CN112801429B - Micro-terrain wind power calculation method, device and system - Google Patents
Micro-terrain wind power calculation method, device and system Download PDFInfo
- Publication number
- CN112801429B CN112801429B CN202110386651.9A CN202110386651A CN112801429B CN 112801429 B CN112801429 B CN 112801429B CN 202110386651 A CN202110386651 A CN 202110386651A CN 112801429 B CN112801429 B CN 112801429B
- Authority
- CN
- China
- Prior art keywords
- wind power
- meteorological
- meteorological element
- calculating
- sequence
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004364 calculation method Methods 0.000 title claims description 44
- 238000000034 method Methods 0.000 claims abstract description 63
- 238000005259 measurement Methods 0.000 claims description 34
- 238000012937 correction Methods 0.000 claims description 19
- 238000013500 data storage Methods 0.000 claims description 17
- 239000011159 matrix material Substances 0.000 claims description 17
- 238000004088 simulation Methods 0.000 claims description 10
- 238000007781 pre-processing Methods 0.000 claims description 8
- 230000005540 biological transmission Effects 0.000 claims description 6
- 230000003321 amplification Effects 0.000 claims description 4
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 4
- 238000012805 post-processing Methods 0.000 claims description 4
- 230000003068 static effect Effects 0.000 claims description 4
- 230000001360 synchronised effect Effects 0.000 claims description 3
- 230000000750 progressive effect Effects 0.000 claims 1
- 230000008569 process Effects 0.000 description 10
- 230000008014 freezing Effects 0.000 description 9
- 238000007710 freezing Methods 0.000 description 9
- 238000010248 power generation Methods 0.000 description 8
- 238000001556 precipitation Methods 0.000 description 5
- 238000002679 ablation Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 239000000126 substance Substances 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000001419 dependent effect Effects 0.000 description 3
- 239000013598 vector Substances 0.000 description 3
- 239000000284 extract Substances 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 230000035772 mutation Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 230000002195 synergetic effect Effects 0.000 description 2
- 241000219094 Vitaceae Species 0.000 description 1
- 239000011248 coating agent Substances 0.000 description 1
- 238000000576 coating method Methods 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 235000021021 grapes Nutrition 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000017105 transposition Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Power Engineering (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Wind Motors (AREA)
Abstract
The invention relates to a method, a device and a system for calculating micro-terrain wind power, wherein the method comprises the following steps: constructing a micro-terrain numerical prediction mode, and determining a first meteorological element sequence based on the numerical prediction mode; acquiring a second meteorological element sequence corresponding to the first meteorological element sequence, and acquiring a wind power observation value, a fan icing thickness observation value and a strong wind speed observation value at the height of a fan hub; correcting meteorological elements based on the first meteorological element sequence and the second meteorological element sequence; calculating wind power based on the corrected meteorological elements and wind power observed values; calculating the fan icing thickness based on the corrected meteorological elements and the fan icing thickness observation value; calculating the strong wind speed at the height of the fan hub based on the corrected meteorological elements and the observed value of the strong wind speed at the height of the fan hub; and calculating the wind power under the target weather condition based on the wind power observed value, the calculated wind power, the fan icing thickness and the strong wind speed at the height of the fan hub.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a method, a device and a system for calculating micro-terrain wind power.
Background
With the rapid increase of the wind power clean energy installation machine and the gradual increase of the scale of the wind power installation machine, the influence of wind power integration on the safety and stability of a power system is gradually highlighted. In recent years, operation accidents including large-scale wind power system tripping have occurred at home and abroad. Extreme meteorological disaster conditions such as freezing and strong wind are important causes of accidents. The wind turbine generator is located in a micro-terrain area, wind power fluctuation is large, blades are frozen in winter, centralized shutdown is achieved, large-area grid disconnection is caused, safe operation of a power grid is seriously threatened, and load supply and demand balance of the power grid is seriously threatened. In addition, the wind turbine generator is mostly located in a strong wind area caused by local terrain influence, and when the weather phenomena such as a strong convection weather system, typhoon and the like cross the border, an event that a fan is emergently locked or even the generator is damaged can be generated, so that the power of a wind power plant is suddenly reduced, and the safe operation of a power grid is impacted.
The existing wind power prediction method can be mainly divided into methods such as a physical model method, a statistical method, a learning method and the like, but the methods all consider the wind power as a stable time sequence, and the power mutation phenomenon caused by mutation events such as freezing of a fan, shutdown of a strong wind and the like is difficult to predict, so that the wind power prediction accuracy is greatly reduced under the extreme disaster conditions.
Disclosure of Invention
In order to solve the technical problems or at least partially solve the technical problems, the invention provides a micro-terrain wind power calculation method, device and system.
The invention provides a micro-terrain wind power calculation method, which comprises the following steps:
constructing a micro-terrain numerical prediction mode, and determining a first meteorological element sequence based on the numerical prediction mode;
acquiring a second meteorological element sequence corresponding to the first meteorological element sequence, and acquiring a wind power observation value, a fan icing thickness observation value and a strong wind speed observation value at the height of a fan hub;
correcting meteorological elements based on the first meteorological element sequence and the second meteorological element sequence; calculating wind power based on the corrected meteorological elements and the wind power observed value; calculating the fan icing thickness based on the corrected meteorological elements and the fan icing thickness observation value; calculating the strong wind speed at the height of the fan hub based on the corrected meteorological elements and the observation value of the strong wind speed at the height of the fan hub;
and calculating the wind power under the target weather condition based on the wind power observed value, the calculated wind power, the fan icing thickness and the strong wind speed at the height of the fan hub.
In some embodiments, the constructing a micro-terrain numerical prediction mode and determining a first weather element sequence based on the numerical prediction mode includes:
with a wind power station as a center, constructing a micro-terrain numerical prediction mode with horizontal resolution nested layer by layer and gradual amplification;
acquiring a terrain elevation, a land type and a vegetation type with the minimum horizontal resolution as mode static data; selecting a target parameterization scheme, and determining the starting time and the ending time of forecasting and the time interval of result output;
acquiring meteorological data in a target numerical prediction mode, and determining initial condition data and boundary condition data of the numerical prediction mode;
performing integral calculation based on the steps to obtain meteorological data at any moment;
repeating the steps to obtain a first meteorological element sequence in a continuous preset time interval.
