CN118095145B - CFSR-based analysis method and CFSR-based analysis device for expected power generation capacity sequence - Google Patents
CFSR-based analysis method and CFSR-based analysis device for expected power generation capacity sequence Download PDFInfo
- Publication number
- CN118095145B CN118095145B CN202410524585.0A CN202410524585A CN118095145B CN 118095145 B CN118095145 B CN 118095145B CN 202410524585 A CN202410524585 A CN 202410524585A CN 118095145 B CN118095145 B CN 118095145B
- Authority
- CN
- China
- Prior art keywords
- data sequence
- sequence
- cfsr
- wind
- predicted
- 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
- 238000010248 power generation Methods 0.000 title claims abstract description 109
- 238000004458 analytical method Methods 0.000 title claims abstract description 38
- 238000000034 method Methods 0.000 claims abstract description 63
- 230000009467 reduction Effects 0.000 claims abstract description 27
- 238000004088 simulation Methods 0.000 claims abstract description 21
- 239000012530 fluid Substances 0.000 claims description 24
- 238000003860 storage Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 11
- 230000001133 acceleration Effects 0.000 claims description 10
- 238000012937 correction Methods 0.000 claims description 7
- 238000011144 upstream manufacturing Methods 0.000 claims description 4
- 238000004891 communication Methods 0.000 claims description 3
- 230000007774 longterm Effects 0.000 abstract description 6
- 238000004590 computer program Methods 0.000 description 13
- 230000000694 effects Effects 0.000 description 8
- 238000012360 testing method Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 3
- 238000007619 statistical method Methods 0.000 description 3
- 230000032683 aging Effects 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000012950 reanalysis Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 239000000443 aerosol Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000001932 seasonal effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000035882 stress Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000012731 temporal analysis Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 238000000700 time series analysis Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Abstract
The invention discloses a CFSR-based analysis method and a CFSR-based analysis device for an expected power generation capacity sequence, and belongs to the technical field of wind power generation. The method comprises the following steps: acquiring a historical wind data sequence of a power prediction tower and a wind data sequence of CFSR; analyzing the correlation between the historical wind data sequence of the power prediction tower and the historical wind data sequence of CFSR in the same period, and calculating the predicted wind data sequence of the power prediction tower according to the ring ratio historical wind data sequence of the power prediction tower; performing simulation operation on the predicted wind data sequence to obtain a predicted theoretical power generation amount sequence of the wind power plant associated with the power prediction tower; and correcting the predicted theoretical power generation amount sequence by using a reduction coefficient to obtain a predicted power generation amount sequence of the wind power plant related to the power prediction tower. The technical scheme of the invention can calculate the predicted generated energy sequence in a long time in the future, and provides a reliable prediction result for the long-term predicted generated energy sequence.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a CFSR-based analysis method, a CFSR-based analysis device, CFSR-based analysis equipment and a storage medium for an expected power generation sequence.
Background
With the continuous accelerated development of the wind power industry, the power grid and the power transaction all need accurate long-term power generation amount prediction, so that the requirements on the prediction accuracy, the time cost and the operation cost of the month-by-month power generation amount of n months in the future are higher. The current technical route of the lunar generating capacity prediction in the industry basically adopts the technical route of short-term power prediction, for example: prediction modes from 24 hours to 7 days and 10 days in the future, and even ultra-short-term power prediction, for example: future 4 hours of prediction, and roll the updated prediction mode every 15 minutes. The short-term technical route or the ultra-short-term technical route needs to perform processes such as cleaning, analysis, statistics, reduction and the like on SCADA (supervisory control and data acquisition system, supervisory Control and Data Acquisition) data, so that the calculation cost and the time cost are relatively high. The short-term technical route or the ultra-short-term technical route needs middle-term weather forecast data of some weather forecast centers, the data length is at most 40 days, and the forecast time is short. The short-term technical route or the ultra-short-term technical route needs to adopt the technical means of machine learning, and resources and complexity are high. How to properly solve the above problems is a problem to be solved in the industry.
Disclosure of Invention
The invention provides a CFSR-based analysis method, a CFSR-based analysis device, CFSR-based analysis equipment and a storage medium, which are used for calculating a predicted required power generation sequence in a longer time in the future, and break through the limitation of the predicted time length of the conventional wind power generation.
According to a first aspect of the present invention, there is provided a method of analyzing an expected power generation amount sequence based on CFSR, the method of analyzing an expected power generation amount sequence based on CFSR comprising:
Acquiring a historical wind data sequence of a power prediction tower and a wind data sequence CFSR, wherein the wind data sequence CFSR is related to the position of the power prediction tower, and the wind data sequence CFSR comprises a CFSR historical wind data sequence and a CFSR predicted wind data sequence;
Analyzing the correlation between the historical wind data sequence of the power prediction tower and the historical wind data sequence of CFSR in the same period by using an MCP method, and calculating a predicted wind data sequence of the power prediction tower according to the ring ratio historical wind data sequence of the power prediction tower;
the predicted wind data sequence is imported into a CFD model for simulation operation, and a predicted theoretical power generation amount sequence of the wind power plant related to the power prediction tower is obtained;
and correcting the predicted theoretical power generation amount sequence by using a reduction coefficient to obtain a predicted power generation amount sequence of the wind power plant related to the power prediction tower.
In one embodiment, prior to applying the historical wind data sequence of the power prediction tower, comprising:
and verifying the integrity and the compliance of the historical wind data sequence of the power prediction tower, wherein the integrity comprises that the data quantity accords with expectations and the time sequence of the data is continuous and correct in sequence, and the compliance comprises that the range, the correlation and the trend are in compliance.
In one embodiment, the analyzing, by the MCP method, correlation between the historical wind data sequence of the power prediction tower and the contemporaneous historical wind data sequence of CFSR, and calculating, according to the ring ratio historical wind data sequence of the power prediction tower, a predicted wind data sequence of the power prediction tower includes:
Analyzing the correlation between the historical wind data sequence of the power prediction tower and the historical wind data sequence of CFSR in the same period through an MCP method;
Analyzing a first predicted wind data sequence of the power prediction tower according to the correlation and the CFSR predicted wind data sequence;
and if the time length of the predicted wind data sequence is longer than that of the CFSR predicted wind data sequence, analyzing a second predicted wind data sequence of the power prediction tower through the ring ratio historical wind data sequence of the power prediction tower, wherein the first predicted wind data sequence and the second predicted wind data sequence together form the predicted wind data sequence.
In one embodiment, the step of importing the predicted wind data sequence into a CFD model to perform a simulation operation to obtain a predicted theoretical power generation amount sequence of the wind farm associated with the power prediction tower includes:
calculating a predicted theoretical power generation sequence of a wind power plant associated with a power prediction tower according to a power curve, a thrust coefficient and wake flow influence of wind power generation sets, wherein the wake flow influence refers to airflow interference among all wind power generation sets in the wind power plant;
and calculating the wake influence through a wind acceleration factor and a wind speed attenuation coefficient, wherein the formula is as follows:
Where notation C wake denotes the wind speed decay factor, notation U downwind (x) denotes the wind speed at a certain position x from the turbine downstream of the turbine, notation U upwind denotes the wind speed upstream unaffected by the turbine, notation C t denotes the thrust factor, notation D rotor denotes the diameter of the turbine rotor, notation x denotes the distance of the upwind fan from the position where the wake is currently to be calculated, and notation k is half the turbulence intensity at the fan hub height.
In one embodiment, the wind acceleration factor comprises:
And calculating an equation of Reynolds average Navier-Stokes through a CFD model to obtain wind acceleration factors of all sites of the wind power plant compared with the power prediction tower, wherein the equation is as follows:
Where symbol ρ is the density of the fluid, symbol ui is the component of the fluid velocity i direction, symbol uj is the component of the fluid velocity j direction, symbol p is the pressure of the fluid, symbol x i and symbol x j represent the spatial variables in direction i and direction j, respectively, symbol μ is the dynamic viscosity of the fluid, symbol f i is the component of the external force acting on the fluid in the i direction, symbol ∂ is the partial derivative.
In one embodiment, the correcting the predicted theoretical power generation amount sequence by using a reduction coefficient to obtain a predicted power generation amount sequence of the wind power plant associated with the power prediction tower includes:
Calculating a reduction coefficient through model deviation, power curve deviation and climate deviation;
And calculating through the reduction coefficient and the predicted theoretical power generation amount sequence to obtain a predicted power generation amount sequence of the wind power plant associated with the power prediction tower.
According to a second aspect of the present invention, there is provided an analysis apparatus based on CFSR expected power generation amount sequences, comprising:
An acquisition module for acquiring a historical wind data sequence of a power prediction tower and a wind data sequence CFSR, wherein the wind data sequence CFSR is related to the position of the power prediction tower, and the wind data sequence CFSR comprises a CFSR historical wind data sequence and a CFSR predicted wind data sequence;
The calculation module is used for analyzing the correlation between the historical wind data sequence of the power prediction tower and the historical wind data sequence of the CFSR in the same period through an MCP method and calculating a predicted wind data sequence of the power prediction tower according to the ring ratio historical wind data sequence of the power prediction tower;
The simulation module is used for guiding the predicted wind data sequence into a CFD model for simulation operation to obtain a predicted theoretical power generation amount sequence of the wind power plant related to the power prediction tower;
And the correction module is used for correcting the predicted theoretical power generation amount sequence by using the reduction coefficient to obtain a predicted power generation amount sequence of the wind power plant related to the power prediction tower.
In one embodiment, the acquisition module, the calculation module, the simulation module, and the correction module are controlled to implement any of the above-described analysis methods based on the expected power generation amount sequence CFSR.
According to a third aspect of the present invention, there is provided an electronic device comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements any of the methods for analyzing the expected power generation sequence based on CFSR.
According to a fourth aspect of the present invention, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement any of the above-described methods of analysing a sequence of expected power generation based on CFSR.
In summary, the invention provides a method and a device for analyzing an expected power generation amount sequence based on CFSR, wherein the method comprises the following steps: acquiring a historical wind data sequence of a power prediction tower and a wind data sequence CFSR, wherein the wind data sequence CFSR is related to the position of the power prediction tower, and the wind data sequence CFSR comprises a CFSR historical wind data sequence and a CFSR predicted wind data sequence; analyzing the correlation between the historical wind data sequence of the power prediction tower and the historical wind data sequence of CFSR in the same period by using an MCP method, and calculating a predicted wind data sequence of the power prediction tower according to the ring ratio historical wind data sequence of the power prediction tower; the predicted wind data sequence is imported into a CFD model for simulation operation, and a predicted theoretical power generation amount sequence of the wind power plant related to the power prediction tower is obtained; and correcting the predicted theoretical power generation amount sequence by using a reduction coefficient to obtain a predicted power generation amount sequence of the wind power plant related to the power prediction tower. According to the technical scheme, the predicted generated energy sequence in a long time in the future can be calculated, the limit of the predicted time length of the conventional wind power generation is broken through, and a reliable prediction result is provided for the long-term predicted generated energy sequence.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for analyzing an expected power generation sequence based on CFSR according to an example embodiment of the present invention;
FIG. 2 is a flow chart of another method for analyzing a sequence of expected power generation based on CFSR according to an embodiment of the present invention;
FIG. 3 is a flowchart of step S12 of an analysis method based on CFSR expected power generation sequence according to an embodiment of the present invention;
FIG. 4 is a flowchart of step S13 of an analysis method based on CFSR expected power generation sequence according to an embodiment of the present invention;
FIG. 5 is a flowchart of step S14 of a method for analyzing a sequence of expected power generation based on CFSR according to an embodiment of the present invention;
FIG. 6 is a block diagram of an analysis device based on CFSR expected power generation sequences provided by an embodiment of the present invention;
Fig. 7 is a block diagram of an electronic device according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a predicted generated energy sequence and a monthly average wind speed based on a CFSR predicted generated energy sequence according to an embodiment of the present invention.
Detailed Description
Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail below with reference to the accompanying drawings and the detailed embodiments. It should be understood that the particular embodiments described herein are meant to be illustrative of the application only and not limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the application by showing examples of the application.
It is noted that relational terms such as first and second, and the like are 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. Moreover, 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 … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As shown in fig. 1, the present invention provides an analysis method of an expected power generation amount sequence based on CFSR, the analysis method of an expected power generation amount sequence based on CFSR comprising:
in step S11, a historical wind data sequence of a power prediction tower and a wind data sequence of CFSR are acquired, wherein the wind data sequence of CFSR is related to the position of the power prediction tower, and the wind data sequence of CFSR comprises a CFSR historical wind data sequence and a CFSR predicted wind data sequence;
in step S12, analyzing the correlation between the historical wind data sequence of the power prediction tower and the historical wind data sequence of CFSR in the same period by an MCP method, and calculating a predicted wind data sequence of the power prediction tower according to the ring ratio historical wind data sequence of the power prediction tower;
In step S13, the predicted wind data sequence is imported into a CFD model to perform simulation operation, so as to obtain a predicted theoretical power generation sequence of the wind farm associated with the power prediction tower;
in step S14, the predicted theoretical power generation amount sequence is corrected by using a reduction coefficient, and a predicted power generation amount sequence of the wind power plant associated with the power prediction tower is obtained.
In one embodiment, a historical wind data sequence of the power prediction tower and a wind data sequence of CFSR (wind data sequence, climate Forecast SYSTEM REANALYSIS) are obtained, wherein the historical wind data sequence of the power prediction tower comprises relevant meteorological parameters such as wind speed, wind direction and the like measured by the power prediction tower in the past period of time, and can be used for predicting future wind speed. CFSR data provides high resolution weather analysis data for global coverage. The data includes not only historical meteorological data, but also model-based future meteorological predictions. To correlate CFSR data with the location of the power prediction tower, CFSR grid point data closest to the wind farm geographic location is typically selected. CFSR is a data set designed and implemented as a global, high-resolution, atmospheric-marine-terrestrial surface-sea ice coupling system. Global coverage of high resolution analysis data published by the national environmental forecast center (NCEP, national Centers for Environmental Prediction) in 2010. CFSR the atmospheric model used was T382L64 with a horizontal resolution of about 38km, which significantly improved the spatial resolution of CFSR compared to other global re-analysis data such as NCEP/NCAR, NCEP/DOE, ERA-40, JRA-25. CFSR is the first analysis dataset of NCEP that considers the coupling of atmosphere and sea and incorporates sea-ice modes. Changes in CO 2, aerosols, and other trace gases were considered between 1979 and 2009. The Special Sensor Microwave/Imager (SSM/I) inverted sea-surface wind farm was assimilated and satellite observed radiance (including TOVS, MSU, ATOVS and GOES) was assimilated using Grid-point Statistical Interpolation (GSI) scheme, although on business ncip has been implemented directly for many years, CFSR is the first time that ncip assimilates satellite radiance directly into his global re-analysis product. By improving the resolution of these modes and the improvement in assimilation techniques CFSR, a more detailed and accurate description of the atmosphere can be made. CFSR the duration of forecast data is 280d, namely 7 months, and is the forecast data with the longest current popular forecast duration. The correlation is analyzed and a predicted wind data sequence is calculated by the MCP method, which is a statistical method for analyzing the correlation between two data sources, here the historical wind data of the power prediction tower and the historical wind data of CFSR. The correlation obtained by the analysis can help to understand the statistical link between the two data sources in wind speed and wind direction. The calculation of the predicted wind data sequence is based on an association between power prediction tower historical data and CFSR historical data, and the MCP method can be used to estimate wind data for the power prediction tower over a future period of time. By using CFSR prediction data, corresponding power prediction tower prediction data is calculated according to the established correlation model. And (3) importing the predicted wind data sequence and the geographic position of the machine site into a CFD (computational fluid dynamics) model for simulating the flow characteristics of wind in the complex terrain and the wind farm layout. The wind speed of each fan can be obtained through simulation by inputting the predicted wind data sequence into the CFD model, and then the predicted theoretical power generation sequence of the wind power plant associated with the power prediction tower is calculated. The predicted theoretical power generation sequence is modified by using reduction coefficients, which take into account various losses that may occur in actual operation of the wind farm, such as mechanical losses, electrical losses, meteorological condition changes, etc., and which are typically statistically derived based on historical operating data. And multiplying the predicted theoretical power generation amount sequence by a reduction coefficient to obtain a more practical predicted power generation amount sequence. The predicted amount of generated energy sequence provides a highly probabilistic and more accurate estimate of the amount of generated energy of the wind farm over a period of time in the future.
According to the technical scheme, the predicted generated energy sequence in a long time in the future can be calculated, the limit of the predicted time length of the conventional wind power generation is broken through, and a reliable prediction result is provided for the long-term predicted generated energy sequence.
In one embodiment, as shown in fig. 2, the method further includes the following step S21:
In step S21, the integrity of the historical wind data sequence of the power prediction tower is checked, wherein the integrity includes that the data amount is consistent with the expectation and that the time sequence of the data is continuous and in correct order, and the compliance includes that the range, the correlation and the trend are all compliant.
In one embodiment, the purpose of the integrity check is to ensure that the amount of data collected is in line with expectations and that the arrangement of the data is sequential and in the correct order in time. It is checked whether the data amount reaches a predetermined number of sampling points. For example, if one power prediction tower expects to record data every 10 minutes, then there should be 6 data points per hour and 144 data points per day. Checking whether these data points are complete can help confirm that the recording function of the device is normal to avoid data loss. Confirm whether the time stamps of the data records are consecutive and arranged in the correct time order, with or without missing or duplicate time points. To ensure the temporal integrity of the data, providing a reliable basis for subsequent time series analysis or time-based predictive models. In a preferred embodiment, the data of the power prediction tower is tested according to GB/T18710-2002 wind farm wind energy resource assessment method, NB/T31147-2018 wind farm engineering wind energy resource measurement and assessment technical Specification and combined with actual conditions, including integrity test and rationality test.
Compliance testing is to ensure that data meets analytical requirements in quality, and is primarily concerned with range testing, correlation testing, and trend testing. It is checked whether the data values are within a reasonably expected range, e.g. wind speed data should be between a physically possible minimum and maximum value, e.g. wind speeds of 0-30 m/s, in order to identify possible sensor faults or data logging errors. Statistical correlations between wind speed data and other relevant meteorological parameters, such as wind direction and temperature, etc., are analyzed. For example, it is expected that wind speeds may vary significantly in certain wind directions, and it may be confirmed whether reasonable physical and statistical relationships between data are maintained, ensuring logical consistency of the data. Checking whether there is an abnormal trend in the dataset, such as a long-term increasing or decreasing wind speed, may be due to environmental changes, equipment aging, or other external influencing factors. Trend analysis helps identify potential anomalies or systematic deviations. Through the above inspection, the quality and availability of the historical wind data of the power prediction tower can be ensured, and the integrity and compliance of the data directly influence the accuracy of wind power prediction.
In one embodiment, as shown in FIG. 3, step S12 includes the following steps S31-S33:
in step S31, analyzing the correlation between the historical wind data sequence of the power prediction tower and the historical wind data sequence of CFSR in synchronization by an MCP method;
in step S32, analyzing a first predicted wind data sequence of the power prediction tower based on the correlation and the CFSR predicted wind data sequence;
in step S33, if the time length of the predicted wind data sequence is greater than the time length of the predicted wind data sequence of CFSR, a second predicted wind data sequence of the power prediction tower is analyzed by the ring ratio historical wind data sequence of the power prediction tower, and the first predicted wind data sequence and the second predicted wind data sequence together form the predicted wind data sequence.
In one embodiment, a wind power prediction strategy is performed using the MCP (measurement-Correlate-prediction) method for analyzing and predicting wind data sequences of a power prediction tower. A historical wind data sequence of the power prediction tower and CFSR (Climate Forecast SYSTEM REANALYSIS) historical wind data sequence time synchronized therewith are collected. Using the MCP method, the correlation between these two data sequences was analyzed. This typically involves statistical analysis, such as calculating correlation coefficients, to determine CFSR the reliability and accuracy of the data in predicting the actual wind speed. This step is to ensure that CFSR model data has sufficient similarity with field measurement data to be used in predicting future wind data sequences for the power prediction tower.
Based on the correlation from the analysis, a first predicted wind data sequence of the power prediction tower is generated, such as adjusting the average and variability of wind speed, in combination with the future wind data sequence provided by CFSR. If CFSR's predicted data time length is insufficient to cover the required prediction period, then the ring ratio historical wind data sequence of the power prediction tower is used to estimate a wind data sequence that is outside of the CFSR's predicted range. Ring ratio analysis is a method of predicting future trends by comparing data over the same time period of history. For example, if CFSR data only covers 7 months into the future, and a prediction of 12 months is required, an additional 5 months can be estimated using the same month wind data sequence for the previous years. And combining the first predicted wind data sequence based on CFSR prediction and the second predicted wind data sequence based on ring ratio historical data to form a complete predicted wind data sequence. It is also desirable to ensure that the two-part data transitions smoothly at the junction. The prediction method of the technical scheme has the advantages that real-time observation data and analysis data are combined, so that accuracy and reliability of wind power prediction are improved.
In one embodiment, as shown in FIG. 4, step S13 includes the following steps S41-S42:
in step S41, a predicted theoretical power generation sequence of a wind farm associated with a power prediction tower is calculated according to a power curve, a thrust coefficient and wake effects of wind turbines, wherein the wake effects refer to airflow interference among wind turbines in the wind farm;
in step S42, the wake effect is calculated from the wind acceleration factor and the wind speed decay factor as follows:
Where notation C wake denotes the wind speed decay factor, notation U downwind (x) denotes the wind speed at a certain position x from the turbine downstream of the turbine, notation U upwind denotes the wind speed upstream unaffected by the turbine, notation C t denotes the thrust factor, notation D rotor denotes the diameter of the turbine rotor, notation x denotes the distance of the upwind fan from the position where the wake is currently to be calculated, and notation k is half the turbulence intensity at the fan hub height.
In one embodiment, the power curve of a wind turbine describes the power generation capacity of the turbine at different wind speeds. Typically, each turbine model has a specific power curve for predicting the theoretical output power at a given wind speed. Thrust coefficient is a parameter in wind turbine design that reflects the thrust produced by the wind acting on the turbine blades. This coefficient directly affects the intensity and characteristics of the wake. Wake effects refer to the situation where the disturbance of the airflow of one turbine affects the other turbines downwind. The wake can reduce the wind speed at the downwind turbine, thereby affecting its power generation efficiency. Within a wind farm, accounting for wake effects is critical to predicting the power production of the entire wind farm.
Calculating the wake effect by wind acceleration factor and wind speed decay factor, equation (1) is as follows:
（1）
Where notation C wake denotes the wind speed decay factor, notation U downwind (x) denotes the wind speed at a certain position x from the turbine downstream of the turbine, notation U upwind denotes the wind speed upstream unaffected by the turbine, notation C t denotes the thrust factor, notation D rotor denotes the diameter of the turbine rotor, notation x denotes the distance of the upwind fan from the position where the wake is currently to be calculated, and notation k is half the turbulence intensity at the fan hub height.
Further, CFD is an abbreviation for computational fluid dynamics, which involves numerical analysis and data structures to solve and analyze fluid flow problems. CFD models are typically used to simulate wind flow patterns in wind farms, taking into account terrain, atmospheric stability, wake effects, and other factors. The Reynolds average Navier-Stokes equation describes the basic principle of fluid motion, including pressure gradients, fluid viscosity, turbulence stresses, and the like. In the wind power field, this equation is used to calculate the speed distribution and direction of the wind at different sites within the wind farm, and is used to evaluate the performance of the turbine. Calculating a Navier-Stokes equation of Reynolds average through a CFD model to obtain wind acceleration factors of all sites of a wind power plant compared with the power prediction tower, wherein the formula (2) is as follows:
（2）
Where symbol ρ is the density of the fluid, symbol ui is the component of the fluid velocity i direction, symbol uj is the component of the fluid velocity j direction, symbol p is the pressure of the fluid, symbol x i and symbol x j represent the spatial variables in direction i and direction j, respectively, symbol μ is the dynamic viscosity of the fluid, symbol f i is the component of the external force acting on the fluid in the i direction, symbol ∂ is the partial derivative. Wherein ui and uj together form the wind speed at the site, and the wind acceleration factor may be derived from analysis of the measured wind speed of the power prediction tower and the wind speeds at the respective sites associated with the power prediction tower.
By combining the formula (1) and the formula (2), the predicted theoretical power generation sequence of the wind power plant can be calculated more accurately. The influence of each turbine is calculated by equation (1) and then the wind speed and pressure distribution are further refined using equation (2). Using the wind speed data and the power curves of the turbines, the power production of each turbine and the entire wind farm can be predicted.
In one embodiment, as shown in FIG. 5, step S14 includes the following steps S51-S52:
In step S51, a reduction coefficient is calculated from the model deviation, the power curve deviation and the climate deviation;
In step S52, a predicted power generation amount sequence of the wind farm associated with the power prediction tower is obtained by calculating the reduction coefficient and the predicted theoretical power generation amount sequence.
In one embodiment, model bias refers to the difference between predicted and actual observations due to model simplification or error. In wind power prediction, model deviation may be caused by simplification of a CFD model, inaccurate parameter setting or boundary condition selection, and the like. The rate curve deviates from the curve of the unit output power at a specific wind speed. In practice, the actual power output may deviate from the standard power curve due to various factors such as machine aging, maintenance conditions, air density variations, etc. Climate bias calculation refers to the effect of long-term climate change or seasonal climate conditions on wind speed. Because there may be differences in the historical climate data and future climate conditions, using the historical data to predict future wind speeds may introduce climate deviations. In view of the above, a reduction coefficient for adjusting the theoretical power generation amount to a predicted power generation amount closer to the actual case can be calculated.
The calculated reduction coefficient is applied to the theoretical power generation sequence to correct the deviation caused by the model, the power curve and the climate. The corrected power generation amount sequence is the predicted power generation amount obtained by taking the actual operation condition into consideration, namely the actual power generation amount possibly achieved by the future actual operation of the wind farm. The calculation of the reduction coefficient generally requires a large amount of historical data and complex statistical analysis, and wind power values in the future are predicted in the future by optimizing wind power plant management.
For example, the prediction of the power generation amount for the next year for a certain 125MW wind project is shown in FIG. 8. Wind speed data and wind direction data are collected over a specified period of time (2021/12/1-2023/10/31). Cleaning these data means to exclude any errors or outliers, ensuring the quality and accuracy of the data. Checking and ensuring the integrity of the data, i.e. the amount of data per month should exceed an expected value of 90%, involves checking for data loss, equipment failure or other factors that may affect the integrity of the data. And collecting synchronous generating capacity data including the online electric quantity and the lost electric quantity. CFSR data covering the predicted period (2023/11-2024/5) and the sent period (2021/12/1-2023/10/31) are downloaded. CFSR data provides historical and predicted climate and weather patterns that are critical to predicting power generation. The measure-correlation-prediction method is used to evaluate the correlation of the data of the power prediction tower and CFSR data. If the correlation coefficient R2 exceeds 0.7 and the two trends agree, then the prediction is considered reliable. Wind flow patterns at different sites were simulated using computational fluid dynamics software. The theoretical power curve and the thrust coefficient of the unit are considered in the simulation, and the month-by-month theoretical power generation capacity is obtained. And comparing the theoretical power generation amount of the generated time period with the actual network power to obtain a reduction coefficient. The reduction coefficient reflects the proportion of the actual power generation amount to the theoretical power generation amount, and various losses and deviations are considered. And multiplying the month-by-month theoretical electric quantity of the predicted period by the reduction coefficient to obtain the generated energy of the predicted period.
In one embodiment, FIG. 6 is a block diagram of an analysis device based on CFSR expected power generation sequences, according to an example embodiment. As shown in fig. 6, the analysis device based on the CFSR expected power generation amount sequence includes an acquisition module 61, a calculation module 62, a simulation module 63, and a correction module 64.
The acquiring module 61 is configured to acquire a historical wind data sequence of a power prediction tower and a wind data sequence CFSR, where the wind data sequence CFSR is related to a position of the power prediction tower, and the wind data sequence CFSR includes a CFSR historical wind data sequence and a CFSR predicted wind data sequence;
The calculating module 62 is configured to analyze, by using an MCP method, a correlation between the historical wind data sequence of the power prediction tower and the contemporaneous historical wind data sequence of CFSR, and calculate, according to the ring ratio historical wind data sequence of the power prediction tower, a predicted wind data sequence of the power prediction tower;
The simulation module 63 is configured to introduce the predicted wind data sequence into a CFD model for performing a simulation operation, so as to obtain a predicted theoretical power generation amount sequence of the wind farm associated with the power prediction tower;
The correction module 64 is configured to correct the predicted theoretical power generation amount sequence by using a reduction coefficient, so as to obtain a predicted power generation amount sequence of the wind farm associated with the power prediction tower.
The acquisition module 61, the calculation module 62, the simulation module 63, and the correction module 64 included in the block diagram of the analysis apparatus for an expected power generation amount sequence based on CFSR are controlled to execute the analysis method for an expected power generation amount sequence based on CFSR set forth in any one of the embodiments described above.
As shown in fig. 7, the present invention provides an electronic device 700, including: a processor 701 and a memory 702 storing computer program instructions;
The processor 701, when executing the computer program instructions, obtains a historical wind data sequence of a power prediction tower and a wind data sequence of CFSR, wherein the wind data sequence of CFSR is related to the position of the power prediction tower, and the wind data sequence of CFSR comprises a CFSR historical wind data sequence and a CFSR predicted wind data sequence; analyzing the correlation between the historical wind data sequence of the power prediction tower and the historical wind data sequence of CFSR in the same period by using an MCP method, and calculating a predicted wind data sequence of the power prediction tower according to the ring ratio historical wind data sequence of the power prediction tower; the predicted wind data sequence is imported into a CFD model for simulation operation, and a predicted theoretical power generation amount sequence of the wind power plant related to the power prediction tower is obtained; and correcting the predicted theoretical power generation amount sequence by using a reduction coefficient to obtain a predicted power generation amount sequence of the wind power plant related to the power prediction tower.
The invention provides a computer readable storage medium, wherein computer program instructions are stored on the computer readable storage medium, when the computer program instructions are executed by a processor, a historical wind data sequence of a power prediction tower and a wind data sequence of CFSR are obtained, the wind data sequence of CFSR is related to the position of the power prediction tower, and the wind data sequence of CFSR comprises a CFSR historical wind data sequence and a CFSR predicted wind data sequence; analyzing the correlation between the historical wind data sequence of the power prediction tower and the historical wind data sequence of CFSR in the same period by using an MCP method, and calculating a predicted wind data sequence of the power prediction tower according to the ring ratio historical wind data sequence of the power prediction tower; the predicted wind data sequence is imported into a CFD model for simulation operation, and a predicted theoretical power generation amount sequence of the wind power plant related to the power prediction tower is obtained; and correcting the predicted theoretical power generation amount sequence by using a reduction coefficient to obtain a predicted power generation amount sequence of the wind power plant related to the power prediction tower.
It is to be understood that the specific features, operations and details described herein before with respect to the method of the invention may be similarly applied to the apparatus and system of the invention, or vice versa. In addition, each step of the method of the present invention described above may be performed by a corresponding component or unit of the apparatus or system of the present invention.
It is to be understood that the various modules/units of the apparatus of the invention may be implemented in whole or in part by software, hardware, firmware, or a combination thereof. Each module/unit may be embedded in the processor of the computer device in hardware or firmware form or independent of the processor, or may be stored in the memory of the computer device in software form for the processor to call to perform the operations of each module/unit. Each module/unit may be implemented as a separate component or module, or two or more modules/units may be implemented as a single component or module.
In one embodiment, a computer device is provided that includes a memory and a processor, the memory having stored thereon computer instructions executable by the processor, the computer instructions, when executed by the processor, directing the processor to perform the steps of the method of the embodiments of the invention. The computer device may be broadly a server, a terminal, or any other electronic device having the necessary computing and/or processing capabilities. In one embodiment, the computer device may include a processor, memory, network interface, communication interface, etc. connected by a system bus. The processor of the computer device may be used to provide the necessary computing, processing and/or control capabilities. The memory of the computer device may include a non-volatile storage medium and an internal memory. The non-volatile storage medium may have an operating system, computer programs, etc. stored therein or thereon. The internal memory may provide an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface and communication interface of the computer device may be used to connect and communicate with external devices via a network. Which when executed by a processor performs the steps of the method of the invention.
The present invention may be implemented as a computer readable storage medium having stored thereon a computer program which, when executed by a processor, causes steps of a method of an embodiment of the present invention to be performed. In one embodiment, a computer program is distributed over a plurality of computer devices or processors coupled by a network such that the computer program is stored, accessed, and executed by one or more computer devices or processors in a distributed fashion. A single method step/operation, or two or more method steps/operations, may be performed by a single computer device or processor, or by two or more computer devices or processors. One or more method steps/operations may be performed by one or more computer devices or processors, and one or more other method steps/operations may be performed by one or more other computer devices or processors. One or more computer devices or processors may perform a single method step/operation or two or more method steps/operations.
Those of ordinary skill in the art will appreciate that the method steps of the present invention may be implemented by a computer program, which may be stored on a non-transitory computer readable storage medium, to instruct related hardware such as a computer device or a processor, which when executed causes the steps of the present invention to be performed. Any reference herein to memory, storage, database, or other medium may include non-volatile and/or volatile memory, as the case may be. Examples of nonvolatile memory include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage, hard disk, solid state disk, and the like. Examples of volatile memory include Random Access Memory (RAM), external cache memory, and the like.
The technical features described above may be arbitrarily combined. Although not all possible combinations of features are described, any combination of features should be considered to be covered by the description provided that such combinations are not inconsistent.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (9)
1. A method of analyzing an expected power generation sequence based on CFSR, comprising:
Acquiring a historical wind data sequence of a power prediction tower and a wind data sequence CFSR, wherein the wind data sequence CFSR is related to the position of the power prediction tower, and the wind data sequence CFSR comprises a CFSR historical wind data sequence and a CFSR predicted wind data sequence;
Analyzing the correlation between the historical wind data sequence of the power prediction tower and the historical wind data sequence of CFSR in the same period by using an MCP method, and calculating a predicted wind data sequence of the power prediction tower according to the ring ratio historical wind data sequence of the power prediction tower;
the predicted wind data sequence is imported into a CFD model for simulation operation, and a predicted theoretical power generation amount sequence of the wind power plant related to the power prediction tower is obtained;
Correcting the predicted theoretical power generation amount sequence by using a reduction coefficient to obtain a predicted power generation amount sequence of the wind power plant related to the power prediction tower;
The method for analyzing the correlation between the historical wind data sequence of the power prediction tower and the historical wind data sequence of CFSR in the same period by using the MCP method, and calculating the predicted wind data sequence of the power prediction tower according to the ring ratio historical wind data sequence of the power prediction tower comprises the following steps:
Analyzing the correlation between the historical wind data sequence of the power prediction tower and the historical wind data sequence of CFSR in the same period through an MCP method;
Analyzing a first predicted wind data sequence of the power prediction tower according to the correlation and the CFSR predicted wind data sequence;
and if the time length of the predicted wind data sequence is longer than that of the CFSR predicted wind data sequence, analyzing a second predicted wind data sequence of the power prediction tower through the ring ratio historical wind data sequence of the power prediction tower, wherein the first predicted wind data sequence and the second predicted wind data sequence together form the predicted wind data sequence.
2. A method of analyzing a sequence of expected power generation based on CFSR as claimed in claim 1, comprising, prior to applying the sequence of historical wind data for the power prediction tower:
and verifying the integrity and the compliance of the historical wind data sequence of the power prediction tower, wherein the integrity comprises that the data quantity accords with expectations and the time sequence of the data is continuous and correct in sequence, and the compliance comprises that the range, the correlation and the trend are in compliance.
3. The method for analyzing an expected power generation amount sequence based on CFSR according to claim 1, wherein the step of introducing the predicted wind data sequence into a CFD model to perform a simulation operation to obtain a predicted theoretical power generation amount sequence of a wind farm associated with the power prediction tower comprises the steps of:
calculating a predicted theoretical power generation sequence of a wind power plant associated with a power prediction tower according to a power curve, a thrust coefficient and wake flow influence of wind power generation sets, wherein the wake flow influence refers to airflow interference among all wind power generation sets in the wind power plant;
And calculating the wake influence through a wind acceleration factor and a wind speed attenuation coefficient, wherein the wind speed attenuation coefficient has the following formula:
Where notation C wake denotes the wind speed decay factor, notation U downwind (x) denotes the wind speed at a certain position x from the turbine downstream of the turbine, notation U upwind denotes the wind speed upstream unaffected by the turbine, notation C t denotes the thrust factor, notation D rotor denotes the diameter of the turbine rotor, notation x denotes the distance of the upwind fan from the position where the wake is currently to be calculated, and notation k is half the turbulence intensity at the fan hub height.
4. A method of analyzing an expected power generation amount sequence based on CFSR as claimed in claim 3, wherein the wind acceleration factor includes:
And calculating a Navier-Stokes equation of Reynolds average through a CFD model to obtain wind acceleration factors of all sites of the wind power plant compared with the power prediction tower, wherein the formula is as follows:
Where symbol ρ is the density of the fluid, symbol u i is the component of the fluid velocity i direction, symbol u j is the component of the fluid velocity j direction, symbol p is the pressure of the fluid, symbol x i and symbol x j represent the spatial variables in direction i and direction j, respectively, symbol μ is the dynamic viscosity of the fluid, symbol f i is the component of the external force acting on the fluid in the i direction, symbol ∂ is the sign of the partial derivative.
5. The method for analyzing an expected power generation amount sequence based on CFSR according to claim 1, wherein the correcting the predicted theoretical power generation amount sequence by using a reduction coefficient to obtain a predicted supposed power generation amount sequence of the wind farm associated with the power prediction tower includes:
Calculating a reduction coefficient through model deviation, power curve deviation and climate deviation;
And calculating through the reduction coefficient and the predicted theoretical power generation amount sequence to obtain a predicted power generation amount sequence of the wind power plant associated with the power prediction tower.
6. An analysis device based on CFSR's expected power generation sequence, comprising:
An acquisition module for acquiring a historical wind data sequence of a power prediction tower and a wind data sequence CFSR, wherein the wind data sequence CFSR is related to the position of the power prediction tower, and the wind data sequence CFSR comprises a CFSR historical wind data sequence and a CFSR predicted wind data sequence;
The calculation module is used for analyzing the correlation between the historical wind data sequence of the power prediction tower and the historical wind data sequence of the CFSR in the same period through an MCP method and calculating a predicted wind data sequence of the power prediction tower according to the ring ratio historical wind data sequence of the power prediction tower;
The simulation module is used for guiding the predicted wind data sequence into a CFD model for simulation operation to obtain a predicted theoretical power generation amount sequence of the wind power plant related to the power prediction tower;
the correction module is used for correcting the predicted theoretical power generation amount sequence by using the reduction coefficient to obtain a predicted power generation amount sequence of the wind power plant related to the power prediction tower;
The calculation module is further used for analyzing the correlation between the historical wind data sequence of the power prediction tower and the historical wind data sequence of CFSR in the same period through an MCP method; analyzing a first predicted wind data sequence of the power prediction tower according to the correlation and the CFSR predicted wind data sequence; and if the time length of the predicted wind data sequence is longer than that of the CFSR predicted wind data sequence, analyzing a second predicted wind data sequence of the power prediction tower through the ring ratio historical wind data sequence of the power prediction tower, wherein the first predicted wind data sequence and the second predicted wind data sequence together form the predicted wind data sequence.
7. The analysis device based on CFSR expected power generation amount sequence according to claim 6, characterized in that: the acquisition module, the calculation module, the simulation module, and the correction module are controlled to perform the method for analyzing the expected power generation amount sequence based on CFSR according to any one of claims 1 to 5.
8. A computing device, comprising:
A communication interface, a processor, a memory;
wherein the memory is for storing program instructions that, when executed by the processor, cause the computing device to implement the method of analyzing a sequence of expected power generation based on CFSR of any one of claims 1 to 5.
9. A computer readable storage medium having stored thereon program instructions, which when executed by a computer cause the computer to implement the method for analysing an expected power generation sequence based on CFSR as claimed in any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410524585.0A CN118095145B (en) | 2024-04-29 | CFSR-based analysis method and CFSR-based analysis device for expected power generation capacity sequence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410524585.0A CN118095145B (en) | 2024-04-29 | CFSR-based analysis method and CFSR-based analysis device for expected power generation capacity sequence |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN118095145A CN118095145A (en) | 2024-05-28 |
| CN118095145B true CN118095145B (en) | 2024-07-16 |
Family
ID=
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110020801A (en) * | 2019-04-08 | 2019-07-16 | 中能电力科技开发有限公司 | A kind of appraisal procedure that wind power plant is lost by external wake effect |
| CN112200346A (en) * | 2020-09-07 | 2021-01-08 | 中国农业大学 | Short-term wind power prediction method for weather fluctuation process division and matching |
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110020801A (en) * | 2019-04-08 | 2019-07-16 | 中能电力科技开发有限公司 | A kind of appraisal procedure that wind power plant is lost by external wake effect |
| CN112200346A (en) * | 2020-09-07 | 2021-01-08 | 中国农业大学 | Short-term wind power prediction method for weather fluctuation process division and matching |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11803676B2 (en) | Scalable system and engine for forecasting wind turbine failure | |
| CN111461444B (en) | Method, system, medium and electronic equipment for predicting unit power of wind power plant | |
| US10215162B2 (en) | Forecasting output power of wind turbine in wind farm | |
| US10742040B2 (en) | System and method of determining forecast error for renewable energy fluctuations | |
| Wan et al. | Development of an equivalent wind plant power-curve | |
| CN112231361B (en) | Wind power project power generation amount evaluation method based on fan operation data | |
| CN102945508B (en) | Model correction based wind power forecasting method | |
| EP3947961B1 (en) | A method for estimating remaining useful life of components of an operational wind turbine | |
| CN115526429A (en) | Decoupling analysis method for wind power prediction error, processor and storage medium | |
| CN110705768B (en) | Wind power generation power prediction optimization method and device for wind power plant | |
| CN117526274A (en) | New energy power prediction method, electronic equipment and storage medium in extreme climate | |
| Natarajan | Damage equivalent load synthesis and stochastic extrapolation for fatigue life validation | |
| Kennedy et al. | A probabilistic model for simulating long-term wind-power output | |
| Liu et al. | A Monte Carlo simulation method for probabilistic evaluation of annual energy production of wind farm considering wind flow model and wake effect | |
| CN111062516A (en) | Fan output prediction method based on GMDH (Gaussian mixture distribution) multivariate processing | |
| CN118095145B (en) | CFSR-based analysis method and CFSR-based analysis device for expected power generation capacity sequence | |
| CN118095145A (en) | CFSR-based analysis method and CFSR-based analysis device for expected power generation capacity sequence | |
| Potter et al. | Wind power data for grid integration studies | |
| Millstein et al. | A database of hourly wind speed and modeled generation for US wind plants based on three meteorological models | |
| Nielsen et al. | A Bayesian network based approach for integration of condition-based maintenance in strategic offshore wind farm O&M simulation models | |
| CN110705769B (en) | New energy power generation power prediction optimization method and device | |
| Todd et al. | An independent analysis of bias sources and variability in wind plant pre‐construction energy yield estimation methods | |
| KR20180023078A (en) | Prediction method of generation quantity in solar energy generation using weather information | |
| CN112749820A (en) | Wind power prediction method and system | |
| Tinnesand et al. | Distributed Wind Resource Assessment Framework: Functional Requirements and Metrics for Performance and Reliability Modeling |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |