Optimal configuration method of inertia damping for virtual synchronous generator grid-connected system considering capacity limitation

Abstract

The present invention discloses an inertia damping optimization configuration method for a virtual synchronous machine grid-connected system considering capacity limitations, comprising the following steps: first, establishing differential equations of three virtual synchronous machines, and connecting to a nine-node system to obtain dynamic response curves of the three virtual synchronous machines; then, under the condition of considering the total capacity limitation of the three virtual synchronous machines, designing an objective function for optimizing the dynamic performance of the virtual synchronous machines; finally, using the White Whale optimization algorithm to iterate and find the optimal inertia and damping configuration of the three virtual synchronous machines. Under the condition of considering capacity limitations, the present invention takes the dynamic performance of the virtual synchronous machine as the optimization target, and uses the White Whale optimization algorithm to iterate and find the optimal inertia and optimal damping of each of the three virtual synchronous machines, which helps to speed up the response speed of the virtual synchronous machine to system disturbances and significantly improves the dynamic performance of the virtual synchronous machine.

Description

Technical Field

[0001] The present invention relates to the field of new power system stability research, and in particular to an inertia damping optimization configuration method for a virtual synchronous machine grid-connected system considering capacity limitations. Background Art

[0002] With the centralized grid connection of large-scale new energy, new energy units do not have the frequency and voltage support capabilities of traditional synchronous units, resulting in prominent frequency stability problems caused by the lack of inertia and damping in the system. Providing inertia support for the system through virtual synchronous machines has become an effective means to improve the frequency stability of the system. However, virtual synchronous machines face the following problems: large-scale provision of virtual inertia and virtual damping requires the system to have a large energy storage unit, and improper setting of control parameters will lead to poor grid-connected adaptability of virtual synchronous machines and reduced regulation capabilities. Therefore, it is necessary to reasonably configure the inertia and damping coefficient of each virtual synchronous machine according to the system state under the condition of total capacity limitation. To this end, the present invention proposes an inertia damping optimization configuration method for a virtual synchronous machine grid-connected system considering capacity limitations, and uses the White Whale optimization algorithm to iteratively find the optimal inertia and optimal damping configuration of each virtual synchronous machine, thereby accelerating the response speed of the virtual synchronous machine to system disturbances and significantly improving the dynamic performance of the virtual synchronous machine. Summary of the invention

[0003] To achieve the above purpose, the technical solution provided by the present invention is:

[0004] S1: Establish differential equations of three virtual synchronous machines and connect them to the nine-node system to obtain dynamic response curves of the three virtual synchronous machines;

[0005] S1-1: According to the rotor motion equation, the differential equation of the active power control loop in the three virtual synchronous machines is established:

[0006]

[0007] In formula (1), i is the number of virtual synchronous machines, i = 1, 2, 3, θ i is the power angle of the virtual synchronous machine; ω i and ω r are the actual angular frequency and reference angular frequency of the virtual synchronous machine respectively; P ref is the reference value of the active power of the virtual synchronous machine; P i is the actual active power of the virtual synchronous machine; D Ci is the damping coefficient; H i is the inertia coefficient;

[0008] S1-2: Connect three virtual synchronous machines to the nine-node system, construct a three-virtual synchronous machine nine-node system, obtain the node information of the system, and calculate the power flow equation to obtain the angular frequency ω of the three virtual synchronous machines at the grid connection point in the steady state. r 、Power angle θ i , Active power P ref , and use these values ​​as reference values;

[0009] S1-3: According to the network equation at the grid connection point, under disturbance conditions, the angular frequency ω of the three virtual synchronous machines at the grid connection point is i , Active power P i will change, and the changed angular frequency ω i , Active power P i As actual value;

[0010] S1-4: The reference values ​​and actual values ​​obtained in step S1-2 and step S1-3, including: reference angular frequency ω r 、Power angle θ i , Reference active power P ref , actual angular frequency ω i , actual active power P i , substitute into the differential equation established in step S1-1, solve the differential equation using the Longze Kutta method, and obtain the dynamic response curves of the three virtual synchronous machines;

[0011] S2: Design the objective function to optimize the dynamic performance of the virtual synchronous machine under the condition of considering the total capacity limitation of the three virtual synchronous machines;

[0012] S2-1: Considering the capacity limitation, that is, the total inertia and total damping of the three virtual synchronous machines remain unchanged when the disturbance event occurs, the penalty function Penalty is designed:

[0013] Penalty = λ·(|H qtytot -H1-H2-H3|+|D qtytot -D C1 -D C2 -D C3 |) (2)

[0014] In formula (2), λ is the penalty coefficient, H qtytot is the total inertia of the three virtual synchronous machines, D qtytot is the total damping of the three virtual synchronous machines, H1, H2, H3 are the inertia values ​​of each virtual synchronous machine, D C1 , D C2 , D C3 is the damping value of each virtual synchronous machine;

[0015] S2-2: In order to optimize the dynamic performance of the virtual synchronous machine, the minimum cumulative sum of angular frequency deviations of each virtual synchronous machine is designed as the objective function Obj:

[0016]

[0017] In formula (3), ω i is the actual angular frequency of the i-th virtual synchronous machine;

[0018] S2-3: Combine the penalty function Penalty in step S2-1 and the objective function Obj in step S2-2 to construct the latest objective function NewObj under the constraints:

[0019]

[0020] S3: Use the White Whale optimization algorithm to iterate and find the optimal inertia and damping configuration of the three virtual synchronous machines;

[0021] S3-1: The White Whale optimization algorithm is based on populations, taking each White Whale as a candidate solution. Therefore, the inertia H of the three virtual synchronous machines to be calculated is i and damping D Ci As the beluga location parameter, the total number of beluga whales is the population size N;

[0022] S3-2: Setting the search interval for the white whale position parameters, randomly generating the initial positions of all white whales within the search interval, and calculating the fitness value of the white whale initial position according to the latest objective function of step S2-3;

[0023] S3-3: For each beluga whale, according to the balance factor B n Decide whether to enter the exploration phase or the development phase:

[0024]

[0025] In formula (5), K is the current iteration number, K max is the maximum number of iterations, and B0 changes randomly between (0,1) in each iteration; when the balance coefficient B n >0.5, it enters the exploration phase. n When ≤0.5, it enters the development stage; as the number of iterations K increases, the probability of entering the development stage increases;

[0026] S3-4: During the exploration phase, the updated position of the beluga whale is determined by the paired swimming behavior of the beluga whale. The specific update method is as follows:

[0027]

[0028] In formula (6), is the new position of the nth beluga whale in the jth dimension, x j (j=1,2,…,6) is a random number selected from 6 dimensions, The nth white whale is at the xth j The position in dimension, W Kr,xj represents the position of a random white whale, a and b are random numbers between (0,1), even and odd represent odd and even numbers, and sin(2πb) and cos(2πb) indicate that the white whale is updated globally in a mirrored or synchronized manner.

[0029] S3-5: During the development phase, the location of the white whale update is determined by the white whale's predation behavior. The specific update method is:

[0030]

[0031] In formula (7), is the new position of the nth beluga whale, is the position of the random white whale, W Kn is the current position of the nth beluga whale, is the optimal white whale position, c and d are random numbers between (0,1), L F is the flight function, 2d(1-K / K max ) is the random jump intensity, indicating that the white whale uses the flying strategy to achieve updates in the local range;

[0032] S3-6: Calculate the fitness values ​​of the white whale positions updated in step S3-4 and step S3-5, and sort them according to their sizes to find the optimal result of the current iteration;

[0033] S3-7: Since some beluga whales may die and fall into the deep sea, the probability X of whale fall is calculated in each iteration to avoid falling into the local optimum:

[0034]

[0035] In formula (8), the probability of whale fall decreases from 0.1 in the initial iteration to 0.05 in the final iteration, indicating that during the optimization process, the closer the beluga whale is to the food source, the less danger it faces;

[0036] At the same time, in order to maintain the population size, the new individual position is established using the individual position, random individual position and whale fall step length:

[0037]

[0038] In formula (9), e, f and g are random numbers between (0, 1), W step It is the whale fall step length;

[0039] S3-8: Repeat steps S3-3 to S3-7 until the maximum number of iterations is reached, and the white whale position with the minimum fitness is obtained, which is the optimal inertia and optimal damping;

[0040] S3-9: Substitute the optimal inertia and optimal damping obtained in step S3-8 into the differential equation to obtain the dynamic response curves of the three virtual synchronous machines after optimization.

[0041] Compared with the prior art, the principles and advantages of this solution are as follows:

[0042] The present invention discloses an inertia damping optimization configuration method for a virtual synchronous machine grid-connected system considering capacity limitations. First, differential equations of three virtual synchronous machines are established and connected to a nine-node system to obtain dynamic response curves of the three virtual synchronous machines. Then, under the condition of considering the total capacity limitation of the three virtual synchronous machines, an objective function for optimizing the dynamic performance of the virtual synchronous machines is designed. Finally, the Beluga optimization algorithm is used to iterate and optimize to obtain the optimal inertia and damping configuration of the three virtual synchronous machines. Under the condition of considering capacity limitations, the present invention takes the dynamic performance of the virtual synchronous machine as the optimization target, and uses the Beluga optimization algorithm to iterate and optimize to obtain the optimal inertia and optimal damping of each of the three virtual synchronous machines, which helps to speed up the response speed of the virtual synchronous machine to system disturbances and significantly improves the dynamic performance of the virtual synchronous machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of an inertia damping optimization configuration method of a virtual synchronous machine grid-connected system considering capacity limitation in an embodiment of the present invention;

[0044] Figure 2 The topology structure of a nine-node system with three virtual synchronous machines in an embodiment of the present invention;

[0045] Figure 3 is a fitness value change curve of three virtual synchronous machines in an embodiment of the present invention after being optimized by the White Whale algorithm;

[0046] Figure 4 are the inertia change curves of the three virtual synchronous machines in the embodiment of the present invention after being optimized by the White Whale algorithm;

[0047] Figure 5 are the damping variation curves of the three virtual synchronous machines in the embodiment of the present invention after being optimized by the White Whale algorithm;

[0048] Figure 6 Angular frequency dynamic response curves of the first virtual synchronous machine before and after optimization in an embodiment of the present invention;

[0049] Figure 7 Angular frequency dynamic response curves of the second virtual synchronous machine before and after optimization in an embodiment of the present invention;

[0050] Figure 8 Angular frequency dynamic response curves of the third virtual synchronous machine before and after optimization in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention will be further described below in conjunction with specific embodiments:

[0052] Figure 1 The flowchart of the inertia damping optimization configuration method of the virtual synchronous machine grid-connected system considering capacity limitation is shown. Figure 2 The figure shows the topological structure of a nine-node system with three virtual synchronous machines. In this system, the circuit structure of each virtual synchronous machine includes: inverter control module, filter circuit module; the control structure includes: active power control loop, reactive power control loop, voltage and current control loop and PWM module, etc. Among them, Q ref Indicates reactive power reference value, Q i Indicates the actual value of reactive power, e i The specific optimization process includes the following steps:

[0053] S1: Establish differential equations of three virtual synchronous machines and connect them to the nine-node system to obtain dynamic response curves of the three virtual synchronous machines;

[0054] S1-1: According to the rotor motion equation, the differential equation of the active power control loop in the three virtual synchronous machines is established:

[0055]

[0056] In formula (10), i is the number of virtual synchronous machines, i = 1, 2, 3, θ i is the power angle of the virtual synchronous machine; ω i and ω r are the actual angular frequency and reference angular frequency of the virtual synchronous machine respectively; P ref is the reference value of the active power of the virtual synchronous machine; P i is the actual active power of the virtual synchronous machine; D Ci is the damping coefficient; H i is the inertia coefficient;

[0057] S1-2: Connect three virtual synchronous machines to the nine-node system, construct a three-virtual synchronous machine nine-node system, obtain the node information of the system, and calculate the power flow equation to obtain the angular frequency ω of the three virtual synchronous machines at the grid connection point in the steady state. r 、Power angle θ i , Active power P ref , and use these values ​​as reference values;

[0058] S1-3: According to the network equation at the grid connection point, under disturbance conditions, the angular frequency ω of the three virtual synchronous machines at the grid connection point is i , Active power P i will change, and the changed angular frequency ω i , Active power P i As actual value;

[0059] S1-4: The reference values ​​and actual values ​​obtained in step S1-2 and step S1-3, including: reference angular frequency ω r 、Power angle θ i , Reference active power P ref , actual angular frequency ω i , actual active power P i , substitute into the differential equation established in step S1-1, solve the differential equation using the Longze Kutta method, and obtain the dynamic response curves of the three virtual synchronous machines;

[0060] S2: Design the objective function to optimize the dynamic performance of the virtual synchronous machine under the condition of considering the total capacity limitation of the three virtual synchronous machines;

[0061] S2-1: Considering the capacity limitation, that is, the total inertia and total damping of the three virtual synchronous machines remain unchanged when the disturbance event occurs, the penalty function Penalty is designed:

[0062] Penalty = λ·(|H qtytot -H1-H2-H3|+|D qtytot -D C1 -D C2 -D C3 |) (11)

[0063] In formula (11), λ is the penalty coefficient, H qtytot is the total inertia of the three virtual synchronous machines, D qtytot is the total damping of the three virtual synchronous machines, H1, H2, H3 are the inertia values ​​of each virtual synchronous machine, D C1 , D C2 , D C3 is the damping value of each virtual synchronous machine;

[0064] S2-2: In order to optimize the dynamic performance of the virtual synchronous machine, the minimum cumulative sum of angular frequency deviations of each virtual synchronous machine is designed as the objective function Obj:

[0065]

[0066] In formula (12), ω i is the actual angular frequency of the i-th virtual synchronous machine;

[0067] S2-3: Combine the penalty function Penalty in step S2-1 and the objective function Obj in step S2-2 to construct the latest objective function NewObj under the constraints:

[0068]

[0069] S3: Use the White Whale optimization algorithm to iterate and find the optimal inertia and damping configuration of the three virtual synchronous machines;

[0070] S3-1: The White Whale optimization algorithm is based on populations, taking each White Whale as a candidate solution. Therefore, the inertia H of the three virtual synchronous machines to be calculated is i and damping D Ci As the beluga location parameter, the total number of beluga whales is the population size N;

[0071] S3-2: Setting the search interval for the white whale position parameters, randomly generating the initial positions of all white whales within the search interval, and calculating the fitness value of the white whale initial position according to the latest objective function of step S2-3;

[0072] S3-3: For each beluga whale, according to the balance factor B n Decide whether to enter the exploration phase or the development phase:

[0073]

[0074] In formula (14), K is the current iteration number, K max is the maximum number of iterations, and B0 changes randomly between (0,1) in each iteration; when the balance coefficient B n >0.5, it enters the exploration phase. n When ≤0.5, it enters the development stage; as the number of iterations K increases, the probability of entering the development stage increases;

[0075] S3-4: During the exploration phase, the updated position of the beluga whale is determined by the paired swimming behavior of the beluga whale. The specific update method is as follows:

[0076]

[0077] In formula (15), is the new position of the nth beluga whale in the jth dimension, x j (j=1,2,…,6) is a random number selected from 6 dimensions, The nth white whale is at the xth j The position in dimension, W Kr,xjrepresents the position of a random white whale, a and b are random numbers between (0,1), even and odd represent odd and even numbers, and sin(2πb) and cos(2πb) indicate that the white whale is updated globally in a mirrored or synchronized manner.

[0078] S3-5: During the development phase, the location of the white whale update is determined by the white whale's predation behavior. The specific update method is:

[0079]

[0080] In formula (16), is the new position of the nth beluga whale, is the position of the random white whale, W Kn is the current position of the nth beluga whale, is the optimal white whale position, c and d are random numbers between (0,1), L F is the flight function, 2d(1-K / K max ) is the random jump intensity, indicating that the white whale uses the flying strategy to achieve updates in the local range;

[0081] S3-6: Calculate the fitness values ​​of the white whale positions updated in step S3-4 and step S3-5, and sort them according to their sizes to find the optimal result of the current iteration;

[0082] S3-7: Since some beluga whales may die and fall into the deep sea, the probability X of whale fall is calculated in each iteration to avoid falling into the local optimum:

[0083]

[0084] In formula (17), the probability of whale fall decreases from 0.1 in the initial iteration to 0.05 in the final iteration, indicating that during the optimization process, the closer the beluga whale is to the food source, the less danger it faces;

[0085] At the same time, in order to maintain the population size, the new individual position is established using the individual position, random individual position and whale fall step length:

[0086]

[0087] In formula (18), e, f and g are random numbers between (0, 1), W step It is the whale fall step length;

[0088] S3-8: Repeat steps S3-3 to S3-7 until the maximum number of iterations is reached, and the white whale position with the minimum fitness is obtained, which is the optimal inertia and optimal damping;

[0089] S3-9: Substitute the optimal inertia and optimal damping obtained in step S3-8 into the differential equation to obtain the dynamic response curves of the three virtual synchronous machines after optimization.

[0090] Figure 3 The figure shows the fitness value change curves of the three virtual synchronous machines after being optimized by the White Whale algorithm. Among them, the minimum fitness value after optimization by the White Whale optimization algorithm is 4.719×10 -4 .

[0091] Figure 4 The figure shows the inertia change curves of the three virtual synchronous machines after being optimized by the White Whale algorithm. Considering the capacity limitation, the inertia configuration before optimization is 200, 200, and 600. After optimization by the White Whale algorithm, the best inertia configuration is 400, 400, and 200.

[0092] Figure 5 The figure shows the damping change curves of the three virtual synchronous machines after being optimized by the White Whale algorithm. Considering the capacity limitation, the damping configuration before optimization is 200, 200, and 600. After optimization by the White Whale algorithm, the best damping configuration is 400, 400, and 200.

[0093] Figure 6 The figure shows the angular frequency dynamic response curve of the first virtual synchronous machine before and after optimization. The maximum angular frequency amplitude before optimization is 1.00025 (pu) and the response time is 12.3s; the maximum angular frequency amplitude after optimization is 1.00013 (pu) and the response time is 9.4s; the angular frequency amplitude is reduced by 0.00012 (pu) and the response time is reduced by 2.9s.

[0094] Figure 7 The figure shows the angular frequency dynamic response curve of the second virtual synchronous machine before and after optimization. The maximum angular frequency amplitude before optimization is 1.0001 (pu) and the response time is 13.1s; the maximum angular frequency amplitude after optimization is 1.00005 (pu) and the response time is 8.3s; the angular frequency amplitude is reduced by 0.00005 (pu) and the response time is reduced by 4.8s.

[0095] Figure 8 The figure shows the angular frequency dynamic response curves of the third virtual synchronous machine before and after optimization. The maximum angular frequency amplitude before optimization is 1.00007 (pu) and the response time is 12.85s; the maximum angular frequency amplitude after optimization is 1.00006 (pu) and the response time is 9.3s; the angular frequency amplitude is reduced by 0.00001 (pu) and the response time is reduced by 3.55s.

[0096] From the above analysis, it can be seen that the inertia damping optimization configuration method of the virtual synchronous machine grid-connected system considering capacity limitation proposed in the present invention helps to speed up the response speed of the virtual synchronous machine to system disturbances and significantly improves the dynamic performance of the virtual synchronous machine.

[0097] The embodiments described above are only preferred embodiments of the present invention and are not intended to limit the scope of implementation of the present invention. Therefore, all changes made according to the shape and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An inertia damping optimization configuration method for a virtual synchronous machine grid-connected system considering capacity constraints, characterized in that: The following steps are involved: S1: Establish differential equations of three virtual synchronous machines and connect them to the nine-node system to obtain dynamic response curves of the three virtual synchronous machines; S2: Design the objective function to optimize the dynamic performance of the virtual synchronous machine under the condition of considering the total capacity limitation of the three virtual synchronous machines; S2-1: Considering the capacity limitation, that is, the total inertia and total damping of the three virtual synchronous machines remain unchanged when the disturbance event occurs, the penalty function Penalty is designed: Penalty=λ·(|H qtytot -H1-H2-H3|+|D qtytot -D C1 -D C2 -D C3 |) (1) In formula (1), λ is the penalty coefficient, H qtytot is the total inertia of the three virtual synchronous machines, D qtytot is the total damping of the three virtual synchronous machines, H1, H2, H3 are the inertia values ​​of each virtual synchronous machine, D C1 , D C2 , D C3 is the damping value of each virtual synchronous machine; S2-2: In order to optimize the dynamic performance of the virtual synchronous machine, the minimum cumulative sum of angular frequency deviations of each virtual synchronous machine is designed as the objective function Obj: In formula (2), i is the number of virtual synchronous machines, i = 1, 2, 3, ω r is the reference angular frequency of the virtual synchronous machine, ω i is the actual angular frequency of the i-th virtual synchronous machine; S2-3: Combine the penalty function Penalty in step S2-1 and the objective function Obj in step S2-2 to construct the latest objective function NewObj under the constraints: S3: The White Whale optimization algorithm is used to iterate and find the optimal inertia and damping configuration of the three virtual synchronous machines.

2. The inertia damping optimization configuration method of a virtual synchronous machine grid-connected system considering capacity limitation according to claim 1 is characterized in that: The step S1 comprises: S1-1: According to the rotor motion equation, the differential equations of the active power control loop in the three virtual synchronous machines are established: In formula (4), θ i is the power angle of the virtual synchronous machine; P ref is the reference value of the active power of the virtual synchronous machine; P i is the actual active power of the virtual synchronous machine; D Ci is the damping coefficient; H i is the inertia coefficient; S1-2: Connect three virtual synchronous machines to the nine-node system, construct a three-virtual synchronous machine nine-node system, obtain the node information of the system, and calculate the power flow equation to obtain the angular frequency ω of the three virtual synchronous machines at the grid connection point in the steady state. r 、Power angle θ i , Active power P ref , and use these values ​​as reference values; S1-3: According to the network equation at the grid connection point, under disturbance conditions, the angular frequency ω of the three virtual synchronous machines at the grid connection point is i , Active power P i will change, and the changed angular frequency ω i , Active power P i As actual value; S1-4: The reference values ​​and actual values ​​obtained in step S1-2 and step S1-3, including: reference angular frequency ω r 、Power angle θ i , Reference active power P ref , actual angular frequency ω i , actual active power P i , substituted into the differential equation established in step S1-1, and the differential equation is solved using the Longze Kutta method to obtain the dynamic response curves of the three virtual synchronous machines.

3. The inertia damping optimization configuration method of a virtual synchronous machine grid-connected system considering capacity limitation according to claim 1 is characterized in that: The step S3 comprises: S3-1: The White Whale optimization algorithm is based on populations, taking each White Whale as a candidate solution. Therefore, the inertia H of the three virtual synchronous machines to be calculated is i and damping D Ci As the beluga location parameter, the total number of beluga whales is the population size N; S3-2: Setting the search interval for the white whale position parameters, randomly generating the initial positions of all white whales within the search interval, and calculating the fitness value of the white whale initial position according to the latest objective function of step S2-3; S3-3: For each beluga whale, according to the balance factor B n Decide whether to enter the exploration phase or the development phase: In formula (5), K is the current iteration number, K max is the maximum number of iterations, and B0 changes randomly between (0,1) in each iteration; when the balance coefficient B n >0.5, it enters the exploration phase. n When ≤0.5, it enters the development stage; as the number of iterations K increases, the probability of entering the development stage increases; S3-4: During the exploration phase, the updated position of the beluga whale is determined by the paired swimming behavior of the beluga whale. The specific update method is as follows: In formula (6), is the new position of the nth beluga whale in the jth dimension, x j (j=1,2,…,6) is a random number selected from 6 dimensions, The nth white whale is at the xth j The position in dimension, represents the position of a random white whale, a and b are random numbers between (0,1), even and odd represent odd and even numbers, and sin(2πb) and cos(2πb) indicate that the white whale is updated globally in a mirrored or synchronized manner. S3-5: During the development phase, the location of the white whale update is determined by the white whale's predation behavior. The specific update method is: In formula (7), is the new position of the nth beluga whale, is the location of a random white whale, is the current position of the nth beluga whale, is the optimal white whale position, c and d are random numbers between (0,1), L F is the flight function, 2d(1-K / K max ) is the random jump intensity, indicating that the white whale uses the flying strategy to achieve updates in the local range; S3-6: Calculate the fitness values ​​of the white whale positions updated in step S3-4 and step S3-5, and sort them according to their sizes to find the optimal result of the current iteration; S3-7: Since some beluga whales may die and fall into the deep sea, the probability X of whale fall is calculated in each iteration to avoid falling into the local optimum: In formula (8), the probability of whale fall decreases from 0.1 in the initial iteration to 0.05 in the final iteration, indicating that during the optimization process, the closer the beluga whale is to the food source, the less danger it faces; At the same time, in order to maintain the population size, new individual positions are established using individual positions, random individual positions, and whale fall step lengths: In formula (9), e, f and g are random numbers between (0,1), W step It is the whale fall step length; S3-8: Repeat steps S3-3 to S3-7 until the maximum number of iterations is reached, and the white whale position with the minimum fitness is obtained, which is the optimal inertia and optimal damping; S3-9: Substitute the optimal inertia and optimal damping obtained in step S3-8 into the differential equation to obtain the dynamic response curves of the three virtual synchronous machines after optimization.

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