Wind power plant equivalent frequency response modeling method considering working condition uncertainty and equivalence method

Abstract

The invention discloses a wind power plant equivalent frequency response modeling method considering working condition uncertainty and an equivalence method, belongs to the technical field of power system modeling and equivalence, and aims to solve the problem that no method capable of considering the working condition uncertainty of units in a wind power plant to perform equivalent aggregation on the frequency response of the whole wind power plant exists at present. According to the method, modeling is carried out on the wind speed distribution uncertainty in a wind power plant by adopting a probability method, a wind power plant wind speed distribution scene is generated based on a three-parameter Weibull distribution function, and the working condition uncertainty of each unit is represented; dividing the wind power plant into a non-frequency modulation area, a comprehensive inertia and overspeed load shedding control area and a comprehensive inertia and variable pitch control area according to the wind speed distribution and the corresponding wind speed during fan output; and based on a partitioning result, determining an equivalent influence coefficient and an equivalent control coefficient by utilizing parameters of each wind turbine generator, obtaining a wind power plant equivalent frequency response model considering working condition uncertainty by combining a wind turbine generator frequency response model, and realizing equivalence based on the model.

Description

Technical Field

[0001] This invention belongs to the field of power system modeling and equivalent technology, specifically relating to a wind farm equivalent frequency response modeling method and equivalent method. Background Technology

[0002] The integration of a high proportion of new energy sources, especially the significant increase in wind power penetration, has highlighted the frequency stability issue of the power system. Furthermore, the uncertainty and volatility of power output from individual wind turbines in wind farms exacerbate the system frequency problem, necessitating the establishment of relevant analytical models for the frequency characteristics of wind farms. To ensure the frequency security of the power system, an equivalent modeling method for the frequency response of wind farms is proposed to analyze their frequency response characteristics, which has become one of the keys to solving current frequency issues.

[0003] Currently, there are several solutions for equivalent modeling of the frequency response of wind farms, such as:

[0004] The "Extended Frequency Response Model of Power System Considering Wind Power Integrated Inertial Control" proposed by Zhou Haiqiang et al., Automation of Electric Power Systems, 2023, 47(08): 198-205, considers the differences in wind speed among wind turbines in a wind farm. It groups the turbines according to the similarity of their dynamic characteristics, thereby aggregating the frequency responses of each turbine in the wind farm to obtain an equivalent frequency response model for a multi-wind-speed wind farm. This model can reflect the frequency response characteristics of the wind farm under integrated inertial control well and has high accuracy. However, this modeling process only considers the overspeed load reduction control of the wind turbines and does not consider pitch angle control as a backup control method for load reduction. It also lacks a description of the uncertainty of the operating conditions.

[0005] Jiang Tao et al. proposed "Practical Frequency Response Model and Parameter Identification Method for Large-Scale Wind Power Grid-Connected Systems Considering Information Privacy," Power System Technology, 2024, 48(12): 4876-4885. This article considers that large-scale wind power grid-connected systems contain multiple wind turbine units, and directly modeling the frequency response can easily lead to high model order and difficulty in solving the problem. Therefore, based on the idea of ​​unit aggregation, multiple wind turbine units are approximated as one unit, thus establishing an equivalent aggregation model of the frequency response of multiple wind turbine units. This model can truly reflect the aggregation characteristics of the frequency response of multiple wind turbine units and has good solution speed and accuracy. However, this modeling process does not consider wind turbine load shedding and backup control, only considers the frequency regulation means of integrated inertia control, and does not consider the impact of the uncertainty of wind turbine operating conditions on the frequency response.

[0006] The article "Frequency Domain Equivalent Modeling Method for Frequency Response Characteristics of Doubly Fed Wind Farms" by Pan Xueping et al., *Power System Technology*, 2025, 49(06): 2465-2475, proposes an equivalent frequency response modeling method for wind farms. This method first establishes a frequency response model for the wind turbine units, then groups the turbines in the wind farm according to different wind speeds. Based on the idea of ​​simulating the trajectory of the total active power change of the turbines in the same group, the parameters of the equivalent frequency response model of the wind farm are obtained through parameter identification, thus establishing an equivalent model. This model can accurately describe the active power output characteristics of wind turbine units under different wind speeds and has high accuracy. Although this modeling process considers various frequency control methods for wind turbines and the influence of wind speed differences on the frequency response, it still lacks a description of the uncertainty of operating conditions.

[0007] In summary, current equivalent modeling methods for wind farm frequency response mainly obtain the wind farm frequency response model through the aggregation of turbine frequency responses. However, these methods lack comprehensive consideration of frequency regulation control and description of the uncertainties in wind turbine operating conditions. Therefore, there is an urgent need for an equivalent frequency response modeling method for wind farms that can account for the uncertainties in wind turbine operating conditions, in order to better describe the dynamic behavior and frequency response characteristics of each turbine within the wind farm. Summary of the Invention

[0008] This invention aims to address the current lack of a method that can effectively aggregate the frequency response of the entire wind farm by taking into account the uncertainties in the operating conditions of the turbines within the wind farm.

[0009] A method for modeling the equivalent frequency response of a wind farm that takes into account operating condition uncertainties includes:

[0010] A probabilistic approach is used to model the uncertainty of wind speed distribution within a wind farm. A wind speed distribution scenario is generated based on a three-parameter Weibull distribution function to characterize the operating uncertainty of each turbine. The wind farm is divided into three zones based on the wind speed distribution and the wind speed corresponding to turbine output: a non-frequency-tuning zone, a combined inertia and overspeed load reduction control zone, and a combined inertia and pitch control zone. Based on the zoning results, equivalent influence coefficients and equivalent control coefficients are determined using the parameters of each wind turbine. Combined with a wind turbine frequency response model established based on multiple frequency control strategies and rotor motion equations, an equivalent frequency response model of the wind farm considering operating uncertainty is obtained.

[0011] Furthermore, the wind speed distribution scenario of the wind farm is generated based on the three-parameter Weibull distribution function. F(x) is the wind speed distribution probability, x is the random wind speed; γ is the location parameter, β is the shape parameter, and η is the scale parameter.

[0012] Furthermore, the process of dividing a wind farm into a non-frequency regulation area, a combined inertia and overspeed load shedding control area, and a combined inertia and pitch control area according to the wind speed distribution and the wind speed corresponding to the wind turbine output includes:

[0013] When the wind speed is less than V1, the corresponding wind turbines do not participate in frequency regulation, and the number of wind turbines , where N is the total number of wind turbines in the wind farm, and F(x < V1) is the probability that the wind speed of the wind turbine is less than V1, that is, the probability that the wind turbine is in the non-frequency regulation area;

[0014] When the wind speed is in the range [V1, V2], the corresponding wind turbines participate in the frequency regulation process through combined inertia and overspeed load shedding control, and the number of wind turbines , where F(V1 < x < V2) is the probability that the wind speed of the wind turbine is in the range [V1, V2), that is, the probability that the wind turbine is in the combined inertia and overspeed load shedding control area;

[0015] When the wind speed is greater than V2, the corresponding wind turbines participate in the frequency regulation process through combined inertia and pitch control, and the number of wind turbines .

[0016] Furthermore, V1 is the wind speed corresponding to when the wind turbine output is 20% of the rated capacity; V2 is the wind speed corresponding to when the wind turbine reaches the maximum speed.

[0017] Furthermore, the equivalent influence coefficient and the equivalent control coefficient include: the influence coefficient of the equivalent speed on active power , the influence coefficient of the equivalent pitch angle on active power , the influence coefficient of the equivalent wind speed on active power , the equivalent output power increment , the equivalent pitch angle time constant , the equivalent virtual inertia coefficient , the equivalent droop coefficient ; H Wi and P mi are the inertia time constant and the active power output of the i-th wind turbine respectively, β i and β 0i are the pitch angle and the steady-state pitch angle of the i-th wind turbine respectively, V i and V 0i are the wind speed and the steady-state wind speed at which the i-th wind turbine is located respectively, ΔP ei is the output power increment of the corresponding wind turbine, T βi , k di and k pi are the pitch angle time constant, the virtual inertia coefficient and the droop coefficient of the corresponding wind turbine respectively.

[0018] Furthermore, the equivalent frequency response model of the wind farm considering the uncertainty of the working conditions is as follows:

[0019]

[0020] In the formula, Δβ F ΔV and Δβ ref_F These represent the increments of the equivalent pitch angle, equivalent wind speed, and equivalent pitch angle command value for each unit, respectively. The equivalent load reduction percentage for the wind farm; ω F ω0 and ω0 represent the equivalent rotational speed and equivalent steady-state rotational speed of the wind farm, respectively; k optF This represents the equivalent maximum power factor of the wind farm.

[0021] Furthermore, in the process of establishing the frequency response model of the wind turbine based on multiple frequency control strategies and rotor motion equations, the multiple frequency control strategies include droop control, virtual inertia control, overspeed load reduction control, and pitch angle control, as detailed below:

[0022]

[0023]

[0024] In the formula, ΔP SI k represents the active power regulation of the generator unit, Δf represents the frequency deviation; p k is the droop coefficient. d P is the virtual inertia coefficient, P0 is the steady-state active power under load reduction control, d% is the load reduction percentage, and P MPPT The steady-state active power of the unit in MPPT mode, ρ is air density, V is wind speed, R is the radius of the wind turbine rotor, and C is the wind turbine rotor radius. p Let λ be the wind energy utilization coefficient. opt β is the optimal tip speed ratio under the current wind speed. opt This is the optimal pitch angle under the current wind speed.

[0025] Furthermore, in the process of establishing the frequency response model of the wind turbine based on various frequency regulation control strategies and rotor motion equations, the wind turbine rotor motion equations are as follows:

[0026]

[0027] In the formula, H W Let ω be the inertial time constant of the wind turbine. m P is the fan speed, t is time, and P is the fan speed. m and P e These are the per-unit values ​​of mechanical power and electromagnetic power, respectively; λ is the tip speed ratio, β is the blade pitch angle, and S... W Rated capacity of the wind turbine; k opt This represents the power factor in MPPT mode.

[0028] Furthermore, the frequency response model of the wind turbine unit, based on various frequency regulation control strategies and rotor motion equations, is as follows:

[0029]

[0030] In the formula, ω0, β0, and V0 represent the angular velocity, pitch angle, and wind speed during stable operation of the wind turbine, respectively. Δβ and ΔV represent the increments of turbine speed, blade pitch angle, and wind speed, respectively, and T represents the wind speed. β Δβ is the pitch angle time constant. ref The increment of the pitch angle command value; ΔP e This represents the output power increment. , and These are the influence coefficients of rotational speed, pitch angle, and wind speed on active power, respectively; H W Let V be the inertial time constant of the wind turbine, and V be the wind speed; k p k is the droop coefficient. d k is the virtual inertia coefficient. opt S represents the power factor in MPPT mode. W This refers to the rated capacity of the wind turbine unit.

[0031] An equivalent wind farm method that takes into account operating condition uncertainties is proposed, which uses the equivalent frequency response model of the wind farm established by the wind farm equivalent frequency response modeling method that takes into account operating condition uncertainties as described above for equivalence.

[0032] The beneficial effects of this invention are:

[0033] To address the current lack of consideration for turbine operating uncertainty in equivalent frequency response modeling methods for wind farms, this invention comprehensively considers the differences in various frequency regulation controls and operating states of wind turbines, and takes into account the difficulty of determining wind speed in wind farms. It utilizes probabilistic statistical methods to account for the impact of operating uncertainty on the frequency response characteristics of wind farms, proposing an equivalent frequency response modeling method for wind farms that considers operating uncertainty, along with a corresponding equivalent method. This method can aggregate the frequency response of the entire wind farm by taking into account the impact of turbine operating uncertainty within the wind farm. The method characterizes the operating uncertainty of wind turbines through the probability distribution of wind speed, divides each turbine into regions based on wind speed intervals, and aggregates them to obtain an equivalent frequency response aggregation model of the wind farm, which can effectively reflect the frequency response characteristics of the wind farm. This method can provide technical support for the analysis and control of new power systems. Attached Figure Description

[0034] Figure 1 This is a schematic diagram illustrating the frequency response modeling process of a wind turbine generator with multiple frequency modulation controls according to the present invention.

[0035] Figure 2This is a schematic diagram of the wind farm frequency response equivalent aggregation steps that take into account operating condition uncertainties in the present invention.

[0036] Figure 3 This is a schematic diagram showing the specific zoning of each turbine unit within the wind farm according to the present invention. Detailed Implementation

[0037] This invention characterizes the operational uncertainty of wind turbine units through the probability distribution of wind speed. Based on wind speed ranges, it divides each unit into regions and aggregates these regions to obtain an equivalent frequency response model of the wind farm, which can effectively reflect the frequency response characteristics of the wind farm. This method can provide technical support for the analysis and control of new power systems.

[0038] The modeling process of this invention is as follows: S1. Taking into account various frequency regulation control strategies, a single-unit frequency response model of the wind turbine is established; S2. The uncertainty of wind speed distribution within the wind farm is modeled using a probabilistic method. A wind speed distribution scenario of the wind farm is generated based on the three-parameter Weibull distribution function to characterize the operating uncertainty of each unit; S3. The operating state of each unit in the wind farm is partitioned according to the corresponding wind speed interval. Combined with the wind speed probability distribution, the equivalent parameters and coefficients of the wind farm equivalent frequency response model are obtained, and a wind farm equivalent frequency response model considering operating uncertainty is established. The invention will now be described in detail with reference to specific embodiments. Specific implementation method one:

[0040] This embodiment is a method for modeling the equivalent frequency response of a wind farm that takes into account uncertainties in operating conditions, and specifically includes the following steps:

[0041] S1. Taking into account various frequency regulation control strategies, a single-unit frequency response model of the wind turbine is established.

[0042] As attached Figure 1 As shown, the various frequency control methods corresponding to wind turbine units include droop control, virtual inertia control, overspeed load reduction control, and pitch angle control, which can be specifically represented by equations (1) and (2):

[0043] (1)

[0044] (2)

[0045] In the formula, ΔP SI k represents the active power regulation of the generator unit, Δf represents the frequency deviation; p k is the droop coefficient. d The values ​​are virtual inertia coefficients, all set based on GB / T 19963.1-2021. P0 is the steady-state active power under load reduction control, d% is the load reduction percentage, and P... MPPTThe steady-state active power of the unit in MPPT mode, ρ is air density, V is wind speed, R is the radius of the wind turbine rotor, and C is the wind turbine rotor radius. p Let λ be the wind energy utilization coefficient. opt β is the optimal tip speed ratio under the current wind speed. opt This represents the optimal pitch angle at the current wind speed. It is worth noting that the wind turbine controls the steady-state active power P0 through overspeed load reduction and variable pitch angle control, reserving backup power to support the frequency regulation stage.

[0046] As attached Figure 1 As shown, the steps for establishing the single-unit frequency response model of the wind turbine include:

[0047] A1. Combining the mechanical characteristic equation and rotor motion equation of the wind turbine, the simplified rotor motion equation of the wind turbine is obtained.

[0048] The mechanical characteristic equation and rotor motion equation of the wind turbine are expressed by equations (3) and (4):

[0049] (3)

[0050] In the formula, T w λ is the mechanical torque, β is the tip speed ratio, and β is the blade pitch angle. * These are intermediate conversion coefficients.

[0051] (4)

[0052] In the formula, J is the moment of inertia of the wind turbine, and T e Where ω is the electromagnetic torque, D is the damping coefficient, and ω is the damping coefficient. m This refers to the fan speed.

[0053] Combining equations (3) and (4), and neglecting the effect of damping, we can obtain a rewritten set of rotor motion equations, which can be specifically expressed by equation (5):

[0054] (5)

[0055] In the formula, H W P is the inertial time constant of the wind turbine. m and P e These are the per-unit values ​​for mechanical power and electromagnetic power, respectively, S W The rated capacity of the wind turbine, k opt This represents the power factor in MPPT mode.

[0056] A2. Based on equations (1) and (2) and step A1, considering the influence of rotational speed, pitch angle and wind speed on active power, the frequency response model of the wind turbine is established as follows:

[0057] (6)

[0058] In the formula, ω0, β0, and V0 represent the angular velocity, pitch angle, and wind speed during stable operation of the wind turbine, respectively. Δβ and ΔV represent the increments of turbine speed, blade pitch angle, and wind speed, respectively, and T represents the wind speed. β Δβ is the pitch angle time constant. ref The increment of the pitch angle command value; ΔP e The output power increment is obtained from the electromagnetic power shown in equation (5) in step A1; , and The coefficients representing the influence of rotational speed, pitch angle, and wind speed on active power are respectively obtained from the mechanical power shown in equation (5) in step A1.

[0059] S2. The uncertainty of wind speed distribution in the wind farm is modeled using a probabilistic method. The wind speed distribution scenario of the wind farm is generated based on the three-parameter Weibull distribution function to characterize the operating uncertainty of each unit.

[0060] The three parameters of the Weibull distribution function are the location parameter, the shape parameter, and the scale parameter, which can be represented by equation (7):

[0061] (7)

[0062] In the formula, F(x) is the wind speed distribution probability, x is the random wind speed; γ is the location parameter, β is the shape parameter, and η is the scale parameter. All three parameters are obtained by fitting the historical wind speed data of the wind farm to the Weibull distribution under the premise that the historical wind speed conforms to the distribution and by parameter identification.

[0063] S3. Divide the operating status of each unit in the wind farm into zones according to the corresponding wind speed range. Combine the wind speed probability distribution to obtain the equivalent parameters and coefficients of the wind farm equivalent frequency response model, and establish a wind farm equivalent frequency response model that takes into account the uncertainty of operating conditions.

[0064] As attached Figure 2 As shown, the process of establishing the equivalent frequency response model of a wind farm is as follows:

[0065] B1, as attached Figure 3 As shown, the wind farm is divided into three regions based on wind speed: the non-frequency modulation zone, the comprehensive inertia and overspeed load reduction control zone, and the comprehensive inertia and pitch control zone. The actual division results are shown in equations (8) to (10).

[0066] B2. Based on the zoning situation, the impact of operating condition uncertainty on the frequency response characteristics of the wind farm is considered through probability and statistics to obtain the number of units in each region.

[0067] The total number of units in the wind farm is N, and the number of units in each area is N1, N2, and N3 respectively. The number of units in each area is obtained according to the wind speed distribution. The specific calculation steps are as follows:

[0068] When the wind speed is less than V1, the corresponding units do not participate in frequency regulation. The number of units N1 is as follows:

[0069] (8)

[0070] In the formula, V1 is the wind speed corresponding to the fan output of 20% of the rated capacity, which is obtained from P m (ω m , β| β=0 ,V1)=k opt ω m 3 / S W =0.2; F(x<V1) is the probability that the fan wind speed is less than V1, that is, the probability that the fan is in the non-frequency-regulation area, which can be calculated by formula (7).

[0071] When the wind speed is in the interval [V1, V2], the corresponding units participate in the frequency-regulation process through the combined inertia and over-speed load shedding control. The number of units N2 is as follows:

[0072] (9)[[ID=​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​WF Equivalent pitch angle time constant T βeq Equivalent virtual inertia coefficient k dw Equivalent droop coefficient k pw :

[0077] (11)

[0078] In the formula, H Wi and P mi β represents the inertial time constant and active power output of the i-th wind turbine, respectively. i and β 0i V represents the pitch angle and steady-state pitch angle of the i-th wind turbine, respectively. i and V 0i Let ΔP be the wind speed and steady-state wind speed at which the i-th wind turbine is located. ei T corresponds to the increase in the unit's output power. βi k di and k pi These are the pitch angle time constant, virtual inertia coefficient, and droop coefficient for the corresponding turbine unit.

[0079] B4. Based on the equivalent coefficients and parameters obtained in step B3, and combined with equation (6) obtained in step A2, the equivalent frequency response model of the wind farm considering the uncertainty of the operating conditions is as follows:

[0080] (12)

[0081] In the formula, Δβ F ΔV and Δβ ref_F These represent the increments of the equivalent pitch angle, equivalent wind speed, and equivalent pitch angle command value for each unit, respectively. ω represents the equivalent load reduction percentage of the wind farm, while ω is the average obtained by summing the load reduction percentages of each unit. F ω0 and ω0 are the equivalent rotational speed and equivalent steady-state rotational speed of the wind farm, respectively, both obtained by superimposing and averaging the rotational speeds of all units in the wind farm; k optF The equivalent maximum power coefficient of the wind farm is also obtained by averaging the power of each unit.

[0082] It should be noted that the core and greatest innovation of this invention lies in the fact that the wind speeds of each wind turbine in a wind farm are not the same, and in reality, it is impossible to determine the wind speed distribution of the entire wind farm in real time. There is an uncertainty in the operating conditions of each wind turbine. By using probabilistic statistical methods to characterize the uncertainty of the wind turbine operating conditions, and based on the established frequency response model of a single wind turbine containing multiple frequency modulation controls, an equivalent frequency response model of the wind farm that takes into account the uncertainty of operating conditions is obtained. Specific Implementation Method Two:

[0084] This embodiment is a wind farm equivalent method that takes into account the uncertainty of operating conditions. First, the wind farm equivalent frequency response model is established using the wind farm equivalent frequency response modeling method that takes into account the uncertainty of operating conditions described in Specific Embodiment 1. Then, the equivalent frequency response model of the wind farm is used for equivalence.

[0085] The above examples of this invention are merely illustrative of the computational model and process of this invention, and are not intended to limit the implementation of this invention. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is impossible to exhaustively list all possible implementations here. Any obvious variations or modifications derived from the technical solutions of this invention are still within the scope of protection of this invention.

Claims

1. A method for modeling the equivalent frequency response of a wind farm considering uncertainties in operating conditions, characterized in that, include: The uncertainty of wind speed distribution in the wind farm is modeled using a probabilistic method. The wind speed distribution scenario of the wind farm is generated based on the three-parameter Weibull distribution function to characterize the operating uncertainty of each unit. According to the wind speed distribution and the wind speed corresponding to the wind turbine output, the wind farm is divided into a non-frequency modulation zone, a comprehensive inertia and overspeed load reduction control zone, and a comprehensive inertia and pitch control zone. Based on the zoning results, the equivalent influence coefficient and equivalent control coefficient are determined using the parameters of each wind turbine. Combined with the wind turbine frequency response model established based on multiple frequency regulation control strategies and rotor motion equations, the equivalent frequency response model of the wind farm considering the uncertainty of operating conditions is obtained.

2. The wind farm equivalent frequency response modeling method considering operating condition uncertainties according to claim 1, characterized in that, Wind farm wind speed distribution scenario generated based on three-parameter Weibull distribution function F(x) is the wind speed distribution probability, x is the random wind speed; γ is the location parameter, β is the shape parameter, and η is the scale parameter.

3. A method for modeling the equivalent frequency response of a wind farm considering operational uncertainties, as described in any one of claims 1 to 2, characterized in that, The process of dividing a wind farm into a non-frequency modulation zone, a combined inertia and overspeed load reduction control zone, and a combined inertia and pitch control zone based on wind speed distribution and the corresponding wind speed at turbine output includes: When the wind speed is less than V1, the corresponding unit does not perform frequency regulation, and the number of units , N is the total number of units in the wind farm, and F(x<V1) is the probability that the wind speed of the fan is less than V1, that is, the probability that the fan is in the non-frequency-regulation area; When the wind speed is in the interval [V1, V2], the corresponding units participate in the frequency regulation process through the comprehensive inertia and over-speed load shedding control, and the number of units , F(V1 < x < V2) is the probability that the wind turbine speed is in the interval [V1, V2)], that is, the probability that the wind turbine is in the comprehensive inertia and over-speed load shedding control area; When the wind speed is greater than V2, the corresponding turbine unit participates in the frequency regulation process through integrated inertia and variable pitch control. The number of turbine units... .

4. The wind farm equivalent frequency response modeling method considering operating condition uncertainties according to claim 3, characterized in that, V1 refers to the wind speed corresponding to 20% of the rated capacity of the fan; V2 refers to the wind speed corresponding to the fan reaching its maximum speed.

5. The wind farm equivalent frequency response modeling method considering operating condition uncertainties according to claim 3, characterized in that, The equivalent influence coefficient and equivalent control coefficient include: the influence coefficient of equivalent rotational speed on active power. The influence coefficient of equivalent pitch angle on active power The influence coefficient of equivalent wind speed on active power Equivalent output power increment Equivalent pitch angle time constant Equivalent virtual inertia coefficient Equivalent droop coefficient H Wi and P mi β represents the inertial time constant and active power output of the i-th wind turbine, respectively. i and β 0i V represents the pitch angle and steady-state pitch angle of the i-th wind turbine, respectively. i and V 0i Let ΔP be the wind speed and steady-state wind speed at which the i-th wind turbine is located. ei T corresponds to the increase in the unit's output power. βi k di and k pi These are the pitch angle time constant, virtual inertia coefficient, and droop coefficient for the corresponding turbine unit.

6. The wind farm equivalent frequency response modeling method considering operating condition uncertainties according to claim 5, characterized in that, The equivalent frequency response model of a wind farm considering operating condition uncertainties is as follows: In the formula, Δβ F ΔV and Δβ ref_F These represent the increments of the equivalent pitch angle, equivalent wind speed, and equivalent pitch angle command value for each unit, respectively. The equivalent load reduction percentage for the wind farm; ω F ω0 and ω0 represent the equivalent rotational speed and equivalent steady-state rotational speed of the wind farm, respectively; k optF This represents the equivalent maximum power factor of the wind farm.

7. A method for modeling the equivalent frequency response of a wind farm considering uncertainties in operating conditions, as described in claim 6, is characterized in that... In establishing the frequency response model of the wind turbine based on various frequency control strategies and rotor motion equations, the various frequency control strategies include droop control, virtual inertia control, overspeed load reduction control, and pitch angle control, as detailed below: In the formula, ΔP SI k represents the active power regulation of the generator unit, Δf represents the frequency deviation; p k is the droop coefficient. d P is the virtual inertia coefficient, P0 is the steady-state active power under load reduction control, d% is the load reduction percentage, and P MPPT The steady-state active power of the unit in MPPT mode, ρ is air density, V is wind speed, R is the radius of the wind turbine rotor, and C is the wind turbine rotor radius. p Let λ be the wind energy utilization coefficient. opt β is the optimal tip speed ratio under the current wind speed. opt This is the optimal pitch angle under the current wind speed.

8. A method for modeling the equivalent frequency response of a wind farm considering uncertainties in operating conditions, as described in claim 7, is characterized in that... In the process of establishing the frequency response model of the wind turbine based on various frequency regulation control strategies and rotor motion equations, the wind turbine rotor motion equations are as follows: In the formula, H W Let ω be the inertial time constant of the wind turbine. m P is the fan speed, t is time, and P is the fan speed. m and P e These are the per-unit values ​​of mechanical power and electromagnetic power, respectively; λ is the tip speed ratio, β is the blade pitch angle, and S... W Rated capacity of the wind turbine; k opt This represents the power factor in MPPT mode.

9. A method for modeling the equivalent frequency response of a wind farm considering operational uncertainties, as described in claim 8, is characterized in that... The frequency response model of the wind turbine generator, based on various frequency regulation control strategies and rotor motion equations, is as follows: In the formula, ω0, β0, and V0 represent the angular velocity, pitch angle, and wind speed during stable operation of the wind turbine, respectively. Δβ and ΔV represent the increments of turbine speed, blade pitch angle, and wind speed, respectively, and T represents the wind speed. β Δβ is the pitch angle time constant. ref The increment of the pitch angle command value; ΔP e This represents the output power increment. , and These are the influence coefficients of rotational speed, pitch angle, and wind speed on active power, respectively; H W Let V be the inertial time constant of the wind turbine, and V be the wind speed; k p k is the droop coefficient. d k is the virtual inertia coefficient. opt S represents the power factor in MPPT mode. W This refers to the rated capacity of the wind turbine unit.

10. A wind farm equivalent method considering operational uncertainties, characterized in that, The wind farm equivalent frequency response model established using the wind farm equivalent frequency response modeling method considering operating condition uncertainties as described in any one of claims 1 to 9 is used for equivalent evaluation.

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