Wind power plant inertia demand quantitative evaluation method considering control parameters

Abstract

The invention provides a wind power plant station inertia demand quantitative evaluation method considering control parameters, and the method comprises the steps: obtaining the system data of a wind power plant station, and building a wind power plant station small signal model based on a virtual synchronous generator (VSG) technology; according to the small signal model, starting from the wind power generation stand-alone system, considering a control parameter virtual inertia J, performing inertia quantitative evaluation analysis on the wind power generation stand-alone system to obtain a stand-alone system inertia stability domain; on the basis of a wind power generation single-machine system, inertia quantitative evaluation analysis of a wind power generation double-machine system, a wind power generation three-machine system and a wind power generation four-machine system is carried out in sequence, and inertia stability domains of the double-machine system, the three-machine system and the wind power generation four-machine system are obtained respectively; and further expanding the number of the fans to n to obtain an empirical formula of the inertia stability domain of the multi-machine system of the wind power plant. The wind power station inertia demand quantitative evaluation method considering the control parameters analyzes the wind power station inertia demand, enriches the frequency control system of a novel power system, enhances the inertia response capability of a new energy station, and has important significance for maintaining the stability of a high-proportion renewable energy station system.

Description

Technical Field

[0001] The present invention relates to the field of inertia quantification requirements for new energy stations, and in particular to an inertia quantification requirement for a wind farm station considering control parameters. Background Art

[0002] my country's total onshore wind energy reserves are considerable. In recent years, the industrialization of offshore wind power has also developed rapidly, with installed capacity continuing to climb. New energy sources are rapidly developing, with wind power accounting for a significant portion of this, and the scale of wind farms is gradually increasing. Further research into wind farm optimization and system control strategies holds great promise for development.

[0003] As the proportion of new energy generators in the power generation mix increases, future power systems will gradually shift from mechanical electromagnetic power systems primarily based on synchronous motors to low-inertia power systems with a high proportion of power electronic equipment and a small number of synchronous motors. Compared to traditional power systems, low-inertia power systems experience significantly higher initial frequency change rates and maximum frequency deviations during power shortages. Traditional frequency control measures suitable for high-inertia power systems are less effective, posing risks to grid frequency stability.

[0004] Therefore, from the perspective of system stability, it is necessary to conduct a quantitative assessment of the inertia demand of wind farms based on virtual synchronous machine technology to provide the necessary inertia support for the system. Summary of the Invention

[0005] In order to solve the problems existing in the prior art, the present invention provides a method for quantitatively evaluating the inertia demand of a wind farm station taking into account control parameters. The method quantitatively analyzes the inertia demand of a wind farm station from the perspective of regulating and controlling the control parameters, and provides the necessary inertia support for the system to maintain the stability of the station. To achieve the above-mentioned purpose, the present invention provides a method for quantitatively evaluating the inertia demand of a wind farm station taking into account control parameters, and the method includes:

[0006] Determine the wind power system topology, obtain wind farm system data, and normalize the data to obtain wind farm system normalized value data;

[0007] Combined with the per-unit data of wind farms, a small signal model of wind farms based on virtual synchronous machine technology is established and analyzed;

[0008] Based on the small signal model, starting from the single-machine system, considering the control parameter virtual inertia J, the critical inertia of the wind power generation single-machine system is quantitatively evaluated and analyzed. By changing J, the inertia stability domain of the single-machine system is obtained.

[0009] Based on the single-machine system, the critical inertia quantitative evaluation and analysis of the dual-machine system, the three-machine system and the four-machine system are carried out in turn. By changing the inertia, the characteristic root loci of the dual-machine system, the three-machine system and the four-machine system are obtained, and then the inertia stability domain of the multi-machine system of the wind farm station is obtained.

[0010] The number of wind turbines is further expanded to n, and the empirical formula for the inertia stability domain of the multi-machine system in the wind farm is obtained.

[0011] Furthermore, the wind farm system topology was determined to be a multi-machine parallel system with multiple wind turbines connected to an AC busbar. The wind power system consists of two components: a wind energy conversion system and a power conversion system. It generates power using a variable speed constant frequency method, connected to the load via full-power back-to-back converters, filters, transformers, and transmission lines.

[0012] Furthermore, the wind turbines in the wind farm system are equivalent to voltage sources when small signal modeling is performed.

[0013] Furthermore, the wind farm station data includes the port voltage data of each inverter, the output voltage and current data of each inverter, the filter inductance, filter capacitance and equivalent resistance data of each LC filter, the equivalent resistance and inductance data of each transmission line, and the AC bus PCC point voltage data.

[0014] Furthermore, the per-unit normalization process is as follows: per-unit value = nominal value / reference value. The nominal value is the data in the wind farm system, and the reference value includes the power reference value, voltage reference value, frequency reference value, and damping reference value.

[0015] Furthermore, a small-signal model of a wind farm based on virtual synchronous machine technology was developed, using virtual synchronous machine control technology for its inverter control method. The small-signal modeling process first selected a reference coordinate system, then, based on coordinate transformation, performed small-signal modeling of the power control loop, filters, and interconnecting and transmission lines. Due to the complexity of multi-machine systems at wind farms, appropriate state variables were selected, and a general small-signal model of a multi-machine parallel wind farm system based on virtual synchronous machines was derived and established from a state-space perspective.

[0016] Furthermore, based on the wind farm small signal model of the virtual synchronous machine technology, appropriate state variables are selected, and a universal small signal model of the virtual synchronous machine multi-machine parallel system is derived and established from the perspective of state space.

[0017] Furthermore, the quantitative assessment and analysis of the critical inertia of a single wind turbine system involves substituting the per-unit system data into a small-signal model to derive the state matrix of the single-unit system. By varying the virtual inertia J, the root loci of the system characteristic roots are obtained, and thus the inertia stability region of the single-unit system is determined.

[0018] Furthermore, a quantitative evaluation and analysis of the critical inertia of the two-machine system, three-machine system, and four-machine system is performed. For the two-machine system, five different power distribution scenarios are selected based on the load fluctuation. For each scenario, the system characteristic root loci are obtained by changing the inertia. Finally, the one with the largest critical stable inertia value among the five scenarios is taken as the final critical stable inertia of the two-machine system, and the inertia stability domain of the single-machine system is obtained.

[0019] For the three-machine system, eight different power distribution situations are selected according to the load fluctuation. For each situation, the system characteristic root loci are obtained by changing the inertia. Finally, the critical stable inertia value with the largest value among the eight situations is taken as the final critical stable inertia of the three-machine system, and then the inertia stability domain of the three-machine system is obtained.

[0020] For the four-machine system, nine different power distribution situations are taken according to the load fluctuation. For each situation, the system characteristic root loci are obtained by changing the inertia. Finally, the critical stable inertia value with the largest value among the nine situations is taken as the final critical stable inertia of the four-machine system, and the inertia stability domain of the four-machine system is obtained.

[0021] Furthermore, different power distribution scenarios in the quantitative evaluation and analysis of critical inertia of dual-machine system, three-machine system and four-machine system are as follows:

[0022] For the dual-machine system, the five different power distribution situations are: Pref1:Pref2=0.1:0.9; Pref1:Pref2=0.2:0.8; Pref1:Pref2=0.3:0.7; Pref1:Pref2=0.4:0.6; Pref1:Pref2=0.5:0.5.

[0023] Where Pref1 / Pref2 are the reference active powers of the two wind turbines respectively.

[0024] For the three-machine system, the eight different power allocation scenarios are: Pref1:Pref2:Pref3=0.5:0.3:0.2; Pref1:Pref2:Pref3=0.5:0.1:0.4; Pref1:Pref2:Pref3=0.6:0.3:0.1; Pref1:Pref2:Pref3=0.6:0.2:0.2; Pref1:Pref2:Pref3=0.7:0.2:0.1; Pref1:Pref2:Pref3=0.8:0.1:0.1; Pref1:Pref2:Pref3=0.4:0.4:0.2; Pref1:Pref2:Pref3=0.4:0.3:0.3.

[0025] Where Pref1 / Pref2 / Pref3 are the reference active powers of the three wind turbines respectively.

[0026] For the four-machine system, the nine different power allocation scenarios are: Pref1:Pref2:Pref3:Pref4=0.3:0.3:0.2:0.2; Pref1:Pref2:Pref3:Pref4=0.3:0.3:0.3:0.1; Pref1:Pref2:Pref3:Pref4=0.4:0.4:0.1:0.1; Pref1:Pref2:Pref3:Pref4=0.4:0.3:0.2:0.1; Pref1:Pref2:Pref3:Pref4=0.4:0 .2:0.2:0.2;Pref1:Pref2:Pref3:Pref4=0.5:0.3:0.1:0.1;Pref1:Pref2:Pref3:Pref4=0.5:0.3:0.1:0.1;Pref1:Pref2:Pr ef3: Pref4 = 0.5: 0.2: 0.2: 0.1; Pref1: Pref2: Pref3: Pref4 = 0.6: 0.2: 0.1: 0.1; Pref1: Pref2: Pref3: Pref4 = 0.7: 0.1: 0.1: 0.1.

[0027] Where Pref1 / Pref2 / Pref3 / Pref4 are the reference active powers of the four wind turbines respectively.

[0028] Furthermore, the empirical formula for the inertia stability domain of the multi-machine system of a wind farm station is derived from the inertia stability domain of the single-machine system, the inertia stability domain of the two-machine system, the inertia stability domain of the three-machine system and the inertia stability domain of the four-machine system through regular deduction.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The proposed method for quantitatively assessing wind farm inertia requirements, taking control parameters into account, addresses the increasing penetration of new energy and power electronics equipment, which reduces grid inertia, weakens its strength, and leads to increasingly serious stability issues. This method analyzes the inertia requirements of wind farms to enrich the frequency control system of new power systems, thereby enhancing the inertia response capability of new energy farms and assisting the power grid in coping with large-capacity load fluctuations. This is of great significance for maintaining frequency stability in systems with a high proportion of renewable energy farms. Simultaneously, starting with small signal modeling and considering control parameters to determine the minimum inertia support required by wind farms, this method forms a method for analyzing and quantitatively assessing the inertia requirements of wind farm power systems and a derived formula, providing a technical foundation for quantitative inertia requirements assessment methods and operational control of current power systems with a high proportion of new energy farms. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0032] Figure 1 A wind farm system architecture diagram of a method for quantitatively evaluating wind farm inertia requirements considering control parameters provided in the first embodiment of the present disclosure;

[0033] Figure 2 A flow chart of a method for quantitatively evaluating wind farm inertia requirements taking control parameters into consideration, provided in the second embodiment of the present disclosure;

[0034] Figure 3 This is a block diagram of the control link of the permanent magnet direct-drive wind turbine based on a virtual synchronous machine provided in the third embodiment of the present disclosure;

[0035] Figure 4 This is a comparison diagram of response curves of single-machine, dual-machine, triple-machine and quad-machine systems of a wind farm station under different virtual inertia (near critical virtual inertia) control settings provided in the fourth embodiment of the present disclosure. DETAILED DESCRIPTION

[0036] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0037] The technical solution of the present invention is described in detail below with reference to specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0038] refer to Figure 1 The first embodiment of the present disclosure provides a wind farm system architecture diagram for a method for quantitatively assessing wind farm inertia requirements that considers control parameters. The wind farm system is constructed by connecting n permanent magnet direct-drive wind turbines to an AC busbar through back-to-back converters, filter systems, and transformers. The wind farm system control strategy is to independently control each wind turbine in the wind farm. Multiple wind turbines are controlled using multi-virtual synchronous machine technology to achieve energy flow balance and ensure the stability of the system's AC busbar. Virtual synchronous machine technology is used in the inverters of the back-to-back converters, containing the target control parameter, the virtual inertia parameter J.

[0039] refer to Figure 2 The second embodiment of the present disclosure provides a flow chart of a method for quantitatively evaluating the inertia demand of a wind farm considering control parameters, including:

[0040] S1: Obtain wind farm system data and establish a small signal model of the wind farm based on virtual synchronous machine technology.

[0041] First, determine the topology of the wind farm station. Figure 1 , obtain wind farm system data. Wind farm data includes the port voltage data of each inverter, the output voltage and current data of each inverter, the filter inductance, filter capacitance and equivalent resistance data of each LC filter, the equivalent resistance and inductance data of each transmission line, and the AC bus PCC point voltage data;

[0042] The data is normalized to obtain the per-unit value data of the wind farm station. The normalization process is as follows: per-unit value = nominal value / reference value. The nominal value is the data in the wind farm station system, and the reference values ​​include power reference value, voltage reference value, frequency reference value, and damping reference value.

[0043] A small-signal model for a wind farm based on virtual synchronous generator technology was established by combining per-unit wind farm data. The small-signal modeling process first selected a reference coordinate system. Based on coordinate transformation, small-signal modeling of the power control loop, filters, and interconnecting and transmission lines was performed. Due to the complexity of multi-machine systems at wind farms, appropriate state variables were selected, and a general small-signal model for multi-machine parallel wind farm systems based on virtual synchronous generators was derived and established from a state-space perspective.

[0044] S2: Based on the small signal model, starting from the wind turbine single-unit system, considering the control parameter virtual inertia J, the inertia quantitative evaluation and analysis of the wind turbine single-unit system is carried out to obtain the inertia stability domain of the single-unit system.

[0045] Based on the small-signal model established by S1, starting with a single wind turbine system, the critical inertia of the wind turbine system is quantitatively evaluated and analyzed by considering the control parameter virtual inertia J. Substituting the per-unit system data into the small-signal model, the state matrix of the single wind turbine system is derived. By varying J, the characteristic root loci of the single wind turbine system are obtained. By observing the characteristic root loci, the inertia stability region of the single wind turbine system is determined.

[0046] S3: Based on the single-machine wind power generation system, the inertia quantitative evaluation and analysis of the wind power generation dual-machine system, three-machine system and four-machine system are carried out in sequence, and the inertia stability domains of the dual-machine system, three-machine system and four-machine system are obtained respectively;

[0047] Based on the analysis of a single wind turbine system by S2, quantitative evaluation and analysis of the critical inertia of a dual-turbine system, a three-turbine system, and a four-turbine system are carried out in turn to obtain the inertia stability domain of the multi-turbine system of the wind farm.

[0048] For the dual-machine system, five different power distribution scenarios were selected based on load fluctuations. For each scenario, the system's characteristic root loci were obtained by varying the inertia. The maximum critical stability inertia value among the five scenarios was selected as the final critical stability inertia of the dual-machine system, thereby determining the inertia stability region of the dual-machine system. The five different power distribution scenarios are: Pref1:Pref2 = 0.1:0.9; Pref1:Pref2 = 0.2:0.8; Pref1:Pref2 = 0.3:0.7; Pref1:Pref2 = 0.4:0.6; and Pref1:Pref2 = 0.5:0.5. Pref1 / Pref2 are the reference active powers of the two wind turbines, respectively.

[0049] For the three-machine system, eight different power distribution situations are selected according to the load fluctuation. For each situation, the system characteristic root loci are obtained by changing the inertia. Finally, the critical stable inertia value with the largest value among the eight situations is taken as the final critical stable inertia of the three-machine system, and then the inertia stability domain of the three-machine system is obtained. The eight different power distribution scenarios are: Pref1:Pref2:Pref3=0.5:0.3:0.2; Pref1:Pref2:Pref3=0.5:0.1:0.4; Pref1:Pref2:Pref3=0.6:0.3:0.1; Pref1:Pref2:Pref3=0.6:0.2:0.2; Pref1:Pref2:Pref3=0.7:0.2:0.1; Pref1:Pref2:Pref3=0.8:0.1:0.1; Pref1:Pref2:Pref3=0.4:0.4:0.2; Pref1:Pref2:Pref3=0.4:0.3:0.3. Where Pref1 / Pref2 / Pref3 are the reference active powers of the three wind turbines.

[0050] For the four-machine system, nine different power distribution scenarios are selected based on the load fluctuation. For each scenario, the system characteristic root loci are obtained by changing the inertia. Finally, the one with the largest critical stable inertia value among the nine scenarios is taken as the final critical stable inertia of the four-machine system, and then the inertia stability domain of the four-machine system is obtained. The nine different power distribution scenarios are: Pref1:Pref2:Pref3:Pref4=0.3:0.3:0.2:0.2; Pref1:Pref2:Pref3:Pref4=0.3:0.3:0.3:0.1; Pref1:Pref2:Pref3:Pref4=0.4:0.4:0.1:0.1; Pref1:Pref2:Pref3:Pref4=0.4:0.3:0.2:0.1; Pref1:Pref2:Pref3:Pref4=0.4:0.2:0.2 .2:0.2; Pref1:Pref2:Pref3:Pref4=0.5:0.3:0.1:0.1; Pref1:Pref2:Pref3:Pref4=0.5:0.3:0.1:0.1; Pref1:Pref2:Pref3:Pref4=0.5:0.2:0.2:0.1; Pref1:Pref2:Pref3:Pref4=0.6:0.2:0.1:0.1; Pref1:Pref2:Pref3:Pref4=0.7:0.1:0.1:0.1. Where Pref1 / Pref2 / Pref3 / Pref4 are the reference active powers of the four wind turbines respectively.

[0051] S4: Further expand the number of wind turbines to n and obtain the empirical formula for the inertia stability domain of the multi-machine system in the wind farm.

[0052] The empirical formulas for the inertia stability domain of a multi-machine system in a wind farm station are derived from the inertia stability domain of a single-machine system, the inertia stability domain of a dual-machine system, the inertia stability domain of a three-machine system, and the inertia stability domain of a four-machine system obtained by S2 and S3 through regular deduction.

[0053] refer to Figure 3The third embodiment of the present disclosure provides a block diagram of the control process for a permanent magnet direct-drive wind turbine based on a virtual synchronous machine. The wind turbine side uses an outer-loop control scheme for the DC bus voltage and an inner-loop control scheme for the motor current. The actual DC voltage is compared with a reference value, and the output of the PI controller is used as the q-axis current reference. After the PI output, decoupling, and feedforward terms, the SVPWM (Space Vector Pulse Width Modulation) is controlled and transmitted to the PMSG-side converter. Load-side control is achieved through the collaboration of the outer and inner-loop controllers to regulate frequency, voltage, and power output. The entire control process is performed within a two-phase rotating coordinate system. The outer-loop control of the VSG can also be called power loop control. The power output from the inverter source passes through a filtering system and is fed back to the outer-loop controller. The VSG's characteristic curve is used to adjust the system's output frequency and voltage to achieve system power balance and ensure system frequency and voltage stability. The inner-loop control scheme, known as voltage and current control, achieves decoupled control of active and reactive power, and frequency and voltage, while also regulating the output current to ensure output power quality and improve the system's dynamic performance. The power outer loop of the VSG contains the control target virtual inertia J.

[0054] refer to Figure 4 , Embodiment 4 of the present disclosure provides a comparison diagram of response curves of single-machine, dual-machine, triple-machine and quad-machine systems of a wind farm under different virtual inertia control settings.

[0055] Standalone system: Figure (a) shows J = J cr1 -1 system response curve, Figure (b) is J=J cr1 System response curve. Among them, J cr1 is the critical inertia of the single-machine system.

[0056] Dual-machine system: Figure (c) shows J=J cr2 -1 system response curve, Figure (d) is J=J cr2 System response curve. Among them, J cr2 is the critical inertia of the dual-machine system.

[0057] Three-machine system: Figure (e) shows J=J cr3 -1 system response curve, Figure (f) is J=J cr3 System response curve. Among them, J cr3 is the critical inertia of the three-machine system.

[0058] Four-machine system: Figure (g) shows J = J cr4 -1 system response curve, Figure (h) is J=J cr4 System response curve. Among them, J cr4 is the critical inertia of the four-machine system.

[0059] Each response curve shows the active power, frequency, grid-side voltage, and current of each system. The response curves for each system demonstrate a critical inertia value that places the system in a critically stable state. Below this critical inertia value, the system becomes unstable, while above or equal to this critical inertia value, the system remains stable. Furthermore, for single-unit and dual-unit wind turbine systems, insufficient system inertia can cause the power provided by the system to fall short of the load's required power, leading to a continuous increase in frequency, exceeding its safe and stable operating range and causing system instability. For three-unit, four-unit, and higher-level wind turbine systems, insufficient system inertia can cause system power and frequency oscillations, leading to system instability.

Claims

1. A quantitative evaluation method for wind farm inertia requirements considering control parameters, characterized in that: include: Obtain wind farm system data and establish a wind farm small signal model based on Virtual Synchronous Generator (VSG) technology; Based on the small signal model, starting from the wind power generation stand-alone system, considering the control parameter virtual inertia J, the inertia quantitative evaluation analysis of the wind power generation stand-alone system is carried out, and the inertia stability domain of the stand-alone system is obtained; Based on the single-machine wind power generation system, the inertia quantitative evaluation and analysis of the wind power generation dual-machine system, three-machine system and four-machine system are carried out in turn, and the inertia stability domains of the dual-machine system, three-machine system and four-machine system are obtained respectively; The number of wind turbines is further expanded to n, and the empirical formula for the inertia stability domain of the multi-machine system in the wind farm is obtained.

2. The method for quantitatively evaluating wind farm inertia requirements considering control parameters according to claim 1, characterized in that: The wind farm system topology is determined to be a multi-machine parallel system with multiple wind turbines connected to an AC bus. The wind power system consists of two components: a wind energy conversion system and a power conversion system. It generates power using a variable speed constant frequency method, connected to the load via full-power back-to-back converters, filters, transformers, and transmission lines.

3. The method for quantitatively evaluating wind farm inertia requirements considering control parameters according to claim 1, characterized in that: The wind turbines in the wind farm system are equivalent to voltage sources when modeling small signals.

4. The method for quantitatively evaluating wind farm inertia requirements considering control parameters according to claim 1, characterized in that: The wind farm station data includes the port voltage data of each inverter, the output voltage and current data of each inverter, the filter inductance, filter capacitance and equivalent resistance data of each LC filter, the equivalent resistance and inductance data of each transmission line, and the AC bus PCC point voltage data.

5. The method for quantitatively evaluating wind farm inertia requirements considering control parameters according to claim 1, characterized in that: The per-unit normalization process is: per-unit value = nominal value / reference value, wherein the nominal value is each data in the wind farm system, and the reference value includes power reference value, voltage reference value, frequency reference value and damping reference value.

6. The method for quantitatively evaluating wind farm inertia requirements considering control parameters according to claim 1, characterized in that: The wind farm small-signal model based on virtual synchronous machine technology utilizes virtual synchronous machine control technology for its inverter control method. The small-signal modeling process involves first selecting a reference coordinate system. Based on coordinate transformation, small-signal modeling of the power control loop, filters, and connecting and transmission lines is performed. Due to the complexity of multi-machine systems at wind farms, appropriate state variables are selected, and a general small-signal model for multi-machine parallel wind farm systems based on virtual synchronous machines is derived and established from a state-space perspective.

7. The method for quantitatively evaluating wind farm inertia requirements considering control parameters according to claim 1, characterized in that: Based on the wind farm small signal model of virtual synchronous machine technology, appropriate state variables are selected and a universal small signal model of virtual synchronous machine multi-machine parallel system is derived and established from the perspective of state space. The quantitative evaluation and analysis of the critical inertia of a single wind power generation system is as follows: the system per-unit value data is substituted into a small signal model to obtain a state matrix of the single-machine system. By changing the virtual inertia J, the root locus of the system characteristic roots is obtained, and then the inertia stability domain of the single-machine system is obtained.

8. The method for quantitatively evaluating wind farm inertia requirements considering control parameters according to claim 1, characterized in that: Quantitative evaluation and analysis of critical inertia of the two-machine system, three-machine system and four-machine system: For the dual-machine system, five different power distribution situations are taken according to the load fluctuation. For each situation, the system characteristic root loci are obtained by changing the inertia. Finally, the critical stable inertia value with the largest value among the five situations is taken as the final critical stable inertia of the dual-machine system, and then the inertia stability domain of the dual-machine system is obtained. For the three-machine system, eight different power distribution situations are selected according to the load fluctuation. For each situation, the system characteristic root loci are obtained by changing the inertia. Finally, the critical stable inertia value with the largest value among the eight situations is taken as the final critical stable inertia of the three-machine system, and then the inertia stability domain of the three-machine system is obtained. For the four-machine system, nine different power distribution situations are taken according to the load fluctuation. For each situation, the system characteristic root loci are obtained by changing the inertia. Finally, the critical stable inertia value with the largest value among the nine situations is taken as the final critical stable inertia of the four-machine system, and the inertia stability domain of the four-machine system is obtained.

9. The method for quantitatively evaluating wind farm inertia requirements considering control parameters according to claim 8, characterized in that: Different power distribution scenarios in the quantitative evaluation and analysis of critical inertia of the two-machine system, three-machine system and four-machine system: For the dual-machine system, the five different power allocation situations are: Pref1:Pref2=0.1:0.9; Pref1:Pref2=0.2:0.8; Pref1:Pref2=0.3:0.7; Pref1:Pref2=0.4:0.6; Pref1:Pref2=0.5:0.

5. Where Pref1 / Pref2 are the reference active powers of the two wind turbines respectively. For the three-machine system, the eight different power allocation scenarios are: Pref1: Pref2: Pref3 = 0.5: 0.3: 0.2; Pref1:Pref2:Pref3=0.5:0.1:0.4; Pref1:Pref2:Pref3=0.6:0.3:0.1; Pref1:Pref2:Pref3=0.6:0.2:0.2; Pref1:Pref2:Pref3=0.7:0.2:0.1; Pref1:Pref2:Pref3=0.8:0.1:0.1; Pref1:Pref2:Pref3=0.4:0.4:0.2; Pref1:Pref2:Pref3=0.4:0.3:0.

3. Where Pref1 / Pref2 / Pref3 are the reference active powers of the three wind turbines respectively. For the four-machine system, the nine different power allocation scenarios are: Pref1: Pref2: Pref3: Pref4 = 0.3: 0.3: 0.2: 0.2; Pref1: Pref2: Pref3: Pref4=0.3: 0.3: 0.3: 0.1; Pref1: Pref2: Pref3: Pref4=0.4: 0.4: 0.1: 0.1; Pref1: Pref2: Pref3: Pref4=0.4: 0.3: 0.2: 0.1; Pref1: Pref2: Pref3: Pref4=0.4: 0.2: 0.2: 0.2; Pref1: Pref2: Pref3: Pref4=0.5: 0.3: 0.1: 0.1; Pref1: Pref2: Pref3: Pref4=0.5: 0.3: 0.1: 0.1; Pref1: Pref2: Pref3: Pref4=0.5: 0.2: 0.2: 0.1; Pref1: Pref2: Pref3: Pref4=0.6: 0.2: 0.1: 0.1; Pref1: Pref2: Pref3: Pref4=0.7:0.1:0.1:0.

1. Where Pref1 / Pref2 / Pref3 / Pref4 are the reference active powers of the four wind turbines respectively.

10. The method for quantitatively evaluating wind farm inertia requirements considering control parameters according to claim 1, characterized in that: The empirical formula for the inertia stability domain of the multi-machine system of the wind farm station is derived from the inertia stability domain of the single-machine system, the inertia stability domain of the dual-machine system, the inertia stability domain of the three-machine system and the inertia stability domain of the four-machine system obtained in claims 7 and 8 through regular deduction.

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