VSG parameter adaptive control method and system based on dynamic fuzzy control

Abstract

The invention discloses a VSG parameter adaptive control method based on dynamic fuzzy control, and the method comprises the steps: inputting an angular frequency deviation [delta] w and an angular frequency change speed dw / dt into a fuzzy controller, and obtaining the telescoping factors [alpha] e and [alpha] ec of an input discourse domain and the telescoping factor [beta] of an output discourse domain; establishing a fuzzy control rule and a membership function of input and output variables; fuzzy reasoning is carried out based on a fuzzy control rule, and the sizes of the input discourse domain and the output discourse domain are adjusted in real time through the scaling factors alpha < e > and alpha < ec > of the input discourse domain and the scaling factor beta of the output discourse domain; and according to the adjusted discourse domain, the rotational inertia and the damping value are obtained through the output variable. The method has the advantages of being small in power impact during disturbance and high in control precision when system disturbance is small, and can meet functional requirements of different working scenes.

Description

Technical Field

[0001] The present invention belongs to the field of power grids and relates to a virtual synchronous machine control technology for a grid-type converter, and in particular to a VSG parameter adaptive control method and system based on dynamic fuzzy control. Background Art

[0002] Grid-connected converters (GFMs) serve as the interface between distributed microgrids and the power grid, and their grid-connected control strategies are directly related to the stable operation of the grid. Currently, grid-connected converters (GFMs) primarily employ virtual synchronous machine (VSG) control to provide inertia and damping for the grid. However, existing fixed-parameter VSGs often perform poorly under system disturbances and exhibit poor robustness. Existing adaptive methods primarily include those based on adaptive functions and fuzzy control.

[0003] The adaptive function-based control method mainly adjusts the gains of the moment of inertia and damping coefficient in real time according to the change amount and change speed of the angular frequency. However, the adaptive function often has high requirements for function design. The power impact is large at the moment of system pre-synchronization, affecting the life and reliability of power electronic components.

[0004] The control method based on fuzzy control is to use the membership function to adjust the moment of inertia and damping coefficient in real time. However, the existing fuzzy control has insufficient control accuracy and poor control effect when the angular frequency variation range is small. Summary of the Invention

[0005] Purpose of the invention: In order to overcome the deficiencies in the prior art, a VSG parameter adaptive control method and system based on dynamic fuzzy control is provided to solve the problems of large power impact and insufficient control accuracy in the existing pre-synchronization control when the error is small.

[0006] Technical solution: To achieve the above-mentioned purpose, the present invention provides a VSG parameter adaptive control method based on dynamic fuzzy control, comprising the following steps:

[0007] S1: Input the angular frequency deviation Δw and the angular frequency change rate dw / dt into the fuzzy controller to obtain the scaling factor α of the input domain e , α ec and the scaling factor β of the output universe;

[0008] S2: Establish fuzzy control rules and membership functions of input and output variables;

[0009] S3: Fuzzy reasoning based on fuzzy control rules, by inputting the scaling factor α of the domain e , α ec The scaling factor β of the input and output domains adjusts the sizes of the input and output domains in real time respectively;

[0010] S4: According to the adjusted domain, the moment of inertia and damping value are obtained through the output variables.

[0011] Furthermore, the scaling factor α of the input domain in step S1 is e , α ec The function expression is:

[0012]

[0013]

[0014] Wherein, e represents the angular frequency deviation Δw and the angular frequency change rate dw / dt, and x represents the variable value.

[0015] Furthermore, the fuzzy control rules in step S2 include: introducing a quantization factor K e , K ec , realizing the conversion between continuous variables and fuzzy discrete variables.

[0016] Furthermore, in step S2, the continuous variables include the input variables e, ec and output variable o of the fuzzy controller, and the fuzzy discrete variables include the error E and the error change rate EC. The specific conversion includes:

[0017]

[0018] In the formula, <> represents the rounding operation of the result, e L and e H Represent the lower limit and upper limit of e and ec respectively.

[0019] Furthermore, the quantization factor K in step S2 e , K ec The expression is as follows:

[0020]

[0021] Among them, 2m is the number of variables in the domain of error E, and 2n is the number of variables in the domain of error change rate EC.

[0022] Furthermore, the fuzzy reasoning in step S3 includes a clarification process, which converts the discrete variable O output by the fuzzy controller into a proportional factor K. o Converted into actual continuous output, expressed as follows:

[0023]

[0024] Among them, L 、o H They represent the lower limit and upper limit of the output variable o respectively.

[0025] Furthermore, the scaling factor K in step S3 o The expression is as follows:

[0026]

[0027] Among them, 2l is the number of variables in the domain of the output variable o.

[0028] Furthermore, the fuzzy control rules in step S2 set five fuzzy subsets {NB, NS, Z0, PS, PB}, where N and P represent negative and positive value divisions respectively; B and S represent large and small respectively; and Z0 is represented as 0.

[0029] The present invention also provides a VSG parameter adaptive control system based on dynamic fuzzy control, comprising:

[0030] The scaling factor establishment module is used to obtain the scaling factor α of the input domain e , α ec and the scaling factor β of the output universe;

[0031] A fuzzy control rule building module is used to build fuzzy control rules and membership functions of input and output variables;

[0032] Fuzzy reasoning module, used to input the scaling factor α of the domain e , α ec The scaling factor β of the input and output domains adjusts the sizes of the input and output domains in real time respectively;

[0033] The parameter output module is used to obtain the moment of inertia and damping values ​​through output variables.

[0034] Beneficial Effects: Compared with the existing technology, the present invention has the advantages of small power impact during disturbance and high control accuracy when the system disturbance is small, and can meet the functional requirements of different working scenarios. It has the following two advantages:

[0035] 1. The present invention realizes dynamic domain adjustment by introducing the domain scaling factor, breaking the inherent contradiction between accuracy and robustness in traditional fuzzy control, providing a more flexible and efficient way for VSG control, and significantly optimizing system performance.

[0036] 2. The optimized fuzzy control rule base in the present invention is constructed based on in-depth research on the influencing mechanism of moment of inertia and damping coefficient. It can more accurately match the system's requirements for moment of inertia and damping coefficient under different operating conditions, effectively improving the system's dynamic response performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic diagram of the control strategy of the method of the present invention;

[0038] Figure 2 This is the control principle diagram of the fuzzy controller;

[0039] Figure 3 is the membership function graph of input and output variables;

[0040] Figure 4 This is a schematic diagram of the operation of a single distributed microgrid grid-connected simulation system;

[0041] Figure 5 This is the output active power diagram of the converter side during the DC microgrid closing process;

[0042] Figure 6 This is the system frequency diagram of the DC microgrid closing process;

[0043] Figure 7 This is the output active power diagram of the converter side when the active load suddenly changes;

[0044] Figure 8 This is the system frequency diagram when the active load changes suddenly. DETAILED DESCRIPTION

[0045] The present invention is further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, modifications of various equivalent forms of the present invention made by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0046] Example 1:

[0047] like Figure 1 As shown, this embodiment provides a VSG parameter adaptive control method based on dynamic fuzzy control, comprising the following steps:

[0048] S1: Input the angular frequency deviation Δw and the angular frequency change rate dw / dt into the fuzzy controller, and obtain the scaling factor α of the input domain by calculation e , α ec and the scaling factor β of the output universe;

[0049] Use function to construct the scaling factor α of the input domain e , α ec , its function expression is:

[0050]

[0051] Where, e represents the angular frequency deviation Δw, ec represents the angular frequency change rate dw / dt, and x represents the variable value.

[0052] The scaling factor β of the output domain is designed based on fuzzy reasoning method;

[0053] S2: Establish fuzzy control rules and membership functions of input and output variables;

[0054] like Figure 2 As shown, the fuzzy control rules include: introducing the quantitative factor K e , K ec , realizing the conversion between continuous variables and fuzzy discrete variables.

[0055] Continuous variables include the input variables e, ec and output variable o of the fuzzy controller, and fuzzy discrete variables include the error E and the error change rate EC. In practical applications, the input variables e, ec and output variable o of the fuzzy controller are all continuous. For example, the value ranges of the input variables e and ec are [e L , e H ] and [ec L ,ec H ], the value range of the output variable o is [o L , o H ], L and H represent the upper and lower limits of the range, respectively. These continuous variables become discrete sets after passing through the membership function. The domain of error E contains 2m variables, and the domain of error change rate EC has 2n variables. Similarly, the domain of output O has 2l variables. Taking error E as an example, the variables are represented as {-m, …, -1, 0, 1, …, m}.

[0056] Based on the above description, the conversion between continuous variables and fuzzy discrete variables specifically includes:

[0057]

[0058] In the formula, <> represents the rounding operation of the result, e L and e H Represent the lower limit and upper limit of e and ec respectively.

[0059] Quantization factor K e , K ec The expression is as follows:

[0060]

[0061] Among them, 2m is the number of variables in the domain of error E, and 2n is the number of variables in the domain of error change rate EC.

[0062] Fuzzy reasoning includes clarification processing, which converts the discrete variable O output by the fuzzy controller into a proportional factor K. o Converted into actual continuous output, expressed as follows:

[0063]

[0064] Among them,L 、o H They represent the lower limit and upper limit of the output variable o respectively.

[0065] Scale factor K o The expression is as follows:

[0066]

[0067] Among them, 2l is the number of variables in the domain of the output variable o.

[0068] In this embodiment, the fuzzy control rules set five fuzzy subsets {NB, NS, Z0, PS, PB}, where N and P represent negative and positive value divisions respectively; B and S represent large and small respectively; and Z0 represents 0.

[0069] The input variables are the angular frequency deviation Δw and the angular frequency change rate dw / dt, and the output variables are the increment ΔJ of the moment of inertia J and the increment ΔD of the damping value D. Their respective membership functions are as follows: Figure 3 As shown in (a) to (d) in the figure.

[0070] In this embodiment, 25 fuzzy control rules are determined based on the adaptive parameter adjustment rules and actual experience, and a fuzzy control rule base is constructed, as shown in Table 1 and Table 2.

[0071] Table 1ΔJ fuzzy control rules

[0072]

[0073] Table 2ΔD fuzzy control rules

[0074]

[0075] The scaling factor is adjusted appropriately based on the angular frequency deviation Δw and the angular frequency change rate dw / dt. When the Δw and dw / dt errors are large, the fuzzy control rules do not need to be refined. However, when the errors become smaller, the existing adaptive control effect is poor. This invention appropriately shrinks the domain to achieve more precise control of the moment of inertia J and the damping coefficient D. The initial domain of β is [0, 1]. The scaling factor β of the output variable and the fuzzy control rules for the input variables Δw and dw / dt are shown in Table 3.

[0076] Table 3 Output variable β fuzzy control rules

[0077]

[0078] S3: Fuzzy reasoning based on fuzzy control rules, by inputting the scaling factor α of the domain e , α ecThe scaling factor β of the input and output domains adjusts the sizes of the input and output domains in real time respectively;

[0079] The specific adjustment methods are as follows:

[0080] First, when the system angular frequency deviation or its rate of change is small, they will shrink the domain (according to the expansion factor α of the input domain e , α ec To improve the system's ability to perceive and process small changes, accurately capture subtle information, and provide a basis for subsequent precise adjustment of parameters, thereby improving control accuracy.

[0081] Second, when the deviation or rate of change is large, the domain is expanded (according to the scaling factor α of the input domain e , α ec To ensure that the system can obtain comprehensive information and enhance its adaptability and robustness to large disturbances.

[0082] The third is to adjust α in real time according to different working conditions. e , α ec and β, optimizing the system control performance.

[0083] S4: According to the adjusted domain, the output variables ΔJ and ΔD are obtained, and the moment of inertia J and the damping value D are obtained by combining the initial moment of inertia J0 and the damping value D0 set by the system.

[0084] Example 2:

[0085] Based on the method of Example 1, this embodiment provides a VSG parameter adaptive control system based on dynamic fuzzy control, including:

[0086] The scaling factor establishment module is used to obtain the scaling factor α of the input domain e , α ec and the scaling factor β of the output universe;

[0087] A fuzzy control rule building module is used to build fuzzy control rules and membership functions of input and output variables;

[0088] Fuzzy reasoning module, used to input the scaling factor α of the domain e , α ec The scaling factor β of the input and output domains adjusts the sizes of the input and output domains in real time respectively;

[0089] The parameter output module is used to obtain the moment of inertia and damping values ​​through output variables.

[0090] Example 3:

[0091] In order to verify the effectiveness and effect of the solution of the present invention, the following simulation experiments are carried out:

[0092] like Figure 4 As shown in the figure, a single distributed microgrid grid-connected simulation system was built, where X f Represents the converter side impedance, U PCC is the voltage at the common coupling point; Xv is the impedance between the converter and the grid side, P V is the active power transmitted to the grid during commutation, Qv is the reactive power transmitted to the grid during commutation, and U g is the grid side voltage, X g is the grid side impedance, VSG stands for virtual synchronous generator; Grid stands for grid; S g It is the grid-connected closing switch;

[0093] The parameter settings during the simulation are shown in Table 4.

[0094] Table 4 Simulation parameters

[0095]

[0096] Condition 1: When the DC microgrid is running in an off-grid state for 1 second, a pre-synchronization closing signal is given to the VSG converter. The total simulation time is 3 seconds. When the VSG is running off-grid, the active load is 1000W. At 1 second and when the grid is closed, the active load increases to 10000W. The pre-synchronization process is implemented based on a phase-locked loop. The system active power and frequency at the closing moment are as follows: Figure 5 and Figure 6 shown.

[0097] pass Figure 5 and Figure 6 It can be seen that at the moment of fixed parameter VSG pre-synchronization closing, a large power and frequency impact will be generated, seriously affecting the power quality of the power grid. After adopting traditional adaptive parameter control and fuzzy adaptive control strategies, the system's active power and frequency overshoots have not improved much. The system's active overshoot is only reduced by 0.1% using traditional adaptive control and by 0.21% using fuzzy control. After adopting the dynamic fuzzy adaptive control strategy provided by the present invention, the system has no overshoot, and the stabilization time is only about 0.13s different from other control strategies. The simulation results show that the control strategy proposed by the present invention effectively improves the power quality of the system at the moment of VSG converter closing.

[0098] Condition 2: When the DC microgrid is connected to the grid and the system active load increases from 10,000W to 12,000W, the total simulation time is 6s. The system active power and frequency during the load mutation are as follows: Figure 7 and Figure 8 shown.

[0099] pass Figure 7 It can be seen that the traditional adaptive VSG control strategy has some improvement in active power overshoot compared to fixed parameter VSG control, but the overshoot is only reduced by 1.67%, which is not very effective. Fuzzy control has lower overshoot than other control methods, reducing it by 54.17% compared to fixed parameter VSG control.

[0100] like Figure 8 As shown, the control strategy proposed in the present invention can effectively improve the frequency overshoot phenomenon of the system, and the stabilization time is shorter than that of other control strategies, which is only 0.35s. This fully proves that the control strategy proposed in this patent can effectively suppress the fluctuation of the system when the system load suddenly changes, has faster adjustment ability, and has better anti-interference ability.

[0101] In summary, the proposed VSG parameter adaptive disturbance optimization control strategy based on dynamic fuzzy control effectively overcomes the shortcomings of traditional control strategies by leveraging its unique dynamic domain regulation, optimized fuzzy control rules, and superior system performance enhancement mechanism. Under various complex operating conditions, such as DC microgrid closing, system load mutations, and grid frequency drops, it not only significantly reduces the power and frequency impacts at the moment of closing, but also greatly mitigates the adverse effects of external disturbances on system stability. This provides a solid foundation for the reliable grid connection and stable operation of distributed microgrids under high-proportion renewable energy access.

Claims

1. A VSG parameter adaptive control method based on dynamic fuzzy control, characterized in that: The steps include: S1: Input the angular frequency deviation Δw and the angular frequency change rate dw / dt into the fuzzy controller to obtain the scaling factor α of the input domain e , α ec and the scaling factor β of the output universe; S2: Establish fuzzy control rules and membership functions of input and output variables; S3: Fuzzy reasoning based on fuzzy control rules, by inputting the scaling factor α of the domain e , α ec The scaling factor β of the input and output domains adjusts the sizes of the input and output domains in real time respectively; S4: According to the adjusted domain, the moment of inertia and damping value are obtained through the output variables.

2. A VSG parameter adaptive control method based on dynamic fuzzy control according to claim 1, characterized in that: The scaling factor α of the input domain in step S1 e , α ec The function expression is: Where, e represents the angular frequency deviation Δw, ec represents the angular frequency change rate dw / dt, and x represents the variable value.

3. The VSG parameter adaptive control method based on dynamic fuzzy control according to claim 1, characterized in that: The fuzzy control rules in step S2 include: introducing a quantization factor K e , K ec , realizing the conversion between continuous variables and fuzzy discrete variables.

4. The VSG parameter adaptive control method based on dynamic fuzzy control according to claim 3 is characterized in that: In step S2, the continuous variables include the input variables e, ec and the output variable o of the fuzzy controller, and the fuzzy discrete variables include the error E and the error change rate EC. The specific conversion includes: In the formula, <> represents the rounding operation of the result, e L and e H Represent the lower limit and upper limit of e and ec respectively.

5. The VSG parameter adaptive control method based on dynamic fuzzy control according to claim 4 is characterized in that: The quantization factor K in step S2 e , K ec The expression is as follows: Among them, 2m is the number of variables in the domain of error E, and 2n is the number of variables in the domain of error change rate EC.

6. The VSG parameter adaptive control method based on dynamic fuzzy control according to claim 5, characterized in that: The fuzzy reasoning in step S3 includes a clarification process, which converts the discrete variable O output by the fuzzy controller into a proportional factor K. o Converted into actual continuous output, expressed as follows: Among them, L 、o H They represent the lower limit and upper limit of the output variable o respectively.

7. The VSG parameter adaptive control method based on dynamic fuzzy control according to claim 6, characterized in that: The scaling factor K in step S3 o The expression is as follows: Among them, 2l is the number of variables in the domain of the output variable o.

8. The VSG parameter adaptive control method based on dynamic fuzzy control according to claim 1, characterized in that: In step S2, the fuzzy control rules set five fuzzy subsets {NB, NS, Z0, PS, PB}, where N and P represent negative and positive value divisions respectively; B and S represent large and small respectively; and Z0 represents 0.

9. A VSG parameter adaptive control system based on dynamic fuzzy control, characterized in that: include: The scaling factor establishment module is used to obtain the scaling factor α of the input domain e , α ec and the scaling factor β of the output universe; A fuzzy control rule building module is used to build fuzzy control rules and membership functions of input and output variables; Fuzzy reasoning module, used to input the scaling factor α of the domain e , α ec The scaling factor β of the input and output domains adjusts the sizes of the input and output domains in real time respectively; The parameter output module is used to obtain the moment of inertia and damping values ​​through output variables.

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