A wind farm frequency regulation power allocation method considering historical fatigue loads of turbines

Abstract

The present invention belongs to the technical field of wind farm active power control and discloses a wind farm frequency modulation power distribution method that takes into account the historical fatigue load of the unit. The method comprises the following steps: collecting the frequency data of the wind farm grid connection point; when the frequency change exceeds the frequency modulation dead zone, calculating the wind farm active power change reference value ΔP farm ; Match the station active power change reference value ΔP in the formed scenario strategy set farm The corresponding optimized active power distribution results of the units. This distribution method is compared with the traditional proportional distribution scheme. While meeting the system frequency response requirements, the present invention improves the problems of sudden increase in fatigue load in wind farms and large differences in fatigue accumulation between units, and verifies the effectiveness of the proposed method.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind farm active power control, and in particular relates to a wind farm frequency modulation power distribution method taking into account historical fatigue loads of units. Background Art

[0002] As the proportion of wind power connected to the grid continues to increase, wind farms are required to be able to participate in grid frequency regulation. Frequent fluctuations in wind turbine output lead to increasing fatigue loads on the units, threatening their safe and stable operation.

[0003] For wind farms to participate in grid frequency regulation, they should be able to quickly track the active power instructions required for frequency regulation. When the required power is lower than the maximum power that the wind farm can generate, it is crucial to coordinate the active power generated by the units within the site.

[0004] Traditional methods for allocating active power to wind farms only consider matching active power requirements, distributing power proportionally based on the generating capacity of the turbines within the site. This approach is fast and efficient, but it fails to consider the impact of turbine fatigue loads on the health and lifespan of the turbines. Based on this, patent application number 201910836163.6, entitled "Method for Active Power Allocation in Wind Farms Considering Turbine Fatigue Loads," proposes a three-tiered allocation structure. The first tier is the wind farm cluster allocation layer, which calculates the available power using wind speed forecasts for each wind farm and distributes cluster active power to wind farms based on the proportion of available power within the wind farms. The second tier is the wind farm allocation layer, which allocates cluster active power reference values ​​received in the first tier to different turbine types using a prioritized ranking method based on cluster analysis results. The third tier is the unit allocation layer, which distributes the active power reference values ​​received by each unit type to each unit. Although this solution proposes to consider the active power distribution from the perspective of unit fatigue load, the consideration and description of fatigue factors in the process are not specific enough, and it is impossible to truly and effectively take into account both tracking active demand instructions and reducing the fatigue load of the wind farm. On this basis, the patent application number is 202011093282.6, and the invention name is Wind Farm Active Power Optimization Distribution Control Method, which proposes to use unit fatigue load sensitivity to describe the unit fatigue load performance, and establishes an optimization model that comprehensively considers tracking active power instructions and station fatigue loads. The above-mentioned patent application considers the distribution of unit active power from the perspective of fatigue load, reducing the fatigue level of units in the station while meeting active demand, but does not consider the historical fatigue status of wind turbines in the optimization process, and does not consider the coordinated distribution of fatigue of units in the station, which increases the number and cost of operation and maintenance of wind farms.

[0005] In view of this, a wind farm frequency regulation power allocation method considering the historical fatigue load of the units is proposed. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a wind farm frequency regulation power allocation method that takes into account the historical fatigue load of the units, solves the problems mentioned in the background technology, and coordinates and optimizes the total fatigue load of the station and the fatigue load differences of the units within the station while meeting the needs of wind farms participating in frequency regulation.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A wind farm frequency regulation power allocation method considering historical fatigue loads of turbines comprises the following steps:

[0009] Collect the frequency data of the wind farm grid connection point, and calculate the reference value of the wind farm active power change ΔP when the frequency change exceeds the frequency regulation dead zone farm ;

[0010] Match the station active power change reference value ΔP in the formed scenario strategy set farm The corresponding unit active power optimization distribution results;

[0011] The matched unit active power command is sent to the unit in the station. The unit generates a wind turbine control variable reference value based on the active power reference value and controls the wind turbine to track the power reference value.

[0012] For each unit's pitch angle β and speed ω r , wind speed V, and the current active power data value P e0 Conduct collection;

[0013] According to the collected fan operating status data, the fan fatigue sensitivity coefficient C, D, and the fan active power adjustable value P are calculated in the fan controller. avail Calculation of

[0014] According to the historical data of active power change reference value of wind farm participating in frequency regulation, a set of active power change set value ΔP is set set ;

[0015] Based on the active power change setpoint ΔP set , fan fatigue sensitivity coefficient C, D, fan active power adjustable value P avail , complete the optimization solution and form a scenario strategy set for matching and calling.

[0016] Preferably, the calculation model of the fan fatigue sensitivity coefficient is

[0017]

[0018]

[0019] Where η gis the gearbox transmission ratio, ω r , β is the wind turbine speed and pitch angle, ω f0 is the fan speed filter value, J g ,J t are the generator and overall equivalent inertia, T m ,F t are the wind turbine mechanical torque and the tower thrust, P e0 is the active power output of the wind turbine at the initial moment, B d ,E d is the coefficient matrix, and the calculation formula is:

[0020]

[0021]

[0022]

[0023] Preferably, the adjustable amount of active power of the fan P avail The calculation of includes the following steps:

[0024] First, calculate the maximum active power that the wind turbine can generate at the current wind speed V. The calculation formula is:

[0025]

[0026] Where ρ is the air density, A is the area swept by the wind wheel, V is the wind speed, C p_max is the maximum wind energy utilization coefficient.

[0027] Preferably, the wind turbine active power can be adjusted upward by min(P max -P e0 ,ΔP i max ), ΔP i max Indicates the maximum amplitude by which the active power of the fan can increase in a short time;

[0028] The amount by which the active power of the fan can be adjusted downward is min(P e0 ,ΔP i max ),ΔP i max Indicates the maximum amplitude by which the active power of the fan can decrease in a short time.

[0029] Preferably, the optimization model of the station controller can be expressed as:

[0030]

[0031]

[0032]

[0033] |P i k -P i k-1 |≤ΔP i max (10)

[0034] Where n is the number of units in the wind farm, λ1,λ2,λ3 are weight coefficients of different targets, Δf is the frequency deviation, ΔT s i is the change of tower bending moment and shaft torque of the i-th unit, T s base is the standard constant value of tower bending moment and shaft torque, k1 and k2 are the fatigue weight coefficients of tower bending moment and shaft torque, is the initial value of the fatigue state of the unit at each optimization; Pi is the active output of wind turbine i, P i min ,P i max are the minimum and maximum active output of the wind turbine respectively; is the active power instruction of the wind farm, P i k ,P i k-1 is the active power output of the i-th unit at time k and time k-1.

[0035] Preferably, the allocation scheme includes: the wind turbine controller receives the optimization allocation instruction P from the station controller WTi ,The fan controller generates a reference value of the control variable to control the fan to reach the operating state; the fan controller needs to calculate the fatigue value generated during the action process and superimpose it on the initial fatigue value to prepare for the next optimization.

[0036] According to another aspect of the present invention, a computer-readable storage medium storing a computer program is provided, wherein when the program is executed by a processor, a wind farm frequency regulation power distribution method considering historical fatigue loads of the units as described in any one of the above items is implemented.

[0037] According to another aspect of the present invention, the present invention provides a device, characterized in that the device includes: one or more processors;

[0038] a memory for storing one or more programs,

[0039] When the one or more programs are executed by the one or more processors, the one or more processors are enabled to execute any one of the above-mentioned methods for allocating frequency regulation power to a wind farm taking into account historical fatigue loads of turbines.

[0040] Beneficial effects of the present invention:

[0041] Based on established fatigue sensitivity coefficients for wind turbines, this paper proposes a method for allocating active power to wind farms participating in frequency regulation that takes into account the historical fatigue loads of the turbines. This method effectively reduces the total fatigue load on the wind farm while meeting frequency response requirements. Furthermore, compared to other existing allocation methods, this method addresses the significant differences in fatigue loads between turbines within a wind farm. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0043] Figure 1 This is a flow chart of the method for allocating active power for wind farm participation in frequency regulation taking into account the historical fatigue status of the units according to the present invention;

[0044] Figure 2 This is a diagram of the wind farm active power distribution control structure of the present invention;

[0045] Figure 3 It is a two-mass model of the transmission chain of the present invention;

[0046] Figure 4 This is a structural diagram of a wind farm according to the present invention;

[0047] Figure 5 1 is a frequency waveform diagram of different allocation methods of the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 any creative efforts shall fall within the scope of protection of the present invention.

[0049] The active power distribution control structure of the wind farm participating in frequency regulation considering the fatigue load of the unit described in this application is as follows Figure 1As shown, it includes a frequency controller, a station active power controller and a wind turbine controller. The frequency controller calculates the required power reference value ΔPf based on the detected grid frequency data. arm , as shown in formula (1):

[0050]

[0051] Where, f m is the frequency value detected by the common connection point, f0 is the frequency base value, K v ,K p are the virtual inertia coefficient and droop coefficient respectively.

[0052] The station active power controller receives the active power instruction ΔP farm , and match the station active power change reference value ΔP in the formed scenario strategy set farm The corresponding unit active power optimization distribution results.

[0053] The matched unit active power instruction is sent to the unit in the station. The unit generates a wind turbine control variable reference value based on the active power reference value and controls the wind turbine to track the power reference value.

[0054] For scenario policy sets, you can construct them as follows:

[0055] For each unit's pitch angle β and speed ω r , wind speed V, and the current active power data value P e0 Conduct collection;

[0056] According to the collected fan operating status data, the fan fatigue sensitivity coefficient C, D, and the fan active power adjustable value P are calculated in the fan controller. avail Calculation of

[0057] According to the historical data of active power change reference value of wind farm participating in frequency regulation, a set of active power change set value ΔP is set set ;

[0058] Based on the active power change setpoint ΔP set , fan fatigue sensitivity coefficient C, D, fan active power adjustable value P avail , complete the optimization solution and form a scenario strategy set for matching and calling.

[0059] The optimization objective function of the constructed optimization model is shown in formula (2), and the constraints of the optimization model are shown in formulas (3) to (5):

[0060]

[0061] P imin ≤P i ≤P i max (3)

[0062]

[0063] |P i k -P i k-1 |≤ΔP i max (5)

[0064] Where n is the number of units in the wind farm, λ1,λ2,λ3 are weight coefficients of different targets, Δf is the frequency deviation, ΔT s i is the change of tower bending moment and shaft torque of the i-th unit, T s base is the standard constant value of tower bending moment and shaft torque, k1 and k2 are the fatigue weight coefficients of tower bending moment and shaft torque, is the initial value of the unit fatigue state during each optimization; P i is the active power output of wind turbine i, P i min ,P i max are the minimum and maximum active output of the wind turbine respectively; is the active power instruction of the wind farm, P i k ,P i k-1 is the active power output of the i-th unit at time k and time k-1.

[0065] The wind turbine controller receives data on the turbine's operating status, including speed, pitch angle, and wind speed, and calculates the turbine's fatigue sensitivity coefficient and the calculated fatigue status value. Furthermore, the wind turbine controller receives optimized dispatch results from the station controller and controls the turbine to track the given values.

[0066] The calculation method of the fan fatigue sensitivity coefficient described in this application is as follows:

[0067] Based on Betz theory, the mechanical power that a fan extracts from wind is:

[0068]

[0069] Where P is the wind energy absorbed by the fan, ρ is the air density, A is the area swept by the wind wheel, V is the wind speed, C is the wind speed, p(λ,β) is the wind energy utilization coefficient, which is a high-order nonlinear function of the pitch angle β and the tip speed ratio λ. Where λ is:

[0070]

[0071] Where R is the radius of the wind wheel, ω m is the wind turbine speed. Wind energy utilization coefficient C p (λ,β) is:

[0072]

[0073] The present invention adopts Figure 2 The two-mass model shown in the figure is used for modeling, and all physical quantities are converted to the low-speed shaft side. The dynamic equation is:

[0074]

[0075] Where, J m and J g are the rotational inertia of the wind wheel and generator respectively; D s is the equivalent damping of the transmission chain shaft system; K s is the equivalent stiffness of the transmission chain shaft system; ω m ,ω g ,ω s are the angular velocities of the wind wheel, generator and transmission chain shafts respectively; T m ,T e ,T s are the torques of the wind wheel, generator and transmission chain shaft system respectively.

[0076] From formula (9), we can get:

[0077]

[0078] make From formula (10), we can get:

[0079]

[0080] The incremental form of formula (11) is:

[0081]

[0082] From formula (9), we can deduce:

[0083]

[0084]

[0085] From formula (13), we can know that:

[0086]

[0087]

[0088] From formula (15) and (16), we can know that:

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] Combining the above formulas, it can be written as the following state space equation:

[0097]

[0098] Where, u=[ΔP e1 ,ΔP e2 ...,ΔP en ],x=[Δω r ,Δβ,Δω f ]', the coefficient matrices are:

[0099]

[0100]

[0101] Discretizing Equation (24), we can obtain the following discrete state space equation:

[0102] x(k+1)=A d x(k)+B d u+E d (27)

[0103] The coefficient matrices are:

[0104]

[0105] According to equations (12) to (14), the axis direction can be obtained:

[0106] ΔT s (k+1)=C shaft ΔP e +D shaft (29)

[0107] Where C shaft ,D shaft is the fatigue sensitivity coefficient, and the calculation formula is as follows:

[0108]

[0109] Similarly, the tower direction can be deduced:

[0110] ΔF t (k+1)=C tower ΔP e +D tower (31)

[0111] Where C tower ,D tower is the fatigue sensitivity coefficient, and the calculation formula is as follows:

[0112]

[0113] Furthermore, the present invention requires continuous iteration of the wind turbine's fatigue state. Strain gauges are installed throughout the shaft and tower to measure alternating stresses. The fatigue loads on the shaft and tower are then calculated using the rainflow counting method. The newly calculated fatigue loads are then superimposed on the original initial fatigue state values ​​and uploaded to the station controller for the next optimization run.

[0114] The present invention is verified by establishing a wind farm containing 10 5MW wind turbines. The wind farm structure is as follows: Figure 3 As shown. The average wind speed of the wind field is set to 13m / s, the turbulence intensity is 0.12, and the station load is 40MW. At the simulation time of 0s, 20s, and 40s, the load is lost by 5MW, the load is increased by 5MW, and the load is lost by 2.5MW. The system frequency under different distribution modes is as follows Figure 4 As shown in the figure, it can be seen that the frequency lowest point of the optimized allocation method adopted by the present invention is consistent with that of the traditional proportional allocation method, but the frequency highest point of the optimized allocation method of the present invention is higher, and the subsequent fluctuation is larger, but it is within the normal frequency fluctuation range. In terms of fatigue load, after rain flow counting analysis and calculation, the equivalent fatigue load of the unit using the allocation scheme adopted by the present invention and the traditional proportional allocation method and the unit without considering the historical fatigue state of the unit are shown in Tables 1 and 2:

[0115] Table 1 Equivalent fatigue load in the direction of the shafting

[0116]

[0117] Table 2 Equivalent fatigue load in tower direction

[0118]

[0119] The above data are sorted and analyzed, as shown in Table 3:

[0120] Table 3 Data processing and analysis results

[0121]

[0122]

[0123] Data processing and analysis show that while meeting the frequency regulation requirements of a wind farm, an active power allocation strategy that disregards the historical fatigue status of wind turbines can minimize the overall fatigue of the wind farm. However, the fatigue status of wind turbines within a site varies significantly. The proposed site active power allocation method, which considers the historical fatigue load of a wind farm, reduces overall site fatigue while coordinating the fatigue status of each site unit, enhancing site health while reducing the economic costs of site operation and maintenance. These examples demonstrate the effectiveness of the present invention.

[0124] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0125] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications are intended to fall within the scope of the present invention.

Claims

1. A wind farm frequency regulation power allocation method considering historical fatigue loads of turbines, characterized in that: The following steps are involved: Collect frequency data of wind farm grid connection points f When the frequency change exceeds the frequency regulation dead zone, calculate the reference value of wind farm active power change Δ P farm ; Match the station active power change reference value Δ in the established scenario strategy set P farm The corresponding unit active power optimization distribution results; The matched unit active power command is sent to the unit in the station. The unit generates a wind turbine control variable reference value based on the active power reference value and controls the wind turbine to track the power reference value. Pitch angle for each unit β , speed ω r , wind speed V , and the current active power data value P e0 Conduct collection; According to the collected fan operating status data, the fan fatigue sensitivity coefficient is calculated in the fan controller. C, D , the fan active power can be adjusted P avail Calculation of According to the historical data of active power change reference value of wind farm participating in frequency regulation, a set of active power change set values ​​Δ P set ; Based on the active power change setpoint Δ P set , fan fatigue sensitivity coefficient C, D , the fan active power can be adjusted P avail , complete the optimization solution and form a scenario strategy set for matching and calling; The optimization model for forming the scenario strategy set can be expressed as: (7) (8) (9) (10) Where, n is the number of units in the wind farm , , are weight coefficients of different objectives, is the frequency deviation, , For the i The changes of tower bending moment and shaft torque of each unit, , is the standard constant value of tower bending moment and shaft torque, k 1、 k 2 is the fatigue weight coefficient of tower bending moment and shaft torque, is the initial value of the unit fatigue state during each optimization; P i For wind turbines i The meritorious contribution, , are the minimum and maximum active output of the wind turbine respectively; is the active power instruction of the wind farm station, , For the i Units in k Time and k- 1 hour of active output.

2. A wind farm frequency modulation power allocation method considering historical fatigue loads of units according to claim 1, characterized in that: The calculation model of the fan fatigue sensitivity coefficient is: (1) (2) Where, η g is the gearbox transmission ratio, ω r， β are the wind turbine speed and pitch angle, ω f0 is the fan speed filter value, J g , J t are the generator and overall equivalent inertia respectively, T m , F t are the wind turbine mechanical torque and the tower thrust, P e0 is the active power output of the wind turbine at the initial moment, B d ,E d is the coefficient matrix, and the calculation formula is: (3) (4) (5)。 3. The wind farm frequency modulation power allocation method considering historical fatigue load of units according to claim 1 is characterized in that: The fan active power adjustable amount P avail The calculation of includes the following steps: First calculate the current wind speed V Under this condition, the maximum active power that the fan can generate is calculated as follows: (6) Where, ρ is the air density, A Indicates the area swept by the wind wheel, V is the wind speed, is the maximum wind energy utilization coefficient.

4. A wind farm frequency modulation power allocation method considering historical fatigue loads of turbines according to claim 3, characterized in that: The wind turbine active power can be increased by min( P max - P e0 , ), Indicates the maximum amplitude by which the active power of the fan can increase in a short time; The amount by which the active power of the fan can be adjusted downward is min( P e0 , ), Indicates the maximum amplitude by which the active power of the fan can decrease in a short time.

5. The wind farm frequency modulation power allocation method considering historical fatigue loads of turbines according to claim 1, characterized in that: The allocation method further includes: the wind turbine controller receives the optimization allocation instruction of the station controller P WTi , the fan controller generates a reference value of the control variable to control the fan to reach the operating state; the fan controller needs to calculate the fatigue value generated during the action process and superimpose it on the initial fatigue value to prepare for the next optimization.

6. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, a wind farm frequency regulation power distribution method considering historical fatigue loads of units as described in any one of claims 1 to 5 is implemented.

7. A device, characterized in that The device comprises: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors execute the wind farm frequency regulation power distribution method considering the historical fatigue load of the unit as described in any one of claims 1 to 5.

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