Method, device and equipment for calculating actual power curve of wind turbine generator and storage medium

Abstract

The invention provides a wind turbine generator actual power curve calculation and detection method, device and equipment and a storage medium, and the method improves the adaptability, rapidity and accuracy of a wind power plant fan actual power curve calculation method through building a fan power curve calculation process adaptive to a wind power plant level analysis scene; through a parameter configuration method of actual power curve calculation, fan requirements of different types and actual site points of the wind power plant are met; data acquisition processes under various conditions are considered, a power curve data screening method under various constraint conditions is adopted, and the rapidity and accuracy of actual power curve calculation for wind power plant analysis are improved; through a selection strategy of wind speed standardization, adaptability and accuracy of an actual power curve calculation method are increased.

Description

Technical Field

[0001] The present application relates to the field of wind power generation technology, and in particular to a method, device, equipment and storage medium for calculating an actual power curve of a wind turbine generator set. Background Art

[0002] Wind turbine operators typically source their wind turbines from multiple wind turbine manufacturers. Mainstream domestic wind turbine manufacturers offer different technology paths, including direct drive, semi-direct drive, and dual-fed (DFIG). They also offer a wide variety of wind turbine PLC brands and models. The definitions of wind turbine status information vary widely, and the data points provided by wind turbine manufacturers to wind turbine operators vary, resulting in varying data quality.

[0003] When analyzing wind farm-level theoretical power generation, power loss, and other indicators over a predetermined time period (e.g., month, year, etc.), wind farm operators need to calculate the actual power curve for each wind turbine at each turbine site. Due to a series of constraints, including varying wind turbine technology and characteristics across various manufacturers, multiple models, varying terrains, and actual turbine site characteristics, it is difficult to fully reference standards such as "GBT18451.2-2003 Wind Turbine Power Characteristics Test" to calculate a standard power curve. Therefore, based on wind farm-level application requirements, the actual power curve at the wind farm level must be calculated based on both the standard and the actual turbine site conditions.

[0004] Among the existing patents related to wind turbine power curves:

[0005] 1) Patent CN108269197B, "Method and device for evaluating wind turbine power characteristics," uses sector division to perform free-stream wind speed correction to obtain evaluation results;

[0006] 2) Patent CN115422503B, "Method for Drawing Power Curves of Wind Turbine Generator Sets," draws power curves based on wind speeds at wind towers and nacelles of several turbines;

[0007] 3) Patent CN107542627B, "A method and system for drawing a power curve of a wind turbine generator set," draws a power curve by correcting wind speed with air density and selecting the generated power;

[0008] 4) Patent CN113139880A, "Method, device, equipment and storage medium for fitting actual power curve of wind turbine generator set", obtains actual power curve by dividing cut-in wind speed and rated wind speed and then processing the data;

[0009] 5) Patent CN114971313 A “A method for fitting a power curve of a wind turbine” fits the power curve by extracting the minimum pitch angle and rated speed of the blades and then filtering the data.

[0010] If the existing patented methods mentioned above are used in wind farm-level analysis scenarios, there may be deficiencies in data screening for preset time periods or adaptability to turbine sites. This makes it difficult to meet the wind farm-level wind turbine actual power curve calculation needs under a series of restrictive conditions, such as wind turbine technology routes from various manufacturers, multiple models, various terrains, actual turbine site characteristics, and wind turbine configurations. Summary of the Invention

[0011] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0012] To this end, the first purpose of this application is to propose a method for calculating the actual power curve of a wind turbine, aiming to improve the adaptability, speed and accuracy of the calculation of the actual power curve of the wind turbine used for wind farm level analysis.

[0013] The second objective of this application is to provide a device for calculating and detecting the actual power curve of a wind turbine generator set.

[0014] The third objective of this application is to provide an electronic device.

[0015] The fourth object of this application is to provide a computer-readable storage medium.

[0016] To achieve the above objectives, the first embodiment of the present application proposes a method for calculating and detecting the actual power curve of a wind turbine generator set, comprising:

[0017] Obtain wind turbine operating system parameters and time series data tables, remove abnormal data in the time series data tables, and perform data standard name conversion, wind speed and direction source selection, wind speed standardization selection, power generation mark identification and extraction, power limit mark synthesis, and data aggregation;

[0018] Based on the wind turbine operating system parameters, the processed time series data table is subjected to data screening processing to obtain a time series data table for actual power curve calculation;

[0019] The Bien method is used to divide the wind speed data in the time series data table into multiple wind speed Bien intervals according to the preset time series interval length, and the power mean value in each wind speed Bien interval is counted to obtain the actual power curve of the wind turbine.

[0020] The wind turbine operating system parameters include at least the power curve calculation function parameters, wind farm model parameters and wind farm wind turbine information;

[0021] Power curve calculation function parameters include wind speed selection, wind direction selection, and wind speed normalization enable;

[0022] Wind farm model parameters include wind farm model, cut-in speed, optimal blade angle, actual power lower limit threshold, cut-in speed lower limit coefficient, power generation screening blade angle threshold, power limit screening blade angle allowable error, rated power Pn lower limit coefficient, wind direction mode, available sector range, and pre-pitch parameters;

[0023] The wind farm turbine information includes the wind farm turbine number, model, and rated power Pn.

[0024] Among them, obtaining the time series data table includes:

[0025] Set a preset time period; wherein the preset time period is a monthly or weekly time interval;

[0026] According to the time sequence of the preset time period, the wind farm number, wind turbine number, data timestamp, wind speed, power, generator speed, blade angle, nacelle position, wind deviation, wind turbine PLC status, power limit status, power generation status, and air density are selected to form a time series data table.

[0027] The time series data table is sequentially subjected to data standard name conversion, wind speed and direction source selection, wind speed standardization selection, power generation mark identification and extraction, power limit mark synthesis, and data aggregation, including:

[0028] Data standard name conversion is to convert the data points of time series data tables with different sources and different data point names into standard names;

[0029] The selection of wind speed and direction sources is based on the consideration of factors such as the influence of impeller turbulence and nacelle shape design on the nacelle wind speed and direction instrument. The wind speed and direction of the nacelle wind radar are the preferred sources. If the wind speed of the wind tower near the machine site is consistent with the wind speed at the machine site, the wind speed of the wind tower is selected. In other cases, the wind speed and direction measured by the nacelle wind speed and direction instrument are used.

[0030] The wind speed normalization selection is to determine whether there is air density data in the time series data table; if so, the wind speed data is normalized based on the air density data; otherwise, determine whether the ambient temperature data in the time series data table is normal; if normal, calculate the air density data based on the ambient temperature data, and normalize the wind speed data based on the air density data;

[0031] Generation tag identification and extraction, and power limit tag synthesis, are performed by identifying and extracting new generation tags (denoted as Prodtag) and power limit tags (denoted as Limtag) from the time series data table using the optimal pitch angle, cut-in speed, and pre-pitch parameters of the wind turbine configuration at the actual turbine site.

[0032] The data aggregation is to aggregate the data in the time series data table into data with fixed time intervals to obtain a time series data table for the data screening step.

[0033] Among them, in the step of identifying and extracting the power generation mark, the screening conditions for identifying and extracting the power generation mark from the time series data table are:

[0034] [Power > Actual power lower limit threshold] and [Generator speed > (Cut-in speed * Cut-in speed lower limit coefficient)] and [Propeller angle < Power generation screening propeller angle threshold]

[0035] The filter conditions for identifying and extracting the power limit marker from the timing data table are:

[0036] If (pre-pitch parameters are configured in the system parameters):

[0037] [Propeller angle > (propeller angle obtained from the pre-pitch parameter table + allowable error of propeller angle filtered by power limit)] & [Power < (rated power Pn * rated power Pn lower limit coefficient)]

[0038] Else:

[0039] [Propeller angle > (optimal propeller angle + allowable error of propeller angle for power-limited screening)] & [Power < (rated power Pn * rated power Pn lower limit coefficient)].

[0040] The step of synthesizing the power limit mark includes:

[0041] A power limit tag is synthesized based on the power limit state, that is, Limtag=Limtag0∪Limtag1; wherein Limtag is the synthesized power limit tag, Limtag1 is the power limit tag extracted from the time series data table, and Limtag0 is the power limit state in the time series data table;

[0042] Add a power limit tag, that is, Limtag=Limtag1.

[0043] The data screening steps include:

[0044] For the time series data table, remove the non-grid-connected data according to the power generation mark;

[0045] Eliminate power-limited data based on power-limited flags;

[0046] Eliminate unusable sector data and data with large wind deviation;

[0047] Unusable data is eliminated according to the power curve markers to complete data screening.

[0048] To achieve the above-mentioned purpose, a second embodiment of the present application provides a device for calculating and detecting an actual power curve of a wind turbine generator set, comprising:

[0049] The data acquisition and conversion module is used to obtain the wind turbine operating system parameters and time series data table, eliminate abnormal data in the time series data table, and perform data standard name conversion, wind speed and direction source selection, wind speed standardization selection, power generation mark identification and extraction, power limit mark synthesis, and data aggregation;

[0050] A data screening module, configured to perform data screening processing on the time series data table based on the wind turbine operating system parameters to obtain a time series data table for actual power curve calculation;

[0051] The wind turbine actual power curve calculation module is used to use the Bien method to divide the wind speed data in the time series data table into multiple wind speed Bien intervals according to the preset time series interval length, and calculate the power mean in each wind speed Bien interval to obtain the wind turbine actual power curve.

[0052] To achieve the above-mentioned purpose, a third embodiment of the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0053] Memory stores computer-executable instructions;

[0054] The processor executes the computer-executable instructions stored in the memory to implement the method of the aforementioned technical solution.

[0055] To achieve the above-mentioned purpose, the fourth embodiment of the present application proposes a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the method of the above-mentioned technical solution.

[0056] Different from the existing technology, the present invention provides a method, device, equipment and storage medium for calculating and detecting the actual power curve of a wind turbine. The method improves the adaptability, speed and accuracy of the actual power curve calculation method of wind turbines in wind farms by establishing a wind turbine power curve calculation process that adapts to wind farm-level analysis scenarios; adapts to the requirements of wind turbines of different models and actual machine sites in wind farms through a parameter configuration method for actual power curve calculation; considers a data acquisition process under multiple conditions and adopts a power curve data screening method under multiple constraint conditions to improve the speed and accuracy of the actual power curve calculation used for wind farm analysis; and increases the adaptability and accuracy of the actual power curve calculation method through a wind speed standardization selection strategy.

[0057] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1The present invention provides a flow chart of a method for calculating and detecting the actual power curve of a wind turbine generator set.

[0059] Figure 2 The present invention provides a data acquisition and conversion process diagram of a method for calculating and detecting the actual power curve of a wind turbine generator set.

[0060] Figure 3 The present invention provides a schematic diagram of data screening under constraint conditions in a method for calculating and detecting the actual power curve of a wind turbine generator set.

[0061] Figure 4 The present invention is a schematic structural diagram of a device for calculating and detecting the actual power curve of a wind turbine generator set. DETAILED DESCRIPTION

[0062] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0063] The following describes a method and device for calculating and detecting the actual power curve of a wind turbine generator system according to an embodiment of the present application with reference to the accompanying drawings.

[0064] Figure 1 This is a flow chart of a method for calculating and detecting the actual power curve of a wind turbine provided in an embodiment of the present application. The method includes the following steps:

[0065] S101: Obtain wind turbine operating system parameters and a time series data table, remove abnormal data in the time series data table, and perform data standard name conversion, wind speed and direction source selection, wind speed standardization selection, power generation mark identification and extraction, power limit mark synthesis, and data aggregation.

[0066] In an embodiment of the present invention, the wind turbine operating system parameters include at least power curve calculation function parameters, wind farm model parameters and wind farm wind turbine information; wherein,

[0067] Power curve calculation function parameters include wind speed selection, wind direction selection, and wind speed normalization enable;

[0068] Wind farm model parameters include wind farm model, cut-in speed, optimal blade angle, actual power lower limit threshold, cut-in speed lower limit coefficient, power generation screening blade angle threshold, power limit screening blade angle allowable error, rated power Pn lower limit coefficient, wind direction mode, available sector range, and pre-pitch parameters;

[0069] The wind farm turbine information includes the wind farm turbine number, model, and rated power Pn.

[0070] Among them, obtaining the time series data table includes:

[0071] Set a preset time period; wherein the preset time period is a monthly or weekly time interval;

[0072] According to the time sequence of the preset time period, the wind farm number, wind turbine number, data timestamp, wind speed, power, generator speed, blade angle, nacelle position, wind deviation, wind turbine PLC status, power limit status, power generation status, and air density are selected to form a time series data table.

[0073] like Figure 2 As shown, the time series data table is sequentially subjected to data standard name conversion, wind speed and direction source selection, wind speed standardization selection, power generation mark identification and extraction, power limit mark synthesis, and data aggregation, including: sequentially subjected to data standard name conversion, wind speed and direction source selection, wind speed standardization selection, power generation mark identification and extraction, power limit mark synthesis, and data aggregation; wherein,

[0074] Data standard name conversion is to convert the data points in time series data tables with different sources and different data point names into standard names.

[0075] In an embodiment of the present invention, when analyzing a wind farm, wind turbine data comes from data sources such as centralized control data, SCADA data, and wind turbine equipment data. The data points are named differently, so standard name conversion of the data points is required.

[0076] The selection of the source of wind speed and direction is based on the factors that the nacelle wind speed and direction instrument is affected by the impeller turbulence and the nacelle shape design. The wind speed and direction of the nacelle wind measuring radar are preferably used as the source of wind speed and direction. If the wind speed of the wind tower near the machine site is consistent with the wind speed at the machine site, the wind speed of the wind tower is selected. In other cases, the wind speed and direction measured by the nacelle wind speed and direction instrument are used.

[0077] Specifically, the wind speed source is selected based on the wind speed selection parameters. The actual wind speed source used can be the wind speed measured by the wind turbine's nacelle anemometer, wind radar, or a wind tower near the turbine site. The wind direction source is selected based on the wind direction selection parameters. The actual wind direction source used can be the wind direction measured by the wind turbine's nacelle anemometer or wind radar.

[0078] The wind speed normalization selection is to determine whether there is air density data in the time series data table; if so, the wind speed data is normalized based on the air density data; otherwise, determine whether the ambient temperature data in the time series data table is normal; if normal, calculate the air density data based on the ambient temperature data, and normalize the wind speed data based on the air density data;

[0079] In an embodiment of the present invention, if wind speed normalization is enabled, the judgment logic is as follows:

[0080] If wind speed normalization is enabled {

[0081] If (air density exists in the time series data table):

[0082] Perform wind speed standardization;

[0083] Else if (the ambient temperature of the timing data table is normal):

[0084] Calculate the simplified air density based on the ambient temperature to normalize the wind speed;

[0085] Else:

[0086] No wind speed normalization is performed;}

[0087] Else:

[0088] No wind speed normalization is performed;

[0089] The wind speed normalization formula is as follows:

[0090]

[0091] Where V is the measured wind speed, V n is the converted wind speed, ρ is the air density, and ρ0 is the standard air density.

[0092] The power generation tag identification and extraction, and the power limit tag synthesis are achieved by identifying and extracting new power generation tags (denoted as Prodtag) and power limit tags (denoted as Limtag) from the timing data table through the optimal blade angle, cut-in speed, and pre-pitch parameters in the wind turbine configuration at the actual machine site.

[0093] In the actual analysis of power generation operators, due to the differences in the whole machine manufacturers and models, data transmission and other restrictions, some sites do not have wind turbine operating information such as power generation status and power limit status. Taking into account the adaptability of the wind turbine actual power curve calculation function to wind turbines at different sites and different machine locations, the optimal blade angle, cut-in speed, pre-pitch parameters and other parameter information in the actual machine location wind turbine configuration can be used to identify and extract new power generation tags (denoted as Prodtag1) and power limit tags (denoted as Limtag1) from the time series data table S without relying on the wind turbine status information. Optionally, the power limit tag Limtag1 and the power limit status (denoted as Limtag0) of the time series data table are synthesized (if not, the new tag is used directly). Finally, the actual power generation tag (denoted as Prodtag) and power limit tag (denoted as Limtag) are obtained.

[0094] If the power generation flag can be extracted from the wind turbine PLC status, then extract it. Otherwise, identify and extract the power generation flag from the time series data table. Note that the definition of wind turbine PLC status varies between different manufacturers or models, and the definition information that can be obtained depends on the manufacturer.

[0095] The screening conditions for identifying and extracting power generation markers from the time series data table are:

[0096] [Power > Actual power lower limit threshold] and [Generator speed > (Cut-in speed * Cut-in speed lower limit coefficient)] and [Propeller angle < Power generation screening propeller angle threshold].

[0097] The steps of synthesizing the power limit mark include:

[0098] A power limit tag is synthesized based on the power limit state, that is, Limtag=Limtag0∪Limtag1; wherein Limtag is the synthesized power limit tag, Limtag1 is the power limit tag extracted from the time series data table, and Limtag0 is the power limit state in the time series data table;

[0099] Add a power limit tag, that is, Limtag=Limtag1.

[0100] The filter conditions for identifying and extracting the power limit marker from the timing data table are:

[0101] If (pre-pitch parameters are configured in the system parameters):

[0102] [Propeller angle > (propeller angle obtained from the pre-pitch parameter table + allowable error of propeller angle filtered by power limit)] & [Power < (rated power Pn * rated power Pn lower limit coefficient)]

[0103] Else:

[0104] [Propeller angle > (optimal propeller angle + allowable error of propeller angle for power-limited screening)] & [Power < (rated power Pn * rated power Pn lower limit coefficient)].

[0105] Data aggregation is to aggregate the data in the time series data table into data at fixed time intervals to obtain a time series data table for the data screening step.

[0106] Due to the existence of multiple data sources, the time series data table obtained in the preset time period may have a time interval of 1s, 1min, 10min, etc., and it is necessary to aggregate it into data with a fixed time interval (usually 10min). The new aggregated time series data table is used as the time series data table for the data screening step.

[0107] S102: Based on the wind turbine operating system parameters, the time series data table is screened to obtain a time series data table for actual power curve calculation.

[0108] Different data sources and specific wind farm wind turbines can provide different data points and machine site parameter information, and there are multiple constraints. Data screening needs to be performed according to the actual situation. The data screening process under constraints is as follows: Figure 3 shown. Figure 3 In the time series data table, the non-grid-connected data is eliminated according to the power generation mark, the power-limited data is eliminated according to the power-limited mark, the unavailable sector data and the data with large wind deviation are eliminated, and finally the unavailable data is eliminated according to the power curve mark to complete the data screening.

[0109] The data screening process is constrained by the parameters and data points available for wind turbines at the actual wind farm site. In this case, the data screening process combines multiple constraints (generation marker screening, power limit marker screening, available sector screening, wind deviation screening, and power curve marker screening). For example, at one wind farm, based on actual turbine conditions, the data screening process only involves generation marker screening and power limit marker screening.

[0110] Available sectors are generally used in scenarios such as wind turbine type certification, third-party testing, and wind turbine sector control. Most wind turbines in actual wind farms do not have available sectors marked, making it impossible to filter the data by available sectors. When available sector filtering is enabled, the nacelle position data in the time series data table is filtered based on the available sector parameters.

[0111] When eliminating data with large wind deviations, in practice, different wind direction modes (e.g., 0° wind direction, 180° wind direction, etc.) are used at wind turbine sites due to the technical styles of different OEMs and turbine models. These modes must be uniformly converted to wind deviations during data screening. Furthermore, enabling wind deviation screening depends on the data characteristics of the wind deviation data points. Wind deviation screening is only enabled when different wind deviations can significantly demonstrate power differences.

[0112] When some PLC data points provided by OEMs contain power curve markers, unusable data is eliminated based on the power curve markers to assist in selecting which rows of data in the time series data table can be used for power curve drawing.

[0113] S103: using the Bien method, dividing the wind speed data in the time series data table into a plurality of wind speed Bien intervals according to a preset time series interval length, and calculating the power mean value in each wind speed Bien interval to obtain an actual power curve of the wind turbine.

[0114] The Bien method is also called the "bin interval method". Its basic principle is to divide the data into several small intervals and process the data using mathematical statistics methods in each interval.

[0115] The time series data obtained after data screening and wind speed standardization is denoted as S flt , the data removed by screening is recorded as S ng , S={S flt ,S ng}. According to the Bien method, S flt Medium wind speeds are divided into multiple wind speed bins based on bin length (typically 0.5 m / s). The power average for each wind speed bin is calculated to generate the actual power curve. Optionally, the power data for each wind speed bin can be quantile filtered using a power quantile threshold before statistics are generated to further eliminate outliers.

[0116] The calculation formula for the power mean P(i) in the i-th wind speed interval (expressed as W(i)) is:

[0117]

[0118] Among them, Ni is the power data P in the wind speed range i,j The number of

[0119] To visually evaluate the accuracy of the actual power curve calculation for wind farm level analysis, S is plotted on the same graph with wind speed as the horizontal axis and power as the vertical axis. flt Wind speed-power scatter relationship, S ng Wind speed-power scatter relationship and actual power curve.

[0120] Figure 4 A schematic structural diagram of a device for calculating and detecting the actual power curve of a wind turbine provided in an embodiment of the present application.

[0121] like Figure 4 As shown, the device 300 includes:

[0122] The data acquisition and conversion module 310 is used to obtain wind turbine operating system parameters and time series data tables, remove abnormal data in the time series data tables, and perform data standard name conversion, wind speed and direction source selection, wind speed standardization selection, power generation mark identification and extraction, power limit mark synthesis, and data aggregation;

[0123] A data screening module 320 is configured to perform data screening processing on the time series data table based on the wind turbine operating system parameters to obtain a time series data table for actual power curve calculation;

[0124] The wind turbine actual power curve calculation module 330 is used to use the Bien method to divide the wind speed data in the time series data table into multiple wind speed Bien intervals according to the preset time series interval length, and calculate the power mean in each wind speed Bien interval to obtain the wind turbine actual power curve.

[0125] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0126] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0127] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0128] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0129] It is important to note that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold beyond these legitimate uses. Furthermore, such collection / sharing should be conducted only after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes the relevant user information before using the feature. Furthermore, any necessary steps must be taken to safeguard and secure access to such personal information and ensure that others with access to personal information comply with its privacy policy and procedures.

[0130] This application contemplates providing implementations that allow users to selectively block the use or access of personal information data. Specifically, this disclosure contemplates providing hardware and / or software to prevent or block access to such personal information data. Risks can be minimized by limiting data collection and deleting data once it is no longer needed. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0131] In the descriptions of the foregoing embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are mutually inconsistent.

[0132] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0133] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0134] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0135] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0136] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0137] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0138] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for calculating the actual power curve of a wind turbine generator system, characterized in that: include: Obtain wind turbine operating system parameters and time series data tables, remove abnormal data in the time series data tables, and perform data standard name conversion, wind speed and direction source selection, wind speed standardization selection, power generation mark identification and extraction, power limit mark synthesis, and data aggregation; Based on the wind turbine operating system parameters, the processed time series data table is subjected to data screening processing to obtain a time series data table for actual power curve calculation; The Bien method is used to divide the wind speed data in the time series data table into multiple wind speed Bien intervals according to the preset time series interval length, and the power mean value in each wind speed Bien interval is counted to obtain the actual power curve of the wind turbine.

2. The method for calculating the actual power curve of a wind turbine generator system according to claim 1, wherein: The wind turbine operating system parameters include at least power curve calculation function parameters, wind farm model parameters and wind farm wind turbine information; wherein, Power curve calculation function parameters include wind speed selection, wind direction selection, and wind speed normalization enable; Wind farm model parameters include wind farm model, cut-in speed, optimal blade angle, actual power lower limit threshold, cut-in speed lower limit coefficient, power generation screening blade angle threshold, power limit screening blade angle allowable error, rated power Pn lower limit coefficient, wind direction mode, available sector range, and pre-pitch parameters; The wind farm turbine information includes the wind farm turbine number, model, and rated power Pn.

3. The method for calculating the actual power curve of a wind turbine generator system according to claim 1, wherein: Get the time series data table, including: Setting a preset time period; wherein the preset time period is a month or week; According to the timing of the preset time period, the wind farm number, wind turbine number, data timestamp, wind speed, power, generator speed, blade angle, nacelle position, wind deviation, wind turbine PLC status, power limit status, power generation status, and air density are selected to form the timing data table.

4. The method for calculating the actual power curve of a wind turbine generator system according to claim 1, wherein: The time series data table is sequentially subjected to data standard name conversion, wind speed and direction source selection, wind speed standardization selection, power generation mark identification and extraction, power limit mark synthesis, and data aggregation, including: The data standard name conversion is to convert the time series data table data with different sources and different data point names into standard names of data points; The selection of the wind speed and direction source is based on the consideration of factors such as the influence of the impeller turbulence and the cabin shape design on the cabin wind speed and direction instrument. The wind speed and direction source is preferably the wind speed and direction of the cabin wind radar. If the wind speed of the wind tower near the machine site is consistent with the wind speed at the machine site, the wind speed of the wind tower is selected. In other cases, the wind speed and direction measured by the cabin wind speed and direction instrument are used. The wind speed normalization selection is to determine whether there is air density data in the time series data table; if so, the wind speed data is normalized based on the air density data; otherwise, determine whether the ambient temperature data in the time series data table is normal; if normal, calculate the air density data based on the ambient temperature data, and normalize the wind speed data based on the air density data; The generation tag identification and extraction and power limit tag synthesis are performed by identifying and extracting a new generation tag (denoted as Prodtag) and a power limit tag (denoted as Limtag) from the time series data table through the optimal blade angle, cut-in speed, and pre-pitch parameters of the wind turbine configuration at the actual turbine site; The data aggregation is to aggregate the data in the time series data table into data with fixed time intervals to obtain a time series data table for the data screening step.

5. The method for calculating the actual power curve of a wind turbine generator system according to claim 4, characterized in that: In the step of identifying and extracting the power generation mark, the screening conditions for identifying and extracting the power generation mark from the time series data table are: [Power > Actual power lower limit threshold] and [Generator speed > (Cut-in speed * Cut-in speed lower limit coefficient)] and [Propeller angle < Power generation screening propeller angle threshold]; The screening conditions for identifying and extracting the power limit mark from the timing data table are: If (pre-pitch parameters are configured in the system parameters): [Propeller angle > (propeller angle obtained from the pre-pitch parameter table + allowable error of propeller angle filtered by power limit)] & [Power < (rated power Pn * rated power Pn lower limit coefficient)] Else: [Propeller angle > (optimal propeller angle + allowable error of propeller angle for power-limited screening)] & [Power < (rated power Pn * rated power Pn lower limit coefficient)].

6. The method for calculating the actual power curve of a wind turbine generator system according to claim 4, characterized in that: The step of synthesizing the power limit mark includes: A power limit tag is synthesized based on the power limit state, that is, Limtag=Limtag0∪Limtag1; wherein Limtag is the synthesized power limit tag, Limtag1 is the power limit tag extracted from the time series data table, and Limtag0 is the power limit state in the time series data table; Add a power limit tag, that is, Limtag=Limtag1.

7. The method for calculating the actual power curve of a wind turbine generator system according to claim 1, wherein: The steps of data screening include: For the time series data table, remove the non-grid-connected data according to the power generation mark; Eliminate power-limited data based on power-limited flags; Eliminate unusable sector data and data with large wind deviation; Unusable data is eliminated according to the power curve markers to complete data screening.

8. A wind turbine actual power curve calculation device, characterized in that: include: The data acquisition and conversion module is used to obtain the wind turbine operating system parameters and time series data table, eliminate abnormal data in the time series data table, and perform data standard name conversion, wind speed and direction source selection, wind speed standardization selection, power generation mark identification and extraction, power limit mark synthesis, and data aggregation; A data screening module, configured to perform data screening processing on the time series data table based on the wind turbine operating system parameters to obtain a time series data table for actual power curve calculation; The wind turbine actual power curve calculation module is used to use the Bien method to divide the wind speed data in the time series data table into multiple wind speed Bien intervals according to the preset time series interval length, and calculate the power mean in each wind speed Bien interval to obtain the wind turbine actual power curve.

9. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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