New energy wind turbine generator virtual inertia parameter optimization setting method

Abstract

The invention discloses a new energy wind turbine generator virtual inertia parameter optimization setting method comprising the following steps: S1, modeling a wind turbine generator and virtual inertia control thereof based on a differential relation, and combining to obtain a virtual inertia control wind turbine generator model; s2, extracting virtual inertia key parameters based on the virtual inertia control wind turbine generator model; s3, constructing a constraint condition of virtual inertia control of the wind turbine generator based on the differential relationship; s4, loop iteration is carried out based on the constraint conditions to adjust the virtual inertia key parameters, a virtual inertia coefficient and a filtering time constant which meet the constraint conditions are obtained, and virtual inertia parameter optimization setting is achieved; according to the method, the virtual inertia parameter can be optimized and set, the inertia response performance of the wind turbine generator is improved, and the frequency stability of a power system is improved.

Description

Technical Field

[0001] This invention belongs to the technical field of wind power new energy access to the power system, and specifically relates to a method for optimizing and tuning the virtual inertia parameters of new energy wind turbine units. Background Technology

[0002] In the process of building a new power system, wind power and other new energy power generation have low marginal costs and no carbon emissions. They are gradually transitioning from alternative power sources to dominant power sources and are a key means to achieve the low-carbon goals of the power system.

[0003] Among them, new energy mainly interfaces with the grid through power electronic inverters. It does not have the mechanical rotational inertia characteristics of synchronous units. The power output and grid frequency changes are decoupled from each other, which seriously weakens the system frequency regulation performance. If an inertia simulation control strategy is not applied, it cannot provide direct inertia to the system. Moreover, after grid connection, it occupies the start-up space of high-inertia conventional units, and the overall rotational inertia of the system continues to deteriorate, resulting in a serious lack of emergency frequency support.

[0004] Therefore, in order to solve the above problems, it is necessary to develop a method for optimizing and tuning the virtual inertia parameters of new energy wind turbines. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for optimizing and tuning the virtual inertia parameters of new energy wind turbines. This method optimizes and tunes the virtual inertia parameters, improves the inertia response performance of wind turbines, and enhances the frequency stability of the power system.

[0006] The objective of this invention is achieved as follows: a method for optimizing and tuning the virtual inertia parameters of a new energy wind turbine generator, comprising the following steps:

[0007] S1. Model the wind turbine and its virtual inertia control based on differential relationships respectively, and jointly obtain the virtual inertia control wind turbine model.

[0008] S2. Key parameters of virtual inertia are extracted from the wind turbine model based on virtual inertia control;

[0009] S3. Construct constraints for virtual inertia control of wind turbine units based on differential relationships;

[0010] S4. Based on the constraints, iteratively adjust the key parameters of the virtual inertia to obtain the virtual inertia coefficient and filtering time constant that satisfy the constraints, thereby achieving the optimization and tuning of the virtual inertia parameters.

[0011] Furthermore, the differential-algebraic equation model of the wind turbine in step S1 is expressed as follows: In the formula: Represents the differential state variable matrix. Represents the intermediate variable matrix of the differential. Represents the state vector. Represents the intermediate vector. Represents an algebraic state variable matrix. Represents an algebraic intermediate variable matrix. This represents the output coefficient matrix. This indicates the electromagnetic power output of the wind turbine.

[0012] Furthermore, in step S1, the wind turbine uses a phase-locked loop (PLL) to achieve grid-connected synchronous operation and virtual inertia control. Therefore, the spatial state equations for the frequency and virtual inertia control are expressed as follows: ,in, , , , , , In the formula: This represents the power corresponding to the virtual inertia. , Indicates the proportional and integral coefficients. , Indicates the voltage magnitude and phase angle at the point of common coupling. Represents mechanical torque. Represents the virtual inertia coefficient. Indicates the phase angle of the phase-locked loop. This represents an intermediate variable in the phase-locked loop. This indicates the measured frequency of the phase-locked loop after filtering.

[0013] Furthermore, the differential algebraic equation model of the virtual inertia control wind turbine in step S1 is expressed as follows: Combining the wind turbine and virtual inertia control models, we obtain: In the formula: , , , , , , , , , The state-space model of a wind turbine controlled by virtual inertia is then expressed as: , , , , .

[0014] Furthermore, the key parameters of virtual inertia in step S2 include the frequency filtering time constant. and virtual inertia coefficient .

[0015] Furthermore, the constraints in step S3 specifically include the inertia response time constant, small disturbance stability, and damping ratio; wherein, the inertia response time constant is selected in the interval [8, 12], the small disturbance stability is determined by the characteristic root, and the damping ratio is determined by the power output pulsation amplitude.

[0016] Furthermore, the iterative adjustment of the key parameters of virtual inertia in step S4 specifically includes the following steps: ① Initialize all parameters and filter the time constant with the initial frequency. and maximum virtual inertia coefficient ② Add virtual inertia coefficient as input; ③ Determine if the response meets the requirements. If yes, proceed to the next step; otherwise, return to step ②. ④ Determine if the constraints are met. If yes, proceed to the next step; otherwise, increase the frequency filtering time constant. And return to step ③; ⑤ Let ⑥ Continue to increase the virtual inertia coefficient ;⑦ Order ⑧ Determine if the constraints are met. If yes, return to step ⑥; otherwise, proceed to the next step. ⑨ Output the current frequency filtering time constant. and the maximum value of the virtual inertia coefficient and minimum value Then, the iteration ends.

[0017] The beneficial effects of this invention are as follows: by constructing a wind turbine model for virtual inertia control, the key parameters of virtual inertia are analyzed and obtained, and constraints for virtual inertia control are constructed. Based on the constraints, the key parameters of virtual inertia are iteratively adjusted to obtain the virtual inertia coefficient and filtering time constant that satisfy the constraints, thereby achieving optimized tuning of virtual inertia parameters, improving the frequency regulation performance of the system, and ensuring the frequency stability of the power system. Attached Figure Description

[0018] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0020] like Figure 1 As shown, a method for optimizing and tuning the virtual inertia parameters of a new energy wind turbine includes the following steps:

[0021] S1. Model the wind turbine and its virtual inertia control based on differential relationships respectively, and combine them to obtain the wind turbine model with virtual inertia control.

[0022] Preferably, the differential algebraic equation model of the wind turbine in step S1 is expressed as: In the formula: Represents the differential state variable matrix. Represents the intermediate variable matrix of the differential. Represents the state vector. Represents the intermediate vector. Represents an algebraic state variable matrix. Represents an algebraic intermediate variable matrix. This represents the output coefficient matrix. This represents the electromagnetic power output of the wind turbine. The wind turbine modeling process includes rotor-side control modeling, transmission chain shaft system modeling, and measurement and modulation stage modeling, which are then combined to obtain the wind turbine model.

[0023] Preferably, in step S1, the wind turbine uses a phase-locked loop (PLL) to achieve grid-connected synchronous operation and virtual inertia control. The spatial state equations for the frequency and virtual inertia control are then expressed as: ,in, , , , , , In the formula: This represents the power corresponding to the virtual inertia. , Indicates the proportional and integral coefficients. , Indicates the voltage magnitude and phase angle at the point of common coupling. Represents mechanical torque. Represents the virtual inertia coefficient. Indicates the phase angle of the phase-locked loop. This represents an intermediate variable in the phase-locked loop. This indicates the measured frequency of the phase-locked loop after filtering.

[0024] Preferably, the differential algebraic equation model of the wind turbine for virtual inertia control in step S1 is expressed as follows: By combining the wind turbine and virtual inertia control models, we obtain: In the formula: , , , , , , , , , The state-space model of a wind turbine controlled by virtual inertia is then expressed as: , , , , .

[0025] S2. The key parameters of virtual inertia are extracted from the wind turbine model based on virtual inertia control.

[0026] Preferably, the key parameters of virtual inertia in step S2 include the frequency filtering time constant. and virtual inertia coefficient .

[0027] S3. Construct the constraints for the virtual inertia control (VIC) of wind turbine units based on differential relationships.

[0028] Preferably, the constraints in step S3 specifically include inertia response time constant, small disturbance stability, and damping ratio; wherein, the inertia response time constant is selected in the interval [8, 12], small disturbance stability is determined by characteristic roots, and the damping ratio is determined by the power output pulsation amplitude, which is selected as 1.5% here.

[0029] S4. Based on the constraints, iteratively adjust the key parameters of the virtual inertia to obtain the virtual inertia coefficient and filtering time constant that satisfy the constraints, thereby achieving the optimization and tuning of the virtual inertia parameters.

[0030] Preferably, the iterative adjustment of the key parameters of virtual inertia in step S4 specifically includes the following steps: ① Initialize all parameters and filter the time constant with the initial frequency. and maximum virtual inertia coefficient ② Add virtual inertia coefficient as input; ③ Determine if the response meets the requirements. If yes, proceed to the next step; otherwise, return to step ②. ④ Determine if the constraints are met. If yes, proceed to the next step; otherwise, increase the frequency filtering time constant. And return to step ③; ⑤ Let ⑥ Continue to increase the virtual inertia coefficient ;⑦ Order ⑧ Determine if the constraints are met. If yes, return to step ⑥; otherwise, proceed to the next step. ⑨ Output the current frequency filtering time constant. and the maximum value of the virtual inertia coefficient and minimum value Then, the iteration ends.

[0031] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing and tuning the virtual inertia parameters of a new energy wind turbine generator, characterized in that: Includes the following steps: S1. Model the wind turbine and its virtual inertia control based on differential relationships respectively, and jointly obtain the virtual inertia control wind turbine model. S2. Key parameters of virtual inertia are extracted from the wind turbine model based on virtual inertia control; S3. Construct constraints for virtual inertia control of wind turbine units based on differential relationships; S4. Based on the constraints, iteratively adjust the key parameters of the virtual inertia to obtain the virtual inertia coefficient and filtering time constant that satisfy the constraints, thereby achieving the optimization and tuning of the virtual inertia parameters.

2. The method for optimizing and tuning the virtual inertia parameters of a new energy wind turbine as described in claim 1, characterized in that: The differential algebraic equation model of the wind turbine in step S1 is expressed as follows: In the formula: Represents the differential state variable matrix. Represents the intermediate variable matrix of the differential. Represents the state vector. Represents the intermediate vector. Represents an algebraic state variable matrix. Represents an algebraic intermediate variable matrix. This represents the output coefficient matrix. This indicates the electromagnetic power output of the wind turbine.

3. The method for optimizing and tuning the virtual inertia parameters of a new energy wind turbine according to claim 2, characterized in that: In step S1, the wind turbine uses a phase-locked loop (PLL) to achieve grid-connected synchronous operation and virtual inertia control. The spatial state equations for the frequency and virtual inertia control are then expressed as: ,in, , , , , , In the formula: This represents the power corresponding to the virtual inertia. , Indicates the proportional and integral coefficients. , Indicates the voltage magnitude and phase angle at the point of common coupling. Represents mechanical torque. Represents the virtual inertia coefficient. Indicates the phase angle of the phase-locked loop. This represents an intermediate variable in the phase-locked loop. This indicates the measured frequency of the phase-locked loop after filtering.

4. The method for optimizing and tuning the virtual inertia parameters of a new energy wind turbine as described in claim 3, characterized in that: The differential-algebraic equation model of the virtual inertia control wind turbine in step S1 is expressed as follows: Combining the wind turbine model and the virtual inertia control model, we obtain: In the formula: , , , , , , , , , The state-space model of a wind turbine controlled by virtual inertia is then expressed as: , , , , .

5. The method for optimizing and tuning the virtual inertia parameters of a new energy wind turbine as described in claim 1, characterized in that: The key parameters of virtual inertia in step S2 include the frequency filtering time constant. and virtual inertia coefficient .

6. The method for optimizing and tuning the virtual inertia parameters of a new energy wind turbine as described in claim 1, characterized in that: The constraints in step S3 specifically include inertia response time constant, small disturbance stability, and damping ratio; wherein, the inertia response time constant is selected in the interval [8, 12], small disturbance stability is determined by characteristic roots, and the damping ratio is determined by the power output pulsation amplitude.

7. The method for optimizing and tuning the virtual inertia parameters of a new energy wind turbine according to claim 1, characterized in that: The iterative adjustment of key parameters of virtual inertia in step S4 specifically includes the following steps: ① Initialize all parameters and filter the time constant with the initial frequency. and maximum virtual inertia coefficient ② Add virtual inertia coefficient as input; ③ Determine if the response meets the requirements. If yes, proceed to the next step; otherwise, return to step ②. ④ Determine if the constraints are met. If yes, proceed to the next step; otherwise, increase the frequency filtering time constant. And return to step ③; ⑤ Let ⑥ Continue to increase the virtual inertia coefficient ;⑦ Order ⑧ Determine if the constraints are met. If yes, return to step ⑥; otherwise, proceed to the next step. ⑨ Output the current frequency filtering time constant. and the maximum value of the virtual inertia coefficient and minimum value Then, the iteration ends.

Images