CN116933570B - Method and device for evaluating power generation capacity in wind power plant redevelopment process - Google Patents
Method and device for evaluating power generation capacity in wind power plant redevelopment process Download PDFInfo
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Abstract
The application provides a method and a device for evaluating the generated energy in a wind power plant redevelopment process, comprising the following steps: acquiring historical operation data of a wind power plant, wherein the historical operation data comprises: measurement data of the wind speed tower; carrying out validity identification on the measured data of the wind speed tower, and determining an application scene corresponding to the wind power plant; under the application scene corresponding to the wind power plant, carrying out simulation deviation analysis on at least two fans associated with the wind speed tower; based on a preset correction model, correcting the wind flow field model of the fan area to be corrected to obtain a corrected wind pattern spectrum; and based on the corrected wind pattern, evaluating the generated energy in the wind power plant redevelopment process. Therefore, the historical operation data of the wind power plant are used for evaluating the generated energy in the wind power plant redevelopment process, and the evaluation of the generated energy in the wind power plant redevelopment process is effectively realized.
Description
Technical Field
The application relates to the field of wind farm development, in particular to a method and a device for evaluating the generated energy in the wind farm redevelopment process.
Background
The wind power generation can be used as clean energy, so that not only can partial thermal power and nuclear power be replaced, but also the emission of pollutants can be reduced. Therefore, wind power is developed, environmental pollution can be reduced while the same electric energy is obtained, and the wind power construction criticizing work is done, so that the method has important significance in accelerating wind power construction, pushing clean energy utilization, realizing clean production and protecting ecological environment.
However, there is currently no relevant research effort for the assessment of the amount of power generation during wind farm redevelopment.
Disclosure of Invention
One technical problem to be solved by the present application is: and the evaluation of the generated energy in the wind power plant redevelopment process is realized.
In order to solve the above technical problems, an embodiment of the present application provides a method for evaluating an electric power generation amount in a redevelopment process of a wind farm, including:
acquiring historical operation data of a wind power plant, wherein the historical operation data comprises: measurement data of a wind speed tower, the measurement data comprising: wind speed, wind direction, temperature or air pressure;
carrying out validity identification on the measured data of the wind speed tower, and determining an application scene corresponding to the wind power plant, wherein the application scene is a first target scene or a second target scene;
under the application scene corresponding to the wind power plant, carrying out simulation deviation analysis on at least two fans related to the wind speed tower, wherein the simulation deviation analysis is wind speed deviation analysis of the actual cabin wind speed of the fans and the simulated cabin wind speed, or trend difference analysis of the actual cabin wind speed change trend of the fans and the simulated cabin wind speed change trend of the fans, and the simulation deviation analysis is used for determining a fan area to be corrected;
carrying out wind flow field model correction on a to-be-corrected wind turbine area based on a preset correction model to obtain a corrected wind pattern spectrum, wherein the preset correction model is a wind flow field model based on wind turbine data, a wind flow field model based on virtual points and a wind flow field model based on mesoscale coupling;
And based on the corrected wind pattern, evaluating the generated energy in the wind power plant redevelopment process.
The embodiment of the application also provides a generating capacity assessment device in a wind power plant redevelopment process, which comprises:
the acquisition module is used for acquiring historical operation data of the wind power plant, wherein the historical operation data comprises: measurement data of a wind speed tower, the measurement data comprising: wind speed, wind direction, temperature or air pressure;
the identification module is used for effectively identifying the measurement data of the wind speed tower and determining an application scene corresponding to the wind power plant, wherein the application scene is a first target scene or a second target scene;
the analysis module is used for carrying out simulation deviation analysis on at least two fans related to the wind speed tower under the application scene corresponding to the wind power plant, wherein the simulation deviation analysis is wind speed deviation analysis of the actual cabin wind speed and the simulated cabin wind speed of the fans, or trend difference analysis of the actual cabin wind speed change trend and the simulated cabin wind speed change trend of the fans, and the simulation deviation analysis is used for determining a fan area to be corrected;
the correction module is used for correcting the wind flow field model of the region of the fan to be corrected based on a preset correction model to obtain a corrected wind pattern spectrum, wherein the preset correction model is a wind flow field model based on fan data, a wind flow field model based on virtual points and a wind flow field model based on mesoscale coupling;
And the evaluation module is used for evaluating the generated energy in the wind power plant redevelopment process based on the corrected wind pattern.
The embodiment of the application also provides a computer device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the power generation amount evaluation method in the re-development process of the wind power plant in any embodiment when executing the computer program.
The embodiment of the application also provides a computer readable storage medium, and a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the steps of the power generation amount evaluation method in the wind farm redevelopment process according to any one of the embodiments are realized.
Through the technical scheme, the power generation amount evaluation method in the wind power plant redevelopment process provided by the application obtains the historical operation data of the wind power plant, wherein the historical operation data comprises: measurement data of a wind speed tower, the measurement data comprising: wind speed, wind direction, temperature or air pressure; carrying out validity identification on the measured data of the wind speed tower, and determining an application scene corresponding to the wind power plant, wherein the application scene is a first target scene or a second target scene; under the application scene corresponding to the wind power plant, carrying out simulation deviation analysis on at least two fans related to the wind speed tower, wherein the simulation deviation analysis is wind speed deviation analysis of the actual cabin wind speed of the fans and the simulated cabin wind speed, or trend difference analysis of the actual cabin wind speed change trend of the fans and the simulated cabin wind speed change trend of the fans, and the simulation deviation analysis is used for determining a fan area to be corrected; carrying out wind flow field model correction on a to-be-corrected wind turbine area based on a preset correction model to obtain a corrected wind pattern spectrum, wherein the preset correction model is a wind flow field model based on wind turbine data, a wind flow field model based on virtual points and a wind flow field model based on mesoscale coupling; and based on the corrected wind pattern, evaluating the generated energy in the wind power plant redevelopment process. Therefore, the historical operation data of the wind power plant are used for evaluating the generated energy in the wind power plant redevelopment process, the application scene is classified by the validity of the measurement data of the wind speed tower, the historical operation data of the wind power plant can be used for evaluating and correcting the generated energy in the redevelopment process under the condition that the wind speed tower exists or not, and the evaluation of the generated energy in the wind power plant redevelopment process is effectively realized.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for evaluating power generation capacity in a wind farm redevelopment process according to an embodiment of the present application.
FIG. 2 is a flow chart of another method for assessing power generation during redevelopment of a wind farm disclosed in an embodiment of the present application.
Fig. 3 is a schematic structural diagram of a power generation amount evaluation device in a wind farm redevelopment process according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in further detail below with reference to the accompanying drawings and examples. The following detailed description of the embodiments and the accompanying drawings are provided to illustrate the principles of the present application and not to limit the scope of the application, which may be embodied in many different forms and not limited to the specific embodiments disclosed herein, but rather to include all technical solutions falling within the scope of the claims.
These embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that: the relative arrangement of parts and steps, the composition of materials, numerical expressions and numerical values set forth in these embodiments should be construed as exemplary only and not limiting unless otherwise specifically stated.
In the description of the present application, unless otherwise indicated, the meaning of "plurality" is greater than or equal to two; the terms "upper," "lower," "left," "right," "inner," "outer," and the like indicate an orientation or positional relationship merely for convenience of description and to simplify the description, and do not indicate or imply that the devices or elements being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus are not to be construed as limiting the present application. When the absolute position of the object to be described is changed, the relative positional relationship may be changed accordingly.
Furthermore, the terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The "vertical" is not strictly vertical but is within the allowable error range. "parallel" is not strictly parallel but is within the tolerance of the error. The word "comprising" or "comprises" and the like means that elements preceding the word encompass the elements recited after the word, and not exclude the possibility of also encompassing other elements.
It should also be noted that, in the description of the present application, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be directly connected or indirectly connected through an intermediate medium. The specific meaning of the terms in the present application can be understood as appropriate by one of ordinary skill in the art. When a particular device is described as being located between a first device and a second device, there may or may not be an intervening device between the particular device and either the first device or the second device.
All terms used herein have the same meaning as understood by one of ordinary skill in the art to which this application pertains, unless specifically defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods, and apparatus should be considered part of the specification.
Fig. 1 is a schematic flow chart of a method for evaluating power generation capacity in a wind farm redevelopment process according to an embodiment of the present application. As shown in fig. 1, the specific process of the power generation amount evaluation method in the wind farm redevelopment process includes:
s110, acquiring historical operation data of a wind power plant, wherein the historical operation data comprises: and measuring data of the wind speed tower.
Wherein the measurement data includes: wind speed, wind direction, temperature or air pressure. The measurement data is taken as basic data of the wind farm, and can also include, but is not limited to: coordinates of a unit (fan), model parameters, SCADA (Supervisory Control And Data Acquisition) data of a step length of 10min, fault logs, a topographic map in the early development process and the position of a wind speed tower.
The wind speed tower is characterized in that monitoring devices such as an anemometer, a wind vane, temperature, air pressure and the like are arranged at different heights of the tower body, the wind speed of a site can be observed all-weather continuously, and measurement data are recorded and stored in a data recorder arranged on the tower body.
S120, carrying out validity identification on the measured data of the wind speed tower, and determining an application scene corresponding to the wind power plant.
The application scene is a first target scene or a second target scene. The application scenario may be used to describe an evaluation scenario for standard evaluation, the first target scenario may be AP1, and the second target scenario may be AP2.
In some embodiments, validity identification is performed on measurement data of a wind speed tower, and an application scene corresponding to a wind power plant is determined, including:
acquiring position and topography information of a wind speed tower and position and topography information of at least two fans associated with the wind speed tower; determining a terrain level corresponding to the wind speed tower based on the position and the terrain information of the wind speed tower; determining the terrain level corresponding to each fan based on the position and the terrain information of each fan associated with the wind speed tower; when the difference value between the terrain level corresponding to the wind speed tower and the terrain level corresponding to the at least one fan is less than or equal to 1, determining the at least one fan as a first reference fan; and determining an application scene corresponding to the wind power plant based on the magnitude relation between the processed data quantity of the wind speed tower in the effective wind direction relative to the first reference fan and a preset data quantity threshold value.
Wherein, based on the position and the topography information of the wind speed tower, determining the topography level corresponding to the wind speed tower may include: and (3) calculating RIX (terrain steep index), terrain inclination angle and maximum ridge step by adopting an application program such as IEC61400-12-1 to obtain the terrain grade corresponding to the wind speed tower.
Based on the position and terrain information of each wind turbine associated with the wind speed tower, determining a terrain level corresponding to each wind turbine may include: and (3) calculating RIX (terrain steep index), terrain inclination angle and maximum ridge step by adopting an application program such as IEC61400-12-1 to obtain the terrain grade corresponding to each fan.
In addition, when it is determined that the difference between the terrain level corresponding to the wind speed tower and the terrain level corresponding to the at least one fan is greater than 1, determining that the application scene corresponding to the wind power plant is the second target scene AP2.
The effective wind direction De of the wind speed tower relative to the first reference fan can be calculated according to IEC61400-12-1 wind generating set power curve test.
The processed data amount in the effective wind direction of the wind speed tower relative to the first reference fan can be the original data amount in the effective wind direction of the wind speed tower relative to the first reference fan, and the processed data amount can be processed according to the processing mode of the conventional wind measuring tower in the processing principle of GB/T18710-2002 wind farm wind energy resource evaluation method, so that invalid data in measured data is eliminated.
It should be noted that the preset data amount threshold may be 3000 pieces (15 min step size)/4500 pieces (10 min step size).
Based on a magnitude relation between a processed data amount of the wind speed tower in an effective wind direction relative to the first reference fan and a preset data amount threshold, determining an application scenario corresponding to the wind farm may include: if the processed data amount of the wind speed tower in the effective wind direction relative to the first reference fan is greater than or equal to a preset data amount threshold value, determining an application scene corresponding to the wind power plant as a first target scene AP1, and if the processed data amount of the wind speed tower in the effective wind direction relative to the first reference fan is less than the preset data amount threshold value, determining the application scene corresponding to the wind power plant as a second target scene AP2. Therefore, classification of evaluation scenes is effectively achieved.
S130, under the application scene corresponding to the wind power plant, performing simulation deviation analysis on at least two fans associated with the wind speed tower.
The simulation deviation analysis is wind speed deviation analysis of the actual cabin wind speed and the simulation cabin wind speed of the fan, or trend difference analysis of the actual cabin wind speed change trend and the simulation cabin wind speed change trend of the fan.
The simulation deviation analysis can be used for determining a fan area to be corrected, wherein the fan area to be corrected is a range area of fan components to be corrected, and at least one fan can be included in the fan area to be corrected.
In some embodiments, when the application scenario is the first target scenario AP1, the simulated deviation analysis is a wind speed deviation analysis of an actual nacelle wind speed of the wind turbine and the simulated nacelle wind speed. The actual nacelle wind speed may be a nacelle wind speed average.
Under the application scene that wind farm corresponds, carry out emulation deviation analysis to at least two fans that wind speed tower is correlated with, include:
simulating a wind farm based on preset wind measurement data to obtain wake flow post-wind speed corresponding to each fan; and comparing the cabin wind speed average value of the fans with the wake wind speed corresponding to each fan, and determining a fan area to be corrected.
The preset wind measurement data may include measurement data of an actual wind measurement tower, measurement data of a laser radar or other wind measurement data.
Based on the comparison of the cabin wind speed average value of the fans and the wake flow wind speed corresponding to each fan, the simulation accuracy of each fan or each area can be obtained through analysis, and therefore the fan area to be corrected is accurately identified. Correspondingly, based on the comparison of the cabin wind speed average value of the fans and the wake wind speed corresponding to each fan, the determining the fan area to be corrected can comprise: if the difference value between the cabin wind speed average value of the fans and the wake wind speed corresponding to at least one fan is larger than a preset threshold value, determining a range area formed by at least one fan as a fan area to be corrected.
In some embodiments, when the application scene is the second target scene AP2, the simulation deviation analysis is a trend difference analysis of an actual nacelle wind speed change trend of the wind turbine and a simulated nacelle wind speed change trend.
Under the application scene that wind farm corresponds, carry out emulation deviation analysis to at least two fans that wind speed tower is correlated with, include:
and comparing the theoretical cabin wind speed of the fans with the wake wind speed corresponding to each fan, and determining the fan area to be corrected.
The simulation accuracy of each fan or each region can be obtained by analysis based on comparison between the theoretical cabin wind speed of the fan and the wake wind speed corresponding to each fan, and therefore the fan region to be corrected can be accurately identified. Correspondingly, based on the comparison of the theoretical cabin wind speed of the fans and the wake wind speed corresponding to each fan, the determining the fan area to be corrected can comprise: and if the difference value between the theoretical cabin wind speed of the fan and the wake wind speed corresponding to at least one fan is larger than a preset threshold value, determining a range area formed by at least one fan as a fan area to be corrected.
S140, based on a preset correction model, correcting the wind flow field model of the fan area to be corrected, and obtaining a corrected wind pattern spectrum.
The preset correction model is a wind flow field model based on fan data, a wind flow field model based on virtual points and a wind flow field model based on mesoscale coupling.
In some embodiments, based on a preset correction model, performing wind flow field model correction on a fan area to be corrected to obtain a corrected wind graph spectrum, including:
and taking the time sequence of the wind speed and the wind direction of the fan in the fan area to be corrected as wind measurement data, and inputting the wind measurement data into a wind flow field model based on the fan data to obtain a first wind map.
The measurement data can be obtained by selecting more than 90% of fans which are not blocked in the correction area of the AP1 scene as calculation fans T2 and taking the time sequence of wind speed and wind direction corresponding to the calculation fans T2 as wind measurement data. And (3) inputting the wind measurement data into a wind flow field model based on the fan data to obtain corrected wind speeds after wake flow of each fan point, and repeating comparison and correction to finally obtain a first wind map S1.
And building a virtual wind measuring tower in the fan area to be corrected, and inputting point position data of the virtual wind measuring tower into a wind flow field model based on virtual points to obtain a second wind map.
The virtual wind measuring tower can be built by utilizing mesoscale data in a fan area to be corrected, point position data of the virtual wind measuring tower are input into a wind flow field model based on virtual points, corrected wind speeds of all fan point position wake flows are obtained, comparison and correction are repeated, and a second wind map S2 is finally obtained.
WRF (The Weather Research and Forecasting Model) simulation is conducted on the fan area to be corrected to obtain a simulation scale grid, grid data of the simulation scale grid are input into a wind flow field model based on mesoscale coupling, and a third wind map is obtained.
The simulation scale grid is a mesoscale grid at the height of N x N200 m (or 400 m) covering the field range through WRF simulation, grid data of the simulation scale grid are input into a wind flow field model based on mesoscale coupling, wind speeds after corrected wake of each fan point are obtained, comparison and correction are repeated, and finally a third wind map S3 is obtained.
The corrected wind pattern is determined based on the first wind pattern, the second wind pattern, and the third wind pattern.
Wherein the modified wind pattern may be one of a first wind pattern, a second wind pattern, and a third wind pattern, such as a first wind pattern/a second wind pattern/a third wind pattern.
The wind pattern spectrum closest to the actual running result can be selected as the corrected wind pattern based on the effectiveness of the first wind pattern, the second wind pattern and the third wind pattern, so that the wind pattern spectrum adopted by the redevelopment wind power plant in the calculation of the generated energy can be more similar to the actual situation by adding the corrected cabin wind speed change trend into the wind flow field model.
And S150, evaluating the generated energy in the wind power plant redevelopment process based on the corrected wind pattern.
For the AP1 scene and the AP2 scene, the generated energy during operation needs to be evaluated, the generated energy is distinguished according to the electric quantity damage types, and the loss coefficient during actual operation is calculated.
In some embodiments, evaluating power generation during a wind farm redevelopment based on the corrected wind profile includes:
determining a loss coefficient of wind power plant redevelopment based on SCADA data and fault logs of the wind power plant; and based on the corrected wind pattern spectrum and loss coefficient, evaluating the generating capacity of the new fan type and point location used in the wind power plant redevelopment process to obtain the generating capacity in the wind power plant redevelopment process.
Wherein the loss factor comprises: electrical loss coefficient, environmental loss coefficient, and fan performance loss coefficient.
Based on the corrected wind pattern spectrum and loss coefficient, performing generating capacity assessment on the model and the point position of a new fan used in the wind power plant redevelopment process to obtain generating capacity in the wind power plant redevelopment process, which can comprise: determining an electrical loss coefficient and an environmental loss coefficient as necessary loss coefficients for wind power plant development based on the energy duty ratio of each loss coefficient; and adding the necessary loss coefficient into the reduction coefficient, and calculating the generating capacity of the model and the point position of the new fan by adopting the corrected map to obtain the final generating capacity. Therefore, the necessary loss coefficient is added into the reduction coefficient, and the future power generation amount can be estimated more fully. The method has a certain guiding significance for the power generation amount evaluation of the wind power plant redevelopment scene, has industrial utilization value in engineering, and has good application prospects in engineering.
In this embodiment, historical operation data of a wind farm is obtained, where the historical operation data includes: measurement data of a wind speed tower, the measurement data comprising: wind speed, wind direction, temperature or air pressure; carrying out validity identification on the measured data of the wind speed tower, and determining an application scene corresponding to the wind power plant, wherein the application scene is a first target scene or a second target scene; under the application scene corresponding to the wind power plant, carrying out simulation deviation analysis on at least two fans related to the wind speed tower, wherein the simulation deviation analysis is wind speed deviation analysis of the actual cabin wind speed of the fans and the simulated cabin wind speed, or trend difference analysis of the actual cabin wind speed change trend of the fans and the simulated cabin wind speed change trend of the fans, and the simulation deviation analysis is used for determining a fan area to be corrected; carrying out wind flow field model correction on a to-be-corrected wind turbine area based on a preset correction model to obtain a corrected wind pattern spectrum, wherein the preset correction model is a wind flow field model based on wind turbine data, a wind flow field model based on virtual points and a wind flow field model based on mesoscale coupling; and based on the corrected wind pattern, evaluating the generated energy in the wind power plant redevelopment process. Therefore, the historical operation data of the wind power plant are used for evaluating the generated energy in the wind power plant redevelopment process, the application scene is classified by the validity of the measurement data of the wind speed tower, the historical operation data of the wind power plant can be used for evaluating and correcting the generated energy in the redevelopment process under the condition that the wind speed tower exists or not, and the evaluation of the generated energy in the wind power plant redevelopment process is effectively realized.
In some embodiments, before performing simulation deviation analysis on at least two fans associated with a wind speed tower in an application scenario corresponding to a wind farm, the method further includes:
carrying out anemometer deviation pretreatment on at least two fans associated with the wind speed tower; and when the application scene is the first target scene, carrying out cabin wind speed correction on at least two fans to obtain wind direction information corresponding to the fans.
For example, when the application scene is the second target scene, performing anemometer deviation preprocessing on at least two fans associated with the wind speed tower, and performing simulation deviation analysis on the at least two fans associated with the wind speed tower. When the application scene is a first target scene, carrying out anemometer deviation preprocessing on at least two fans related to the wind speed tower, carrying out cabin wind speed correction on the at least two fans, and executing simulation deviation analysis on the at least two fans related to the wind speed tower after wind direction information corresponding to the fans is obtained.
The wind speed deviation preprocessing is used for obtaining the theoretical cabin wind speed of the fan at an effective time point, wherein the effective time point is a time point in an effective time sequence corresponding to the fan, and the effective time sequence is obtained based on SCADA data of the fan. Therefore, the deviation of the wind speed trend of the fan caused by the measurement error of the anemometer can be eliminated by preprocessing the deviation of the anemometer of the cabin wind speed.
In some embodiments, anemometer bias pre-processing is performed on at least two fans associated with a wind speed tower, comprising:
classifying at least two fans based on the model, the hub height and the control mode of each fan, wherein each fan corresponds to a power curve; for fans of the same class, determining the fan corresponding to the optimal power curve as a second reference fan; removing an invalid time sequence of the fan based on the fan state recorded in the SCADA data corresponding to the fan and the energy control mode to determine an effective time sequence corresponding to the fan; and performing cubic spline interpolation on a power curve corresponding to the second reference fan based on the power value under the effective time sequence corresponding to the fan to obtain the theoretical cabin wind speed of the fan at the effective time point.
The optimal power curve can be determined based on fan time, availability and unit infinite electricity condition.
Classifying fans of the same class according to the types of fans, the heights of hubs and control modes, selecting a power curve of the fan with the optimal expression form as a reference power curve, eliminating time sequences of the fan Ti in abnormal operation and shutdown states according to fan states and energy control modes recorded in SCADA data of the fan Ti, and performing cubic spline interpolation on the reference power curve according to power values of the fan Ti in the effective time sequences, so as to obtain a theoretical wind speed value of the fan Ti at the time point, and taking the theoretical wind speed value as a theoretical cabin wind speed of the fan Ti at the time point. Thus, the theoretical nacelle wind speed of the wind turbine at the effective point in time is determined.
In some embodiments, nacelle wind speed correction is performed on at least two fans to obtain wind direction information corresponding to the fans, including:
screening an initial time sequence of the absolute wind direction of the second reference fan in the effective wind direction of the first reference fan; performing time axis matching on the basis of the measured data of the second reference fan and the measured data of the wind speed tower to obtain an updating time sequence; performing linear fitting on the basis of the fan wind speed in the updated time sequence and the measured wind speed of the wind speed tower to obtain a first fitting parameter and a second fitting parameter; and determining wind direction information corresponding to the fan based on the first fitting parameter and the second fitting parameter, wherein the wind direction information comprises a cabin wind speed average value of the fan.
Wherein the first fitting parameter may be the slope of the fitting curveaThe second fitting parameter may be the intercept of the fitting curveb。
The time axis matching is carried out on the data of the fan T1 of the screened time sequence and the wind speed data of the wind speed tower by screening the time sequence of the absolute wind direction of the reference fan T1 in the effective wind direction De, and the time axis matching is combinedForming a new time sequence array DF1, and linearly fitting the wind speed of the fan in DF1 and the wind speed of the wind speed tower to obtain a slopeaAnd intercept of bThe method comprises the steps of carrying out a first treatment on the surface of the This slope isaAnd intercept ofbCan be applied to all wind directions of T1 and other Ti fans.
Fig. 2 is a flowchart of another method for evaluating the power generation amount in the redevelopment process of the wind farm according to the embodiment.
The method comprises the steps of identifying the position and the data effectiveness of a wind power prediction tower (such as a wind speed tower) by acquiring basic data (such as historical operation data) of a wind power plant, distinguishing a calculation mode according to an identification result, and classifying the calculation mode into a standard evaluation AP1 and a brief evaluation AP2; aiming at scenes AP1 and AP2, the full-field fans are required to be classified, and anemometer deviation preprocessing is carried out; aiming at a scene AP1, cabin wind speed correction is needed, specifically, a cabin wind speed correction function and the cabin wind speed correction of the same type of unit are effectively calculated; for the scenes AP1 and AP2, simulation deviation analysis is required, for the scene AP1, deviation analysis of actual cabin wind speed and simulation wind speed is required, and for the scene AP2, trend difference analysis of actual cabin wind speed change trend and simulation wind speed is required; for the scenes AP1 and AP2, the wind flow field model needs to be corrected in one of the following three modes: simulation correction based on a fan, simulation correction based on virtual points and simulation correction based on mesoscale coupling; aiming at scenes AP1 and AP2, the generated energy during transportation is required to be evaluated, the generated energy is distinguished according to the type of electric quantity damage, and the loss coefficient during actual operation is calculated; aiming at the scenes AP1 and AP2, the wind power plant redevelopment generating capacity is required to be evaluated by combining the corrected wind graph spectrum and the loss coefficient.
In the embodiment, the evaluation scenes are classified by the position of the wind power prediction tower and the validity of the data, so that the evaluation correction of the generated energy in the redevelopment process can be performed by utilizing the operation data of the wind power plant under the condition that whether the wind power prediction tower exists or not is ensured. By preprocessing the deviation of the anemometer on the cabin wind speed, the deviation of the trend of the wind speed of the fan caused by the measurement error of the anemometer can be eliminated. And classifying the lost electric quantity through the fan operation data, so as to calculate the necessary loss coefficient in the wind power plant. By adding the corrected cabin wind speed change trend into the wind flow field model, the wind graph spectrum adopted by the redevelopment wind power plant when the generated energy is calculated can be closer to the actual situation. The necessary loss coefficient is added into the reduction coefficient, so that the future power generation amount can be fully estimated. The method has a certain guiding significance for the power generation amount evaluation of the wind power plant redevelopment scene, has industrial utilization value in engineering, and has good application prospect in engineering.
Fig. 3 is a schematic structural diagram of a power generation amount evaluation device in a wind farm redevelopment process according to the present embodiment. The power generation amount evaluation device in the wind farm redevelopment process may include: the acquisition module 310, the identification module 320, the analysis module 330, the correction module 340, and the evaluation module 350.
The obtaining module 310 is configured to obtain historical operation data of the wind farm, where the historical operation data includes: measurement data of a wind speed tower, the measurement data comprising: wind speed, wind direction, temperature or air pressure.
The identification module 320 is configured to identify validity of measurement data of the wind speed tower, and determine an application scenario corresponding to the wind farm, where the application scenario is a first target scenario or a second target scenario.
The analysis module 330 is configured to perform a simulation deviation analysis on at least two fans associated with the wind speed tower in an application scenario corresponding to the wind farm, where the simulation deviation analysis is a wind speed deviation analysis of an actual cabin wind speed of the fans and a simulated cabin wind speed, or a trend difference analysis of an actual cabin wind speed variation trend of the fans and a simulated cabin wind speed variation trend of the fans, where the simulation deviation analysis is used to determine a fan area to be corrected.
The correction module 340 is configured to perform wind flow field model correction on a wind turbine area to be corrected based on a preset correction model, so as to obtain a corrected wind pattern spectrum, where the preset correction model is a wind flow field model based on wind turbine data, a wind flow field model based on virtual points, and a wind flow field model based on mesoscale coupling.
And the evaluation module 350 is used for evaluating the electric energy generation amount in the wind power plant redevelopment process based on the corrected wind pattern.
In some embodiments, the identification module 320 is specifically configured to:
acquiring position and topography information of a wind speed tower and position and topography information of at least two fans associated with the wind speed tower; determining a terrain level corresponding to the wind speed tower based on the position and the terrain information of the wind speed tower; determining the terrain level corresponding to each fan based on the position and the terrain information of each fan associated with the wind speed tower; when the difference value between the terrain level corresponding to the wind speed tower and the terrain level corresponding to the at least one fan is less than or equal to 1, determining the at least one fan as a first reference fan; and determining an application scene corresponding to the wind power plant based on the magnitude relation between the processed data quantity of the wind speed tower in the effective wind direction relative to the first reference fan and a preset data quantity threshold value.
In some embodiments, further comprising: and a processing module.
The processing module is used for carrying out anemometer deviation preprocessing on at least two fans related to the wind speed tower, the anemometer deviation preprocessing is used for obtaining the theoretical cabin wind speed of the fans at effective time points, the effective time points are time points in effective time sequences corresponding to the fans, and the effective time sequences are obtained based on SCADA data of the fans; and when the application scene is the first target scene, carrying out cabin wind speed correction on at least two fans to obtain wind direction information corresponding to the fans.
In some embodiments, the processing module is specifically configured to:
classifying at least two fans based on the model, the hub height and the control mode of each fan, wherein each fan corresponds to a power curve; for fans of the same class, determining that the fan corresponding to the optimal power curve is a second reference fan, and determining the optimal power curve based on the fan time and the availability; removing an invalid time sequence of the fan based on the fan state recorded in the SCADA data corresponding to the fan and the energy control mode to determine an effective time sequence corresponding to the fan; and performing cubic spline interpolation on a power curve corresponding to the second reference fan based on the power value under the effective time sequence corresponding to the fan to obtain the theoretical cabin wind speed of the fan at the effective time point.
In some embodiments, the processing module is specifically configured to:
screening an initial time sequence of the absolute wind direction of the second reference fan in the effective wind direction of the first reference fan; performing time axis matching on the basis of the measured data of the second reference fan and the measured data of the wind speed tower to obtain an updating time sequence; performing linear fitting on the basis of the fan wind speed in the updated time sequence and the measured wind speed of the wind speed tower to obtain a first fitting parameter and a second fitting parameter; and determining wind direction information corresponding to the fan based on the first fitting parameter and the second fitting parameter, wherein the wind direction information comprises a cabin wind speed average value of the fan.
In some embodiments, when the application scenario is the first target scenario, the simulated deviation analysis is a wind speed deviation analysis of an actual nacelle wind speed of the wind turbine and the simulated nacelle wind speed.
The analysis module 330 is specifically configured to:
simulating a wind farm based on preset wind measurement data to obtain wake flow post-wind speed corresponding to each fan; and comparing the cabin wind speed average value of the fans with the wake wind speed corresponding to each fan, and determining a fan area to be corrected.
In some embodiments, when the application scene is the second target scene, the simulation deviation analysis is a trend difference analysis of an actual nacelle wind speed change trend of the wind turbine and a simulated nacelle wind speed change trend.
The analysis module 330 is specifically configured to:
and comparing the theoretical cabin wind speed of the fans with the wake wind speed corresponding to each fan, and determining the fan area to be corrected.
In some embodiments, the correction module 340 is specifically configured to:
taking the time sequence of the wind speed and the wind direction of the fan in the fan area to be corrected as wind measurement data, and inputting the wind measurement data into a wind flow field model based on the fan data to obtain a first wind map; building a virtual wind measuring tower in a fan area to be corrected, and inputting point position data of the virtual wind measuring tower into a wind flow field model based on virtual points to obtain a second wind map; performing WRF simulation on the fan area to be corrected to obtain a simulation scale grid, and inputting grid data of the simulation scale grid into a wind flow field model based on mesoscale coupling to obtain a third wind map; the corrected wind pattern is determined based on the first wind pattern, the second wind pattern, and the third wind pattern.
In some embodiments, the evaluation module 350 is specifically configured to:
based on SCADA data and fault logs of the wind turbine, determining loss coefficients of re-development of the wind power plant, wherein the loss coefficients comprise: electrical loss coefficient, environmental loss coefficient, and fan performance loss coefficient; and based on the corrected wind pattern spectrum and loss coefficient, evaluating the generating capacity of the new fan type and point location used in the wind power plant redevelopment process to obtain the generating capacity in the wind power plant redevelopment process.
The power generation amount evaluation device in the wind power plant redevelopment process provided by the application can execute the method embodiment, and the specific implementation principle and technical effects can be seen in the method embodiment, and the detailed description is omitted herein.
The embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device includes a memory 410 and a processor 420 communicatively coupled to each other via a system bus. It should be noted that only computer devices having components 410-420 are shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.
The computer device may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The computer device can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 410 includes at least one type of readable storage medium including non-volatile memory (non-volatile memory) or volatile memory, such as flash memory (flash memory), hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random access memory (random access memory, RAM), read-only memory (ROM), erasable programmable read-only memory (erasable programmable read-only memory, EPROM), electrically erasable programmable read-only memory (electrically erasable programmable read-only memory, EEPROM), programmable read-only memory (programmable read-only memory, PROM), magnetic memory, RAM, optical disk, etc., which may include static or dynamic. In some embodiments, the memory 410 may be an internal storage unit of a computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory 410 may also be an external storage device of a computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), or the like, which are provided on the computer device. Of course, memory 410 may also include both internal storage units of a computer device and external storage devices. In this embodiment, the memory 410 is typically used to store an operating system installed on a computer device and various types of application software, such as program codes of the above-described methods. In addition, the memory 410 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 420 is typically used to perform the overall operations of the computer device. In this embodiment, the memory 410 is used for storing program codes or instructions, the program codes include computer operation instructions, and the processor 420 is used for executing the program codes or instructions stored in the memory 410 or processing data, such as the program codes for executing the above-mentioned method.
Herein, the bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, a peripheral component interconnect (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus system may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
Another embodiment of the present application also provides a computer-readable medium, which may be a computer-readable signal medium or a computer-readable medium. A processor in a computer reads computer readable program code stored in a computer readable medium, such that the processor is capable of performing the functional actions specified in each step or combination of steps in the above-described method; a means for generating a functional action specified in each block of the block diagram or a combination of blocks.
The computer readable medium includes, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared memory or semiconductor system, apparatus or device, or any suitable combination of the foregoing, the memory storing program code or instructions, the program code including computer operating instructions, and the processor executing the program code or instructions of the above-described methods stored by the memory.
The definition of memory and processor may refer to the description of the embodiments of the computer device described above, and will not be repeated here.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The functional units or modules in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all or part of the technical solution contributing to the prior art or in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Thus, various embodiments of the present application have been described in detail. In order to avoid obscuring the concepts of the present application, some details known in the art are not described. How to implement the solutions disclosed herein will be fully apparent to those skilled in the art from the above description.
Although some specific embodiments of the present application have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present application. It will be understood by those skilled in the art that the foregoing embodiments may be modified and equivalents substituted for elements thereof without departing from the scope and spirit of the present application. In particular, the technical features mentioned in the respective embodiments may be combined in any manner as long as there is no structural conflict.
Claims (9)
1. The method for evaluating the generated energy in the redevelopment process of the wind farm is characterized by comprising the following steps of:
acquiring historical operation data of a wind farm, wherein the historical operation data comprises: measurement data of a wind speed tower, the measurement data comprising: wind speed, wind direction, temperature or air pressure;
carrying out validity identification on the measurement data of the wind speed tower, and determining an application scene corresponding to the wind power plant, wherein the application scene is a first target scene or a second target scene;
Under the application scene corresponding to the wind power plant, performing simulation deviation analysis on at least two fans related to the wind speed tower, wherein the simulation deviation analysis is wind speed deviation analysis of actual cabin wind speed and simulated cabin wind speed of the fans, or trend difference analysis of actual cabin wind speed change trend and simulated cabin wind speed change trend of the fans, and the simulation deviation analysis is used for determining a fan area to be corrected;
carrying out wind flow field model correction on the to-be-corrected wind turbine area based on a preset correction model to obtain a corrected wind pattern spectrum, wherein the preset correction model is a wind flow field model based on wind turbine data, a wind flow field model based on virtual points and a wind flow field model based on mesoscale coupling; based on a preset correction model, carrying out wind flow field model correction on the fan region to be corrected to obtain a corrected wind pattern spectrum, wherein the wind pattern spectrum comprises the following components: taking the time sequence of the wind speed and the wind direction of the fan in the fan area to be corrected as wind measurement data, and inputting the wind measurement data into the wind flow field model based on the fan data to obtain a first wind map; building a virtual wind measuring tower in the fan area to be corrected, and inputting point position data of the virtual wind measuring tower into the wind flow field model based on the virtual points to obtain a second wind map; performing WRF simulation on the fan area to be corrected to obtain a simulation scale grid, and inputting grid data of the simulation scale grid into the wind flow field model based on mesoscale coupling to obtain a third wind map; selecting a wind pattern spectrum closest to an actual running result as a corrected wind pattern based on the effectiveness of the first wind pattern, the second wind pattern and the third wind pattern;
And evaluating the generated energy in the wind power plant redevelopment process based on the corrected wind pattern.
2. The method of claim 1, wherein the identifying of validity of the measurement data of the wind speed tower, determining an application scenario corresponding to the wind farm, comprises:
acquiring the position and the topographic information of the wind speed tower and the position and the topographic information of at least two fans associated with the wind speed tower;
determining a terrain grade corresponding to the wind speed tower based on the position and the terrain information of the wind speed tower; determining a terrain level corresponding to each fan based on the position and the terrain information of each fan associated with the wind speed tower;
when the difference value between the terrain level corresponding to the wind speed tower and the terrain level corresponding to at least one fan is less than or equal to 1, determining that at least one fan is a first reference fan;
and determining an application scene corresponding to the wind power plant based on the magnitude relation between the processed data amount of the wind speed tower in the effective wind direction relative to the first reference fan and a preset data amount threshold value.
3. The method according to claim 2, further comprising, before performing simulation deviation analysis on at least two fans associated with the wind speed tower in the application scenario corresponding to the wind farm:
Carrying out anemometer deviation preprocessing on at least two fans associated with the wind speed tower, wherein the anemometer deviation preprocessing is used for obtaining the theoretical cabin wind speed of the fans at effective time points, the effective time points are time points in effective time sequences corresponding to the fans, and the effective time sequences are obtained based on SCADA data of the fans;
and when the application scene is the first target scene, carrying out cabin wind speed correction on at least two fans to obtain wind direction information corresponding to the fans.
4. A method according to claim 3, wherein the anemometer bias pre-processing of at least two fans associated with the wind tower comprises:
classifying at least two fans based on the model, the hub height and the control mode of each fan, wherein each fan corresponds to a power curve;
for fans of the same category, determining a fan corresponding to an optimal power curve as a second reference fan, wherein the optimal power curve is determined based on fan time and availability;
removing an invalid time sequence of the fan based on the fan state and the energy control mode recorded in the SCADA data corresponding to the fan so as to determine an effective time sequence corresponding to the fan;
And performing cubic spline interpolation on a power curve corresponding to the second reference fan based on the power value under the effective time sequence corresponding to the fan to obtain the theoretical cabin wind speed of the fan at the effective time point.
5. The method of claim 4, wherein performing nacelle wind speed correction on at least two fans to obtain wind direction information corresponding to the fans comprises:
screening an initial time sequence of the absolute wind direction of the second reference fan in the effective wind direction of the first reference fan;
performing time axis matching based on the measurement data of the second reference fan and the measurement data of the wind speed tower to obtain an updating time sequence;
performing linear fitting on the basis of the fan wind speed in the updated time sequence and the measured wind speed of the wind speed tower to obtain a first fitting parameter and a second fitting parameter;
and determining wind direction information corresponding to the fan based on the first fitting parameter and the second fitting parameter, wherein the wind direction information comprises a cabin wind speed average value of the fan.
6. The method of claim 5, wherein when the application scenario is the first target scenario, the simulated deviation analysis is a wind speed deviation analysis of an actual nacelle wind speed of the wind turbine and a simulated nacelle wind speed;
Under the application scene corresponding to the wind power plant, performing simulation deviation analysis on at least two fans associated with the wind speed tower, wherein the simulation deviation analysis comprises the following steps:
simulating the wind power plant based on preset wind measurement data to obtain the wake flow post wind speed corresponding to each fan;
and comparing the cabin wind speed average value of the fans with the wake wind speed corresponding to each fan, and determining a fan area to be corrected.
7. The method according to claim 6, wherein when the application scene is the second target scene, the simulation deviation analysis is a trend difference analysis of an actual cabin wind speed change trend of the wind turbine and a simulated cabin wind speed change trend;
under the application scene corresponding to the wind power plant, performing simulation deviation analysis on at least two fans associated with the wind speed tower, wherein the simulation deviation analysis comprises the following steps:
and comparing the theoretical cabin wind speed of the fans with the wake wind speed corresponding to each fan, and determining a fan area to be corrected.
8. The method of claim 1, wherein evaluating power generation during the wind farm redevelopment based on the corrected wind profile comprises:
Determining a loss coefficient of redevelopment of the wind farm based on the SCADA data and fault log of the wind turbine, the loss coefficient comprising: electrical loss coefficient, environmental loss coefficient, and fan performance loss coefficient;
and based on the corrected wind pattern and the loss coefficient, evaluating the generating capacity of a new fan model and a point position used in the wind farm redevelopment process to obtain the generating capacity in the wind farm redevelopment process.
9. An electric power generation amount evaluation device in a wind farm redevelopment process, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring historical operation data of a wind power plant, and the historical operation data comprises: measurement data of a wind speed tower, the measurement data comprising: wind speed, wind direction, temperature or air pressure;
the identification module is used for carrying out validity identification on the measurement data of the wind speed tower and determining an application scene corresponding to the wind power plant, wherein the application scene is a first target scene or a second target scene;
the analysis module is used for carrying out simulation deviation analysis on at least two fans related to the wind speed tower under the application scene corresponding to the wind power plant, wherein the simulation deviation analysis is wind speed deviation analysis of the actual cabin wind speed and the simulation cabin wind speed of the fans, or trend difference analysis of the actual cabin wind speed change trend and the simulation cabin wind speed change trend of the fans, and the simulation deviation analysis is used for determining a fan area to be corrected;
The correction module is used for carrying out wind flow field model correction on the to-be-corrected wind turbine area based on a preset correction model to obtain a corrected wind graph spectrum, wherein the preset correction model is a wind flow field model based on wind turbine data, a wind flow field model based on virtual points and a wind flow field model based on mesoscale coupling; the correction module is specifically configured to: taking the time sequence of the wind speed and the wind direction of the fan in the fan area to be corrected as wind measurement data, and inputting the wind measurement data into the wind flow field model based on the fan data to obtain a first wind map; building a virtual wind measuring tower in the fan area to be corrected, and inputting point position data of the virtual wind measuring tower into the wind flow field model based on the virtual points to obtain a second wind map; performing WRF simulation on the fan area to be corrected to obtain a simulation scale grid, and inputting grid data of the simulation scale grid into the wind flow field model based on mesoscale coupling to obtain a third wind map; selecting a wind pattern spectrum closest to an actual running result as a corrected wind pattern based on the effectiveness of the first wind pattern, the second wind pattern and the third wind pattern;
And the evaluation module is used for evaluating the generated energy in the wind power plant redevelopment process based on the corrected wind spectrum.
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| CN111680408A (en) * | 2020-05-26 | 2020-09-18 | 中国能源建设集团广东省电力设计研究院有限公司 | Wind resource map drawing method and device for offshore wind power |
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