CN116169727B - Old wind farm reconstruction project generating capacity assessment method and system without anemometry data - Google Patents
Old wind farm reconstruction project generating capacity assessment method and system without anemometry data Download PDFInfo
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Abstract
The application relates to a method and a system for evaluating the generated energy of a reconstruction project of an old wind farm without anemometry data, comprising the following steps: according to SCADA data of the old wind power plant, calculating actual power generation capacity of each fan; selecting a reference machine position; calculating an actual electric quantity scale factor of each fan of the old wind power plant relative to a reference machine position; calculating a power generation amount correction coefficient of the mesoscale wind data based on the actual power generation amount of the old wind power plant; and (3) based on the actual electric quantity scale factors, reconstructing the position arrangement of the wind power plant, and calculating and correcting the generated energy of the reconstructed wind power plant. The application solves the evaluation of the generated energy without the original wind measuring data project by means of the scale wind data and SCADA data in the wind power plant.
Description
Technical Field
The application relates to a method, a system, equipment and a medium for evaluating the generated energy of an old wind power plant reconstruction project without anemometry data, which are used in the field of evaluating the generated energy of a wind power plant.
Background
Early-built wind farms are often approaching or reaching the life span or are faced with rebuilding conditions due to low power generation efficiency. The traditional wind power project generating capacity assessment method is to input wind measurement data, topographic data and fan data of a wind power project, simulate wind resources through a CFD model and calculate the generating capacity of the project. In the power generation amount evaluation, anemometry data is indispensable.
However, the old wind farm rebuilding project may lose original anemometry data due to long time, which makes it difficult to evaluate the power generation amount of the rebuilding project. In this case, it is common practice to perform anemometry in an existing wind farm and collect anemometry data or anemometry data in a wind turbine SCADA.
Because the wind power plant is built, the flow field is completely changed compared with the flow field under the natural condition before the wind turbine is installed, wind measurement data collected by the existing method cannot reflect the actual change condition of wind in the free flow field, and further the generated energy of a project cannot be accurately estimated by direct simulation.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the prior art. Therefore, the application aims to provide a method, a system, equipment and a medium for evaluating the generated energy of an old wind farm reconstruction project without an original wind measurement data project.
In order to achieve the aim of the application, the application adopts the following technical scheme:
in a first aspect, the method for evaluating the power generation capacity of the reconstruction project of the old wind farm without anemometry data provided by the application comprises the following steps:
according to SCADA data of the old wind power plant, calculating actual power generation capacity of each fan;
selecting a reference machine position;
calculating an actual electric quantity scale factor of each fan of the old wind power plant relative to a reference machine position;
calculating a power generation amount correction coefficient of the mesoscale wind data based on the actual power generation amount of the old wind power plant;
and (3) based on the actual electric quantity scale factors, reconstructing the position arrangement of the wind power plant, and calculating and correcting the generated energy of the reconstructed wind power plant.
Further, the calculating the actual power generation amount of each fan according to the SCADA data of the old wind power plant comprises the following steps:
collecting the recent power generation data of each fan in the SCADA system of the old wind power plant, eliminating the period of serious data loss, eliminating the machine sites with more data loss, and defining the machine sites of the effective SCADA data as OT i Annual actual power generation capacity OAEP of each fan is counted i 。
Further, the selecting the reference machine bit includes: selecting a reference machine position OT in an old wind farm 0 Extracting the actualAnnual energy production OAEP 0 。
Further, the calculating the actual electric quantity scale factor of each fan of the old wind power plant relative to the reference machine position includes:
calculating theoretical wake loss WK of each fan of old wind power plant i ；
Calculating annual actual power generation capacity OAEP 'of each fan of old wind power plant without wake loss' i ：
OAEP′ i ＝OAEP i /(1-WK i )；
Calculating OT (relative to a reference machine position) of each machine position of old wind power plant 0 Is the electric quantity scale factor K of (2) i ：
K i ＝OAEP′ i /OAEP′ 0 。
Further, the calculating the power generation amount correction coefficient of the mesoscale wind data based on the actual power generation amount of the old wind farm includes:
extracting reference machine position OT 0 Theoretical power generation meso_oaep at mesoscale data 0 ；
Statistics of old and old wind farm reference machine position OT 0 Determining a reduction coefficient of the old wind power plant according to the availability of the fans, wherein the reduction coefficient of the old wind power plant comprises a reduction coefficient D of the availability of the fans 1 Reduction coefficient D of fan power curve 2 Integrated reduction coefficient value D 3 ；
Calculating a power generation amount correction coefficient C of mesoscale wind data 0 ：C 0 Actual annual energy production OAEP of=old wind farm reference machine 0 (theoretical Power production under mesoscale wind data Meso_OAEP) 0 *D 1 *D 2 *D 3 )。
Further, the step of performing the arrangement of the machine positions of the reconstructed wind power plant based on the actual electric quantity scale factors, and calculating and correcting the simulated power generation amount of the reconstructed wind power plant comprises the following steps:
based on the old wind power plant generating capacity scale factor, carrying out wind power plant reconstruction position arrangement, wherein the position of the wind power plant reconstruction is recorded as NT j ；
Rebuilding wind power plant wind resource simulation and theoretical generating capacity calculation;
correcting the simulated generated energy of the reconstructed wind power plant and calculating the Internet surfing electric quantity of the reconstructed wind power plant.
Further, correcting the simulated power generation amount of the reconstructed wind power plant and calculating the internet power of the reconstructed wind power plant, comprising:
calculation of NT j With nearby OT i The theoretical power generation difference without wake is recorded as delta AEP' j ,
Extracting reference machine position OT 0 No wake theoretical power generation NAEP' 0 ；
Rebuilding correction of theoretical electric quantity of a wind power plant, comprising:
by means of a power scaling factor K i OT for reconstruction of items i Correcting the theoretical generating capacity of the machine without wake flow, and NAEP i ＝K i *NAEP′ 0 ；
By ΔAEP' j And NAEP i Correction of OT i New machine position NT in the vicinity j Theoretical power generation without wake, NAEP j ＝NAEP i +ΔAEP′ j ；
Calculating wake loss WK 'of reconstructed wind power plant' j ；
Calculating the Internet surfing electric quantity of the reconstructed wind power plant:
wherein D is 0 For the comprehensive reduction coefficient of the wind farm, D 0 ＝D 1 *D 2 *D 3 ，C 0 Is a correction factor for the mesoscale data.
In a second aspect, the present application further provides a system for evaluating the power generation capacity of an old wind farm reconstruction project without anemometry data, including:
the actual power generation amount acquisition unit is configured to count the actual power generation amount of each fan according to SCADA data of the old wind power plant;
a machine position selection unit configured to select a reference machine position;
the calculating unit is configured to calculate the actual electric quantity scale factors of all fans of the old wind power plant relative to the reference machine position;
a correction coefficient calculation unit configured to calculate a power generation amount correction coefficient of the mesoscale wind data based on an actual power generation amount of the old wind farm;
and the generating capacity calculation unit is configured to perform the arrangement of the positions of the reconstructed wind power plant based on the actual electric quantity scale factors, and calculate and correct the simulated generating capacity of the reconstructed wind power plant.
In a third aspect, the present application also provides a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods.
In a fourth aspect, the present application also provides an electronic device, including: one or more processors, memory, and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods.
The application adopts the technical proposal and has the following characteristics: in the application, in the evaluation of the generated energy of the reconstruction project of the old wind power plant, because the historical operation data of the fans, namely SCADA data, are introduced, the generated energy of the original fans is synchronously compared, so that the real distribution condition of wind resources of the whole wind power plant is obtained, and then the evaluation of the fan arrangement and the generated energy of the reconstruction project is carried out according to the distribution of the wind resources and the SCADA data of the actual operation, so that the obtained evaluation result is more accurate and reliable than the evaluation of the single use of a small number of wind measuring tower data. Therefore, the application solves the generating capacity assessment without the original wind measurement data project by means of the scale wind data and SCADA data in the wind power plant, and can be widely applied to the generating capacity assessment of the old wind power plant reconstruction project.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Like parts are designated with like reference numerals throughout the drawings. In the drawings:
FIG. 1 is a schematic diagram of old wind farm location arrangement and reference location selection according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a position arrangement of a rebuilt wind farm according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a position arrangement of an old and a rebuilt wind farm according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the application.
Detailed Description
It is to be understood that the terminology used herein is for the purpose of describing particular example embodiments only, and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises," "comprising," "includes," "including," and "having" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order described or illustrated, unless an order of performance is explicitly stated. It should also be appreciated that additional or alternative steps may be used.
Although the terms first, second, third, etc. may be used herein to describe various elements, components, regions, layers and/or sections, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms may be only used to distinguish one element, component, region, layer or section from another region, layer or section. Terms such as "first," "second," and other numerical terms when used herein do not imply a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the example embodiments.
For ease of description, spatially relative terms, such as "inner," "outer," "lower," "upper," and the like, may be used herein to describe one element or feature's relationship to another element or feature as illustrated in the figures. Such spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures.
Because wind measurement data collected by the existing method cannot reflect the actual change condition of wind in the free flow field, and further cannot evaluate the generated energy of a project accurately through direct simulation. The application provides a method, a system, equipment and a medium for evaluating the generating capacity of a reconstruction project of an old wind farm without anemometry data, which comprise the following steps: firstly, counting the actual power generation amount of each fan according to SCADA data of an old wind power plant; selecting a reference machine position, and calculating an actual electric quantity scale factor of each fan of the old wind power plant relative to the reference machine position; calculating an electric energy generation capacity correction coefficient of the mesoscale wind data by using the actual electric energy generation capacity of the old wind power plant, the theoretical electric energy generation capacity calculated by the mesoscale data and the counted depreciation coefficient of the old wind power plant; using the generated energy scale factors of the old wind power plant, arranging the machine position of the reconstructed wind power plant, and selecting the periphery of the point position with high electric quantity scale factors by the machine position of the reconstructed wind power plant; calculating the theoretical power generation capacity of a reconstructed project by using mesoscale wind data, reconstructing a power curve of the model of the project and reconstructing a machine position point of a wind field; and correcting the theoretical power generation amount of the reconstructed wind power plant and calculating the online power. Therefore, the application solves the evaluation of the generated energy of the reconstruction project of the old wind power plant without the original wind measurement data by means of the scale wind data and SCADA data in the wind power plant.
Exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.
Embodiment one: the method for evaluating the power generation capacity of the reconstruction project of the old wind farm without the anemometry data provided by the embodiment comprises the following steps:
s1, counting the actual power generation amount of each fan according to SCADA data of an old wind power plant
In this embodiment, collect and arrange and count the actual electric quantity of each fan of old wind power plant all the year round, specifically: collecting the recent power generation data of each fan in the SCADA system of the old wind power plant, eliminating the period of serious data loss, eliminating the machine sites with more data loss, and defining the machine sites of the effective SCADA data as OT i Annual actual power generation capacity OAEP of each fan is counted i As shown in fig. 1.
Further, the effective SCADA data is effective data after missing and unreasonable data is removed.
Further, the machine sites with more missing rejected data can be selected, the annual effective SCADA data integrity rate of the selected machine sites is required to be more than 95%, otherwise, the machine sites are abandoned.
Further, the period of serious missing of the removed data can be the period of the selected machine position in the same period of the whole year, the effective SCADA data of each fan should be continuous, and the data synchronous missing rate is within 5%.
S2, selecting a reference machine position
In this embodiment, as shown in fig. 1, a representative machine is selected from the old wind farm as the reference machine OT 0 The virtual wind tower position of the new and old wind power plant is also used as the virtual wind tower position to extract the actual annual energy generation capacity OAEP of the new and old wind power plant 0 . Wherein, the representative machine position OT 0 The method has the advantages that the method selects the situation that the arrangement at the machine position is concentrated as much as possible, the landform features have similarity with other machine positions of the wind field, and the method can basically represent the average wind resource and average power generation level of the whole field.
S3, calculating actual electric quantity scale factors of all fans of the old wind power plant relative to the reference machine position
In this embodiment, the above process includes:
s31, calculating theoretical wake loss WK of each fan of the old wind power plant i
In the present embodiment, OT is set 0 The position is the position of a virtual anemometer tower, and OT is downloaded and used 0 The mesoscale data of the position can be used for calculating the theoretical generating capacity and wake loss of each fan of the old wind power plant under the mesoscale data by adopting a CFD model, wherein the theoretical generating capacity is Meso_OAEP i And wake loss of WK i 。
Further, the mesoscale data is also called as analysis data, and is obtained by inputting various historical observation data (including ground observation, satellites, radars, sonde, buoys, airplanes, ships and the like) into a meteorological numerical model, correcting errors, meshing and coordinating with other observation data to assimilate, and extracting weather parameters from each grid point and different layers in the model.
S32, calculating annual actual power generation capacity OAEP 'of each fan of old wind power plant without wake loss' i ：
OAEP′ i ＝OAEP i /(1-WK i )
S33, calculating OT (relative to a reference machine position) of each machine position of the old wind power plant 0 Is the electric quantity scale factor K of (2) i ：
K i ＝OAEP′ i /OAEP′ 0
S4, calculating an electric energy generation capacity correction coefficient of the mesoscale wind power data by using the actual electric energy generation capacity of the presage wind power plant, the theoretical electric energy generation capacity calculated by the mesoscale data and the counted presage wind power plant reduction coefficient.
In this embodiment, the above process includes:
s41, extracting the reference machine position OT in S31 0 Theoretical power generation meso_oaep at mesoscale data 0 。
S42, counting old wind power plant reference machine position OT 0 Determining a reduction coefficient of the old wind power plant according to the availability of the fans, wherein the reduction coefficient of the old wind power plant comprises a reduction coefficient D of the availability of the fans 1 Folding of fan power curveCoefficient of subtraction D 2 Integrated reduction coefficient value D 3 。
In this embodiment, the fan availability factor D 1 = (total power generation amount in the prescribed period-power generation amount loss due to malfunction and maintenance)/total power generation amount in the prescribed period.
In the embodiment, the reference machine position OT of the old wind farm is measured 0 The actual power curve of the fan, the actual power curve guarantee rate of the fan is calculated, and the reduction coefficient D of the fan power curve is determined 2 (wherein K in the formula is D 2 ). The actual power curve is measured by using a cabin top laser radar method, and the guarantee rate of the actual power curve is calculated by using the following method.
The power curve coincidence proportion calculating method comprises the following steps:
wherein K is D 2 ，F(v i ) For the probability of the ith wind speed interval, obtaining by using a Weibull cumulative distribution function; p (P) real (v i ) The actual power is the actual power curve of the middle value of the ith wind speed interval; p (P) 0 (v i ) Theoretical power curve values specified for the contract.
In this embodiment, the remaining integrated reduction coefficient value D except for the fan availability factor reduction and the power curve reduction is determined 3 ，D 3 Including the reduction caused by electrical loss, environmental impact, software simulation errors, etc., the current industry owner, design house and fan manufacturers respectively take fixed values, such as owner D 3 Taking 0.85 and 0.9 for fan manufacturers, as examples and without limitation.
S43, calculating the generated energy correction coefficient of the mesoscale wind data
And because of the difference between the mesoscale wind data and the actual wind speed of the wind farm, the simulated generated energy has errors, and the generated energy errors generated by the wind speed difference are corrected by using a correction coefficient method. The calculation method of the generated energy correction coefficient of the reference machine position of the old wind power plant comprises the following steps:
power generation amount correction coefficient C of reference machine position 0 Actual annual energy production OAEP of=old wind farm reference machine 0 (theoretical Power production under mesoscale wind data Meso_OAEP) 0 *D 1 *D 2 *D 3 )
S5, reconstructing project machine position arrangement, wind resource simulation, generating capacity calculation and generating capacity correction
In this embodiment, the above process includes:
s51, reconstructing the arrangement of the wind power plant positions based on the old wind power plant power generation amount scale factors
Specifically, as shown in fig. 2, combining the actual power generation amount of each station of the old wind power plant, performing station arrangement on the reconstructed wind power plant, and selecting K as much as possible during station arrangement i Around the machine position with large factors, the machine position of the reconstructed wind power plant is recorded as NT j 。
S52, reconstructing wind power plant wind resource simulation and theoretical generating capacity calculation
Specifically, as shown in FIG. 3, the virtual anemometer tower position OT 0 And wherein the scale wind data, fresh fan power curve, topography data, NT j And OT i Inputting CFD software to simulate wind resources of reconstructed wind power plant and theoretical generating capacity NAEP 'without wake at new and old machine sites' j 、NAEP′ i 。
S53, correcting the simulated generated energy of the reconstructed wind power plant, comprising:
s531, calculate NT j With nearby OT i The theoretical power generation difference without wake is recorded as delta AEP' j ,
ΔAEP′ j ＝NAEP′ j -NAEP′ i 。
S532, extracting a reference machine bit OT from the result of S52 0 No wake theoretical power generation NAEP' 0
S533, correction of theoretical electric quantity of reconstructed wind power plant
By means of a power scaling factor K i OT for reconstruction of items i The theoretical power generation amount of the machine without wake flow is corrected,NAEP i ＝K i *NAEP′ 0 。
by ΔAEP' j And NAEP i Correction of OT i New machine position NT in the vicinity j Theoretical power generation without wake:
NAEP j ＝NAEP i +ΔAEP′ j
s534, calculating wake loss of reconstructed wind power plant
Specifically, the virtual anemometer tower position OT 0 And wherein the scale wind data, new fan power curve, terrain data, new wind farm site NT j Inputting CFD software, calculating wake loss of the reconstructed wind power plant, and recording as WK '' j 。
S535, calculating and rebuilding wind power plant internet surfing electric quantity
The network electricity quantity of the reconstructed wind power plant is recorded as grid_NAEP:
wherein D is 0 For the comprehensive reduction coefficient of the wind farm, D 0 ＝D 1 *D 2 *D 3 ，C 0 Is a correction factor for the mesoscale data.
The method provided by the application can be used for calculating the Internet surfing electric quantity of the reconstructed wind power plant, and can be used for investment decision analysis and evaluation of reconstructed projects.
Embodiment two: the first embodiment provides a method for evaluating the power generation capacity of the old wind farm reconstruction project without anemometry data, and correspondingly, the embodiment provides a system for evaluating the power generation capacity of the old wind farm reconstruction project without anemometry data. The system provided by the embodiment can implement the method for evaluating the generating capacity of the old wind farm reconstruction project without the anemometry data, and the system can be realized by software, hardware or a combination of software and hardware. For convenience of description, the present embodiment is described while being functionally divided into various units. Of course, the functions of the units may be implemented in the same piece or pieces of software and/or hardware. For example, the system may include integrated or separate functional modules or functional units to perform the corresponding steps in the methods of embodiment one. Because the system of the embodiment is basically similar to the method embodiment, the description process of the embodiment is simpler, and the relevant points can be seen from the part of the description of the first embodiment.
The old wind power plant reconstruction project generating capacity evaluation system without anemometry data provided by the embodiment comprises:
the actual power generation amount acquisition unit is configured to count the actual power generation amount of each fan according to SCADA data of the old wind power plant;
a machine position selection unit configured to select a reference machine position;
the calculating unit is configured to calculate the actual electric quantity scale factors of all fans of the old wind power plant relative to the reference machine position;
a correction coefficient calculation unit configured to calculate a power generation amount correction coefficient of the mesoscale wind data based on an actual power generation amount of the old wind farm;
and the generating capacity calculating unit is configured to perform reconstruction wind power plant position arrangement based on the actual electric quantity scale factors, and calculate and correct the generating capacity of the reconstruction wind power plant.
Embodiment III: the present embodiment provides an electronic device corresponding to the method for evaluating the power generation capacity of the old wind farm reconstruction project without anemometry data provided in the first embodiment, where the electronic device may be an electronic device for a client, for example, a mobile phone, a notebook computer, a tablet computer, a desktop computer, etc., so as to execute the method in the first embodiment.
As shown in fig. 4, the electronic device includes a processor, a memory, a communication interface, and a bus, where the processor, the memory, and the communication interface are connected by the bus to complete communication with each other. The bus may be an industry standard architecture (ISA, industry Standard Architecture) bus, a peripheral component interconnect (PCI, peripheral Component) bus, or an extended industry standard architecture (EISA, extended Industry Standard Component) bus, among others. The memory stores a computer program that can be executed on the processor, and when the processor executes the computer program, the computer program is executed to perform the method as described below, and the implementation principle and technical effects are similar to those of the embodiment, and are not repeated herein. It will be appreciated by those skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computing device to which the present inventive arrangements may be applied, and that a particular computing device may include more or less components than those shown, or may combine some of the components, or have a different arrangement of components.
In a preferred embodiment, the logic instructions in the memory described above may be implemented in the form of software functional units and stored in a computer readable storage medium when sold or used as a stand alone product. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to 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 (RAM, random Access Memory), an optical disk, or other various media capable of storing program codes.
In a preferred embodiment, the processor may be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), or other general purpose processor, which is not limited herein.
Embodiment four: the present embodiment provides a computer program product, which may be a computer program stored on a computer readable storage medium, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer is capable of executing the method provided in the above embodiment, and its implementation principles and technical effects are similar to those of the embodiment and are not repeated herein.
In a preferred embodiment, the computer-readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device, such as, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the foregoing. The computer-readable storage medium stores computer program instructions that cause a computer to perform the method provided by the first embodiment described above.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In the description of the present specification, reference to the term "one preferred embodiment", etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the embodiments of the present specification. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application 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 technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (7)
1. The method for evaluating the power generation capacity of the old wind farm reconstruction project without the anemometry data is characterized by comprising the following steps of:
according to SCADA data of the old wind power plant, calculating actual power generation capacity of each fan;
selecting a reference machine position;
calculating an actual electric quantity scale factor of each fan of the old wind power plant relative to a reference machine position comprises the following steps:
calculating theoretical wake loss WK of each fan of old wind power plant i ；
Calculating annual actual power generation capacity OAEP 'of each fan of old wind power plant without wake loss' i ：
OAEP′ i ＝OAEP i /(1-WK i )；
Wherein OAEP i Annual actual power generation of each fan;
calculating OT (relative to a reference machine position) of each machine position of old wind power plant 0 Is the actual electric quantity scale factor K of (2) i ：
K i ＝OAEP′ i /OAEP′ 0 ；
Based on the actual power generation amount of the old wind power plant, calculating a power generation amount correction coefficient of the mesoscale wind data comprises the following steps:
extracting reference machine position OT 0 Theoretical power generation meso_oaep under mesoscale wind data 0 ；
Statistics of old and old wind farm reference machine position OT 0 Determining a reduction coefficient of the old wind power plant according to the availability of the fans, wherein the reduction coefficient of the old wind power plant comprises a reduction coefficient D of the availability of the fans 1 Reduction coefficient D of fan power curve 2 Integrated reduction coefficient value D 3 ；
Calculating a power generation amount correction coefficient C of mesoscale wind data 0 ：C 0 Actual annual energy production OAEP of=old wind farm reference machine 0 (theoretical Power production under mesoscale wind data Meso_OAEP) 0 *D 1 *D 2 *D 3 )；
Based on the actual electric quantity scale factor, the method for reconstructing the position arrangement of the wind power plant, calculating and correcting the generated energy of the reconstructed wind power plant comprises the following steps:
based on the old wind power plant generating capacity scale factor, carrying out wind power plant reconstruction position arrangement, wherein the position of the wind power plant reconstruction is recorded as NT j ；
Rebuilding wind power plant wind resource simulation and theoretical generating capacity calculation;
correcting the simulated generated energy of the reconstructed wind power plant and calculating the Internet surfing electric quantity of the reconstructed wind power plant.
2. The method for evaluating the power generation capacity of the reconstruction project of the old wind farm according to claim 1, wherein the step of counting the actual power generation capacity of each fan according to the SCADA data of the old wind farm comprises the steps of:
collecting the recent power generation data of each fan in the SCADA system of the old wind power plant, eliminating the period of serious data loss, eliminating the machine sites with more data loss, and defining the machine sites of the effective SCADA data as OT i Annual actual power generation capacity OAEP of each fan is counted i 。
3. The method for evaluating the power generation capacity of an old wind farm reconstruction project without anemometry data according to claim 1, wherein the selecting the reference machine position comprises: selecting a reference machine position OT in an old wind farm 0 Extracting the actual annual energy production OAEP 0 。
4. The method for evaluating the power generation capacity of an old wind farm reconstruction project without anemometry data according to claim 1, wherein correcting the simulated power generation capacity of the reconstructed wind farm and calculating the online power of the reconstructed wind farm comprises:
calculation of NT j With nearby OT i The theoretical power generation difference without wake is recorded as delta AEP' j ,
Extracting reference machine position OT 0 No wake theoretical power generation NAEP' 0 ；
Correction of the simulated power generation of a rebuilt wind farm, comprising:
by using the actual electric quantity scale factor K i OT for reconstruction of items i Correcting the theoretical generating capacity of the machine without wake flow, and NAEP i ＝K i *NAEP′ 0 ；
By ΔAEP' j And NAEP i Correction of OT i New machine position NT in the vicinity j Theoretical power generation without wake, NAEP j ＝NAEP i +ΔAEP′ j ；
Calculating wake loss WK 'of reconstructed wind power plant' j ；
Calculating the Internet surfing electric quantity of the reconstructed wind power plant:
wherein D is 0 For the comprehensive reduction coefficient of the wind farm, D 0 ＝D 1 *D 2 *D 3 。
5. An old wind power plant reconstruction project generating capacity evaluation system without anemometry data, which is characterized by comprising:
the actual power generation amount acquisition unit is configured to count the actual power generation amount of each fan according to SCADA data of the old wind power plant;
a machine position selection unit configured to select a reference machine position;
the calculating unit is configured to calculate an actual electric quantity scale factor of each fan of the old wind power plant relative to a reference machine position, and comprises the following steps:
calculating theoretical wake loss WK of each fan of old wind power plant i ；
Calculating annual actual power generation capacity OAEP 'of each fan of old wind power plant without wake loss' i ：
OAEP′ i ＝OAEP i /(1-WK i )；
Wherein OAEP i Annual actual power generation of each fan;
calculating OT (relative to a reference machine position) of each machine position of old wind power plant 0 Is the actual electric quantity scale factor K of (2) i ：
K i ＝OAEP′ i /OAEP′ 0 ；
A correction coefficient calculation unit configured to calculate a power generation amount correction coefficient of the mesoscale wind data based on an actual power generation amount of the old wind farm, comprising:
extracting reference machine position OT 0 Theoretical power generation meso_oaep under mesoscale wind data 0 ；
Statistics of old and old wind farm reference machine position OT 0 Determining a reduction coefficient of the old wind power plant according to the availability of the fans, wherein the reduction coefficient of the old wind power plant comprises a reduction coefficient D of the availability of the fans 1 Reduction coefficient D of fan power curve 2 Integrated reduction coefficient value D 3 ；
Calculating a power generation amount correction coefficient C of mesoscale wind data 0 ：C 0 Actual annual energy production OAEP of=old wind farm reference machine 0 (theoretical Power production under mesoscale wind data Meso_OAEP) 0 *D 1 *D 2 *D 3 )；
The generating capacity calculating unit is configured to perform reconstruction wind power plant position arrangement based on the actual electric quantity scale factor, calculate and correct the generating capacity of the reconstruction wind power plant, and comprises the following steps:
based on the old wind power plant generating capacity scale factor, carrying out wind power plant reconstruction position arrangement, wherein the position of the wind power plant reconstruction is recorded as NT j ；
Rebuilding wind power plant wind resource simulation and theoretical generating capacity calculation;
correcting the simulated generated energy of the reconstructed wind power plant and calculating the Internet surfing electric quantity of the reconstructed wind power plant.
6. A computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-4.
7. An electronic device, comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-4.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106250656A (en) * | 2016-08-23 | 2016-12-21 | 中国华能集团清洁能源技术研究院有限公司 | The complicated landform wind field design platform of the big data of a kind of combination and method |
| CN106321368A (en) * | 2016-08-12 | 2017-01-11 | 峰能斯格尔（北京）可再生能源科技有限公司 | Offshore wind power plant wake flow loss measuring method based on running draught fan SCADA data |
| CN108665102A (en) * | 2018-05-11 | 2018-10-16 | 中国船舶重工集团海装风电股份有限公司 | Method based on the real-time generated energy of mesoscale data prediction wind power plant |
| CN108717593A (en) * | 2018-04-16 | 2018-10-30 | 浙江运达风电股份有限公司 | A kind of microcosmic structure generated energy appraisal procedure based on wind wheel face equivalent wind speed |
| CN110264002A (en) * | 2019-06-20 | 2019-09-20 | 龙源（北京）风电工程设计咨询有限公司 | Wind power plant microcosmic structure schemes evaluation method based on clustering |
| WO2019184161A1 (en) * | 2018-03-29 | 2019-10-03 | 北京金风科创风电设备有限公司 | Mesoscale data-based automatic wind turbine layout method and device |
| CN112818590A (en) * | 2021-01-13 | 2021-05-18 | 中国长江三峡集团有限公司 | Method for evaluating power generation capacity of wind power plant and establishing micro-site selection model |
| CN113567164A (en) * | 2021-07-20 | 2021-10-29 | 中国华能集团清洁能源技术研究院有限公司 | Systematic evaluation and prediction method for technical improvement demand of wind power plant |
| CN115358606A (en) * | 2022-08-26 | 2022-11-18 | 中国电建集团西北勘测设计研究院有限公司 | Method and system for evaluating energy efficiency of in-service wind power plant under gentle terrain |
-
2023
- 2023-02-14 CN CN202310109975.7A patent/CN116169727B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106321368A (en) * | 2016-08-12 | 2017-01-11 | 峰能斯格尔（北京）可再生能源科技有限公司 | Offshore wind power plant wake flow loss measuring method based on running draught fan SCADA data |
| CN106250656A (en) * | 2016-08-23 | 2016-12-21 | 中国华能集团清洁能源技术研究院有限公司 | The complicated landform wind field design platform of the big data of a kind of combination and method |
| WO2019184161A1 (en) * | 2018-03-29 | 2019-10-03 | 北京金风科创风电设备有限公司 | Mesoscale data-based automatic wind turbine layout method and device |
| CN108717593A (en) * | 2018-04-16 | 2018-10-30 | 浙江运达风电股份有限公司 | A kind of microcosmic structure generated energy appraisal procedure based on wind wheel face equivalent wind speed |
| CN108665102A (en) * | 2018-05-11 | 2018-10-16 | 中国船舶重工集团海装风电股份有限公司 | Method based on the real-time generated energy of mesoscale data prediction wind power plant |
| CN110264002A (en) * | 2019-06-20 | 2019-09-20 | 龙源（北京）风电工程设计咨询有限公司 | Wind power plant microcosmic structure schemes evaluation method based on clustering |
| CN112818590A (en) * | 2021-01-13 | 2021-05-18 | 中国长江三峡集团有限公司 | Method for evaluating power generation capacity of wind power plant and establishing micro-site selection model |
| WO2022151890A1 (en) * | 2021-01-13 | 2022-07-21 | 中国长江三峡集团有限公司 | Method for establishing power generating capacity evaluation and micro-siting model for wind farms |
| CN113567164A (en) * | 2021-07-20 | 2021-10-29 | 中国华能集团清洁能源技术研究院有限公司 | Systematic evaluation and prediction method for technical improvement demand of wind power plant |
| CN115358606A (en) * | 2022-08-26 | 2022-11-18 | 中国电建集团西北勘测设计研究院有限公司 | Method and system for evaluating energy efficiency of in-service wind power plant under gentle terrain |
Non-Patent Citations (2)
| Title |
|---|
| Optimum siting of wind turbine generators;Jangamshetti Suresh H;《Power&Energy Society》;第8-13页 * |
| 基于 Scada 数据的风电场改扩建数值模拟研究;王洋等;《南方能源建设》;第92-96页 * |
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