In some embodiments, the constructing a micro-terrain numerical prediction mode with a horizontal resolution nested layer by layer and amplified step by centering on the wind power plant station includes:
selecting a mesoscale weather mode as a coarse grid mode with the horizontal resolution of more than 1km multiplied by 1 km; selecting a large vortex simulated weather mode as a fine grid mode with the horizontal resolution of less than 1km multiplied by 1 km;
with a wind power station as a center, horizontal resolutions of 30m multiplied by 30m, 90m multiplied by 90m, 270m multiplied by 270m, … … and 30 multiplied by 3 are sequentially constructednm×30×3nm series of grids to form the numerical forecasting pattern.
In some embodiments, the time interval of the result output is less than or equal to 15 minutes, and the continuous preset period is equal to or greater than 60 days.
In some embodiments, said modifying meteorological elements based on said first sequence of meteorological elements and said second sequence of meteorological elements comprises:
constructing a regression equation based on the first meteorological element sequence and the second meteorological element sequence;
and correcting the first meteorological element sequence by using a regression equation.
In some embodiments, said calculating wind power based on said modified meteorological element and said wind power observation comprises:
constructing a measurement equation at any moment based on the corrected meteorological elements and the wind power observed value;
for any moment, constructing a forecasting equation by using a least square method based on the wind power observed value and the measurement equation;
determining the regression coefficient estimation value at the next moment based on the forecast equation at two adjacent moments, and determining the variance of the dynamic noise of the regression coefficient;
determining residual errors at all times based on the measurement equation and the forecast equation, and determining a variance matrix of measurement noise;
based on the measurement equation, the forecast equation, the variance of the dynamic noise of the regression coefficient and the variance matrix of the measurement noise, applying a generalized least square method to construct a power recurrence relation between two adjacent moments;
and calculating the wind power based on the power recurrence relation.
In some embodiments, said calculating a wind power at a target weather condition based on said wind power observation, said calculated wind power, said wind turbine icing thickness, and said wind speed at high wind at said wind turbine hub height comprises:
establishing a wind power loss model under target weather conditions such as icing and strong wind disasters based on the wind power observation value, the calculated wind power, the fan icing thickness and the strong wind speed at the height of the fan hub;
determining wind power at a target weather condition based on the calculated wind power and the wind power loss model.
The invention also provides a micro-terrain wind power calculation device, which comprises:
the first sequence determination module is used for constructing a micro-terrain numerical prediction mode and determining a first meteorological element sequence based on the numerical prediction mode;
the second sequence acquisition module is used for acquiring a second meteorological element sequence corresponding to the first meteorological element sequence and acquiring a wind power observation value, a fan icing thickness observation value and a strong wind speed observation value at the height of a fan hub;
the data correction module is used for correcting meteorological elements based on the first meteorological element sequence and the second meteorological element sequence; calculating wind power based on the corrected meteorological elements and the wind power observed value; calculating the fan icing thickness based on the corrected meteorological elements and the fan icing thickness observation value; calculating the strong wind speed at the height of the fan hub based on the corrected meteorological elements and the observation value of the strong wind speed at the height of the fan hub;
and the wind power calculation module is used for calculating the wind power under the target weather condition based on the wind power observation value, the calculated wind power, the calculated fan icing thickness and the strong wind speed at the height of the fan hub.
The invention also provides a micro-terrain wind power calculation system, which comprises: the system comprises a micro-terrain numerical weather forecasting module, a meteorological element correcting module and a power recursion calculating module;
the micro-terrain numerical weather forecast module comprises:
the data transmission submodule is used for acquiring meteorological full court data;
the data preprocessing submodule is used for carrying out grid interpolation by utilizing the meteorological full court data based on the grid resolution of each layer of the nested mode to form data required by the operation of the driving numerical calculation submodule;
the numerical calculation submodule is used for carrying out numerical calculation on the basis of data formed by the data preprocessing submodule, a target parameterization scheme, the simulation starting time and the simulation ending time to obtain a theoretical value;
the mode result post-processing submodule is used for extracting the theoretical value based on the time interval of result output, the longitude and latitude coordinates and the altitude of the wind power plant to obtain a first meteorological element sequence;
the meteorological element correction module is used for establishing a statistical relationship between an observed value and a theoretical value of each element by using a least square method based on the first meteorological element sequence and a second meteorological element sequence which is synchronous with the first meteorological element sequence, so as to realize correction of the theoretical value;
and the power recursion calculation module is used for determining the wind power of each current moment and the wind power of the next moment based on the wind power observation value and the meteorological elements corrected in the same period, and sequentially and circularly iterating to calculate the wind power of each moment.
In some embodiments, the system further comprises: the system comprises a front-end service module and a data storage module;
the data storage module is used for storing various data and/or files acquired or generated by other modules in the system;
the front-end service module is used for reading the data and/or the files in the data storage module and displaying the data and/or the files based on a query request of a user.
Compared with the prior art, the technical scheme provided by the embodiment of the invention has the following advantages:
the technical scheme provided by the embodiment of the invention provides a micro-terrain wind power calculation method, device and system considering extreme disasters, which can accurately forecast the icing growth and ablation conditions of a wind turbine generator and a strong wind shutdown event, and calculate the wind power loss caused by the icing growth and the ablation conditions and the strong wind shutdown event, so that the theoretical value of the wind power is corrected, the wind power forecasting accuracy under the extreme disasters is greatly improved, the optimization of new energy power generation scheduling is assisted, and the safe and stable operation of a power grid is guaranteed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a method for calculating a micro-terrain wind power according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a micro-terrain wind power calculation apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a micro-terrain wind power calculation system according to an embodiment of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention may be more clearly understood, a solution of the present invention will be further described below. It should be noted that the embodiments of the present invention and features of the embodiments may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those described herein; it is to be understood that the embodiments described in this specification are only some embodiments of the invention, and not all embodiments.
The technical problem to be solved by the embodiment of the invention is as follows: aiming at the problem that the existing wind power prediction technology cannot predict the power drop caused by extreme disaster weather such as fan blade icing and strong wind shutdown, the invention provides a micro-terrain wind power calculation method (also called prediction method), device and system considering extreme disasters. Therefore, the icing growth and the ablation of the wind turbine generator can be accurately predicted, the strong wind shutdown event is also accurately predicted, and the wind power loss caused by the icing growth and the ablation can be calculated, so that the predicted value of the wind power is corrected, the wind power prediction accuracy under the extreme disaster condition is greatly improved, the optimization of new energy power generation scheduling is assisted, and the safe and stable operation of a power grid is guaranteed.
The following describes an exemplary method, apparatus and system for calculating micro-terrain wind power according to an embodiment of the present invention with reference to fig. 1 to 3.
Fig. 1 is a schematic flow chart of a method for calculating a micro-terrain wind power according to an embodiment of the present invention. Referring to fig. 1, the method includes:
s110, constructing a micro-terrain numerical prediction mode, and determining a first meteorological element sequence based on the numerical prediction mode.
Wherein, the first meteorological element sequence is a meteorological element prediction sequence.
Therefore, a meteorological element prediction sequence, namely meteorological element prediction values of different dimensions at different moments can be obtained based on the micro-terrain numerical prediction mode.
In some embodiments, this step may include the following steps.
The method comprises the following steps: and (3) taking the wind power station as a center, and constructing a micro-terrain numerical prediction mode with horizontal resolution nested layer by layer and gradual amplification.
The method comprises the following steps of 1, namely, 1 set of micro-terrain numerical prediction mode which is nested layer by layer and amplified step by step is established by taking a wind power station as a center. Specifically, the method can comprise the following steps:
selecting a mesoscale weather mode as a coarse grid mode with the horizontal resolution of more than 1km multiplied by 1 km; and selecting a large vortex simulated weather mode as a fine grid mode with the horizontal resolution of less than 1km multiplied by 1 km.
Namely step 1.1: selecting a medium-scale Weather mode such as WRF (the Weather Research and Forecasting model) as a coarse grid mode with the horizontal resolution of more than 1km multiplied by 1km, and selecting a Weather mode such as Large Eddy Simulation (LES) as a fine grid mode with the horizontal resolution of LESs than 1km multiplied by 1 km.
In other embodiments, other weather patterns meeting the scale requirement may also be selected, and the embodiment of the present invention is not limited by contrast.
Then, with the wind power station as the center, the horizontal resolution of 30 mx 30m, 90 mx 90m, 270 mx 270m,……、30×3nm×30×3nAnd m series of grids to form a numerical forecasting mode.
Namely step 1.2: with a wind power station as a center, a series of grids with horizontal resolutions of 30m × 30m, 90m × 90m, 270m × 270m, 840m × 840m … … and the like are sequentially constructed, and the number of atmospheric vertical layers is set.
The horizontal resolution of the highest precision level can reach 30m multiplied by 30 m.
In other embodiments, the accuracy of the horizontal grid, the number of nested layers (for example, 6 layers, 8 layers, or other layers), and the number of vertical layers may all be set according to the requirements of the prediction method, which is not limited by the embodiment of the present invention.
Step two: acquiring a terrain elevation, a land type and a vegetation type with the minimum horizontal resolution as mode static data; and selecting a target parameterization scheme, and determining the starting time and the ending time of the forecast and the time interval of the result output. Optionally, the time interval for the result output is less than or equal to 15 minutes.
Namely step 1.3: in combination with the above, the terrain elevation, land type, vegetation type with a resolution of 30m × 30m are selected as model static data, an appropriate parameterization scheme is selected, the start time and the end time of prediction are set, and the output time interval of the model result is set to be equal to or less than 15 minutes.
Wherein the suitable parameterization scheme is a parameterization scheme determined based on basic data under the limitation of the forecasting demand, and the parameterization scheme can limit the development trend of the physical process.
Step three: acquiring meteorological data in a target numerical prediction mode, and determining initial condition data and boundary condition data of the numerical prediction mode.
Namely step 1.4: downloading meteorological data in a target numerical prediction mode such as Chinese GRAPES data, European center fine grid data or GFS data of the national environment prediction center, and the like, and decoding and interpolating the data according to the horizontal resolution and the vertical resolution determined in the step 1.2 to obtain initial condition data and boundary condition data for driving the mode (namely, the numerical prediction mode) selected in the step 1.1.
Step four: and performing integral prediction calculation based on the steps to obtain meteorological data at any moment.
Namely step 1.5: according to the setting of the step 1.3, integral prediction calculation is carried out by using a numerical mode, and wind speed, wind direction and temperature data of several layers, such as the ground temperature, the ground pressure, the relative humidity, the precipitation, the ground clearance 10m, 30m, 50m, 70m … …, the hub height (namely the height upper limit value) of the fan, and the like, required by wind power prediction are obtained.
In other embodiments, the height values of different heights above the ground may be set according to the requirements of the prediction method, and the embodiment of the present invention is not limited thereto.
Step five: and repeating the steps to obtain a meteorological element prediction sequence in a continuous preset time period. Optionally, the continuous preset period is equal to or greater than 60 days.
I.e. step 1.6, repeating the foregoing steps 1.2 to 1.5 to obtain a wind farm micro-terrain meteorological element prediction sequence with a horizontal resolution of 30m x 30m for a continuous period of time (the continuous period of time is equal to or greater than 60 days).
Thus, a micro-terrain meteorological element prediction sequence is obtained.
And S120, acquiring a second meteorological element sequence corresponding to the first meteorological element sequence, and acquiring a wind power observation value, a fan icing thickness observation value and a strong wind speed observation value at the height of a fan hub.
Wherein the second meteorological element sequence is a meteorological element observation sequence.
Namely step 2: acquiring meteorological elements such as temperature, pressure, wind, humidity, precipitation and the like with 15-minute resolution ratio in the same period of the wind power plant and the numerical prediction simulation in the step 1 to obtain a plurality of meteorological element observation values at different moments, namely a meteorological element observation sequence; and acquiring observation values of forecast objects such as corresponding station power (namely wind power), fan icing thickness, high wind speed at the height of a fan hub and the like.
