the present invention, the terms "first," "second," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
Example 1
In one embodiment, referring to fig. 1 of the specification, the present invention provides a flow chart of a short-term power prediction method based on a icing state of a wind farm.
The invention provides a short-term power prediction lifting method based on wind power plant icing state prediction, which comprises the following steps:
s101: e text data of each fan in the wind power plant, wind tower data reported by the wind power plant and historical icing thickness of the fan are obtained, wherein the E text data of the fan records actual output power.
It should be noted that, the historical operation data of the wind farm are all in the E text data, the obtained wind tower data can calculate the actual output power of each fan, and the obtained historical icing thickness of the fan is used as a main influencing factor to adjust and optimize the initial predicted output power.
S102: and comparing the actual output power of the fan with the initial predicted output power obtained by prediction calculation according to the wind measuring tower data by taking the historical icing state as an influence factor.
The method and the device have the advantages that the influence caused by the icing of the fan is ignored in the power prediction of the fan in the prior art, and the thickness of the icing of the fan is taken as the only variable based on the initial predicted output power of the fan in the prior art. And analyzing the change of the initial predicted output power under the condition of a certain fan icing thickness through the acquired historical data, taking the actual output power of the fan as a result obtained by the influence of the fan icing state, and comparing the actual output power with the initial predicted output power to obtain the change relation among the three.
S103: and according to the comparison result, establishing a power-assisted prediction model taking initial predicted output power as input and actual output power as output.
It should be noted that, when the thickness of the fan ice coating is less than 1.8mm, the fan can normally output according to the initial predicted output power, but when the thickness of the fan ice coating is greater than 10mm, in order to ensure the operation safety of the fan and avoid damage of the fan, an alarm is usually sent directly to stop the fan, and when the thickness of the fan ice coating is between 1.8 and 10mm, the thickness of the fan ice coating and the actual output power are in a general negative correlation, so that the correlation among the actual output power of the fan, the initial output power and the thickness of the fan ice coating is calculated, and a power-assisted prediction model is established.
In one possible implementation, S103 specifically includes:
s1031: selecting predicted output power and actual output power with ice coating thickness of 1.8mm, and calculating first ratio theta between actual output power and predicted output power 1 。
S1032: selecting predicted output power and actual output power with icing thickness of 10mm, and calculating actual outputA second ratio θ between power and predicted output power 2 。
S1033: calculating a correlation coefficient theta according to the first proportion and the second proportion:
θ＝(θ 1 -θ 2 )/(1.8-10)。
s1034: according to the comparison result, building a power-assisted prediction model:
wherein P represents the actual output power, θ represents the correlation coefficient, and P 0 And b represents the predicted output power and b represents the fan icing thickness.
S104: acquiring numerical meteorological data and geographic data of each fan, and correcting the numerical meteorological data to obtain corrected temperature, corrected humidity and corrected wind speed, wherein the numerical meteorological data comprises: meteorological temperature, meteorological humidity and meteorological wind speed, the geographic data of fan include: the altitude of the fan and the total height of the terrain where the fan is located.
It should be noted that, a large number of researches indicate that main meteorological factors causing icing of the fans are temperature, humidity and wind speed, the positions of fan distribution in the wind farm often have differences, different fans can be placed at different geographic positions, in the geographic positions, because the altitude at which the fans are located is different, the meteorological data of the positions at which the fans are located are slightly different from the meteorological data given by weather forecast, so that numerical meteorological data are obtained from time to time, the numerical meteorological data are corrected according to the different geographic positions of each fan, the meteorological data of the positions at which the fans are located are prevented from being roughly unified, and larger errors are avoided.
In one possible implementation, S104 specifically includes:
s1041: calculating a corrected temperature T according to the meteorological temperature h ：
Wherein T is 0 Indicating meteorological temperature, h indicating fan altitude, τ indicating air temperature deceleration coefficient, τ=1.08.
In a region where the altitude changes greatly, the three-dimensional change of the air temperature is large, and the sudden drop of the air temperature is one of the main causes of icing on the fan blade, in general, the temperature drops at a certain air temperature after every fixed distance of the altitude rise.
S1042: calculating corrected humidity E according to meteorological humidity h ：
E h ＝E 0 exp(-c×h)
Wherein E is 0 Representing meteorological humidity, c=4.45×10-4/m represents a correlation coefficient related to altitude.
It should be noted that, the humidity of different altitudes will change according to the change of the altitude, in general, the higher the altitude is, the smaller the water pressure in the air will be, and the blower is generally located in the area with higher altitude, so the humidity of the blower will be relatively lower, the air humidity in the vertical state is analyzed, the air humidity in the altitude of the blower is calculated, and an important air humidity factor is provided for whether the blower is iced or not.
S1043: and calculating a corrected wind speed according to the meteorological wind speed.
In one possible implementation, S1043 specifically includes:
S1043A: according to the meteorological wind speed, the process of calculating the corrected wind speed is as follows:
introducing a mountain terrain function:
tanα＝H/L 1
wherein L is 1 The distance from the mountain top to the mountain waist in the horizontal direction is represented by H, the height of the mountain where the fan is located is represented by H, and the altitude of the fan is represented by H;
S1043B: calculating wind speed correction coefficient eta at mountain top 1 ：
Wherein k is 1 =2.2 represents the conversion factor of the peak of the mountain top;
S1043C: calculating wind speed correction coefficient eta of mountain foot 2 ：
K 3 ＝0.01+0.73×tanα-0.72×tanα 2
Wherein K is 2 Represents mountain top height conversion factor, K 3 Represents mountain gradient conversion factor H G
Representing wind field height;
S1043D: calculating a wind speed correction coefficient of any point:
wherein K is 4 Representing a wind field acceleration effect correction factor of a side wind slope mountain land;
S1044E: calculating a corrected wind speed of the position of the fan:
U h ＝U 0 +η 3
U h indicating corrected wind speed, U 0 Representing meteorological wind speeds.
It should be noted that, the higher the altitude, the higher the wind speed, and particularly, the wind speed in the mountain area, the acceleration effect will be generated on the wind field in the mountain area, and the windward slope, the leeward slope and the crosswind slope in the mountain area have different acceleration effects. Therefore, different wind speed correction coefficients are calculated for the windward slope, the leeward slope and the side wind slope of the mountain area based on the prior art, and then numerical meteorological data are corrected to obtain more accurate corrected wind speed of the fan.
S105: and calculating the inertia coefficient, the retention coefficient and the freezing coefficient of the fan by combining the corrected temperature, the corrected humidity and the corrected wind speed.
In the practical application process, different correction temperatures, correction humidity and correction wind speeds can influence the capability of air droplets attached to the fan blades, so that the fan inertia coefficient, the retention coefficient and the freezing coefficient in the fan rotation process are analyzed, the icing thickness of the fan blades can be more accurately obtained, and the prediction error is reduced.
In the rotating process of the fan, the larger the relative speed between the inertia coefficient of the fan blade and the liquid drops is, the liquid drops in the air can change along with the wind speed in the air, and the smaller the viscous force of the liquid drops is. Under the influence of the inertia coefficient of the corresponding fan, the retention coefficient of the fan is the amount of the liquid drops which permanently reside on the fan blades, when the fan rotates, part of water is thrown out of the fan blades along with the speed difference between the liquid drops and the blades after the liquid drops adhere to the blades, and the rest icing is permanently left on the fan blades. The analysis of the freezing coefficient of the fan is related to the law of conservation of energy, in the process of freezing liquid drops, water is frozen and subjected to phase change, the surface of the blade generates friction heat, the liquid drops impact on the blade to generate heat conversion, the water is frozen and then is further cooled to the ambient temperature, part of heat can be released, the liquid drops are evaporated and subjected to heat conversion in the attaching process, the conversion of various energies is analyzed in detail, the freezing coefficient of the liquid drops can be obtained, and the change condition of the icing thickness of the fan with different altitudes can be obtained more accurately.
In one possible implementation, S105 specifically includes:
s1051: calculating the inertia coefficient of the fan by taking the corrected wind speed as an independent variable:
α 1 ＝0.020479+0.036092v+2.61987×10 -3 v 3 +8.14453×10 -6 v 4 -7.87608×10 -8 v 5 。
s1052: taking retention coefficient alpha based on structure of fan 2 ＝1。
S1053: and analyzing the energy change before and after the fan is iced, establishing a thermodynamic equilibrium equation on the surface of the fan according to the law of conservation of energy, and calculating the freezing coefficient.
In one possible embodiment, S1053 specifically includes:
S1053A: calculate the heat released by the water phase change to ice at 0℃ f ：
q f ＝α 1 α 2 α 3 vwL f
Wherein L is f Represents the heat of dissolution of ice, L at 0 DEG C f ＝332.4×1000J/kg；
Calculating heat q converted from kinetic energy by collision of liquid water in air and ice surface k ：
q k ＝α 1 α 2 v 3 w；
Calculate the heat q released by the water cooling to ambient temperature under 0 deg.c d ：
q d ＝α 1 α 2 α 3 vwc l (-Ts)
Wherein c l Represents the specific heat capacity of ice in solid state, T s Representing ambient temperature;
calculating energy loss q due to air convection c ：
q c ＝ο(T s -T a )
Wherein o represents the convective heat transfer coefficient, unit J/(m) 2 ·K)，T a ＝T h Representing the current temperature of the surface of the fan;
calculating the latent heat loss q caused by evaporation of liquid drops e ：
X＝0.622hLe/(c w p a )
q e ＝X[e(T s )-e(T a )]
Wherein X represents an evaporation coefficient, L e ＝2.51×10 6 The evaporation latent heat of water at the temperature T is expressed as J/kg, and e (T) is the saturated water pressure of ice on the ice-coated surface at the temperature T as kPa;
calculating the amount of heat q absorbed by the liquid droplets as they continue to rise from the supercooled state to 0℃ l ：
q l ＝α 1 α 2 α 3 vwc w (-T a )
Wherein c w Specific heat capacity representing the state of liquid water;
calculating the heat loss q caused by radiation r ：
q r ＝4εσ R (T a +273.15) 3 (T s -T a )
Wherein epsilon represents the emissivity of the ice surface, and the value is 0.95 and sigma R Representing constant and taking the value of 5.567 multiplied by 10 -8 W/(m 2 ·K 4 )；
Calculating the heat q taken away by liquid water without freezing s ：
q s ＝α 1 α 2 (1-α 3 )vwc w (-T a )；
S1053B: establishing a thermodynamic equilibrium equation of the surface of the fan:
q f +q k +q d ＝q c +q e +q l +q r +q s ；
S1053C: converting thermodynamic equilibrium equation of fan surface, calculating freezing coefficient alpha 3 ：
Wherein S is 1 Represents the windward area of the fan blade S 2 Representing the total area of the fan blade.
S106: and establishing an icing thickness calculation model according to the inertia coefficient, the retention coefficient and the freezing coefficient of the fan.
In one possible implementation, S106 is specifically:
s1061: the ice coating thickness calculation model is as follows:
wherein alpha is 1 Represents the inertia coefficient of the fan, alpha 2 Representing retention coefficient, alpha 3 The freezing coefficient, w corrected humidity and v corrected wind speed, and b the icing thickness.
S107: introducing a fan icing condition, and entering S108 when the corrected temperature, the corrected humidity and the corrected wind speed meet the fan icing condition, otherwise entering S113.
It can be understood that when the liquid drops are frozen, the air temperature, the humidity and the wind speed have a critical value, the liquid drops are not frozen under the condition that the critical value is not reached, and the icing condition can occur once the critical value is exceeded.
In one possible embodiment, the fan icing condition is specifically a rapid change in wind speed over a short period of time and is greater than 1m/s, the temperature drops below-2 ℃ and the humidity rises above 90% and remains above 1 minute.
S108: and respectively calculating the correlation degree of the correction temperature, the correction temperature and the correction wind speed with the ice coating thickness by using a gray correlation degree method, and correcting the ice coating thickness calculation model.
In one possible implementation, S108 specifically includes:
s1081: respectively carrying out data normalization on the corrected temperature, the corrected wind speed and the icing thickness:
wherein x is k The input data is represented by a representation of the input data,
representing normalized data.
S1082: calculating a correlation coefficient xi of each correction data and the ice coating thickness i (k)：
Wherein λ represents a resolution coefficient, the value λ=0.5, b 0 (k) Represents the thickness of the ice coating after normalization, x i (k) And representing the normalized data of the ith modified meteorological data.
S1083: calculating the relevance r of the correction temperature, the correction temperature and the correction wind speed with the ice coating thickness according to the relevance coefficient i ：
S1084: correcting the icing calculation model according to the corrected temperature, the corrected temperature and the correlation degree of the corrected wind speed and the icing thickness:
it can be understood that the influence degree of different factors on icing in the icing process of liquid drops in air is different, and the influence of different temperatures, humidity and wind speeds on the icing process of liquid drops is further analyzed by a gray correlation degree method, so that different weights are distributed to different factors, the calculated icing thickness is closer to the real condition, and the final prediction accuracy is improved.
S109: and calculating the thickness of the ice coating by using the corrected ice coating thickness calculation model.
S110: and obtaining initial predicted output power according to the wind measuring tower data prediction calculation.
S111: and inputting the icing thickness of the fan and the initial predicted output power into a power-assisted prediction model, and determining the final predicted output power.
It should be noted that, the initial output power obtained by calculation according to the wind measuring tower data does not consider the influence of the fan icing, the invention calculates the initial predicted output power, calculates the icing thickness according to the corrected icing thickness calculation model, brings the two to the power-assisted prediction model established according to the historical meteorological data, and determines the final predicted output power of the fan.
S112: and reporting the final predicted output power.
S113: and repeating S104-S112 at preset time intervals.
It can be understood that, after the final predicted output power is predicted, along with the real-time change of the numerical meteorological data, in order to ensure the accuracy of prediction, the predicted final predicted output power needs to be predicted and reported in real time, so that the initial predicted output power is optimized, and the prediction accuracy is improved.
Compared with the prior art, the invention has at least the following beneficial technical effects:
according to the invention, influence factor historical icing thickness is introduced, a power-assisted prediction model taking initial predicted output power as input and actual output power as output is established, then acquired numerical meteorological data are corrected according to geographic data of each fan, influence of the icing thickness of the fan caused by the geographic position of the fan is reduced, then the correlation degree between the corrected data and the icing thickness is calculated by using a gray correlation degree method, the differential numerical meteorological data are adjusted, the prediction accuracy of the icing thickness of the fan is improved, the actual output power of the fan is obtained according to the predicted icing thickness, the influence of icing on the initial predicted output power is reduced, the prediction accuracy of the actual output power of the fan is improved, the power scheduling accuracy is improved, and economic loss is avoided.
Example 2
In one embodiment, referring to fig. 2 of the specification, the present invention provides a schematic structural diagram of a short-term power prediction system based on icing status of a wind farm.
The invention provides a short-term power prediction system 30 based on a wind farm icing state, which comprises:
the acquiring module 301 is configured to acquire E text data of each fan in a wind farm, wind tower data reported by the wind farm, and a historical icing thickness of the fan, where the E text data of the fan records actual output power;
the comparison module 302 is configured to compare the actual output power of the fan with the initial predicted output power obtained by prediction calculation according to the wind tower data, with the historical icing state as an influencing factor;
the first establishing module 303 is configured to establish, according to the comparison result, a power-assisted prediction model with predicted output power as input and actual output power as output;
the correction module 304 is configured to obtain numerical meteorological data and geographic data of each fan, correct the numerical meteorological data, and obtain a corrected temperature, a corrected humidity, and a corrected wind speed, where the numerical meteorological data includes: meteorological temperature, meteorological humidity and meteorological wind speed, the geographic data of fan include: the altitude of the fan and the total altitude of the terrain where the fan is located;
the first calculation module 305 is configured to calculate a fan inertia coefficient, a retention coefficient, and a freezing coefficient in combination with the corrected temperature, the corrected humidity, and the corrected wind speed;
the second establishing module 306 is configured to establish an ice-coating thickness calculation model according to the fan inertia coefficient, the retention coefficient and the freezing coefficient;
the introducing module 307 is configured to introduce a fan icing condition, enter S108 if the corrected temperature, the corrected humidity and the corrected wind speed satisfy the fan icing condition, and enter S110 if not;
the correction module 308 is configured to calculate the correlation between the corrected temperature, and the corrected wind speed and the ice thickness by using a gray correlation method, and correct the ice thickness calculation model;
a second calculation module 309, configured to calculate the ice thickness using the corrected ice thickness calculation model;
the prediction module 310 is configured to predict and obtain an initial predicted output power according to the anemometer tower data;
the determining module 311 is configured to input the fan icing thickness and the initial predicted output power to the power-assisted prediction model, and determine a final predicted output power;
an output module 312, configured to report the final predicted output power;
a repetition module 313, configured to repeat S104-S112 at preset time intervals.
In one possible implementation, the first establishing module 303 is specifically configured to:
selecting predicted output power and actual output power with ice coating thickness of 1.8mm, and calculating first ratio theta between actual output power and predicted output power 1 ；
Selecting predicted output power and actual output power with icing thickness of 10mm, and calculating second ratio theta between actual output power and predicted output power 2 ；
Calculating a correlation coefficient theta according to the first proportion and the second proportion:
θ＝(θ 1 -θ 2 )/(1.8-10)；
according to the comparison result, building a power-assisted prediction model:
wherein P represents the actual output power, θ represents the correlation coefficient, and P 0 And b represents the predicted output power and b represents the fan icing thickness.
In one possible implementation, the correction module 304 is specifically configured to:
calculating a corrected temperature T according to the meteorological temperature h ：
Wherein T is 0 Indicating meteorological temperature, h indicating fan altitude, τ indicating air temperature deceleration coefficient, τ=1.08;
calculating corrected humidity E according to meteorological humidity h ：
E h ＝E 0 exp(-c×h)
Wherein E is 0 Representing meteorological humidity, c=4.45×10-4/m representing a correlation coefficient related to altitude;
and calculating a corrected wind speed according to the meteorological wind speed.
In one possible embodiment, the corrected wind speed is calculated by:
according to the meteorological wind speed, the process of calculating the corrected wind speed is as follows:
introducing a mountain terrain function:
tanα＝H/L 1
wherein L is 1 The distance from the mountain top to the mountain waist in the horizontal direction is represented by H, the height of the mountain where the fan is located is represented by H, and the altitude of the fan is represented by H;
calculating wind speed correction coefficient eta at mountain top 1 ：
Wherein k is 1 =2.2 represents the conversion factor of the peak of the mountain top;
calculating wind speed correction coefficient eta of mountain foot 2 ：
K 3 ＝0.01+0.73×tanα-0.72×tanα 2
Wherein K is 2 Represents mountain top height conversion factor, K 3 Represents mountain gradient conversion factor H G
Representing wind field height;
calculating wind speed correction coefficient of any point
Wherein K is 4 Representing a wind field acceleration effect correction factor of a side wind slope mountain land;
calculating a corrected wind speed of the position of the fan:
U h ＝U 0 +η 3
U h indicating corrected wind speed, U 0 Representing meteorological wind speeds.
In one possible implementation, the computing module 305 is specifically configured to:
calculating the inertia coefficient of the fan by taking the corrected wind speed as an independent variable:
α 1 ＝0.020479+0.036092v+2.61987×10 -3 v 3 +8.14453×10 -6 v 4 -7.87608×10 -8 v 5 ；
taking retention coefficient alpha based on structure of fan 2 ＝1；
And analyzing the energy change before and after the fan is iced, establishing a thermodynamic equilibrium equation on the surface of the fan according to the law of conservation of energy, and calculating the freezing coefficient.
In one possible implementation manner, the freezing coefficient is calculated specifically as follows:
calculate the heat released by the water phase change to ice at 0℃ f ：
q f ＝α 1 α 2 α 3 vwL f
Wherein L is f Represents the heat of dissolution of ice, L at 0 DEG C f ＝332.4×1000J/kg；
Calculating heat q converted from kinetic energy by collision of liquid water in air and ice surface k ：
q k ＝α 1 α 2 v 3 w；
Calculate the heat q released by the water cooling to ambient temperature under 0 deg.c d ：
q d ＝α 1 α 2 α 3 vwc l (-Ts)
Wherein c l Represents the specific heat capacity of ice in solid state, T s Representing ambient temperature;
calculating energy loss q due to air convection c ：
q c ＝ο(T s -T a )
Wherein o represents the convective heat transfer coefficient, unit J/(m) 2 ·K)，T a ＝T h Representing the current temperature of the surface of the fan;
calculating the latent heat loss q caused by evaporation of liquid drops e ：
X＝0.622hLe/(c w p a )
q e ＝X[e(T s )-e(T a )]
Wherein X represents an evaporation coefficient, L e ＝2.51×10 6 The evaporation latent heat of water at the temperature T is expressed as J/kg, and e (T) is the saturated water pressure of ice on the ice-coated surface at the temperature T as kPa;
calculating the amount of heat q absorbed by the liquid droplets as they continue to rise from the supercooled state to 0℃ l ：
q l ＝α 1 α 2 α 3 vwc w (-T a )
Wherein c w Specific heat capacity representing the state of liquid water;
calculating the heat loss q caused by radiation r ：
q r ＝4εσ R (T a +273.15) 3 (T s -T a )
Wherein epsilon represents the emissivity of the ice surface, and the value is 0.95 and sigma R Representing constant and taking the value of 5.567 multiplied by 10 -8 W/(m 2 ·K 4 )；
Calculating the heat q taken away by liquid water without freezing s ：
q s ＝α 1 α 2 (1-α 3 )vwc w (-T a )；
Establishing a thermodynamic equilibrium equation of the surface of the fan:
q f +q k +q d ＝q c +q e +q l +q r +q s ；
converting thermodynamic equilibrium equation of fan surface, calculating freezing coefficient alpha 3 ：
Wherein S is 1 Represents the windward area of the fan blade S 2 Representing the total area of the fan blade.
In one possible implementation, the second establishing module is specifically configured to:
the ice coating thickness calculation model is as follows:
wherein alpha is 1 Represents the inertia coefficient of the fan, alpha 2 Representing retention coefficient, alpha 3 The freezing coefficient, w corrected humidity and v corrected wind speed, and b the icing thickness.
In one possible embodiment, the fan icing condition is specifically a rapid change in wind speed over a short period of time and is greater than 1m/s, the temperature drops below-2 ℃ and the humidity rises above 90% and remains above 1 minute.
In one possible implementation, the correction module is specifically configured to:
respectively carrying out data normalization on the corrected temperature, the corrected wind speed and the icing thickness:
wherein x is k The input data is represented by a representation of the input data,
representing normalized data;
calculating a correlation coefficient xi of each correction data and the ice coating thickness i (k)：
Wherein λ represents a resolution coefficient, the value λ=0.5, b 0 (k) Represents the thickness of the ice coating after normalization, x i (k) Representing the normalized data of the ith modified meteorological data;
calculating the relevance r of the correction temperature, the correction temperature and the correction wind speed with the ice coating thickness according to the relevance coefficient i ：
Correcting the icing calculation model according to the corrected temperature, the corrected temperature and the correlation degree of the corrected wind speed and the icing thickness:
the short-term power prediction system 30 based on the icing state of the wind farm provided by the invention can realize each process realized in the method embodiment, and in order to avoid repetition, the description is omitted here.
The virtual system provided by the invention can be a system, and can also be a component, an integrated circuit or a chip in a terminal.
Compared with the prior art, the invention has at least the following beneficial technical effects:
according to the invention, influence factor historical icing thickness is introduced, a power-assisted prediction model taking initial predicted output power as input and actual output power as output is established, then acquired numerical meteorological data are corrected according to geographic data of each fan, influence of the icing thickness of the fan caused by the geographic position of the fan is reduced, then the correlation degree between the corrected data and the icing thickness is calculated by using a gray correlation degree method, the differential numerical meteorological data are adjusted, the prediction accuracy of the icing thickness of the fan is improved, the actual output power of the fan is obtained according to the predicted icing thickness, the influence of icing on the initial predicted output power is reduced, the prediction accuracy of the actual output power of the fan is improved, the power scheduling accuracy is improved, and economic loss is avoided.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. The short-term power prediction method based on the icing state of the wind power plant is characterized by comprising the following steps of:
s101: e text data of each fan in a wind power plant, wind tower data reported by the wind power plant and historical icing thickness of the fan are obtained, wherein the E text data of the fan records actual output power;
s102: comparing the actual output power of the fan with the initial predicted output power obtained by predictive calculation according to the wind measuring tower data by taking the historical icing state as an influence factor;
s103: according to the comparison result, a power-assisted prediction model taking initial predicted output power as input and actual output power as output is established;
s104: acquiring numerical meteorological data and geographic data of each fan, and correcting the numerical meteorological data to obtain corrected temperature, corrected humidity and corrected wind speed, wherein the numerical meteorological data comprises: meteorological temperature, meteorological humidity and meteorological wind speed, the geographic data of fan includes: the altitude of the fan and the total altitude of the terrain where the fan is located;
s105: calculating an inertia coefficient, a retention coefficient and a freezing coefficient of the fan by combining the corrected temperature, the corrected humidity and the corrected wind speed;
s106: establishing an icing thickness calculation model according to the fan inertia coefficient, the retention coefficient and the freezing coefficient;
s107: introducing a fan icing condition, and entering S108 when the corrected temperature, the corrected humidity and the corrected wind speed meet the fan icing condition, otherwise entering S113;
s108: respectively calculating the correlation degree of the correction temperature, the correction temperature and the correction wind speed with the ice coating thickness by using a gray correlation degree method, and correcting the ice coating thickness calculation model;
s109: calculating the ice coating thickness by using the corrected ice coating thickness calculation model;
s110: calculating according to the anemometer tower data to obtain initial predicted output power;
s111: inputting the ice coating thickness of the fan and the initial predicted output power into the power-assisted prediction model to determine final predicted output power;
s112: reporting the final predicted output power;
s113: and repeating S104-S112 at preset time intervals.
2. The short-term power prediction method according to claim 1, wherein the step S103 specifically includes:
s1031: selecting the predicted output power and the actual output power with the icing thickness of 1.8mm, and calculating a first ratio theta between the actual output power and the predicted output power 1 ；
S1032: selecting the predicted output power and the actual output power of the icing thickness of 10mm, and calculating a second ratio theta between the actual output power and the predicted output power 2 ；
S1033: calculating the correlation coefficient θ from the first ratio and the second ratio:
θ＝(θ 1 -θ 2 )/(1.8-10)；
s1034: according to the comparison result, the power-assisted prediction model is established:
wherein P represents the actual output power, θ represents a correlation coefficient, and P 0 And b represents the fan icing thickness.
3. The short-term power prediction method according to claim 1, wherein the step S104 specifically includes:
s1041: calculating the corrected temperature T according to the meteorological temperature h ：
Wherein T is 0 Indicating the meteorological temperature, wherein h indicates the altitude of the fan, τ indicates an air temperature speed reduction coefficient, and τ=1.08;
s1042: calculating the corrected humidity E according to the meteorological humidity h ：
E h ＝E 0 exp(-c×h)
Wherein E is 0 Representing the meteorological humidity, c=4.45×10-4/m representing a correlation coefficient related to altitude;
s1043: and calculating the corrected wind speed according to the meteorological wind speed.
4. The short-term power prediction method according to claim 3, wherein the S1043 specifically includes:
S1043A: according to the meteorological wind speed, the process of calculating the corrected wind speed is as follows:
introducing a mountain terrain function:
tanα＝H/L 1
wherein L is 1 The distance from the mountain top to the mountain waist in the horizontal direction is represented by H, the height of the mountain where the fan is located is represented by H, and the altitude of the fan is represented by H;
S1043B: calculating wind speed correction coefficient eta at mountain top 1 ：
Wherein k is 1 =2.2 represents mountainA conversion factor of the top highest point;
S1043C: calculating wind speed correction coefficient eta of mountain foot 2 ：
K 3 ＝0.01+0.73×tanα-0.72×tanα 2
Wherein K is 2 Represents mountain top height conversion factor, K 3 Represents mountain gradient conversion factor H G
Representing wind field height;
S1043D: calculating a wind speed correction coefficient of any point:
wherein K is 4 Representing a wind field acceleration effect correction factor of a side wind slope mountain land;
S1044E: calculating a corrected wind speed of the position of the fan:
U h ＝U 0 +η 3
U h representing the corrected wind speed, U 0 Representing the meteorological wind speed.
5. The short-term power prediction method according to claim 1, wherein S105 is specifically:
s1051: calculating the inertia coefficient of the fan by taking the corrected wind speed as an independent variable:
α 1 ＝0.020479+0.036092v+2.61987×10 -3 v 3 +8.14453×10 -6 v 4 -7.87608×10 -8 v 5 ；
s1052: based on the structure of the fan, taking the retention coefficient alpha 2 ＝1；
S1053: and analyzing the energy change before and after the fan is iced, establishing a thermodynamic equilibrium equation on the surface of the fan according to an energy conservation law, and calculating the freezing coefficient.
6. The short-term power prediction method according to claim 4, wherein the step S1053 specifically includes:
S1053A: calculate the heat released by the water phase change to ice at 0℃ f ：
q f ＝α 1 α 2 α 3 vwL f
Wherein L is f Represents the heat of dissolution of ice, L at 0 DEG C f ＝332.4×1000J/kg；
Calculating heat q converted from kinetic energy by collision of liquid water in air and ice surface k ：
q k ＝α 1 α 2 v 3 w；
Calculate the heat q released by the water cooling to ambient temperature under 0 deg.c d ：
q d ＝α 1 α 2 α 3 vwc l (-Ts)
Wherein c l Represents the specific heat capacity of ice in solid state, T s Representing ambient temperature;
calculating energy loss q due to air convection c ：
q c ＝ο(T s -T a )
Wherein o represents the convective heat transfer coefficient, unit J/(m) 2 ·K)，T a ＝T h Representing the current temperature of the surface of the fan;
calculating the latent heat loss q caused by evaporation of liquid drops e ：
X＝0.622hLe/(c w p a )
q e ＝X[e(T s )-e(T a )]
Wherein X represents an evaporation coefficient, L e ＝2.51×10 6 The evaporation latent heat of water at the temperature T is expressed as J/kg, and e (T) is the saturated water pressure of ice on the ice-coated surface at the temperature T as kPa;
calculating the amount of heat q absorbed by the liquid droplets as they continue to rise from the supercooled state to 0℃ l ：
q l ＝α 1 α 2 α 3 vwc w (-T a )
Wherein c w Specific heat capacity representing the state of liquid water;
calculating the heat loss q caused by radiation r ：
q r ＝4εσ R (T a +273.15) 3 (T s -T a )
Wherein epsilon represents the emissivity of the ice surface, and the value is 0.95 and sigma R Representing constant and taking the value of 5.567 multiplied by 10 -8 W/(m 2 ·K 4 )；
Calculating the heat q taken away by liquid water without freezing s ：
q s ＝α 1 α 2 (1-α 3 )vwc w (-T a )；
S1053B: establishing a thermodynamic equilibrium equation of the surface of the fan:
q f +q k +q d ＝q c +q e +q l +q r +q s ；
S1053C: converting thermodynamic equilibrium equation of the surface of the fan, and calculating the freezing coefficient alpha 3 ：
Wherein S is 1 Represents the windward area of the fan blade S 2 Representing the total area of the fan blade.
7. The short-term power prediction method according to claim 1, wherein S106 is specifically:
s1061: the ice coating thickness calculation model is as follows:
wherein alpha is 1 Representing the inertia coefficient of the fan, alpha 2 Representing the retention coefficient, alpha 3 And (5) representing the freezing coefficient, w representing the corrected humidity and v representing the corrected wind speed, and b representing the icing thickness.
8. The short-term power prediction method according to claim 1, characterized in that the fan icing condition is in particular that the wind speed is drastically changed in a short time and is more than 1m/s, the temperature is reduced to less than-2 ℃ and the humidity is increased to above 90% and maintained for more than 1 minute.
9. The short-term power prediction method according to claim 1, wherein the step S108 specifically includes:
s1081: respectively carrying out data normalization on the corrected temperature, the corrected wind speed and the icing thickness:
wherein x is k The input data is represented by a representation of the input data,
representing normalized data;
S1082：calculating a correlation coefficient xi of each correction data and the icing thickness i (k)：
Wherein λ represents a resolution coefficient, the value λ=0.5, b 0 (k) Represents the thickness of the ice coating after normalization, x i (k) Representing the normalized data of the ith modified meteorological data;
s1083: calculating the correlation r between the corrected temperature, the corrected temperature and the corrected wind speed and the ice coating thickness according to the correlation coefficient i ：
S1084: correcting the icing computing model according to the corrected temperature, the corrected temperature and the correlation degree of the corrected wind speed and the icing thickness:
10. a short-term power prediction system based on a wind farm icing condition, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring E text data of each fan in a wind power plant, wind tower data reported by the wind power plant and historical icing thickness of the fans, wherein the E text data of the fans records actual output power;
the comparison module is used for comparing the actual output power of the fan with the initial predicted output power obtained by prediction calculation according to the wind measuring tower data by taking the historical icing state as an influence factor;
the first building module is used for building a power-assisted prediction model taking initial predicted output power as input and actual output power as output according to a comparison result;
the correction module is used for acquiring numerical meteorological data and geographic data of each fan, correcting the numerical meteorological data to obtain corrected temperature, corrected humidity and corrected wind speed, wherein the numerical meteorological data comprises: meteorological temperature, meteorological humidity and meteorological wind speed, the geographic data of fan includes: the altitude of the fan and the total altitude of the terrain where the fan is located;
the first calculation module is used for calculating the inertia coefficient, the retention coefficient and the freezing coefficient of the fan by combining the corrected temperature, the corrected humidity and the corrected wind speed;
the second building module is used for building an icing thickness calculation model according to the fan inertia coefficient, the retention coefficient and the freezing coefficient;
the introducing module is used for introducing a fan icing condition, and entering S108 when the corrected temperature, the corrected humidity and the corrected wind speed meet the fan icing condition, or else entering S113;
the correction module is used for respectively calculating the correlation degree of the correction temperature, the correction temperature and the correction wind speed with the icing thickness by using a gray correlation degree method and correcting the icing thickness calculation model;
the second calculation module is used for calculating the ice coating thickness by using the corrected ice coating thickness calculation model;
the prediction module is used for predicting and obtaining initial predicted output power according to the anemometer tower data;
the determining module is used for inputting the ice coating thickness of the fan and the initial predicted output power into the power-assisted prediction model to determine final predicted output power;
the output module is used for reporting the final predicted output power;
and the repeating module is used for repeating the steps S104-S112 at preset time intervals.
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| CN117638926B (en) * | 2024-01-25 | 2024-04-05 | 国能日新科技股份有限公司 | New energy power prediction method and device based on icing and power coupling modeling |
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