A method and system for identifying the power curve of a wind turbine

Abstract

The present invention proposes a method and system for identifying the power curve of a wind turbine. The method includes: establishing a fluid mechanics model of a wind farm, calculating the time series data of the wind speed of each wind turbine point from the wind speed measured by the wind tower; correlating the calculated wind speed data with the SCADA wind speed data of the wind turbine at the same time to obtain a linear fitting curve, and fitting a relationship function to obtain free flow wind speed data; making the free flow wind speed data and the power data of the wind turbine generator set of SCADA correspond in time to obtain the scatter points of the power curve of the wind turbine generator set; removing abnormal points from the scatter points of the power curve of the wind turbine generator set; fitting the scatter points of the power curve of the wind turbine generator set after the abnormal points are removed into a curve as the identification result of the power curve of the wind turbine generator set. The scheme proposed by the present invention uses computational fluid dynamics to simulate the free flow wind speed, thereby identifying the power curve of the wind turbine generator set, so that the identification result is closer to the actual operation result.

Description

Technical Field

[0001] The present invention belongs to the field of wind farms, and particularly relates to a method and system for identifying the power curve of a wind turbine. Background Art

[0002] Through the retrieval of databases such as domestic and foreign papers, academic conferences, scientific and technological literature, patents, etc., it is found that: the power characteristic is an important basic attribute of a wind turbine, which is directly related to the economic and technical level of a wind power generation unit. At present, the general method for identifying the power characteristic curve refers to the standard IEC61400-12-1 "Power Characteristics Test of Wind Turbines". However, this method requires using two anemometers of the same model to measure the wind speeds at two positions, and then obtaining the air flow correction coefficient within each wind direction interval. This method has a long test time and requires moving the wind turbine, resulting in a huge workload.

[0003] In the article "Correction of Wind Turbine Power Curve Based on Calculation of Equivalent Inflow Wind Speed of Wind Turbine", a method is proposed to inversely iterate the equivalent inflow wind speed by using the operating parameters of the wind turbine, fit the functional relationship between it and the nacelle wind speed, and correct the theoretical power curve to obtain the nacelle wind speed-power curve. This method requires long-term operating data as training data to achieve the correction of the free inflow wind speed. At the same time, the method of inferring the free inflow only based on the operating parameters of the wind turbine introduces errors in the data acquisition process of the wind turbine.

[0004] In the patent "A Method and System for Identifying the Power Characteristic Curve of a Wind Turbine and Its Process", a method is proposed to determine the operating data envelope of a wind turbine according to the actually measured operating data of the wind turbine, and then identify the power characteristic curve of the wind turbine by using the genetic algorithm based on the operating data envelope of the wind turbine. This method ignores the deviation between the free inflow wind speed and the wind speed collected at the downwind of the wind wheel during the operation of the wind turbine.

[0005] It can be seen that there is currently a lack of a method in the industry that can quickly and economically achieve accurate identification of the power curve of a wind turbine. The main reason is the difficulty in identifying the free inflow wind speed. Therefore, how to use the computational fluid dynamics model to obtain the accurate feedforward free inflow wind speed of the wind power generation unit to improve the accuracy of the power characteristic curve has important practical significance.

[0006] Disadvantages of the Prior Art

[0007] In the process of identifying the power curve of a wind turbine, directly using the operating data of the wind turbine without free inflow wind speed correction, or only using the operating data of the wind turbine during the process of free inflow wind speed correction, makes the deviation between the power curve identification result and the actual power curve situation. Summary of the Invention

[0008] To solve the above technical problems, the present invention proposes a technical solution for a method of identifying the power curve of a wind turbine to solve the above technical problems.

[0009] The first aspect of the present invention discloses a method for identifying the power curve of a wind turbine, the method comprising:

[0010] Identifying the power curve of the wind turbine by simulating the free incoming wind speed through fluid mechanics and through the regression algorithm of the measured power curve of the wind turbine; the specific steps include:

[0011] Step S1, obtaining the wind measurement tower data inside or around the wind farm; and verifying the integrity and rationality of the wind measurement data;

[0012] Step S2, based on the Reynolds-averaged Navier-Stokes equations, establishing a fluid mechanics model of the wind farm, and inferring the time series data of the wind speed at each wind turbine point from the measured wind speed of the wind measurement tower;

[0013] Step S3, correlating the inferred wind speed data with the SCADA wind speed data of the wind turbine at the same time to obtain a linear fitting curve, and fitting out a relationship function;

[0014] Step S4, converting the SCADA wind speed data into free incoming wind speed data through the relationship function;

[0015] Step S5, corresponding the free incoming wind speed data and the power data of the wind turbine in the SCADA in time to obtain the scatter points of the wind turbine power curve; by introducing the power fluctuation caused by wind turbulence, establishing a model of the change of the power of the wind turbine with the wind speed under the action of real turbulent wind;

[0016] Step S6, removing the abnormal points from the scatter points of the wind turbine power curve;

[0017] Step S7, fitting the scatter points of the wind turbine power curve after removing the abnormal points to the change model as the identification result of the wind turbine power curve.

[0018] According to the method of the first aspect of the present invention, in the step S1, the method for verifying the integrity and rationality of the wind measurement data includes:

[0019] Verifying and checking the wind measurement tower data, and judging the integrity and rationality of the wind measurement tower data:

[0020] Detecting unreasonable data and missing measurement data; calculating the completeness rate of the effective data of the wind measurement tower, and the completeness rate of the effective data should reach 90%.

[0021] According to the method of the first aspect of the present invention, in the step S1, the calculation method of the completeness rate of the effective data includes:

[0022]

[0023] According to the method of the first aspect of the present invention, in the step S2, the method for establishing a hydrodynamic model of a wind farm based on the Reynolds-averaged Navier-Stokes equations and deducing the time series data of the wind speed at each wind turbine location from the measured wind speed of a wind measurement tower includes:

[0024] Taking the time series wind speed and turbulence of the wind measurement tower as independent variables, setting the upper boundary, side boundary, ground boundary, and inlet and outlet boundary conditions, and performing a closure operation using a modified k-ε turbulence model to solve the Reynolds-averaged Navier-Stokes equations to obtain the parameter values at each grid point in the flow field; according to the parameter values, deducing the time series data of the wind speed at each wind turbine location from the measured wind speed of the wind measurement tower.

[0025] According to the method of the first aspect of the present invention, in the step S3, the method for correlating the deduced wind speed data with the SCADA wind speed data of the wind turbine at the same time to obtain a linear fitting curve and fitting out a relationship function includes:

[0026] Obtaining the multiple relationship and residual between the deduced wind speed and the SCADA wind speed through the fitting curve to obtain a relationship function, U scada = a·U cfd + b;

[0027] wherein, U scada is the SCADA wind speed, a is the multiple relationship, U cfd is the deduced wind speed, and b is the residual.

[0028] According to the method of the first aspect of the present invention, in the step S5, the method for establishing a model of the power of a wind turbine varying with the wind speed under the action of real turbulent wind by introducing the power fluctuation caused by wind turbulence includes:

[0029] Since the change of the wind speed is random and conforms to the characteristics of a Markov process,

[0030] P(t) = p stat (U) + pΔ(t)

[0031] In the formula, P(t) represents the power generated at each moment under the action of real turbulent wind, which is determined by U(t) at time t; p stat (U) represents the power corresponding to the average value of U(t) within a given time period; pΔ(t) represents the power fluctuation caused by wind turbulence at time t; U is the average value of U(t); U(t) is the free-stream wind speed.

[0032] According to the method of the first aspect of the present invention, in the step S6, the method for removing abnormal points from the scatter points of the wind turbine power curve includes:

[0033] Process the power data of the wind turbines in the SCADA and the free incoming wind speed data in intervals, and remove the recording points where the generated power exceeds several times the power variance within the statistical interval as bad points.

[0034] The second aspect of the present invention discloses a wind turbine power curve identification system, which includes:

[0035] A first processing module, configured to obtain the anemometer data inside or around the wind farm; and verify the integrity and rationality of the anemometer data;

[0036] A second processing module, configured to establish a hydrodynamic model of the wind farm based on the Reynolds-averaged Navier-Stokes equations, and deduce the time-series data of the wind speed at each wind turbine location from the measured wind speed of the anemometer;

[0037] A third processing module, configured to correlate the deduced wind speed data with the SCADA wind speed data of the wind turbine at the same time to obtain a linear fitting curve and fit out a relationship function;

[0038] A fourth processing module, configured to convert the SCADA wind speed data into free incoming wind speed data through the relationship function;

[0039] A fifth processing module, configured to correspond the free incoming wind speed data and the power data of the wind turbines in the SCADA in time to obtain the scatter points of the wind turbine power curve; by introducing the power fluctuation caused by wind turbulence, establish a model of the change of the power of the wind turbine with the wind speed under the action of real turbulent wind;

[0040] A sixth processing module, configured to remove abnormal points from the scatter points of the wind turbine power curve;

[0041] A seventh processing module, configured to fit the scatter points of the wind turbine power curve after removing abnormal points to the change model as the identification result of the wind turbine power curve.

[0042] According to the system of the second aspect of the present invention, the first processing module is specifically configured to, the verification of the integrity and rationality of the anemometer data includes:

[0043] Verify and check the anemometer data, and judge the integrity and rationality of the anemometer data:

[0044] Detect unreasonable data and missing data; calculate the integrity rate of the valid data of the anemometer tower, and the integrity rate of the valid data should reach 90%.

[0045] According to the system of the second aspect of the present invention, the first processing module is specifically configured that the calculation of the integrity rate of the valid data includes:

[0046]

[0047] According to the system of the second aspect of the present invention, the second processing module is specifically configured that the establishment of the hydrodynamic model of the wind farm based on the Reynolds-averaged Navier-Stokes equations, and the time-series data of the wind speed at each wind turbine location deduced from the measured wind speed of the anemometer tower includes:

[0048] Taking the time-series wind speed and turbulence of the anemometer tower as independent variables, setting the upper boundary, side boundary, ground boundary and inlet and outlet boundary conditions, and performing a closed operation using the modified k-ε turbulence model to solve the Navier-Stokes equations based on Reynolds averaging to obtain the parameter values at each grid point in the flow field; according to the parameter values, deduce the time-series data of the wind speed at each wind turbine location from the measured wind speed of the anemometer tower.

[0049] According to the system of the second aspect of the present invention, the third processing module is specifically configured that the correlation of the deduced wind speed data with the SCADA wind speed data of the wind turbine at the same time to obtain a linear fitting curve, and the fitting of the relationship function includes:

[0050] Obtain the multiple relationship and residual between the deduced wind speed and the SCADA wind speed through the fitting curve to obtain the relationship function, U scada =a·U cfd +b;

[0051] Wherein, U scada is the SCADA wind speed, a is the multiple relationship, U cfd is the deduced wind speed, and b is the residual.

[0052] According to the system of the second aspect of the present invention, the fifth processing module is specifically configured that in the step S5, the establishment of the power variation model of the wind turbine under the action of real turbulent wind by introducing the power fluctuation caused by wind turbulence includes:

[0053] Since the change of the wind speed is random and conforms to the characteristics of the Markov process,

[0054] P(t)=p stat (U)+pΔ(t)

[0055] Wherein, P(t) represents the power generated at each moment under the action of real turbulent wind, which is determined by U(t) at time t; p stat p(U) represents the power corresponding to the average value of U(t) within a given time period; pΔ(t) represents the power fluctuation caused by wind turbulence at time t; U is the average value of U(t); U(t) is the free incoming flow wind speed.

[0056] According to the system of the second aspect of the present invention, the sixth processing module is specifically configured to, in step S6, remove abnormal points from the scatter points of the wind turbine power curve, including:

[0057] Process the power data of the wind turbine in SCADA and the free incoming flow wind speed data in intervals, and remove the recording points where the generated power exceeds several times the power variance within the statistical interval as bad points.

[0058] The third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps in a method for identifying the power curve of a wind turbine according to any one of the first aspects disclosed in the present invention are implemented.

[0059] The fourth aspect of the present invention discloses a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in a method for identifying the power curve of a wind turbine according to any one of the first aspects disclosed in the present invention are implemented.

[0060] The solution proposed by the present invention uses computational fluid dynamics to simulate the free incoming flow wind speed, thereby identifying the power curve of the wind turbine, making the identification result closer to the actual operation result. Description of the Drawings

[0061] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0062] Figure 1 It is a flowchart of a method for identifying the power curve of a wind turbine according to an embodiment of the present invention;

[0063] Figure 2 It is a flowchart of a method for identifying the power curve of a wind turbine according to an embodiment of the present invention;

[0064] Figure 3A scatter plot and a linear fitting curve of the estimated wind speed and SCADA recorded wind speed time series according to an embodiment of the present invention;

[0065] Figure 4 A free stream wind speed diagram according to an embodiment of the present invention;

[0066] Figure 5 A diagram showing a bad pixel removal result according to an embodiment of the present invention;

[0067] Figure 6 According to the embodiment of the present invention, after 5 screenings, there are no bad pixels in the remaining measurement values, and the remaining scattered points are fitted into regions, which is a power curve recognition result diagram;

[0068] Figure 7 A structural diagram of a wind turbine power curve identification system according to an embodiment of the present invention;

[0069] Figure 8 The figure is a structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0071] A first aspect of the present invention discloses a method for identifying a power curve of a wind turbine generator set. Figure 1 FIG. 1 is a flow chart of a method for identifying a wind turbine power curve according to an embodiment of the present invention. Figure 1 and Figure 2 As shown, the method includes:

[0072] The free flow wind speed is simulated by fluid mechanics and the power curve of the wind turbine is identified by the regression algorithm of the measured power curve of the wind turbine. The specific steps include:

[0073] Step S1, obtaining wind tower data inside or around the wind farm; and verifying the integrity and rationality of the wind measurement data;

[0074] Step S2: Based on the Reynolds-averaged Navier-Stokes equation, a fluid mechanics model of the wind farm is established, and the time series data of the wind speed at each wind turbine point is calculated from the wind speed measured by the wind tower;

[0075] Step S3: Correlate the calculated wind speed data with the SCADA wind speed data of the wind turbine at the same time to obtain a linear fitting curve and fit out a relationship function;

[0076] Step S4: Convert the SCADA wind speed data into free incoming flow wind speed data through the relationship function;

[0077] Step S5: Correlate the free incoming flow wind speed data with the power data of the wind turbine in the SCADA in terms of time to obtain the scatter points of the wind turbine power curve; by introducing the power fluctuations caused by wind turbulence, establish a variation model of the power of the wind turbine under the action of real turbulent wind with respect to the wind speed;

[0078] Step S6: Eliminate the abnormal points from the scatter points of the wind turbine power curve;

[0079] Step S7: Fit the scatter points of the wind turbine power curve after eliminating the abnormal points to the variation model as the identification result of the wind turbine power curve.

[0080] In step S1, obtain the anemometer tower data inside or around the wind farm; and verify the integrity and rationality of the anemometer data.

[0081] In some embodiments, in the step S1, the method for verifying the integrity and rationality of the anemometer data includes:

[0082] Verify and check the anemometer tower data to judge the integrity and rationality of the anemometer tower data:

[0083] Detect the unreasonable data and the missing data; calculate the completeness rate of the valid data of the anemometer tower, and the completeness rate of the valid data should reach 90%.

[0084] The calculation method of the completeness rate of the valid data includes:

[0085]

[0086] In step S2, based on the Reynolds-averaged Navier-Stokes equations, establish a hydrodynamic model of the wind farm, and deduce the time-series data of the wind speed at the location of each wind turbine from the measured wind speed of the anemometer tower.

[0087] In some embodiments, in the step S2, the method for establishing a hydrodynamic model of the wind farm based on the Reynolds-averaged Navier-Stokes equations and deducing the time-series data of the wind speed at the location of each wind turbine from the measured wind speed of the anemometer tower includes:

[0088] Taking the sequential wind speed and turbulence of the anemometer tower as independent variables, setting the upper boundary, side boundary, ground boundary, and inlet and outlet boundary conditions, and using the modified k-ε turbulence model for closed operation to solve the Navier-Stokes equation based on Reynolds-averaged to obtain the parameter values at each grid point in the flow field; then, according to the parameter values, the sequential data of the wind speed at each wind turbine location is deduced from the measured wind speed of the anemometer tower.

[0089] Specifically, the Navier-Stokes equation is an equation that describes viscous Newtonian fluids in fluid mechanics. The governing equation for describing air flow using the Navier-Stokes equation is:

[0090]

[0091] In the formula, - Unsteady acceleration; - Pressure gradient; - Air viscosity; f is the force of other effects.

[0092] For incompressible Newtonian fluids, only the convection term is in a non-linear form; the convective acceleration is the change in velocity caused by the change of fluid flow with space. In addition, the continuity equation:

[0093]

[0094] The turbulence equation uses the modified k-ε turbulence model;

[0095] Due to the non-closure of the Reynolds-averaged Navier-Stokes equation, a turbulence model is introduced to close the equations.

[0096]

[0097] In the formula, S ij - Turbulent deformation tensor, Ω ij - Turbulent rotation tensor, ε - Turbulent dissipation rate, ρ - Air density, k - Turbulent kinetic energy.

[0098] In step S3, the deduced wind speed data is correlated with the SCADA wind speed data of the wind turbine at the same time to obtain a linear fitting curve and fit out a relationship function.

[0099] In some embodiments, in the step S3, the method of correlating the deduced wind speed data with the SCADA wind speed data of the wind turbine at the same time to obtain a linear fitting curve and fit out a relationship function includes:

[0100] Obtaining the multiple relationship and residual between the deduced wind speed and the SCADA wind speed through the fitting curve to obtain the relationship function, U scada= a·U cfd + b;

[0101] where U scada is the SCADA wind speed, a is the multiple relationship, U cfd is the estimated wind speed, and b is the residual.

[0102] In step S4, convert the SCADA wind speed data into free stream wind speed data through the relationship function.

[0103] In step S5, correspond the free stream wind speed data and the power data of the SCADA wind turbine in terms of time to obtain the scatter points of the wind turbine power curve; by introducing the power fluctuations caused by wind turbulence, establish a variation model of the power of the wind turbine under the action of real turbulent wind with respect to the wind speed.

[0104] In some embodiments, in step S5, the method for establishing a variation model of the power of the wind turbine under the action of real turbulent wind by introducing the power fluctuations caused by wind turbulence includes:

[0105] Since the change of wind speed is random and conforms to the characteristics of a Markov process,

[0106] P(t) = p stat (U) + pΔ(t)

[0107] In the formula, P(t) represents the power generated at each moment under the action of real turbulent wind, which is determined by U(t) at time t; p stat (U) represents the power corresponding to the average value of U(t) within a given time period; pΔ(t) represents the power fluctuation caused by wind turbulence at time t; U is the average value of U(t); U(t) is the free stream wind speed.

[0108] In step S6, eliminate the abnormal points from the scatter points of the wind turbine power curve.

[0109] In some embodiments, in step S6, the method for eliminating the abnormal points from the scatter points of the wind turbine power curve includes:

[0110] Process the power data of the SCADA wind turbine and the free stream wind speed data in intervals, and eliminate the recording points where the generated power exceeds several times the power variance within the statistical interval as bad points.

[0111] In step S7, fit the scatter points of the wind turbine power curve after eliminating the abnormal points to the variation model as the identification result of the wind turbine power curve.

[0112] In summary, the solution proposed by the present invention can simulate the free incoming wind speed by computational fluid dynamics, thereby identifying the power curve of the wind turbine, making the identification result closer to the actual operation result. Specific embodiments

[0114] (1) Adopt a fluid dynamics model, and infer the wind speed at the No. 1 position of a wind farm from the measured wind speed of a wind measurement tower in a wind farm in Liaoning.

[0115] (2) Make the correlation analysis diagram of the inferred wind speed and the wind speed recorded by the SCADA of the wind turbine as follows. The upper diagram is the scatter plot and the linear fitting curve of the time series of the inferred wind speed and the SCADA-recorded wind speed, and the residual of the linear fitting is as Figure 3 shown.

[0116] (3) It is obtained that the wind speed recorded by the SCADA of the wind turbine is 0.7727 times the free incoming wind speed, and the mean value of the residual is close to 0.

[0117] (4) According to the correlation relationship in (3), divide the wind speed data recorded by the SCADA of the wind turbine by 0.7727 as the free incoming wind speed, and obtain a scatter plot after correlating with the SCADA power data, as Figure 4 shown;

[0118] (5) Use the Pauta criterion to process the scatter data in intervals, and remove the recording points where the generated power exceeds three times the power variance within the statistical interval as bad points. The removal result is as Figure 5 shown;

[0119] (6) After 5 screenings, there are no bad points among the remaining measured values, and the remaining scatter points are fitted into the power curve identification results in sequence, as Figure 6 shown.

[0120] The second aspect of the present invention discloses a wind turbine power curve identification system. Figure 7 As shown in the structure diagram of a wind turbine power curve identification system according to an embodiment of the present invention; as Figure 7 shown, the system 100 includes:

[0121] The first processing module 101 is configured to obtain the wind measurement tower data inside or around the wind farm; and verify the integrity and rationality of the wind measurement data;

[0122] The second processing module 102 is configured to establish a fluid dynamics model of the wind farm based on the Reynolds-averaged Navier-Stokes equations, and infer the time series data of the wind speed at each wind turbine position from the measured wind speed of the wind measurement tower.

[0123] The third processing module 103 is configured to associate the calculated wind speed data with the SCADA wind speed data of the same time of the wind turbine to obtain a linear fitting curve and fit a relationship function.

[0124] The fourth processing module 104 is configured to convert the SCADA wind speed data into free incoming flow wind speed data through the relationship function.

[0125] The fifth processing module 105 is configured to correspond the free incoming flow wind speed data and the power data of the wind turbine generator set of the SCADA in time to obtain the scatter points of the wind turbine generator set power curve; by introducing the power fluctuation caused by wind turbulence, establish a variation model of the power of the wind turbine generator set with the wind speed under the action of real turbulent wind.

[0126] The sixth processing module 106 is configured to eliminate the abnormal points from the scatter points of the wind turbine generator set power curve.

[0127] The seventh processing module 107 is configured to fit the scatter points of the wind turbine generator set power curve after eliminating the abnormal points to the variation model as the identification result of the wind turbine generator set power curve.

[0128] According to the system of the second aspect of the present invention, the first processing module 101 is specifically configured that the verification of the integrity and rationality of the anemometry data includes:

[0129] Verify and check the anemometer tower data, and judge the integrity and rationality of the anemometer tower data:

[0130] Detect unreasonable data and missing measurement data; calculate the integrity rate of the effective data of the anemometer tower, and the integrity rate of the effective data should reach 90%.

[0131] According to the system of the second aspect of the present invention, the first processing module 101 is specifically configured that the calculation of the integrity rate of the effective data includes:

[0132]

[0133] According to the system of the second aspect of the present invention, the second processing module 102 is specifically configured that the establishment of the hydrodynamic model of the wind farm based on the Reynolds-averaged Navier-Stokes equation and the derivation of the time series data of the wind speed at each wind turbine point position from the measured wind speed of the anemometer tower include:

[0134] Taking the time-series wind speed and turbulence of the anemometer tower as independent variables, setting the upper boundary, side boundary, ground boundary, and inlet and outlet boundary conditions, and using the modified k-ε turbulence model for closed-loop operation to solve the Navier-Stokes equation based on Reynolds-averaged to obtain the parameter values at each grid point in the flow field; according to the parameter values, the time-series data of the wind speed at each wind turbine location is deduced from the measured wind speed of the anemometer tower.

[0135] According to the system of the second aspect of the present invention, the third processing module 103 is specifically configured to associate the deduced wind speed data with the SCADA wind speed data of the wind turbine at the same time to obtain a linear fitting curve, and the fitted relationship function includes:

[0136] Obtaining the multiple relationship and residual between the deduced wind speed and the SCADA wind speed through the fitting curve to obtain the relationship function, U scada = a·U cfd + b;

[0137] Where, U scada is the SCADA wind speed, a is the multiple relationship, U cfd is the deduced wind speed, and b is the residual.

[0138] According to the system of the second aspect of the present invention, the fifth processing module 105 is specifically configured to, in the step S5, establish a power variation model of the wind turbine under the action of real turbulent wind by introducing the power fluctuation caused by wind turbulence, including:

[0139] Since the change of wind speed is random and conforms to the characteristics of the Markov process,

[0140] P(t) = p stat (U) + pΔ(t)

[0141] In the formula, P(t) represents the power generated at each moment under the action of real turbulent wind, which is determined by U(t) at time t; p stat (U) represents the power corresponding to the average value of U(t) in a given time period; pΔ(t) represents the power fluctuation caused by wind turbulence at time t; U is the average value of U(t); U(t) is the free-stream wind speed.

[0142] According to the system of the second aspect of the present invention, the sixth processing module 106 is specifically configured to, in the step S6, remove the abnormal points from the scatter points of the wind turbine power curve, including:

[0143] Processing the power data of the wind turbines in the SCADA and the free-stream wind speed data in intervals, and removing the recording points where the generated power exceeds several times the power variance in the statistical interval as bad points.

[0144] The third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps in a method for identifying a power curve of a wind turbine generator set according to any one of the first aspects disclosed in the present invention are implemented.

[0145] Figure 8 As shown in the structure diagram of an electronic device according to an embodiment of the present invention, Figure 8 As shown, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be implemented through WIFI, a carrier network, near field communication (NFC), or other technologies. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the electronic device, or an external keyboard, a touchpad, or a mouse, etc.

[0146] Those skilled in the art can understand that Figure 8 the structure shown in is only a structure diagram of a part related to the technical solution of the present disclosure, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine some components, or have a different component arrangement.

[0147] The fourth aspect of the present invention discloses a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in a method for identifying a power curve of a wind turbine generator set according to any one of the first aspects disclosed in the present invention are implemented.

[0148] Please note that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification. The above embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the invention patent. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present application, several deformations and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method for identifying the power curve of a wind turbine, characterized in that, the method includes: simulating the free incoming wind speed through fluid mechanics and identifying the power curve of the wind turbine through the regression algorithm of the measured power curve of the wind turbine; the specific steps include: Step S1: Obtain the wind measurement tower data inside or around the wind farm; and verify the integrity and rationality of the wind measurement data; Step S2: Based on the Reynolds-averaged Navier-Stokes equations, establish a fluid mechanics model of the wind farm, and deduce the time-series data of the wind speed at each wind turbine location from the measured wind speed of the wind measurement tower; Step S3: Correlate the deduced wind speed data with the SCADA wind speed data of the wind turbine at the same time to obtain a linear fitting curve and fit out a relationship function; Step S4: Convert the SCADA wind speed data into free incoming wind speed data through the relationship function; Step S5: Corresponding the free incoming wind speed data and the power data of the SCADA wind turbine in time to obtain the scatter points of the wind turbine power curve; by introducing the power fluctuation caused by wind turbulence, establish a model of the change of the power of the wind turbine under the action of real turbulent wind with the wind speed; Step S6: Eliminate the abnormal points from the scatter points of the wind turbine power curve; Step S7: Fit the scatter points of the wind turbine power curve after eliminating the abnormal points to the change model as the identification result of the wind turbine power curve.

2. A method for identifying the power curve of a wind turbine according to claim 1, characterized in that, in the step S1, the method for verifying the integrity and rationality of the wind measurement data includes: Verifying and checking the wind measurement tower data to judge the integrity and rationality of the wind measurement tower data: Detecting unreasonable data and missing data; calculating the integrity rate of the effective data of the wind measurement tower, and the integrity rate of the effective data should reach 90%.

3. A method for identifying the power curve of a wind turbine according to claim 2, characterized in that, in the step S1, the calculation method of the integrity rate of the effective data includes:

4. A method for identifying the power curve of a wind turbine according to claim 1, characterized in that, in the step S2, the method for establishing a fluid mechanics model of the wind farm based on the Reynolds-averaged Navier-Stokes equations and deducing the time-series data of the wind speed at each wind turbine location from the measured wind speed of the wind measurement tower includes: Taking the time-series wind speed and turbulence of the wind measurement tower as independent variables, and setting the upper boundary, side boundary, ground boundary and inlet and outlet boundary conditions, and using the modified k-ε turbulence model for closed operation to solve the Navier-Stokes equations based on Reynolds-averaged to obtain the parameter values at each grid point in the flow field; according to the parameter values, deduce the time-series data of the wind speed at each wind turbine location from the measured wind speed of the wind measurement tower.

5. A method for identifying the power curve of a wind turbine according to claim 1, characterized in that, in the step S3, the method for correlating the deduced wind speed data with the SCADA wind speed data of the wind turbine at the same time to obtain a linear fitting curve and fit out a relationship function includes: The multiple relationship and residuals between the estimated wind speed and the SCADA wind speed are obtained through the fitting curve, and a relationship function, U scada = a·U cfd + b; Among them, U scada is the SCADA wind speed, a is the multiple relationship, U cfd is the estimated wind speed, and b is the residual error.

6. A method for identifying the power curve of a wind turbine, according to claim 1, characterized in that, in the step S5, the method of establishing a variation model of the power of the wind turbine under the action of real turbulent wind by introducing the power fluctuation caused by wind turbulence includes: Since the change of wind speed is random and conforms to the characteristics of a Markov process, P(t) = p stat (U) + pΔ(t) Wherein, P(t) represents the power generated at each moment under the action of real turbulent wind, which is determined by U(t) at time t; p stat (U) represents the power corresponding to the average value of U(t) within a given time period; pΔ(t) represents the power fluctuation caused by wind turbulence at time t; U is the average value of U(t); U(t) is the free incoming flow velocity.

7. A method for identifying the power curve of a wind turbine, according to claim 1, characterized in that, in the step S6, the method of removing outliers from the scatter points of the wind turbine power curve includes: Processing the power data of the wind turbines in the SCADA and the free incoming wind speed data in intervals, and removing the recording points where the generated power exceeds several times the power variance within the statistical interval as bad points.

8. A system for identifying the power curve of a wind turbine, characterized in that, the system includes: A first processing module, configured to obtain the anemometer tower data inside or around the wind farm; and verify the integrity and rationality of the anemometry data; A second processing module, configured to establish a hydrodynamic model of the wind farm based on the Reynolds-averaged Navier-Stokes equations, and deduce the time-series data of the wind speed at each wind turbine location from the measured wind speed of the anemometer tower; A third processing module, configured to correlate the deduced wind speed data with the SCADA wind speed data of the wind turbine at the same time to obtain a linear fitting curve and fit out a relationship function; A fourth processing module, configured to convert the SCADA wind speed data into free incoming wind speed data through the relationship function; A fifth processing module, configured to correspond the free incoming wind speed data and the power data of the wind turbines in the SCADA in time to obtain the scatter points of the wind turbine power curve; establish a variation model of the power of the wind turbine under the action of real turbulent wind by introducing the power fluctuation caused by wind turbulence; A sixth processing module, configured to remove outliers from the scatter points of the wind turbine power curve; A seventh processing module, configured to fit the scatter points of the wind turbine power curve after removing outliers to the variation model as the identification result of the wind turbine power curve.

9. An electronic device, characterized in that, the electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps in a method for identifying the power curve of a wind turbine according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that, a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps in a method for identifying the power curve of a wind turbine according to any one of claims 1 to 7 are implemented.

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