S130, correcting meteorological elements based on the first meteorological element sequence and the second meteorological element sequence; calculating wind power based on the corrected meteorological elements and wind power observed values; calculating the fan icing thickness based on the corrected meteorological elements and the fan icing thickness observation value; and calculating the strong wind speed at the height of the fan hub based on the corrected meteorological elements and the observed value of the strong wind speed at the height of the fan hub.
Therefore, correction of meteorological elements can be achieved, and based on the meteorological elements, the wind power, the fan icing thickness and the strong wind speed at the height of the fan hub are combined with the observed values of the wind power, the fan icing thickness and the strong wind speed at the height of the fan hub to respectively perform preliminary prediction on the wind power, the fan icing thickness and the strong wind speed at the height of the fan hub at the future moment. The method comprises a meteorological element correction process, a wind power preliminary prediction process, a fan icing thickness preliminary prediction process and a strong wind speed preliminary prediction process at the height of a fan hub.
In some embodiments, the meteorological element correction process may include:
constructing a regression equation based on the meteorological element prediction sequence and the meteorological element observation sequence;
and correcting the meteorological element prediction sequence by using a regression equation.
Specifically, the method comprises the following steps:
and step 3: forecasting sequence by using meteorological elements obtained in step 1.6
And the observation sequence obtained in step 2
And (3) constructing a regression equation, and correcting (namely correcting) the meteorological elements predicted by the micro-terrain numerical mode.
Wherein, S represents a certain meteorological element, the superscript p represents a predicted value, the superscript o represents an observed value, the subscripts 1, 2, … …, n represent the 1 st, 2 nd, … … th, n th time step, and the regression equation can be expressed as follows:
In the formula (I), the compound is shown in the specification,
representing the predicted value of the meteorological element after correction,b 0 、b 1 the representative regression coefficient can be obtained by constructing a regression equation and fitting. By using the formula (1), the meteorological elements such as temperature, pressure, wind, humidity, precipitation and the like obtained by the micro-terrain numerical mode calculation can be corrected and calculated.
In some embodiments, the wind power preliminary prediction process may include:
constructing a measurement equation at any moment based on the corrected meteorological elements and wind power observed values;
for any moment, constructing a forecasting equation by using a least square method based on the wind power observed value and the measurement equation;
determining the regression coefficient estimation value at the next moment based on the forecast equation at two adjacent moments, and determining the variance of the dynamic noise of the regression coefficient;
determining residual errors at all times based on a measurement equation and a prediction equation, and determining a variance matrix of measurement noise;
based on a measurement equation, a prediction equation, the variance of dynamic noise of a regression coefficient and a variance matrix of measurement noise, applying a generalized least square method to construct a power recurrence relation of two adjacent moments;
and predicting the wind power based on the power recurrence relation.
Specifically, it may be step 4: and constructing a Kalman filtering recursion system to perform preliminary prediction of theoretical wind power. The method specifically comprises the following steps:
step 4.1: and (3) constructing a measurement equation by using the station power data P acquired in the step (2) and the meteorological elements obtained in the step (3) after the micro-terrain correction, wherein the measurement equation comprises the following steps:
Wherein the content of the first and second substances,P t represents a forecast, i.e. an observed value of wind power, and
，X t represents a matrix of predictor factors, and
，
is a regression coefficient matrix, and
，
to measure noise, arelDimension random vector;P t in the expression (2), the superscript T denotes transposition, and the subscript T denotes the tth time.
Based on the method, the forecasting equation is constructed by utilizing the power data P of the wind power station and applying a least square method, and the forecasting equation is as follows:
Wherein the content of the first and second substances,
representing a predicted value of the wind power,
which is representative of the predictor factor(s),
representing the regression coefficient estimate at time t.
Thus, a predicted value, an observed value and a regression coefficient estimated value of the wind power at the time t are obtained.
Step 4.2, taking
The data of the time is obtained by repeating the step 4.1
Time-of-day regression coefficient estimation
。
The regression coefficient estimation value at the current moment can be obtained by performing regression fitting on data at previous historical moments.
Exemplarily, there are independent variable data and dependent variable data of the first 30 times, and the independent variable data of the 31 st time is known; and predicting the regression coefficient of the independent variable data and the dependent variable at the 31 st moment by using the regression relationship constructed by the independent variable data and the dependent variable data at the first 30 moments to obtain the estimated value of the regression coefficient at the 31 st moment.
In another embodiment, the regression coefficient predicted at the next time may be obtained using data at any time.
Step 4.3: estimating regression coefficientsβThe variance W of the dynamic noise of (2) is as follows:
Step 4.4: according to step 4.1, the component-by-component residuals can be calculated according to equation (3)
Thereby obtaining a variance matrix V of the measurement noise as follows:
Wherein, the residual error can be obtained by subtracting the predicted value of the wind power of the formula (3) from the observed value of the wind power of the formula (2), and the residual error is obtained at each different time (example t time)lThe residual of the dimension.
Step 4.5: assuming an estimate of the regression coefficient at time t
Is the error variance matrix ofC t It is a m × m dimensional square matrix. Wherein, the error variance matrix at 0 moment is 0, that is, the moment:
The error variance matrix at each subsequent time isC t Are all not 0 and can be obtained recursively.
Step 4.6:
is a good approximation of the station power, i.e. its dynamic noise and measurement noise
Are all random vectors with mutually independent mean value 0, if the variance of the two vectors is W and V respectively. Then applying the generalized least squares method, we can obtain:
Thus, the formula (7) (which is a formula group consisting of the above formulas in this paragraph and represents the recursion relationship between two adjacent times) represents the recursion relationship from time t-1 to time t, and the recursions are sequentially performed at a time step of 15 minutes, and are sequentially determined in steps 4.1 to 4.5
Equivalence, namely the theoretical wind power predicted value of the wind power plant at each future moment can be realized
And (4) calculating.
Step 4 is similar to the above-mentioned method,a recursion relation is constructed, so that the fan icing thickness of the wind power plant at each future moment can be realizedDHigh wind speed at the position of hub heightW sp And (4) calculating.
S140, predicting the wind power under the target weather condition based on the wind power observation value, the predicted wind power, the fan icing thickness and the strong wind speed at the height of the fan hub.
The target weather conditions may include freezing, strong wind and other disaster weather conditions. Therefore, the final predicted value of the power of the wind power plant considering the disaster weather characteristics such as freezing and strong wind can be calculated.
In some embodiments, this step may include:
establishing a wind power loss model under target weather conditions such as icing and strong wind disasters based on the wind power observation value, the predicted wind power, the fan icing thickness and the strong wind speed at the height of a fan hub;
based on the predicted wind power and the wind power loss model, wind power at the target weather condition is determined.
Specifically, the method can comprise the following steps:
step 6: according to the wind power plant power P collected in the step 2 and the theoretical power calculated in the step 4P l Thickness of ice coating of fanDAnd the high wind speed at the hub heightW sp Establishing a fan power loss model under the disaster conditions of icing and strong wind:
Wherein the content of the first and second substances,f 1 (D)represents the functional relationship between wind power and fan icing thickness, which may be a piecewise function. Exemplarily, when the icing thickness of the fan is below 0.5mm, the fan normally operates, and the wind power is kept stable; when the icing thickness of the fan is 0.5mm-3mm, the wind power is in a linear decreasing trend; when the icing thickness of the fan is more than 3mm, the fan is shut down, namely does not run, and the wind power drops suddenly and is 0.
Wherein the content of the first and second substances,f 2 (W sp )represents the functional relationship between wind power and the wind speed of a strong wind at the hub height (referred to simply as "wind speed" in this paragraph), which may be a piecewise function. Exemplarily, when the wind speed is below 3m/s, the fan is in a wind waiting state; when the wind speed is 3m/s-25m/s, the fan is in a normal operation state, and the wind power is basically kept stable; when the wind speed exceeds 25m/s, the fan is switched into a running state, namely the fan is automatically locked and stops rotating, and the wind power is suddenly reduced to be 0.
In other embodiments, other functional relationships may also be adopted for the functional relationship between the wind power and the fan icing thickness and the functional relationship between the wind power and the high wind speed at the hub height, which is neither described nor limited in this embodiment of the present invention.
And 7: by combining the steps 4 and 6, the final predicted value of the wind power under the target weather conditions of the disaster weather characteristics such as freezing and strong wind can be calculatedP P ：
In this way, accurate prediction of wind power under target weather conditions may be achieved.
The micro-terrain wind power calculation method, namely the micro-terrain wind power prediction method provided by the embodiment of the invention, at least has the following beneficial effects: the principle is clear, the operation is convenient, and the practical value is high; the method can consider the enhancement effect of the microtopography on the wind speed of the wind power plant area, fully consider the influence of the cut-out operation state of the wind turbine caused by freezing of the wind turbine and gust of strong wind on the power, and accurately predict the power of the wind power plant; and important basic data are provided for optimal scheduling of new energy power generation.
On the basis of the foregoing embodiments, an embodiment of the present invention further provides a micro-terrain wind power calculation apparatus, also referred to as a micro-terrain wind power prediction apparatus, which may be used to perform the steps of any one of the foregoing methods to achieve corresponding beneficial effects, and it can be understood with reference to the foregoing, and the same parts are not described in detail hereinafter.
Fig. 2 is a schematic structural diagram of a micro-terrain wind power calculation device according to an embodiment of the present invention. Referring to fig. 2, the apparatus may include: the first sequence determination module 210, which may also be referred to as a prediction sequence determination module, is configured to construct a micro-terrain numerical prediction mode, and determine a meteorological element prediction sequence based on the numerical prediction mode; the second sequence obtaining module 220, which may also be referred to as an observation sequence obtaining module, is configured to obtain a meteorological element observation sequence corresponding to the meteorological element prediction sequence, and obtain a wind power observation value, a fan icing thickness observation value, and a strong wind speed observation value at a fan hub height; a data correction module 230, configured to correct the meteorological element based on the meteorological element prediction sequence and the meteorological element observation sequence; calculating wind power based on the corrected meteorological elements and wind power observed values; calculating the fan icing thickness based on the corrected meteorological elements and the fan icing thickness observation value; calculating the strong wind speed at the height of the fan hub based on the corrected meteorological elements and the observed value of the strong wind speed at the height of the fan hub; and the wind power calculation module 240 is configured to calculate the wind power under the target weather condition based on the wind power observed value, the calculated wind power, the fan icing thickness, and the strong wind speed at the height of the fan hub.
In the micro-terrain wind power calculation device provided by the embodiment of the invention, the synergistic effect of the modules can be combined with the enhancement effect of the micro-terrain on the wind speed of the wind power plant region, the influence of the cut-out operation state of the wind turbine caused by freezing and strong wind gust on the power is fully considered, and the power of the wind power plant is accurately predicted; important basic data are provided for optimal scheduling of new energy power generation; and the principle is clear, the operation is convenient, and the practical value is very high.
On the basis of the foregoing embodiments, an embodiment of the present invention further provides a micro-terrain wind power calculation system, also referred to as a micro-terrain wind power prediction system, which may be used to perform the steps of any one of the foregoing methods to achieve corresponding beneficial effects, and the foregoing may be understood with reference to the above description, and the same parts are not described in detail below.
Fig. 3 is a schematic structural diagram of a micro-terrain wind power calculation system according to an embodiment of the present invention. Referring to fig. 3, the system may include: a micro-terrain numerical weather forecast module 310, a meteorological element correction module 320 and a power recursion calculation module 330; the micro-terrain numerical weather forecast module 310 includes: the data transmission submodule 311 is configured to obtain meteorological full court data; the data preprocessing submodule 312 is configured to perform grid interpolation by using the meteorological full field data based on the grid resolution of each layer in the nested mode, and form data required by the operation of the driving numerical computation submodule; the numerical value calculation submodule 313 performs numerical value prediction calculation based on data formed by the data preprocessing submodule, the target parameterization scheme, the simulation starting time and the simulation ending time to obtain a predicted value, namely a theoretical value; the mode result post-processing submodule 314 is used for extracting a predicted value based on the time interval of result output, the longitude and latitude coordinates and the altitude of the wind power plant to obtain a meteorological element prediction sequence; the meteorological element correction module 320 is used for establishing a statistical relationship between an observed value and a predicted value of each element by using a least square method based on the meteorological element prediction sequence and a meteorological element observation sequence which is synchronous with the meteorological element prediction sequence, so as to realize correction of the predicted value; the power recursion calculation module 330 is configured to determine wind powers at each current time and at a next time based on the wind power observation value and the corrected meteorological elements at the same time, and perform loop iteration in sequence to predict the wind power at each time.
Illustratively, the data transmission sub-module 311 stores basic information such as a download address, an account number, a password, and a storage location for downloading data of a global course such as a european center, a U.S. GFS, and the like, selects a corresponding data transmission mode according to characteristics of the data to be downloaded, carries out data download at regular time, and stores the data to the data storage module. The data preprocessing sub-module 312 stores information such as horizontal resolution and vertical resolution of each layer of grid in the nested mode, and performs horizontal and spatial grid interpolation on the data downloaded by the data transmission module according to the information to form all data required for driving the operation of the numerical calculation module. The numerical calculation submodule 313 stores information such as simulation start time, simulation end time, parameter schemes and the like of the numerical mode, reads result data of the data preprocessing module, and carries out numerical prediction calculation. The mode result post-processing submodule 314 stores information such as longitude and latitude coordinates and altitude of the wind power plant, extracts predicted values of elements such as temperature, humidity, air pressure, wind speed, wind direction and precipitation of the position of the wind power plant within 15 minutes, and stores the predicted values in the data storage module.
Illustratively, the meteorological element correction module 320 can read actual observed values and numerical mode predicted values of meteorological elements such as temperature, humidity, barometric pressure, wind speed, wind direction, precipitation and the like at the position of the wind farm in the data storage module, establish statistical relationships between the observed values and the predicted values of the elements by using a least square method, correct the numerical mode predicted values, and store the corrected meteorological element values in the data storage module.
Illustratively, the power recursion calculation module 330 can extract a wind power observed value for a period of time (not less than 30 days) and meteorological element predicted values such as temperature, wind speed, wind direction, and air pressure after correction at the same time from a data storage module (shown below), calculate values of each parameter in formula (3) at the current moment and wind power predicted values, iterate the calculated values into formula (3) to calculate wind power predicted values at the next moment, and iteratively calculate theoretical wind power predicted values at each moment in a sequential loop.
In the micro-terrain wind power prediction system provided by the embodiment of the invention, the synergistic effect of the modules can be combined with the enhancement effect of the micro-terrain on the wind speed of the wind power plant region, the influence of the cut-out operation state of the wind turbine caused by freezing and strong wind gust on the power is fully considered, and the power of the wind power plant is accurately predicted; important basic data are provided for optimal scheduling of new energy power generation; and the principle is clear, the operation is convenient, and the practical value is very high.
In some embodiments, with continued reference to fig. 3, the system further comprises: a wind power forecast front-end traffic module 340 (also referred to simply as "front-end traffic module") and a forecast system data storage module 350 (also referred to simply as "data storage module"); the prediction system data storage module 350 is used for storing various types of data and/or files acquired or generated by other modules in the system; the wind power forecast front-end service module 340 is configured to read data and/or files in the forecast system data storage module and display the data and/or files based on a query request of a user.
The prediction system data storage module 350 can store various data and files generated by the above modules in the system. The wind power prediction front-end service module 340 can read various live and predicted data in the data storage module and display the data in the form of a webpage, can perform statistical analysis and display on the wind power prediction level, and can generate wind power prediction messages reported to the regional main stations every day and upload the messages to the regional main stations at regular time.
The micro-terrain wind power calculation system provided by the embodiment of the invention has the advantages of clear working principle, convenience in operation and high practical value; the method can accurately predict the power of the wind power plant by combining the enhancement effect of the microtopography on the wind speed of the wind power plant area and the influence of the cut-out operation state of the wind turbine caused by the freezing of the wind turbine and the gust of strong wind on the power; important basic data are provided for optimizing and scheduling new energy power generation, the new energy power generation scheduling is assisted to be optimized, and the safe and stable operation of a power grid is guaranteed.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A micro-terrain wind power calculation method is characterized by comprising the following steps:
constructing a micro-terrain numerical prediction mode, and determining a first meteorological element sequence based on the numerical prediction mode; the first meteorological element sequence is a meteorological element prediction sequence;
acquiring a second meteorological element sequence corresponding to the first meteorological element sequence, and acquiring a wind power observation value, a fan icing thickness observation value and a strong wind speed observation value at the height of a fan hub; the second meteorological element sequence is a meteorological element observation sequence;
correcting meteorological elements based on the first meteorological element sequence and the second meteorological element sequence; calculating wind power based on the corrected meteorological elements and the wind power observed value; calculating the fan icing thickness based on the corrected meteorological elements and the fan icing thickness observation value; calculating the strong wind speed at the height of the fan hub based on the corrected meteorological elements and the observation value of the strong wind speed at the height of the fan hub;
calculating wind power under a target weather condition based on the wind power observation value, the calculated wind power, the fan icing thickness and the strong wind speed at the height of the fan hub;
said modifying meteorological elements based on said first sequence of meteorological elements and said second sequence of meteorological elements, comprising:
constructing a regression equation based on the first meteorological element sequence and the second meteorological element sequence;
correcting the first meteorological element sequence by using a regression equation;
calculating wind power based on the corrected meteorological elements and the wind power observed value, including:
constructing a measurement equation at any moment based on the corrected meteorological elements and the wind power observed value;
for any moment, constructing a forecasting equation by using a least square method based on the wind power observed value and the measurement equation;
determining the regression coefficient estimation value at the next moment based on the forecast equation at two adjacent moments, and determining the variance of the dynamic noise of the regression coefficient;
determining residual errors at all times based on the measurement equation and the forecast equation, and determining a variance matrix of measurement noise;
based on the measurement equation, the forecast equation, the variance of the dynamic noise of the regression coefficient and the variance matrix of the measurement noise, applying a generalized least square method to construct a power recurrence relation between two adjacent moments;
and calculating the wind power based on the power recurrence relation.
2. The method according to claim 1, wherein the constructing a micro-terrain numerical prediction mode and determining a first meteorological element sequence based on the numerical prediction mode comprises:
with a wind power station as a center, constructing a micro-terrain numerical prediction mode with horizontal resolution nested layer by layer and gradual amplification; the micro-terrain numerical prediction modes comprise at least two numerical prediction modes which have different scales and are nested;
acquiring a terrain elevation, a land type and a vegetation type with the minimum horizontal resolution as mode static data; selecting a target parameterization scheme, and determining the starting time and the ending time of forecasting and the time interval of result output;
acquiring meteorological data of the numerical forecasting mode of each scale, and determining initial condition data and boundary condition data of the numerical forecasting mode of each scale;
performing integral calculation based on the steps to obtain meteorological data at any moment;
repeating the steps to obtain a first meteorological element sequence in a continuous preset time interval.
3. The method according to claim 2, wherein the wind power plant station is used as a center to construct a micro-terrain numerical prediction mode with horizontal resolution nested layer by layer and progressive amplification, and the method comprises the following steps:
selecting a mesoscale weather mode as a coarse grid mode with the horizontal resolution of more than 1km multiplied by 1 km; selecting a large vortex simulated weather mode as a fine grid mode with the horizontal resolution of less than 1km multiplied by 1 km;
with a wind power station as a center, horizontal resolutions of 30m multiplied by 30m, 90m multiplied by 90m, 270m multiplied by 270m, … … and 30 multiplied by 3 are sequentially constructednm×30×3nm series of grids to form the numerical forecasting pattern.
4. The method according to claim 2, characterized in that the time interval of the result output is less than or equal to 15 minutes and the continuous preset period is equal to or greater than 60 days.
5. The method of claim 1, wherein calculating the wind power at a target weather condition based on the wind power observation, the calculated wind power, the wind turbine icing thickness, and the high wind speed at the wind turbine hub height comprises:
establishing a wind power loss model under the target weather conditions of icing and strong wind disasters based on the wind power observation value, the calculated wind power, the fan icing thickness and the strong wind speed at the height of the fan hub;
determining wind power at a target weather condition based on the predicted wind power and the wind power loss model.
6. A micro-terrain wind power computing device, comprising:
the first sequence determination module is used for constructing a micro-terrain numerical prediction mode and determining a first meteorological element sequence based on the numerical prediction mode; the first meteorological element sequence is a meteorological element prediction sequence;
the second sequence acquisition module is used for acquiring a second meteorological element sequence corresponding to the first meteorological element sequence and acquiring a wind power observation value, a fan icing thickness observation value and a strong wind speed observation value at the height of a fan hub; the second meteorological element sequence is a meteorological element observation sequence;
the data correction module is used for correcting meteorological elements based on the first meteorological element sequence and the second meteorological element sequence; calculating wind power based on the corrected meteorological elements and the wind power observed value; calculating the fan icing thickness based on the corrected meteorological elements and the fan icing thickness observation value; calculating the strong wind speed at the height of the fan hub based on the corrected meteorological elements and the observation value of the strong wind speed at the height of the fan hub;
the wind power calculation module is used for calculating the wind power under the target weather condition based on the wind power observation value, the calculated wind power, the calculated fan icing thickness and the strong wind speed at the height of the fan hub;
the data correction module is used for correcting meteorological elements based on the first meteorological element sequence and the second meteorological element sequence, and comprises:
constructing a regression equation based on the first meteorological element sequence and the second meteorological element sequence;
correcting the first meteorological element sequence by using a regression equation;
the data correction module is used for calculating wind power based on the corrected meteorological elements and the wind power observed value, and comprises:
constructing a measurement equation at any moment based on the corrected meteorological elements and the wind power observed value;
for any moment, constructing a forecasting equation by using a least square method based on the wind power observed value and the measurement equation;
determining the regression coefficient estimation value at the next moment based on the forecast equation at two adjacent moments, and determining the variance of the dynamic noise of the regression coefficient;
determining residual errors at all times based on the measurement equation and the forecast equation, and determining a variance matrix of measurement noise;
based on the measurement equation, the forecast equation, the variance of the dynamic noise of the regression coefficient and the variance matrix of the measurement noise, applying a generalized least square method to construct a power recurrence relation between two adjacent moments;
and calculating the wind power based on the power recurrence relation.
7. A micro-terrain wind power calculation system, comprising: the system comprises a micro-terrain numerical weather forecasting module, a meteorological element correcting module and a power recursion calculating module;
the micro-terrain numerical weather forecast module comprises:
the data transmission submodule is used for acquiring meteorological full court data;
the data preprocessing submodule is used for carrying out grid interpolation by utilizing the meteorological full court data according to the grid resolution of the numerical prediction mode of each layer with different scales in the micro-terrain numerical prediction mode based on the nested mode to form data required by the operation of the driving numerical calculation submodule;
the numerical calculation submodule is used for storing the simulation starting time, the simulation ending time and the parameter scheme of the numerical prediction mode and carrying out numerical calculation by combining the data formed by the data preprocessing submodule to obtain a theoretical value;
the mode result post-processing submodule is used for extracting the theoretical value based on the time interval of result output, the longitude and latitude coordinates and the altitude of the wind power plant to obtain a first meteorological element sequence;
the meteorological element correction module is used for establishing a statistical relationship between an observed value and a theoretical value of each element by using a least square method based on the first meteorological element sequence and a second meteorological element sequence which is synchronous with the first meteorological element sequence, namely constructing a regression equation, and correcting the first meteorological element sequence by using the regression equation to realize the correction of the theoretical value; the first meteorological element sequence is a meteorological element prediction sequence, and the second meteorological element sequence is a meteorological element observation sequence;
the power recursion calculation module is used for acquiring a wind power observation value, a fan icing thickness observation value and a strong wind speed observation value at the height of a fan hub, determining the wind power of each current moment and the wind power of the next moment based on the wind power observation value and the corrected meteorological elements in the same period, and sequentially and circularly iterating to calculate the wind power of each moment;
the power recursion calculation module is specifically configured to:
calculating wind power based on the corrected meteorological elements and the wind power observed value; calculating the fan icing thickness based on the corrected meteorological elements and the fan icing thickness observation value; calculating the strong wind speed at the height of the fan hub based on the corrected meteorological elements and the observation value of the strong wind speed at the height of the fan hub;
calculating wind power under a target weather condition based on the wind power observation value, the calculated wind power, the fan icing thickness and the strong wind speed at the height of the fan hub;
wherein the calculating wind power based on the corrected meteorological element and the wind power observed value comprises:
constructing a measurement equation at any moment based on the corrected meteorological elements and the wind power observed value;
for any moment, constructing a forecasting equation by using a least square method based on the wind power observed value and the measurement equation;
determining the regression coefficient estimation value at the next moment based on the forecast equation at two adjacent moments, and determining the variance of the dynamic noise of the regression coefficient;
determining residual errors at all times based on the measurement equation and the forecast equation, and determining a variance matrix of measurement noise;
based on the measurement equation, the forecast equation, the variance of the dynamic noise of the regression coefficient and the variance matrix of the measurement noise, applying a generalized least square method to construct a power recurrence relation between two adjacent moments;
and calculating the wind power based on the power recurrence relation.
8. The system of claim 7, further comprising: the system comprises a front-end service module and a data storage module;
the data storage module is used for storing various data and/or files acquired or generated by other modules in the system;
the front-end service module is used for reading the data and/or the files in the data storage module and displaying the data and/or the files based on a query request of a user.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110386651.9A CN112801429B (en) | 2021-04-12 | 2021-04-12 | Micro-terrain wind power calculation method, device and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110386651.9A CN112801429B (en) | 2021-04-12 | 2021-04-12 | Micro-terrain wind power calculation method, device and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112801429A CN112801429A (en) | 2021-05-14 |
| CN112801429B true CN112801429B (en) | 2021-07-13 |
Family
ID=75816773
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110386651.9A Active CN112801429B (en) | 2021-04-12 | 2021-04-12 | Micro-terrain wind power calculation method, device and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112801429B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20070119285A (en) * | 2006-06-15 | 2007-12-20 | 한국에너지기술연구원 | Forecasting method of wind power generation by classification of wind speed patterns |
| JP2013108462A (en) * | 2011-11-22 | 2013-06-06 | Fuji Electric Co Ltd | System and program for predicting wind power generated electricity |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102005760B (en) * | 2010-11-18 | 2012-12-26 | 西北电网有限公司 | Universal wind power short-term forecasting method |
| CN102269124B (en) * | 2011-06-30 | 2013-04-03 | 内蒙古电力勘测设计院 | Ultra-short term wind power station generated power forecasting system |
| CN103001249B (en) * | 2012-11-15 | 2016-01-20 | 中国电力科学研究院 | Based on the method for forecasting short-term power in wind power station of BP neural net |
| US10386543B2 (en) * | 2016-08-01 | 2019-08-20 | International Business Machines Corporation | Temporal bias correction in wind forecasting |
| CN109948864A (en) * | 2019-03-29 | 2019-06-28 | 国网湖南省电力有限公司 | A kind of mima type microrelief regional wind power icing prediction technique and system |
| CN111950764B (en) * | 2020-07-03 | 2024-03-22 | 国网冀北电力有限公司 | Wind power prediction correction method for power grid under extreme weather conditions |
-
2021
- 2021-04-12 CN CN202110386651.9A patent/CN112801429B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20070119285A (en) * | 2006-06-15 | 2007-12-20 | 한국에너지기술연구원 | Forecasting method of wind power generation by classification of wind speed patterns |
| JP2013108462A (en) * | 2011-11-22 | 2013-06-06 | Fuji Electric Co Ltd | System and program for predicting wind power generated electricity |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112801429A (en) | 2021-05-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Stathopoulos et al. | Wind power prediction based on numerical and statistical models | |
| CN102945508B (en) | Model correction based wind power forecasting method | |
| He et al. | Spatiotemporal analysis of offshore wind field characteristics and energy potential in Hong Kong | |
| KR101383617B1 (en) | Method and apparatus for predicting daily solar radiation level | |
| CN112506994B (en) | Power equipment flood hidden danger point monitoring and early warning method and related device | |
| CN102930177A (en) | Wind speed forecasting method based on fine boundary layer mode for wind farm in complex terrain | |
| CN101741085A (en) | Method for forecasting short-term wind-electricity power | |
| CN115345076A (en) | Wind speed correction processing method and device | |
| Tieo et al. | Suitability of power-law extrapolation for wind speed estimation on a tropical island | |
| CN112541654A (en) | Regional wind energy resource fine evaluation method combining regional climate, local daily change and multi-scale interaction characteristics | |
| CN108808671A (en) | A kind of short-term wind speed DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM method of wind power plant | |
| CN115203934A (en) | Mountain area water-reducing downscaling method based on Logistic regression | |
| CN111666725A (en) | Anemometer tower planning and site selection method and system suitable for wind power plant with non-complex terrain | |
| CN117172037B (en) | Distributed hydrologic forecasting method, device, computer equipment and medium | |
| CN114483485A (en) | Method for improving wind speed prediction of Nudging wind power plant observation data | |
| CN112801429B (en) | Micro-terrain wind power calculation method, device and system | |
| CN106355264B (en) | Reference crop evapotranspiration combined prediction method | |
| Balog et al. | Hourly forecast of solar radiation up to 48h with two runs of weather research forecast model over Italy | |
| Kang et al. | Wind resource assessment and potential development of wind farms along the entire coast of South Korea using public data from the Korea meteorological administration | |
| Mamedova | Methodology for Assessing Meteorological Observation Data to Account for Wind Potential in The Design of a Wind Power Plant | |
| Ayana et al. | A compendious approach for renewable energy assessment based on satellite and ground truth data: Bilate catchment, Rift Valley Basin, Ethiopia | |
| Kubik et al. | Reanalysis: an improved data set for simulating wind generation | |
| Lira-Argüello et al. | Forecast of the wind speed using the regional atmospheric modeling system (RAMS) and weather research and forecasting (WRF) models | |
| CN112070320B (en) | Ultra-short-term wind power prediction method and system based on dynamic harmonic regression | |
| Jiang et al. | Offshore Wind Energy Assessment Considering Different Wake Effect Models |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |