Virtual synchronous generator converter model control parameter identification method and system

Abstract

The invention discloses a virtual synchronous generator converter model control parameter identification method and system. A traditional control parameter identification method is low in convergence speed, insufficient in precision and difficult to adapt to precise control requirements under complex working conditions. An intelligent optimization method based on a particle swarm optimization algorithm has high convergence efficiency, but is easy to fall into local optimum. The virtual synchronous generator converter model control parameter identification method comprises the following steps: an analysis control link: constructing a virtual synchronous generator converter control model suitable for a distributed power supply; selecting a to-be-identified parameter and a to-be-identified observed quantity in the control model, generating standard test data, and constructing a fitness function; and improving a particle swarm algorithm, taking the constructed fitness function as an optimization target, and performing parameter optimization by using the algorithm to complete identification. By optimizing the control parameter identification process, not only is the control precision improved, but also the problems of slow parameter convergence, insufficient precision and the like in a traditional identification method are effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed power supply model parameter control, and relates to a virtual synchronous generator converter model control parameter identification method and system for distributed power supply. Background Art

[0002] With the large-scale access of distributed power sources such as photovoltaic and wind power, the stability and dynamic response capabilities of the power grid are facing challenges. Virtual synchronous generator (VSG) technology enhances the stability of distributed power grid connection by simulating the inertia and damping characteristics of traditional synchronous generators. However, traditional control parameter identification methods have slow convergence speed and insufficient accuracy, and are difficult to adapt to the precise control requirements under complex working conditions. Intelligent optimization methods based on particle swarm optimization algorithm have high convergence efficiency, but are prone to fall into local optimality.

[0003] Therefore, how to improve the particle swarm algorithm to enhance the accuracy and speed of control parameter identification has become a key issue in current research. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the defects of the above-mentioned prior art and provide a method and system for identifying control parameters of a virtual synchronous generator converter model for distributed power sources, which significantly improves the identification accuracy and convergence speed of the control parameters of the virtual synchronous generator converter by optimizing the control parameter identification process.

[0005] To this end, the present invention adopts the following technical solution.

[0006] In a first aspect, the present invention adopts a virtual synchronous generator converter model control parameter identification method, which comprises the steps of:

[0007] S1, analyze the control link and build a virtual synchronous generator converter control model suitable for distributed power sources;

[0008] S2, select the parameters and observations to be identified in the control model, generate standard test data, and construct a fitness function;

[0009] S3, improves the particle swarm algorithm, takes the constructed fitness function as the optimization target, and uses the algorithm to perform parameter optimization to complete the identification.

[0010] Furthermore, in step S1, the virtual synchronous generator converter control model is as follows:

[0011] The virtual synchronous generator converter adjusts active power and reactive power through a control link to stabilize the output frequency and voltage; the control link includes an active-frequency control link and a reactive-voltage control link;

[0012] The active power-frequency control link simulates the traditional synchronous generator rotor motion equation, as shown in formula (1):

[0013]

[0014] The reactive power-voltage control link simulates the voltage regulation equation of a traditional synchronous generator, as shown in equation (2):

[0015]

[0016] Among them, D vsg , H are the damping coefficient and inertia constant of the virtual synchronous generator VSG respectively; P set , Q set are active power reference value and reactive power reference value respectively; P vsg , Q vsg are the actual value of active power and the actual value of reactive power respectively; ω vsg is the angular frequency of VSG; ω g is the actual working angular frequency of the power grid; K is the voltage gain coefficient of the reactive-voltage control link, D u is the droop coefficient of the reactive-voltage control link; U vsg is the converter output voltage; U n is a given reference voltage.

[0017] Furthermore, in step S2, the active-frequency control link is affected by the damping coefficient and the inertia constant, and the reactive-voltage control link is affected by the voltage gain coefficient and the droop coefficient, so the following are selected:

[0018]

[0019] Among them, Pa is the parameter to be identified, and Ob is the observation to be identified.

[0020] Furthermore, in step S2, after generating the standard test data, the standard active power reference value P of the VSG is collected. test Compared with the standard reactive power reference value Q test .

[0021] Furthermore, in step S2, the particle swarm fitness function is:

[0022]

[0023] Among them, P i vsg is the active power of VSG at time i; P i test is the standard active power of VSG at time i; Q i vsgis the reactive power of VSG at time i; Q i test is the standard reactive power of VSG at time i, Fit1 is the active fitness function, and Fit2 is the reactive fitness function.

[0024] Furthermore, in step S3, the particle swarm algorithm is improved as follows:

[0025]

[0026] Among them, w is the inertia weight, c 1 、c 2 are the individual learning factor and the social learning factor, i is the current iteration number, maxgen is the maximum iteration number; w_start, c 1 _start, c 2 _start are the starting values ​​of inertia weight, individual learning factor and social learning factor respectively; w_end, c 1 _end, c 2 _end are the end values ​​of inertia weight, individual learning factor and social learning factor respectively.

[0027] Furthermore, in step S3, the particle swarm algorithm population is initialized as follows:

[0028] Set the population size to M, the particle dimension to N, and the initial position and speed of each particle to be as follows:

[0029]

[0030] Among them, X 0 、V 0 are the initial position and velocity of the particle, Lx 0 ,Lv 0 are the Latin hypercube normalized sampling position matrix and velocity matrix, respectively. is the Hadamard product representing element-by-element multiplication, X max 、V max are the position and maximum velocity of the particle, X min 、V min are the minimum position and velocity of the particle respectively.

[0031] Furthermore, in step S3, after the algorithm is initialized, the fitness function value of each particle is selected and evaluated according to the parameters to be identified. In each iteration, the new individual optimum of the particle is calculated to update the individual, and the group optimum is updated by comparing the individual optimum positions of all particles. After the accuracy requirement is met, the iteration is stopped to output the optimal fitness value and the parameter value to be identified.

[0032] The particle position and velocity are updated as follows:

[0033]

[0034] Among them, X i (t+1), V i (t+1) is the position and velocity of the particle at time t+1, X i (t), V i (t) is the particle position and velocity at time t, Pi(t) is the optimal position of the individual at time t, g(t) is the optimal position of the group at time t, w is the inertia weight, r 1 、r 2 All are random numbers in the interval [0,1].

[0035] In a second aspect, the present invention provides a virtual synchronous generator converter model control parameter identification system, comprising:

[0036] Control model building unit: Analyze the control link and build a virtual synchronous generator converter control model suitable for distributed power sources;

[0037] Fitness function construction unit: selects the parameters to be identified and the observations to be identified in the control model, generates standard test data, and constructs the fitness function;

[0038] Parameter identification unit: Improve the particle swarm algorithm, take the constructed fitness function as the optimization target, and use the algorithm to optimize the parameters to complete the identification.

[0039] The beneficial effects of the present invention are as follows: Compared with the prior art, the present invention significantly improves the identification accuracy and convergence speed of the control parameters of the virtual synchronous generator converter by optimizing the control parameter identification process, enhances the dynamic response capability and control stability of the distributed power grid-connected system, and under load fluctuations or uncertain conditions, the optimized parameters can better adapt to actual conditions.

[0040] The present invention not only improves the control accuracy, but also effectively solves the problems of slow parameter convergence and insufficient accuracy in traditional identification methods. It has important engineering application value and can be widely used in intelligent grid-connected control of distributed energy such as photovoltaic and wind power. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 It is a flow chart of a control parameter identification method of a virtual synchronous generator converter model of the present invention;

[0043] Figure 2 It is an architecture diagram of a control parameter identification system of a virtual synchronous generator converter model of the present invention;

[0044] Figure 3 is a control block diagram of the virtual synchronous generator converter model of the present invention (θvsg and E in the figure represent the phase angle and amplitude of the voltage at the AC side port of the virtual synchronous machine converter, respectively);

[0045] Figure 4 is a curve diagram of the optimal fitness function value of the active observation quantity in the simulation example of the present invention;

[0046] Figure 5 is a curve diagram of the optimal fitness function value of reactive power observation in the simulation example of the present invention;

[0047] Figure 6 It is a diagram of active identification parameters and simulation results in a simulation example of the present invention;

[0048] Figure 7 It is a diagram of reactive power identification parameters and simulation results in the simulation example of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Example 1

[0051] This embodiment provides a control parameter identification method for a virtual synchronous generator converter model: first, by establishing a VSG converter control model suitable for distributed power sources, key converter control parameters are determined; second, the parameters to be identified and related observations are selected, a suitable fitness function is constructed, and the particle swarm optimization algorithm is improved in combination with actual working conditions to improve the convergence speed and identification accuracy of the algorithm; finally, the improved particle swarm algorithm is used to perform parameter optimization to ensure accurate identification of control parameters and meet the system's requirements for accuracy and stability.

[0052] The control parameter identification method of the virtual synchronous generator converter model is as follows: Figure 1 As shown, the steps are as follows:

[0053] S1, analyze the control link and build a virtual synchronous generator converter control model suitable for distributed power sources;

[0054] S2, select the parameters and observations to be identified in the control model, generate standard test data, and construct a fitness function;

[0055] S3, improves the particle swarm algorithm, takes the constructed fitness function as the optimization target, and uses the algorithm to perform parameter optimization to complete the identification.

[0056] Specifically, in step S1, the virtual synchronous generator converter control model is as follows:

[0057] The virtual synchronous generator converter adjusts active power and reactive power through a control link to stabilize the output frequency and voltage; the control link includes an active-frequency control link and a reactive-voltage control link;

[0058] The active power-frequency control link simulates the traditional synchronous generator rotor motion equation, as shown in formula (1):

[0059]

[0060] The reactive power-voltage control link simulates the voltage regulation equation of a traditional synchronous generator, as shown in equation (2):

[0061]

[0062] Among them, D vsg , H are the damping coefficient and inertia constant of the virtual synchronous generator VSG respectively; P set , Q set are active power reference value and reactive power reference value respectively; P vsg , Q vsg are the actual value of active power and the actual value of reactive power respectively; ω vsg is the angular frequency of VSG; ω g is the actual working angular frequency of the power grid; K is the voltage gain coefficient of the reactive-voltage control link, D u is the droop coefficient of the reactive-voltage control link; U vsg is the converter output voltage; U n is a given reference voltage.

[0063] Specifically, in step S2, the active-frequency control link is affected by the damping coefficient and the inertia constant, and the reactive-voltage control link is affected by the voltage gain coefficient and the droop coefficient, so the following are selected:

[0064]

[0065] Among them, Pa is the parameter to be identified, and Ob is the observation to be identified.

[0066] In step S2, after generating the standard test data, the standard active power reference value P of the VSG is collected. test Compared with the standard reactive power reference value Q test .

[0067] In step S2, the particle swarm fitness function is:

[0068]

[0069] Among them, P i vsg is the active power of VSG at time i; P i test is the standard active power of VSG at time i; Q i vsg is the reactive power of VSG at time i; Q i test is the standard reactive power of VSG at time i, Fit1 is the active fitness function, and Fit2 is the reactive fitness function.

[0070] Specifically, in step S3, the particle swarm algorithm is improved as follows:

[0071]

[0072] Among them, w is the inertia weight, c 1 、c 2 are the individual learning factor and the social learning factor, i is the current iteration number, maxgen is the maximum iteration number; w_start, c 1 _start, c 2 _start are the starting values ​​of inertia weight, individual learning factor and social learning factor respectively; w_end, c 1 _end, c 2 _end are the end values ​​of inertia weight, individual learning factor and social learning factor respectively.

[0073] In step S3, the particle swarm algorithm population is initialized as follows:

[0074] Set the population size to M, the particle dimension to N, and the initial position and speed of each particle to be as follows:

[0075]

[0076] Among them, X 0 、V 0 are the initial position and velocity of the particle, Lx 0 ,Lv 0are the Latin hypercube normalized sampling position matrix and velocity matrix, respectively. is the Hadamard product representing element-by-element multiplication, X max 、V max are the position and maximum velocity of the particle, X min 、V min are the minimum position and velocity of the particle respectively.

[0077] In step S3, after the algorithm is initialized, the fitness function value of each particle is selected and evaluated according to the parameters to be identified. In each iteration, the new individual optimum of the particle is calculated to update the individual, and the group optimum is updated by comparing the individual optimal positions of all particles. After the accuracy requirement is met, the iteration is stopped to output the optimal fitness value and the parameter value to be identified.

[0078] The particle position and velocity are updated as follows:

[0079]

[0080] Among them, X i (t+1), V i (t+1) is the position and velocity of the particle at time t+1, X i (t), V i (t) is the particle position and velocity at time t, Pi(t) is the optimal position of the individual at time t, g(t) is the optimal position of the group at time t, w is the inertia weight, r 1 、r 2 All are random numbers in the interval [0,1].

[0081] Example 2

[0082] This embodiment provides a virtual synchronous generator converter model control parameter identification system. Figure 2 As shown, it consists of a control model building unit, a fitness function building unit and a parameter identification unit.

[0083] Control model building unit: Analyze the control link and build a virtual synchronous generator converter control model suitable for distributed power sources.

[0084] Fitness function construction unit: selects the parameters to be identified and the observations to be identified in the control model, generates standard test data, and constructs the fitness function.

[0085] Parameter identification unit: Improve the particle swarm algorithm, take the constructed fitness function as the optimization target, and use the algorithm to optimize the parameters to complete the identification.

[0086] In the control model building unit, the virtual synchronous generator converter control model is as follows:

[0087] The virtual synchronous generator converter adjusts active power and reactive power through a control link, wherein the control link includes an active-frequency control link and a reactive-voltage control link;

[0088] The active power-frequency control link simulates the traditional synchronous generator rotor motion equation, as shown in formula (1):

[0089]

[0090] The reactive power-voltage control link simulates the voltage regulation equation of a traditional synchronous generator, as shown in equation (2):

[0091]

[0092] Among them, D vsg , H are the damping coefficient and inertia constant of the virtual synchronous generator VSG respectively; P set , Q set are active power reference value and reactive power reference value respectively; P vsg , Q vsg are the actual value of active power and the actual value of reactive power respectively; ω vsg is the angular frequency of VSG; ω g is the actual working angular frequency of the power grid; K is the voltage gain coefficient of the reactive-voltage control link, D u is the droop coefficient of the reactive-voltage control link; U vsg is the converter output voltage; U n is a given reference voltage.

[0093] It should be noted that each unit in the above-mentioned virtual synchronous generator converter model control parameter identification system can be fully or partially implemented by software, hardware and their combination. The above-mentioned units can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned units. For the specific definition of a virtual synchronous generator converter model control parameter identification system, please refer to the definition of a virtual synchronous generator converter model control parameter identification method mentioned above. The two have the same functions and effects, which will not be repeated here.

[0094] Simulation Example

[0095] This simulation example is based on the Matlab simulation software. Figure 3 The VSG converter control model shown in the figure uses the method described in Example 1 to identify the control link parameters, which are as follows:

[0096] First, set the standard identification parameter value, D vsg ,H,Du , K are 15, 20, 60, 30 respectively, and the simulation conditions are set. The simulation time is 0.5 seconds. Multiple stages are set to test the dynamic response of the virtual synchronous generator converter: the active power command P within 0 to 0.2 seconds set The reactive power command Q is 120 kW, and then increases to 140 kW in 0.2 to 0.5 seconds to simulate a power step change; set During the whole simulation process, the load is kept constant at 10kvar; a 20kW active load disturbance is introduced during 0.3 to 0.4 seconds to simulate the dynamic load fluctuation in the power grid. The active and reactive power output curves of the VSG converter generated by the simulation under this condition are used as standard test data.

[0097] Secondly, determine the range of identification parameters, D vsg ,H,D u , K ranges from [10,30], [40,80], [10,40], [10,50]; initialize the particle swarm algorithm, select the corresponding identification parameters and observation values, D vsg 、H selects P vsg is the observed value, Fit1 is the fitness function, D u , K chooses Q vsg is the observation value and Fit2 is the fitness function.

[0098] Finally, the improved algorithm is used for iterative optimization. The number of iterations is set to 100 and the population size is set to 20. After reaching the number of iterations, the optimal parameters are output as the identification parameters. The identification error is calculated using the relative error to verify the validity of the identification parameters. The simulation results show that the identification parameters can correctly simulate the active and reactive output curves of the VSG according to the working conditions. The following Table 1 shows the identification results of the identification parameters. Figure 4 and Figure 5 is the optimal fitness function value curve, Figure 6 and Figure 7 This is the simulation result diagram of identification parameters.

[0099] Table 1 Parameter identification results

[0100]

[0101] In summary, this simulation example confirms the feasibility of the virtual synchronous generator converter model control parameter identification method of the present invention, improves the identification accuracy and convergence speed of the virtual synchronous generator converter control parameters, enhances the dynamic response capability and control stability of the distributed power grid-connected system, and realizes the effective identification of the virtual synchronous generator converter control parameters.

[0102] The above disclosure is only the preferred embodiment of the present invention, showing and describing the basic principle, main features and advantages of the present invention. However, the present invention is not limited thereto, and any transformation and changes made by any technician in the field without creative work and without departing from the principle of the present invention should fall within the protection scope of the present invention. The protection scope of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for identifying control parameters of a virtual synchronous generator converter model, characterized in that: Includes steps: S1, analyze the control link and build a virtual synchronous generator converter control model suitable for distributed power sources; S2, select the parameters and observations to be identified in the control model, generate standard test data, and construct a fitness function; S3, improves the particle swarm algorithm, takes the constructed fitness function as the optimization target, and uses the algorithm to perform parameter optimization to complete the identification.

2. The virtual synchronous generator converter model control parameter identification method according to claim 1, characterized in that: In step S1, the virtual synchronous generator converter control model is as follows: The virtual synchronous generator converter adjusts active power and reactive power through a control link, wherein the control link includes an active-frequency control link and a reactive-voltage control link; The active power-frequency control link simulates the traditional synchronous generator rotor motion equation, as shown in formula (1): The reactive power-voltage control link simulates the voltage regulation equation of a traditional synchronous generator, as shown in equation (2): Among them, D vsg , H are the damping coefficient and inertia constant of the virtual synchronous generator VSG respectively; P set , Q set are active power reference value and reactive power reference value respectively; P vsg , Q vsg are the actual value of active power and the actual value of reactive power respectively; ω vsg is the angular frequency of VSG; ω g is the actual working angular frequency of the power grid; K is the voltage gain coefficient of the reactive-voltage control link, D u is the droop coefficient of the reactive-voltage control link; U vsg is the converter output voltage; U n is a given reference voltage.

3. The virtual synchronous generator converter model control parameter identification method according to claim 2, characterized in that: In step S2, the active-frequency control link is affected by the damping coefficient and the inertia constant, and the reactive-voltage control link is affected by the voltage gain coefficient and the droop coefficient, so the following are selected: Among them, Pa is the parameter to be identified, and Ob is the observation to be identified.

4. The virtual synchronous generator converter model control parameter identification method according to claim 3, characterized in that: In step S2, after generating the standard test data, the standard active power reference value P of the VSG is collected. test Compared with the standard reactive power reference value Q test .

5. The virtual synchronous generator converter model control parameter identification method according to claim 4, characterized in that: In step S2, the particle swarm fitness function is: Among them, P i vsg is the active power of VSG at time i; P i test is the standard active power of VSG at time i; Q i vsg is the reactive power of VSG at time i; Q i test is the standard reactive power of VSG at time i, Fit1 is the active fitness function, and Fit2 is the reactive fitness function.

6. The virtual synchronous generator converter model control parameter identification method according to claim 1, characterized in that: In step S3, the particle swarm algorithm is improved as follows: Among them, w is the inertia weight, c1 and c2 are the individual learning factor and social learning factor respectively, i is the current number of iterations, and maxgen is the maximum number of iterations; w_start, c1_start, and c2_start are the starting values ​​of the inertia weight, individual learning factor, and social learning factor respectively; w_end, c1_end, and c2_end are the ending values ​​of the inertia weight, individual learning factor, and social learning factor respectively.

7. The virtual synchronous generator converter model control parameter identification method according to claim 6, characterized in that: In step S3, the particle swarm algorithm population is initialized as follows: Set the population size to M, the particle dimension to N, and the initial position and speed of each particle to be as follows: Among them, X0 and V0 are the initial position and velocity of the particle, Lx0 and Lv0 are the Latin hypercube standardized sampling position matrix and velocity matrix, . is the Hadamard product representing element-by-element multiplication, X max 、V max are the position and maximum velocity of the particle, X min 、V min are the minimum position and velocity of the particle respectively.

8. The virtual synchronous generator converter model control parameter identification method according to claim 7, characterized in that: In step S3, after the algorithm is initialized, the fitness function value of each particle is selected and evaluated according to the parameters to be identified. In each iteration, the new individual optimum of the particle is calculated to update the individual, and the group optimum is updated by comparing the individual optimal positions of all particles. After the accuracy requirement is met, the iteration is stopped to output the optimal fitness value and the parameter value to be identified. The particle position and velocity are updated as follows: Among them, X i (t+1), V i (t+1) is the position and velocity of the particle at time t+1, X i (t), V i (t) is the position and velocity of the particle at time t, Pi(t) is the optimal position of the individual at time t, g(t) is the optimal position of the group at time t, w is the inertia weight, and r1 and r2 are both random numbers in the interval [0,1].

9. A virtual synchronous generator converter model control parameter identification system, characterized in that: include: Control model building unit: Analyze the control link and build a virtual synchronous generator converter control model suitable for distributed power sources; Fitness function construction unit: selects the parameters to be identified and the observations to be identified in the control model, generates standard test data, and constructs the fitness function; Parameter identification unit: Improve the particle swarm algorithm, take the constructed fitness function as the optimization target, and use the algorithm to optimize the parameters to complete the identification.

10. The virtual synchronous generator converter model control parameter identification system according to claim 9, characterized in that: In the control model building unit, the virtual synchronous generator converter control model is as follows: The virtual synchronous generator converter adjusts active power and reactive power through a control link, wherein the control link includes an active-frequency control link and a reactive-voltage control link; The active power-frequency control link simulates the traditional synchronous generator rotor motion equation, as shown in formula (1): The reactive power-voltage control link simulates the voltage regulation equation of a traditional synchronous generator, as shown in equation (2): Among them, D vsg , H are the damping coefficient and inertia constant of the virtual synchronous generator VSG respectively; P set , Q set are active power reference value and reactive power reference value respectively; P vsg , Q vsg are the actual value of active power and the actual value of reactive power respectively; ω vsg is the angular frequency of VSG; ω g is the actual working angular frequency of the power grid; K is the voltage gain coefficient of the reactive-voltage control link, D u is the droop coefficient of the reactive-voltage control link; U vsg is the converter output voltage; U n is a given reference voltage.

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