Double-layer optimization method for short-circuit current and transient stability of virtual synchronous generator system

Abstract

The invention belongs to the field of data processing, and provides a virtual synchronous generator system short-circuit current and transient stability double-layer optimization method, which comprises the following steps: determining to-be-optimized parameters related to short-circuit current in a virtual synchronous generator system; establishing a double-layer interaction optimization model of the short-circuit current and the transient stability by taking the minimum short-circuit current of the virtual synchronous generator system as an optimization target and taking the transient stability reaching the standard as a constraint condition; and according to the double-layer interaction optimization model, determining a preliminary optimization result corresponding to the to-be-optimized parameter under the condition that the short-circuit current is relatively small, and correcting the preliminary optimization result by using transient stability to obtain a double-layer optimization solution result of the to-be-optimized parameter. According to the scheme provided by the invention, the short-circuit current and transient stability of the virtual synchronous generator system can be considered, efficient optimization of multi-target parameters is realized, and the dynamic stability and operation safety of the virtual synchronous generator system are improved.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to a double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system. Background Art

[0002] Virtual Synchronous Generator (VSG) is a power generation unit that simulates the operating characteristics of traditional synchronous generators based on modern power electronics technology and advanced control strategies. The virtual synchronous generator improves the dynamic stability of the power grid by implementing the inertia and damping characteristics of traditional synchronous generators on power electronic equipment.

[0003] However, under fault conditions, virtual synchronous generators have the same power angle instability problem as traditional synchronous generators, and virtual synchronous generators cannot withstand excessive fault currents. Due to the weak overcurrent capacity of virtual synchronous generators, it is usually necessary to set up a corresponding short-circuit current limiting link. After adding the short-circuit current limiting link, the transient characteristics of the virtual synchronous generator change, and the complexity of transient analysis increases.

[0004] The traditional virtual synchronous generator system has the following deficiencies in short-circuit current and transient stability control optimization: First, the coordinated optimization capability of short-circuit current and transient stability is poor. Current research on grid-connected virtual synchronous generator grid-type converter systems mostly focuses on the optimization of single short-circuit current control or the control optimization of transient stability, resulting in poor robustness and dynamic stability of virtual synchronous generator systems in different fault scenarios.

[0005] Second, there is a lack of efficient strategies for multi-objective parameter optimization. The control parameters of the virtual synchronous generator system (such as inertia parameters, damping coefficient, etc.) have an important impact on the response of the power grid short-circuit current. However, there is currently a lack of methods that can efficiently and accurately optimize these control parameters, especially in complex scenarios where multiple parameters are coupled with each other. Traditional optimization schemes are difficult to provide accurate parameter adjustment mechanisms, resulting in the virtual synchronous generator system being unable to effectively suppress the rise of short-circuit current in the event of a sudden fault, resulting in insufficient system operation safety. Summary of the invention

[0006] The present invention provides a double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system, which is used to solve the defect that traditional optimization schemes cannot ensure the dynamic stability and operation safety of the virtual synchronous generator system.

[0007] The present invention provides a double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system, comprising: Determine the parameters to be optimized related to short-circuit current in the virtual synchronous generator system; Taking the minimization of short-circuit current of virtual synchronous generator system as optimization objective and transient stability compliance as constraint condition, a two-layer interactive optimization model of short-circuit current and transient stability is established. According to the two-layer interactive optimization model, the preliminary optimization results corresponding to the parameters to be optimized are determined when the short-circuit current is small, and the preliminary optimization results are corrected using transient stability to obtain the two-layer optimization solution results of the parameters to be optimized.

[0008] According to the two-level optimization method for short-circuit current and transient stability of a virtual synchronous generator system provided by the present invention, the parameters to be optimized related to the short-circuit current in the virtual synchronous generator system are determined, including: Obtain the transient current expression and steady-state current expression of the short-circuit current in the virtual synchronous generator system; Extracting multiple parameter components from the transient current expression and the steady-state current expression; A key component is determined from the multiple parameter components, and a parameter to be optimized related to the short-circuit current is determined according to the influencing factors corresponding to the amplitude and attenuation coefficient of each key component.

[0009] According to the two-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system provided by the present invention, the key components include: an inherent non-power frequency periodic attenuation component, an inherent power frequency periodic attenuation component, and a free power frequency periodic attenuation component; The parameters to be optimized include: reactive power integral coefficient, virtual resistance, virtual inductance and power angle parameter.

[0010] According to the two-level optimization method for short-circuit current and transient stability of a virtual synchronous generator system provided by the present invention, a two-level interactive optimization model of short-circuit current and transient stability is established with minimizing the short-circuit current of the virtual synchronous generator system as the optimization target and meeting the transient stability standard as the constraint condition, including: Based on the genetic algorithm, the optimization model of the main layer is established with the minimum short-circuit current of the virtual synchronous generator system as the optimization target; An auxiliary layer optimization model is established by using an exponential penalty factor and taking transient stability compliance as a constraint. The auxiliary layer optimization model is integrated with the main layer optimization model to obtain a two-layer interactive optimization model of short-circuit current and transient stability.

[0011] According to the two-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system provided by the present invention, a main layer optimization model is established based on a genetic algorithm with the short-circuit current of the virtual synchronous generator system being minimized as the optimization target, including: Determine respectively the initial values ​​of the system physical parameters of the virtual synchronous generator system, the initial values ​​of some parameters to be optimized and the initial parameters related to the genetic algorithm; Based on the initial values ​​of the physical parameters of the system, the initial values ​​of some parameters to be optimized and the initial parameters related to the genetic algorithm, population individuals are generated within a set range to establish an initial population; Taking the minimization of short-circuit current of virtual synchronous generator system as the optimization objective, a fitness function related to short-circuit current is established. Based on the initial values ​​of the system physical parameters, the initial values ​​of some parameters to be optimized, the initial parameters related to the genetic algorithm and the fitness function, the cyclic iteration strategy of the initial population is determined and the main layer optimization model is established.

[0012] According to the two-level optimization method for short-circuit current and transient stability of a virtual synchronous generator system provided by the present invention, a preliminary optimization result corresponding to the parameter to be optimized when the short-circuit current is small is determined, including: Determine the power angle parameters of each population individual based on the initial values ​​of the system physical parameters and the initial values ​​of some parameters to be optimized; Determine whether the power angle parameter of each population individual is within the set power angle range, and obtain the power angle determination result; Determine the short-circuit current corresponding to the population individual for which the power angle judgment result is yes, and calculate the fitness value based on the short-circuit current and the fitness function; According to the fitness value, a preliminary optimization result corresponding to the parameter to be optimized is obtained when the short-circuit current is small.

[0013] According to the double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system provided by the present invention, the initial values ​​of the system physical parameters include: virtual internal potential, grid frequency, active power, reactive power and voltage; The initial parameters related to the genetic algorithm include: the maximum number of iterations, the number of candidate solutions per generation, and the initial mutation probability.

[0014] According to the double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system provided by the present invention, the short-circuit current includes: transient current and steady-state current; The fitness function is:

[0015] in, represents fitness, and are coefficient values, and , i zt represents the transient current, i wt Represents the steady-state current.

[0016] According to the two-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system provided by the present invention, an auxiliary layer optimization model is established by taking transient stability compliance as a constraint condition through an exponential penalty factor, including: During the initial population cycle iteration process in the main layer optimization model, judging whether the transient stability of each population individual meets the standard, and obtaining the stability judgment result; Determine the target population individuals for which the stability judgment result is negative, and determine the exponential penalty factor corresponding to the target population individuals under the current number of loop iterations; The penalty strategy of the target population under different numbers of loop iterations is determined based on an exponential penalty factor, and an auxiliary layer optimization model is established.

[0017] According to the two-level optimization method for short-circuit current and transient stability of a virtual synchronous generator system provided by the present invention, the exponential penalty factor is:

[0018] in, represents the exponential penalty factor corresponding to the target population individual under the i-th cycle iteration, Represents the penalty coefficient constant.

[0019] The double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system provided by the present invention determines the parameters to be optimized related to the short-circuit current in the virtual synchronous generator system, takes the minimum short-circuit current of the virtual synchronous generator system as the optimization target, and takes the transient stability compliance as the constraint condition, establishes a double-layer interactive optimization model for short-circuit current and transient stability, determines the preliminary optimization results corresponding to the parameters to be optimized when the short-circuit current is small according to the double-layer interactive optimization model, and uses transient stability to correct the preliminary optimization results to obtain the double-layer optimization solution results of the parameters to be optimized. Due to the establishment of the double-layer interactive optimization model, the optimization process can take into account the short-circuit current and transient stability of the virtual synchronous generator system, realize the efficient optimization of multi-objective parameters, and thus improve the dynamic stability and operation safety of the virtual synchronous generator system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1It is a flow chart of a double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system provided by an embodiment of the present invention; Figure 2 It is a flow chart of the process of realizing the double-layer optimization of short-circuit current and transient stability by the double-layer interactive optimization model; Figure 3 It is a schematic diagram of the output current of the virtual synchronous generator system before the slight voltage drop; Figure 4 This is a schematic diagram of the output current of the virtual synchronous generator system after a slight voltage drop. Figure 5 This is a schematic diagram of frequency changes before and after a slight voltage drop; Figure 6 It is a schematic diagram of the data of the optimization iteration process of the improved genetic algorithm in the mild voltage drop stage; Figure 7 It is a schematic diagram of the output current of the virtual synchronous generator before the moderate voltage drop; Figure 8 It is a schematic diagram of the output current of the virtual synchronous generator after a moderate voltage drop; Fig. 9 It is a schematic diagram comparing the frequency changes before and after the moderate voltage drop; Fig.10 It is a data schematic diagram of the optimization iteration process of the improved genetic algorithm in the moderate voltage drop stage; Fig.11 It is a schematic diagram of the output current of the virtual synchronous generator before the severe voltage drop; Fig.12 It is a schematic diagram of the output current of the virtual synchronous generator after a severe voltage drop; Fig.13 This is a schematic diagram comparing the frequency changes before and after a severe voltage drop; Fig.14 It is a data schematic diagram of the optimization iteration process of the improved genetic algorithm during the severe voltage drop stage. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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.

[0023] Combine the following Figures 1 to 14 The detailed scheme of the double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system provided by an embodiment of the present invention is described.

[0024] Figure 1 It is a flow chart of a double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system provided in an embodiment of the present invention.

[0025] like Figure 1 As shown, the two-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system provided by an embodiment of the present invention can be executed by a computer or server with data processing and data receiving capabilities. The method mainly includes the following steps: Step 110: Determine the parameters to be optimized related to the short-circuit current in the virtual synchronous generator system.

[0026] In this embodiment, the parameters to be optimized refer to parameters that can affect the short-circuit current, and specifically may be multiple parameters that affect the amplitude and attenuation coefficient of the transient component of the short-circuit current.

[0027] Step 120: Taking minimizing the short-circuit current of the virtual synchronous generator system as the optimization target and taking reaching the transient stability standard as the constraint condition, a two-layer interactive optimization model of short-circuit current and transient stability is established.

[0028] In this embodiment, the optimization process takes minimizing the short-circuit current of the virtual synchronous generator system as the optimization target, and takes meeting the transient stability standard as a constraint condition. The resulting two-layer interactive optimization model can achieve two-layer optimization of short-circuit current and transient stability.

[0029] Step 130: Based on the two-layer interactive optimization model, determine the preliminary optimization results corresponding to the parameters to be optimized when the short-circuit current is small, and use transient stability to correct the preliminary optimization results to obtain the two-layer optimization solution results of the parameters to be optimized.

[0030] The solution provided in this embodiment can achieve double-layer optimization of the short-circuit current and transient stability of the virtual synchronous generator system through a double-layer interactive optimization model. In particular, under fault conditions, it can simultaneously achieve iterative optimization of the short-circuit current and enhancement of the transient stability, which significantly improves the dynamic response capability of the virtual synchronous generator system. When a fault occurs in the power grid, the system can recover quickly and stably, ensuring the overall stability and operational safety of the power grid.

[0031] In one embodiment, determining the parameters to be optimized related to the short-circuit current in the virtual synchronous generator system specifically includes: The first step is to obtain the transient current expression and steady-state current expression of the short-circuit current in the virtual synchronous generator system.

[0032] It can be understood that the transient current expression of the short-circuit current specifically includes: an expression of the transient current of phase A changing with time, an expression of the transient current of phase B changing with time, and an expression of the transient current of phase C changing with time. Specifically, the expression of the transient current of phase A changing with time is as follows: (1) In this embodiment, the expression of the transient current of phase B changing with time is as follows: (2) In this embodiment, the expression of the transient current of phase C changing with time is as follows: (3) in, represents the transient current value of phase A at time t, represents the transient current value of phase B at time t, represents the transient current value of phase C at time t, and Both represent the correlation coefficient of the system steady-state characteristics, represents the virtual impedance, represents the phase angle associated with the virtual impedance, represents the system rated angular frequency, represents the power angle parameter, and Both represent coefficients related to transient processes. represents the angular frequency associated with the transient process, represents the time constant, d 1 Represents the coefficient related to transient attenuation.

[0033] Furthermore, the above coefficients , , and The expressions are as follows: (4) (5) (6) (7) in, represents the virtual internal potential, d 1 d 2 and d 3 Both represent coefficients related to transient attenuation, U and U 0 represent the fault voltage and initial voltage respectively, Represents the time constant.

[0034] In this embodiment, the steady-state current expression is as follows: (8) in, represents the steady-state current value at time t, and Both represent the correlation coefficient of the system steady-state characteristics, represents the virtual impedance, represents the phase angle associated with the virtual impedance, represents the system rated angular frequency, Indicates the power angle parameter.

[0035] The second step is to extract multiple parameter components in the transient current expression and the steady-state current expression.

[0036] It can be understood that in the above transient current expression and steady-state current expression, multiple parameter components such as steady-state power frequency component, inherent non-power frequency periodic attenuation component, inherent power frequency periodic attenuation component and free power frequency periodic attenuation component can be extracted, as shown in Table 1 below.

[0037] Table 1 Comparison of parameter components in transient current expression and steady-state current expression

[0038] From Table 1 above, we can see that the three-phase fault current (i.e. short-circuit current) has a steady-state power frequency component and three transient components, which are respectively The inherent non-power frequency periodic attenuation component of attenuation , with time constant The attenuated inherent power frequency periodic attenuation component and the time constant The decay component of the free power frequency period .

[0039] It should be noted that due to the time constant The value of is only related to the internal parameters of the virtual synchronous generator and depends on the inherent properties of the grid-side converter. Therefore, the time constant The dominant attenuation component is called the intrinsic attenuation component. The dominant decay component, due to the time constant The value of depends not only on the internal impedance, but also on the voltage drop amplitude and voltage drop angle, so The dominant decay component is called the free decay component.

[0040] The third step is to determine the key components from the multiple parameter components, and determine the parameters to be optimized related to the short-circuit current according to the influencing factors corresponding to the amplitude and attenuation coefficient of each key component.

[0041] In this embodiment, the above three transient components of the short-circuit current are taken as key components, that is, the key components specifically include: an inherent non-power frequency periodic attenuation component, an inherent power frequency periodic attenuation component and a free power frequency periodic attenuation component.

[0042] The amplitude and attenuation coefficient of the transient component of the short-circuit current are jointly affected by multiple factors, which mainly include fault parameters, control parameters and power angle parameters. Specifically, among the fault parameters, the fault voltage has a direct impact on the amplitude of the short-circuit current. The control parameters include reactive integral coefficient, virtual resistance, virtual inductance, etc., which determine the attenuation characteristics and response speed of the short-circuit current. In addition, the power angle parameter is also a key factor, which will have a significant impact on the changing trend of the fault current. Therefore, the parameters to be optimized in this embodiment specifically include: reactive integral coefficient, virtual resistance, virtual inductance and power angle parameters.

[0043] Among the above-mentioned influencing factors, the change in the amplitude of the fault voltage will significantly affect the initial value of the short-circuit current. A higher voltage drop usually leads to a larger short-circuit current amplitude, which affects the transient response characteristics of the current. The reactive integral coefficient reflects the reactive regulation capability of the system. The larger its value is, the more sensitive the system is to voltage disturbances, and the decay process of the short-circuit current will be faster accordingly. Virtual resistance and virtual inductance, as electrical parameters inside the virtual synchronous generator, mainly affect the damping and response speed of the short-circuit current. The virtual resistance mainly controls the decay rate of the short-circuit current, while the virtual inductance affects the phase and waveform characteristics of the current.

[0044] In addition, the effect of the change of power angle parameters on the short-circuit current is more complicated, because it not only involves the change of the system operating state, but also the phase regulation and dynamic response characteristics of the current. The change of power angle parameters is reflected in the time-related trigonometric function, and its regulation effect on the current amplitude is nonlinear and dynamic, which makes the transient characteristics of the short-circuit current complex and diverse with the change of power angle parameters. Specifically, when the power angle parameters change, the transient component of the short-circuit current will fluctuate in amplitude and attenuation rate. This fluctuation reflects the difference in the response characteristics of the system at different power angles.

[0045] In one embodiment, taking the minimization of the short-circuit current of the virtual synchronous generator system as the optimization target and taking the transient stability compliance as the constraint condition, a two-layer interactive optimization model of short-circuit current and transient stability is established, specifically including: Firstly, the main layer optimization model is established based on the genetic algorithm with the minimization of the short-circuit current of the virtual synchronous generator system as the optimization objective.

[0046] It is understandable that short-circuit current is an important indicator that affects the safety of power system equipment and power electronic components. Excessive short-circuit current may cause equipment damage. Therefore, it is necessary to reduce the short-circuit current as much as possible in the control of the virtual synchronous generator system.

[0047] Then, an auxiliary layer optimization model is established through an exponential penalty factor with transient stability as a constraint.

[0048] Whether the virtual synchronous generator system can recover to a stable state after a fault (such as a short circuit) occurs is crucial to the safe operation of the power system. The virtual synchronous generator system needs to have good transient response characteristics to cope with various faults that may occur in the power grid. Therefore, it is necessary to ensure that the transient stability meets the standards in the optimization process.

[0049] Finally, the auxiliary layer optimization model is integrated with the main layer optimization model to obtain a two-layer interactive optimization model of short-circuit current and transient stability.

[0050] This embodiment constructs a two-layer interactive optimization model to optimize the virtual synchronous generator system from two aspects: minimizing the short-circuit current and improving the transient stability of the system. In each optimization iteration, a preliminary optimization result is first obtained by optimizing the short-circuit current, and then the preliminary optimization result is corrected and evaluated using the transient stability check. In this way, the final two-layer optimization solution not only has a smaller short-circuit current, but also meets the transient stability requirements of the power grid for the virtual synchronous generator system, thereby ensuring rapid system recovery when a fault occurs.

[0051] In one embodiment, a main layer optimization model is established based on a genetic algorithm with the short-circuit current of the virtual synchronous generator system as the optimization target, including: In the first step, the initial values ​​of the system physical parameters of the virtual synchronous generator system, the initial values ​​of some parameters to be optimized, and the initial parameters related to the genetic algorithm are determined respectively.

[0052] On the one hand, this embodiment defines system physical parameters related to the virtual synchronous generator system, and the initial values ​​of the system physical parameters specifically include: virtual internal potential, virtual resistance, virtual inductance, grid frequency, active power, reactive power, and voltage, etc. These parameters are the basic components of the virtual synchronous generator system, and the specific structure of the current power system is defined by these parameters, providing the required conditions for subsequent short-circuit current calculation, transient stability analysis, etc.

[0053] On the other hand, this embodiment defines initial parameters related to the genetic algorithm, which specifically include: a maximum number of iterations, a number of candidate solutions per generation, and an initial mutation probability.

[0054] In practical applications, the maximum number of iterations of the genetic algorithm can be set to 100, which defines the number of candidate solutions per generation, i.e., the population size, which can be set to 50. At the same time, this embodiment also sets an initial mutation probability for controlling the mutation operation in the genetic algorithm. The initial mutation probability can be set to 0.2, which can be dynamically adjusted as the number of iterations increases.

[0055] In addition, this embodiment also defines a A matrix is ​​used to store the initial values ​​of some parameters to be optimized for each individual in the population. The initial values ​​of some parameters to be optimized for each individual in the population include the initial values ​​of five key parameters: reactive integral coefficient, virtual resistance, virtual inductance, inertia constant and damping coefficient.

[0056] In the second step, based on the initial values ​​of the system's physical parameters, the initial values ​​of some parameters to be optimized and the initial parameters related to the genetic algorithm, population individuals are generated within the set range to establish the initial population.

[0057] Within the set range, the initial population individuals are randomly generated to ensure that the relevant parameters of the population individuals are within the reasonable upper and lower limits. For each population individual, the initial values ​​of the reactive integral coefficient, virtual resistance, virtual inductance, inertia constant and damping coefficient are randomly generated, and the initial power angle parameters are calculated.

[0058] In this embodiment, the setting ranges corresponding to the above multiple parameters can be seen in Table 2 below.

[0059] Table 2 Parameter setting range comparison table

[0060] In the third step, taking the minimization of the short-circuit current of the virtual synchronous generator system as the optimization objective, a fitness function related to the short-circuit current is established.

[0061] The fourth step is to determine the cyclic iteration strategy of the initial population and establish the main layer optimization model based on the initial values ​​of the system physical parameters, the initial values ​​of some parameters to be optimized, the initial parameters related to the genetic algorithm and the fitness function.

[0062] In one embodiment, determining a preliminary optimization result corresponding to the parameter to be optimized when the short-circuit current is small specifically includes: In the first step, the power angle parameters of each population individual are determined based on the initial values ​​of the system physical parameters and the initial values ​​of some parameters to be optimized.

[0063] The second step is to determine whether the power angle parameter of each population individual is within the set power angle range and obtain the power angle determination result.

[0064] In this embodiment, the power angle parameter specifically includes the actual calculated power angle value and the power angle value of the virtual impedance. The specific calculation formula of the actual calculated power angle value is as follows: (9) in, Indicates the actual calculated power angle value, P indicates active power, represents the virtual internal potential, Indicates the fault voltage, Represents virtual impedance.

[0065] In practical applications, it is necessary to perform constraint checks on the actual calculated power angle value to ensure that it is within the first set power angle range [30°, 70°]. If it does not meet this set range, it needs to be regenerated.

[0066] In this embodiment, in addition to the constraint check of the actual calculated power angle value obtained by the above calculation, the power angle value of the virtual impedance also needs to be constrained. The power angle value of the virtual impedance can be calculated as follows: (10) in, Indicates the power angle value of virtual impedance, represents the system rated angular frequency, represents the virtual inductance, Represents a virtual resistor.

[0067] In the constraint checking phase, it is necessary to ensure that the power angle value of the virtual impedance is within the second set power angle range [80°, 90°].

[0068] That is to say, to determine whether the power angle parameter of each population individual is within the set power angle range, two steps are required, namely, determining whether the actual calculated power angle value is within the first set power angle range, and determining whether the power angle value of the virtual impedance is within the second set power angle range.

[0069] The third step is to determine the short-circuit current corresponding to the population individual whose power angle judgment result is yes, and calculate the fitness value based on the short-circuit current and the fitness function.

[0070] In this embodiment, the power angle judgment result is yes, which means that the actually calculated power angle value is within the first set power angle range, and the power angle value of the virtual impedance is within the second set power angle range.

[0071] It can be understood that the main purpose of defining the fitness function is to calculate the short-circuit current of each population individual at different time points, including transient current and steady-state current, and minimize these two current values ​​as the fitness function.

[0072] In this embodiment, the fitness function is defined as the sum of the squares of the transient current and the steady-state current, so that both can be controlled simultaneously during the optimization process.

[0073] It should be noted that the small short-circuit current refers to the situation where the fitness value is less than the preset fitness threshold.

[0074] In a specific implementation, the short-circuit current includes: a transient current and a steady-state current; The fitness function is specifically: (11) in, represents the fitness value, and are coefficient values, and , i zt represents the transient current, i wt Represents the steady-state current.

[0075] The fourth step is to obtain the preliminary optimization results corresponding to the parameters to be optimized when the short-circuit current is small based on the fitness value.

[0076] In this embodiment, when the fitness value is small, it means that the short-circuit current is small, and thus, a preliminary optimization result corresponding to the parameter to be optimized when the short-circuit current is small can be obtained.

[0077] In one embodiment, an auxiliary layer optimization model is established by using an exponential penalty factor with transient stability being achieved as a constraint condition, specifically including: First, in the initial population cycle iteration process in the main layer optimization model, it is determined whether the transient stability of each population individual meets the standard and the stability judgment result is obtained.

[0078] In practical applications, one or more standard values ​​of transient stability evaluation indicators can be set in advance, and then the actual indicator value corresponding to the transient stability evaluation of each population individual is obtained and compared with the standard value of the evaluation indicator, so as to determine whether the transient stability of each population individual meets the standard.

[0079] Then, the target population individuals whose stability judgment result is negative are determined, and the exponential penalty factors corresponding to the target population individuals under the current number of loop iterations are determined.

[0080] In this embodiment, the exponential penalty factor is specifically: (12) in, represents the exponential penalty factor corresponding to the target population individual under the i-th cycle iteration, Represents the penalty coefficient constant.

[0081] Finally, the penalty strategy of the target population under different numbers of loop iterations is determined based on the exponential penalty factor, and the auxiliary layer optimization model is established.

[0082] In this embodiment, based on the exponential penalty factor, the expression of the fitness function can be adjusted as follows: (13) in, represents the adjusted fitness value corresponding to the i-th loop iteration, represents the fitness value corresponding to the i-th loop iteration, Indicates the dynamic coefficient value. When the stability judgment result is negative The value is 1, and the stability judgment result is yes The value is 0, in Represents the exponential penalty factor corresponding to the target population individual in the i-th loop iteration.

[0083] This embodiment uses an exponential penalty factor to deal with population individuals that violate transient stability constraints. The purpose is to allow population individuals to be more exploratory in the early stage of iteration and to accelerate the elimination of unqualified population individuals in the later stage to improve the optimization convergence speed and solution quality.

[0084] It can be seen from the formula of the exponential penalty factor that in the initial stage, the exponential penalty factor is small, allowing some unqualified individuals in the population to participate in the evolution, thereby increasing the diversity of solutions and preventing premature entrapment in the local optimal solution. In the later stage, as the number of generations increases, the value of the exponential penalty factor increases exponentially, causing the fitness value of unqualified individuals in the population to increase rapidly and eventually be eliminated quickly. In this way, in the early stage of iteration, the system has a strong exploratory nature, and quickly converges to the optimal solution in the later stage of iteration.

[0085] In addition, this embodiment also introduces an elite algorithm in the optimization phase. The elite algorithm is also called the elite retention strategy, which is an improved strategy in the genetic algorithm, used to retain the best individuals in the population to ensure that they will not be lost due to random mutation or crossover during the evolution process. The introduction of the elite algorithm solves the problem that the classical genetic algorithm is prone to quality degradation and loss of the optimal solution due to randomness, thereby significantly improving the stability and convergence speed of the genetic algorithm.

[0086] In the elite algorithm, the best performing individuals in each generation are directly retained and copied to the next generation. This means that even if less ideal new individuals are generated through crossover and mutation in the next generation, the optimal solution will still be retained, ensuring the global optimal performance of the algorithm.

[0087] It can be seen that the core idea of ​​the elite algorithm can be summarized as follows: First, retain the best individuals: in each generation, the individuals with the highest (i.e., best) fitness value in the current population are directly copied to the next generation without crossover and mutation operations. This allows the characteristics of the best individuals to be passed on to the offspring, thereby speeding up the evolution of the overall population.

[0088] Second, reduce the possibility of degradation: The genetic algorithm itself is a randomized global search algorithm that explores the solution space through crossover and mutation. However, during the evolution process, random mutation may make the current optimal solution worse or even lost, leading to degradation. The elite retention strategy significantly reduces the possibility of solution degradation by retaining the best individuals in each generation.

[0089] Third, accelerated convergence: By retaining the best individuals in the population, the elite algorithm can accelerate the convergence of the genetic algorithm, especially when the search space of the problem is large and the optimization goal is complex, which enables the elite algorithm to find a near-optimal solution more quickly.

[0090] The main functions of the elite algorithm in this embodiment are as follows: First, ensure that the optimal solution is not lost: In the two-level optimization process, it is necessary to first ensure that the short-circuit current is minimized, while also ensuring the transient stability of the system. In this complex optimization problem, the elite retention strategy plays a key role. By directly retaining the optimal solution, the risk of losing a good control parameter combination due to mutation and crossover operations is avoided.

[0091] Second, improve the convergence speed: Combined with the elite algorithm, the system can ensure that there is an optimal solution in each generation of iteration, which can effectively narrow the search space, thereby speeding up the convergence speed and reducing the optimization time.

[0092] Third, prevent premature degeneration: By retaining the best population individuals in each generation, the elite retention strategy can prevent the optimal solution from degenerating during random crossover and mutation. This is especially important in the optimization process of genetic algorithms, because the uncontrollable factors brought about by randomness may lead to a decline in the quality of the solution.

[0093] Fourth, combined with exponential penalty factors: This embodiment introduces an exponential penalty factor on the basis of elite retention, allowing diversity in the early stage and accelerating the elimination of unqualified individuals in the later stage. In this way, elite individuals can lead the population to gradually approach the global optimal solution, while the exponential penalty factor ensures that the optimization process is not easy to fall into the local optimum. This strategy combination effectively balances the contradiction between "exploration" and "exploitation" of the genetic algorithm.

[0094] In this embodiment, the process of realizing the short-circuit current and transient stability double-layer optimization by using the double-layer interactive optimization model constructed by the improved genetic algorithm can be seen in Figure 2 , the specific process is as follows: Step 201: Initialize genetic algorithm related parameters to form an initial population.

[0095] Step 202: Setting the genetic generation number of the genetic algorithm, that is, the maximum number of iterations.

[0096] Step 203: Calculate the short-circuit current of each generation (population) of individuals, and analyze the relevant parameters in the short-circuit current formula (expression), that is, the parameters to be optimized.

[0097] Step 204: Determine whether the virtual impedance angle (power angle parameter) and system stability (transient stability) meet the requirements, that is, determine whether the power angle parameter is within the set power angle range, and determine whether the transient stability meets the standard.

[0098] Step 205: If the above judgment results are all yes, then directly calculate the steady-state value and peak value corresponding to the short-circuit current, that is, the maximum value of the steady-state current value and the transient current value of the short-circuit current.

[0099] Step 206: If at least one of the above judgment results is negative, an (exponential) penalty factor is introduced to penalize individuals that do not meet the requirements (ie, individuals of the target population), and then the steady-state value and peak value corresponding to the short-circuit current are calculated.

[0100] Step 207: Generate the fitness according to the steady-state value, the peak value and the penalty value (ie, the exponential penalty factor), that is, calculate the fitness value according to the fitness function.

[0101] Step 208: elite retention strategy, that is, introducing an elite retention strategy in the optimization process to retain the population individuals with the best performance in the current generation.

[0102] Step 209: Perform crossover and mutation operations to generate a new population. When performing crossover and mutation operations, some good population individuals can be selected for crossover and mutation to generate a new population. New population individuals are generated by randomly selecting two parent individuals for crossover and setting the crossover ratio coefficient to ensure the diversity of the population. At the same time, mutation operations are performed on the basis of crossover to further enhance the diversity of new individuals, and dynamic adjustment of mutation probability is set to retain good population individuals in the early stage of iteration, and the global exploration ability is improved through mutation with a higher probability in the later stage.

[0103] Step 210: Update population and iterate. In the process of updating population, the newly generated population can be used as the next generation population for the next round of iteration. Through iteration, individual parameters are continuously optimized to find a control parameter combination that can meet the optimal fitness.

[0104] Step 211: determine whether the genetic generation is the set value, that is, determine whether the genetic generation has reached the maximum number of iterations. If the determination result is no, add 1 to the genetic generation and continue the genetic iteration.

[0105] Step 212: If the judgment result of step 211 is yes, the final optimization parameters are output, that is, the double-layer optimization solution results of the parameters to be optimized are output, including reactive integral coefficient, virtual resistance, virtual inductance, inertia constant, damping coefficient and other data, and the genetic iteration process ends.

[0106] After the genetic iteration process is completed, the final power angle of the virtual impedance can be calculated based on the double-layer optimization solution results, and the impedance characteristics used to evaluate the system can be output. Afterwards, the optimization process of the genetic algorithm can be plotted to observe the change of the fitness value with the number of iterations to verify the optimization effect. Specifically, the minimum fitness value of each generation can be displayed through a curve chart to verify the optimization process of the genetic algorithm. The optimization effect of the algorithm can be judged by observing whether the fitness curve has a significant decrease. The final output parameters can be further verified later, such as checking their impact on short-circuit current and transient stability to ensure their effectiveness and stability in engineering applications.

[0107] In order to verify the correctness and superiority of the above-mentioned two-level optimization method for short-circuit current and transient stability of virtual synchronous generator system, this embodiment builds a virtual synchronous generator into an infinite power grid model in Matlab / Simulink simulation software, and the parameters to be optimized (i.e., key control parameters before optimization) can refer to Table 3. Three grid fault voltage drops of different severity are set: mild, moderate, and severe.

[0108] Through the above-mentioned two-level optimization method of short-circuit current and transient stability of the virtual synchronous generator system, the key control parameters before and after optimization are obtained, as shown in Table 3.

[0109] Table 3 Key control parameters before and after optimization

[0110] Combined with the above Table 3, the output current of the new energy before and after parameter optimization will be compared according to the severity of the voltage drop, so as to illustrate the superiority of the proposed short-circuit current suppression method.

[0111] In the mild voltage drop stage, a three-phase symmetrical fault in the power grid is set to cause the voltage at the grid connection point to drop to 0.8pu. The fault occurs at t=3 seconds and is cleared after 0.3 seconds. The output currents of the virtual synchronous generator system before and after the mild voltage drop are as follows: Figure 3 and Figure 4 The frequency change comparison before and after the slight voltage drop is shown in Figure 5 As shown in the figure, the optimization iteration process of the improved genetic algorithm in this case is as follows Figure 6 shown.

[0112] According to the optimization results, the genetic algorithm converged to stability around the 70th generation. After optimizing the parameters, the "impact" value of the short-circuit current dropped significantly: the "impact" value when the fault occurred dropped from 257A to 199A, a decrease of 22%, and the "impact" value when the fault was cleared dropped from 287A to 230A, a decrease of 20%, while the "steady-state" component of the short-circuit current did not change significantly. At the same time, it can be seen that the offset of the rotor frequency has been reduced after optimization, indicating that the synchronous stability of the system has been enhanced, which indirectly shows that the algorithm takes into account the synchronous stability of the system while iteratively optimizing the short-circuit current.

[0113] In the medium voltage drop stage, a three-phase symmetrical fault in the grid is set to cause the voltage at the grid connection point to drop to 0.5pu. The fault occurs at t=3 seconds and is cleared after 0.3 seconds. The output currents of the virtual synchronous generator before and after the medium voltage drop are as follows: Figure 7 and Figure 8 The frequency change comparison before and after the moderate voltage drop is shown in Fig. 9 As shown in the figure, the optimization iterative process of the improved genetic algorithm in this case is as follows Fig.10 shown.

[0114] According to the optimization results, the genetic algorithm converged to stability around the 130th generation. After optimizing the parameters, the "shock" value when a fault occurs is reduced from 447A to 271A, a decrease of 39%, and the "shock" value when the fault is cleared is reduced from 349A to 284A, a decrease of 19%. The "steady-state" component of the short-circuit current has not changed. At the same time, it can be seen that the offset of the rotor frequency is reduced after optimization, indicating that the synchronous stability of the system is enhanced.

[0115] In the severe voltage drop stage, a three-phase symmetrical fault in the power grid is set to cause the voltage at the grid connection point to drop to 0.2pu. The fault occurs at t=3 seconds and is cleared after 0.3 seconds. The output currents of the virtual synchronous generator before and after the severe voltage drop are respectively as follows: Fig.11 and Fig.12 The frequency change comparison before and after the severe voltage drop is shown in Fig.13 As shown in the figure, the optimization iteration process of the improved genetic algorithm in this case is as follows Fig.14 shown.

[0116] According to the optimization results, the genetic algorithm converged to stability around the 70th generation. After optimizing the parameters, the "shock" value when the fault occurred dropped from 621A to 323A, a decrease of 48%, and the "shock" value when the fault was cleared dropped from 332A to 313A, a decrease of 6%, and the "steady state" did not change. At the same time, it can be seen that the deviation of the rotor frequency has been reduced after optimization, indicating that the synchronous stability of the system has been enhanced.

[0117] According to the superposition theorem, the short-circuit current can be divided into a free component and a forced component. The free component is determined by the system parameters and is induced when a fault occurs and when the fault is cleared; the forced component is determined by the no-load electromotive force of the virtual synchronous machine. Considering the need for reactive power support of the system, the command to issue reactive power is not changed during the fault process. Therefore, the forced component of the short-circuit current, that is, the steady-state component, does not change before and after optimization. The optimization of the short-circuit current is mainly to optimize the transient free component. After optimization, the impact of this component on the system can be significantly reduced. When the fault is serious, the impact can be limited to about 50% of the original.

[0118] At the same time, since the synchronous stability of the system is taken into account in the optimization process, it can be ensured that the optimized parameters of the system can ensure the synchronous stability of the system without power angle instability even in the case of serious faults.

[0119] In summary, the two-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system provided by the embodiment of the present invention has at least the following beneficial effects: First, by defining a fitness function to evaluate the impact of short-circuit current on equipment and systems and whether each population individual is stable during transient processes, the impact of short-circuit current on equipment and systems is minimized. This solves the problem that traditional optimization solutions are not accurate enough in evaluating multiple key parameters in the system.

[0120] Second, the introduction of an improved genetic algorithm uses an elite strategy to retain the best individuals and introduces an exponential penalty factor, which can better handle constraint violations, improve the overall convergence speed and optimization performance, ensure rapid iteration to the global optimum in the high-dimensional control parameter space, and improve the optimization efficiency and reliability of the short-circuit current.

[0121] Third, by performing multi-objective optimization on key control parameters in the virtual synchronous generator system, especially under fault conditions, iterative optimization of short-circuit current and enhancement of transient stability are achieved simultaneously, significantly improving the dynamic response capability of the virtual synchronous generator system. When a fault occurs in the power grid, the system can recover quickly and stably, ensuring the overall stability of the power grid.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A two-level optimization method for short-circuit current and transient stability of a virtual synchronous generator system, characterized in that: include: Determine the parameters to be optimized related to short-circuit current in the virtual synchronous generator system; Taking the minimization of short-circuit current of virtual synchronous generator system as optimization objective and transient stability compliance as constraint condition, a two-layer interactive optimization model of short-circuit current and transient stability is established. According to the two-layer interactive optimization model, the preliminary optimization results corresponding to the parameters to be optimized are determined when the short-circuit current is small, and the preliminary optimization results are corrected using transient stability to obtain the two-layer optimization solution results of the parameters to be optimized.

2. The double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system according to claim 1 is characterized in that: Determine the parameters to be optimized related to short-circuit current in the virtual synchronous generator system, including: Obtain the transient current expression and steady-state current expression of the short-circuit current in the virtual synchronous generator system; Extracting multiple parameter components from the transient current expression and the steady-state current expression; A key component is determined from the multiple parameter components, and a parameter to be optimized related to the short-circuit current is determined according to the influencing factors corresponding to the amplitude and attenuation coefficient of each key component.

3. The double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system according to claim 2 is characterized in that: The key components include: inherent non-power frequency periodic attenuation component, inherent power frequency periodic attenuation component and free power frequency periodic attenuation component; The parameters to be optimized include: reactive power integral coefficient, virtual resistance, virtual inductance and power angle parameter.

4. The double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system according to claim 1 is characterized in that: Taking the minimization of short-circuit current of the virtual synchronous generator system as the optimization objective and the transient stability compliance as the constraint condition, a two-layer interactive optimization model of short-circuit current and transient stability is established, including: According to the genetic algorithm, the optimization model of the main layer is established with the minimum short-circuit current of the virtual synchronous generator system as the optimization target; An auxiliary layer optimization model is established by using an exponential penalty factor and taking transient stability compliance as a constraint. The auxiliary layer optimization model is integrated with the main layer optimization model to obtain a two-layer interactive optimization model of short-circuit current and transient stability.

5. The double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system according to claim 4 is characterized in that: According to the genetic algorithm, the short-circuit current of the virtual synchronous generator system is minimized as the optimization goal, and the main layer optimization model is established, including: Determine respectively the initial values ​​of the system physical parameters of the virtual synchronous generator system, the initial values ​​of some parameters to be optimized and the initial parameters related to the genetic algorithm; Based on the initial values ​​of the physical parameters of the system, the initial values ​​of some parameters to be optimized and the initial parameters related to the genetic algorithm, population individuals are generated within a set range to establish an initial population; Taking the minimization of short-circuit current of virtual synchronous generator system as the optimization objective, a fitness function related to short-circuit current is established. Based on the initial values ​​of the system physical parameters, the initial values ​​of some parameters to be optimized, the initial parameters related to the genetic algorithm and the fitness function, the cyclic iteration strategy of the initial population is determined and the main layer optimization model is established.

6. The double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system according to claim 5 is characterized in that: Determine the preliminary optimization results corresponding to the parameters to be optimized when the short-circuit current is small, including: Determine the power angle parameters of each population individual based on the initial values ​​of the system physical parameters and the initial values ​​of some parameters to be optimized; Determine whether the power angle parameter of each population individual is within the set power angle range, and obtain the power angle determination result; Determine the short-circuit current corresponding to the population individual for which the power angle judgment result is yes, and calculate the fitness value based on the short-circuit current and the fitness function; According to the fitness value, a preliminary optimization result corresponding to the parameter to be optimized is obtained when the short-circuit current is small.

7. The double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system according to claim 5, characterized in that: The initial values ​​of the system physical parameters include: virtual internal potential, grid frequency, active power, reactive power and voltage; The initial parameters related to the genetic algorithm include: the maximum number of iterations, the number of candidate solutions per generation, and the initial mutation probability.

8. The double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system according to claim 5, characterized in that: The short-circuit current includes: transient current and steady-state current; The fitness function is: in, represents fitness, and are coefficient values, and , i zt represents the transient current, i wt Represents the steady-state current.

9. The double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system according to claim 4, characterized in that: Through the exponential penalty factor, the auxiliary layer optimization model is established with transient stability as the constraint condition, including: During the initial population cycle iteration process in the main layer optimization model, judging whether the transient stability of each population individual meets the standard, and obtaining the stability judgment result; Determine the target population individuals for which the stability judgment result is negative, and determine the exponential penalty factor corresponding to the target population individuals under the current number of loop iterations; The penalty strategy of the target population under different numbers of loop iterations is determined based on an exponential penalty factor, and an auxiliary layer optimization model is established.

10. The double-layer optimization method for short-circuit current and transient stability of a virtual synchronous generator system according to claim 9, characterized in that: The exponential penalty factor is: in, represents the exponential penalty factor corresponding to the target population individual under the i-th cycle iteration, Represents the penalty coefficient constant.

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