Active frequency supporting method and system for network-building type energy storage converter based on genetic algorithm optimization

Abstract

The invention discloses a genetic algorithm optimization-based active frequency support method and system for a network-building type energy storage converter, and belongs to the technical field of network-building type energy storage converter control. The method comprises the following steps: constructing a second-order motion equation of a virtual synchronous machine of a network construction type energy storage converter, analyzing an influence rule of virtual inertia and a damping coefficient on the stability of a network construction system, and designing an arc tangent function dynamic adjustment mechanism based on angular frequency deviation, an arc tangent function is used as a dynamic adjustment coefficient to establish a first correlation model of the angular frequency deviation and the virtual inertia and a second correlation model of the angular frequency deviation and the damping coefficient, so that the virtual inertia and the damping coefficient are adaptively matched according to the intensity of frequency disturbance; and performing synchronous optimization on the to-be-optimized dynamic adjustment coefficient and configuring the control parameter so as to optimize the active frequency of the network construction type energy storage converter. According to the method, the problem that the virtual inertia and the damping coefficient cannot be adaptively matched according to the disturbance intensity in a traditional virtual synchronous control strategy is solved.

Description

Technical Field

[0001] This invention relates to an active frequency support method and system for a grid-type energy storage converter based on genetic algorithm optimization, belonging to the field of grid-type energy storage converter control technology. Background Technology

[0002] Against the backdrop of a global low-carbon energy transition, the installed capacity of new energy sources, primarily wind and solar power, continues to climb. The combined operation mode of "new energy + energy storage" has become a crucial support for achieving the "dual carbon" goals. The integration of energy storage can not only solve the problem of renewable energy consumption but also improve the dynamic characteristics of the system. In this context, grid-based control, as a key technology for the operation of energy storage converters, has become a research focus for both academia and industry in terms of control strategy innovation.

[0003] The most commonly used grid-connected technology for energy storage converters is virtual synchronous generator (VSG) control. In the VSG control architecture, virtual inertia and damping coefficient are key parameters characterizing the dynamic properties of the virtual synchronous generator, and their dynamic co-optimization plays a decisive role in improving the response characteristics of VSG control under various operating conditions. Existing literature mainly focuses on small-signal modeling of VSG control, analyzing the impact of virtual inertia and damping coefficient on the transient process of VSG control; or it studies the power angle characteristics of the synchronous generator and combines them with VSG control technology, employing a virtual inertia variable control method. However, this method has not fully considered the influence of the damping coefficient. To address the shortcomings of existing research, it is necessary to adaptively optimize the VSG control parameters under different operating conditions to achieve the best active frequency support capability for grid-connected energy storage converter systems. Summary of the Invention

[0004] The purpose of this invention is to provide an active frequency support method and system for grid-type energy storage converters based on genetic algorithm optimization. By using the arctangent function as the dynamic adjustment coefficient to establish a first correlation model between angular frequency deviation and virtual inertia, and a second correlation model between angular frequency deviation and damping coefficient, the virtual inertia and damping coefficient are adaptively matched according to the intensity of frequency disturbance. Furthermore, a genetic algorithm is used to synchronously optimize the dynamic adjustment coefficient, thereby solving the problem that the virtual inertia and damping coefficient cannot adaptively match according to the disturbance intensity in traditional virtual synchronous control strategies.

[0005] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution.

[0006] In a first aspect, the present invention provides an active frequency support method for a grid-type energy storage converter based on genetic algorithm optimization, comprising:

[0007] The influence of virtual inertia and damping coefficient on the stability of the grid-type energy storage converter is analyzed by constructing the second-order motion equation of the virtual synchronous machine.

[0008] Based on the aforementioned influence law, a dynamic adjustment mechanism based on the arctangent function of angular frequency deviation is designed;

[0009] Based on the dynamic adjustment mechanism of the arctangent function, the first correlation model between angular frequency deviation and virtual inertia and the second correlation model between angular frequency deviation and damping coefficient are established by using the arctangent function as the dynamic adjustment coefficient, so that the virtual inertia and damping coefficient are adaptively matched according to the intensity of frequency disturbance.

[0010] A genetic algorithm is used to simultaneously optimize the dynamic adjustment coefficients to be optimized in the first association model and the second association model to obtain a set of optimal dynamic adjustment coefficient combinations;

[0011] The control parameters of the grid-type energy storage converter are configured according to the optimal dynamic adjustment coefficient combination to optimize the active frequency of the grid-type energy storage converter.

[0012] Furthermore, the second-order equation of motion of the virtual synchronous machine of the grid-type energy storage converter is expressed as:

[0013] ;

[0014] In the formula, For virtual inertia, The rate of change of angular frequency, For angular frequency components, For time components, For mechanical torque, For electromagnetic torque, For damping torque, For mechanical power, Electromagnetic power, The damping coefficient is... Angular frequency, This refers to the grid synchronization angular velocity.

[0015] Furthermore, based on the aforementioned influence law, a dynamic adjustment mechanism for the arctangent function based on angular frequency deviation is designed, including:

[0016] Based on the preset grid angular frequency, angular frequency and angular frequency change rate, a complete oscillation cycle of the angular frequency oscillation curve is divided into four characteristic intervals, including the first characteristic interval, the second characteristic interval, the third characteristic interval and the fourth characteristic interval.

[0017] The virtual inertia is dynamically adjusted based on the angular frequency deviation and the rate of change of angular frequency within the first and second characteristic intervals.

[0018] The damping coefficient is dynamically adjusted based on the angular frequency deviation within the first and second characteristic intervals.

[0019] Furthermore, based on the preset grid angular frequency, angular frequency, and rate of change of angular frequency, a complete oscillation cycle of the angular frequency oscillation curve is divided into four characteristic intervals, including:

[0020] If the preset grid angular frequency is greater than the angular frequency and the rate of change of the angular frequency is greater than or equal to 0, then it is divided into the first characteristic interval.

[0021] If the preset grid angular frequency is greater than the angular frequency and the rate of change of angular frequency is less than 0, then it is divided into the second characteristic interval.

[0022] If the preset power grid angular frequency is less than or equal to the angular frequency and the rate of change of the angular frequency is less than 0, then it is classified as the third characteristic interval;

[0023] If the preset grid angular frequency is less than or equal to the angular frequency and the rate of change of the angular frequency is greater than 0, then it is classified as the fourth characteristic interval.

[0024] Furthermore, the influence of the virtual inertia and damping coefficient on the stability of the network system is characterized by the input power and output power response characteristics of the network system, which are expressed based on a second-order transfer function as follows:

[0025] ;

[0026] In the formula, This represents the input power and output power response characteristics of the network system. This represents the per-unit value of synchronous power. Represents a variable in the complex frequency domain. Indicates the rated power.

[0027] Furthermore, the first association model is represented as:

[0028] ;

[0029] In the formula, This is the initial value of the virtual inertia. For angular frequency deviation, For adaptive startup threshold, This is the virtual inertia increment gain coefficient, used to determine the rate of change of virtual inertia with angular frequency. The magnitude of the increase, It is the arctangent function. The first input scaling factor is used to adjust the sensitivity of the virtual inertia increment of the arctangent function. This is the virtual inertia decrement gain coefficient, used to determine the rate of change of virtual inertia with angular frequency. The magnitude of the reduction The second input scaling factor is used to adjust the sensitivity of the virtual inertia reduction of the current arctangent function.

[0030] Furthermore, the second association model is represented as:

[0031] ;

[0032] In the formula, The initial value of the damping coefficient. This is the damping coefficient increment gain coefficient, used to measure the damping coefficient as a function of angular frequency deviation. The magnitude of the increase, The third input scaling factor is used to adjust the damping coefficient increment sensitivity of the current arctangent function.

[0033] Furthermore, a genetic algorithm is used to simultaneously optimize the dynamic adjustment coefficients to be optimized in the first association model and the second association model, obtaining a set of optimal dynamic adjustment coefficient combinations, including:

[0034] The dynamic adjustment coefficient to be optimized is used as the decision variable, and the dynamic adjustment coefficient to be optimized includes a first input scaling coefficient, a second input scaling coefficient, and a third input scaling coefficient.

[0035] The encoding is performed using binary encoding, and an initial population containing a number of individuals is randomly generated.

[0036] Construct a fitness function based on system frequency deviation;

[0037] Repeat the following steps until the preset maximum number of generations is reached:

[0038] Each individual in the population is decoded into a set of dynamic adjustment coefficients, which are then substituted into the first association model and the second association model respectively. Simulation is performed within a preset frequency intensity perturbation range, and the fitness value of each individual is calculated based on the fitness function.

[0039] Individuals whose fitness values ​​are within a preset range are evaluated based on the ITAE index, and a new generation of population is generated through selection, crossover, and mutation operations.

[0040] After the iteration is completed, the individual with the best fitness value in the population is decoded to obtain a set of optimal dynamic adjustment coefficient combinations.

[0041] Furthermore, the fitness function is expressed as:

[0042] ;

[0043] In the formula, The fitness value corresponding to the fitness function. For the current moment, This represents the system frequency deviation.

[0044] Secondly, the present invention provides an active frequency support system for a grid-type energy storage converter based on genetic algorithm optimization, comprising:

[0045] The modeling and analysis module is used to analyze the influence of virtual inertia and damping coefficient on the stability of the grid-type energy storage converter by constructing the second-order motion equation of the virtual synchronous machine.

[0046] The mechanism design module is used to design a dynamic adjustment mechanism based on the arctangent function of the angular frequency deviation according to the influence law;

[0047] The model building module is used to establish a first correlation model between angular frequency deviation and virtual inertia and a second correlation model between angular frequency deviation and damping coefficient based on the dynamic adjustment mechanism of the arctangent function, respectively using the arctangent function as the dynamic adjustment coefficient, so that the virtual inertia and damping coefficient are adaptively matched according to the intensity of frequency disturbance.

[0048] The optimization solution module is used to simultaneously optimize the dynamic adjustment coefficients to be optimized in the first association model and the second association model using a genetic algorithm, so as to obtain a set of optimal dynamic adjustment coefficient combinations.

[0049] The configuration application module is used to configure the control parameters of the grid-type energy storage converter according to the optimal dynamic adjustment coefficient combination, so as to optimize the active frequency of the grid-type energy storage converter.

[0050] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0051] 1. This invention achieves adaptive matching of virtual inertia and damping coefficient to frequency disturbance intensity by constructing a dynamic adjustment mechanism based on arctangent function of angular frequency deviation and using dynamic adjustment coefficient combination optimized by genetic algorithm. This effectively improves the dynamic response capability and system stability of grid-type energy storage converter in active frequency support process, overcomes the problem of response lag or overshoot under traditional fixed parameter configuration, and solves the problem that virtual inertia and damping coefficient cannot be adaptively matched according to disturbance intensity in traditional virtual synchronous control strategy.

[0052] 2. This invention achieves adaptive matching adjustment of virtual inertia and damping coefficient throughout the entire frequency disturbance cycle by using a dynamic adjustment mechanism of arctangent function driven by angular frequency deviation and accurately dividing four characteristic intervals. This significantly improves the dynamic response accuracy and anti-interference capability of grid-type energy storage converters to frequency disturbances and solves the problem of response lag or overshoot under traditional fixed parameter configuration.

[0053] 3. This invention uses a genetic algorithm to simultaneously optimize multiple dynamic adjustment coefficients, and combines ITAE index evaluation with binary encoding evolution strategy to obtain a set of optimal dynamic adjustment coefficient combinations, thereby achieving the global optimal configuration of control parameters and effectively enhancing the stability and robustness of the system under complex frequency disturbance scenarios. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating an active frequency support method for a grid-type energy storage converter based on genetic algorithm optimization, provided in an embodiment of the present invention.

[0055] Figure 2 This is a schematic diagram of the main circuit topology and control system structure of the grid-type energy storage converter provided in an embodiment of the present invention;

[0056] Figure 3 This is a schematic diagram of root locus diagrams with different virtual inertia and damping coefficients provided in the embodiments of the present invention;

[0057] Figure 4 This is a schematic diagram of the angular frequency oscillation curve provided in an embodiment of the present invention;

[0058] Figure 5 This is a schematic diagram of the genetic algorithm solution process provided in an embodiment of the present invention;

[0059] Figure 6 This is a schematic diagram of the fitness curve provided in an embodiment of the present invention;

[0060] Figure 7 This is a schematic diagram illustrating the changes in output frequency of different methods under sudden events provided in the embodiments of the present invention;

[0061] Figure 8 This is a schematic diagram of the adaptive values ​​of virtual inertia and damping coefficient provided in an embodiment of the present invention. Detailed Implementation

[0062] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0063] Example 1

[0064] like Figure 1 As shown in the figure, this embodiment introduces an active frequency support method for grid-type energy storage converters based on genetic algorithm optimization, including:

[0065] Step 1: Analyze the influence of virtual inertia and damping coefficient on the stability of the grid-type energy storage converter by constructing the second-order motion equation of the virtual synchronous machine.

[0066] This invention constructs the second-order motion equation of the virtual synchronous machine of a grid-type energy storage converter, and quantitatively analyzes the influence of virtual inertia and damping coefficient on the frequency stability of the system. This provides a precise theoretical basis for the subsequent design of adaptive adjustment mechanisms, avoids the blindness of traditional empirical parameter configuration, and improves the scientific nature of parameter optimization.

[0067] Step 2: Design a dynamic adjustment mechanism based on the arctangent function of angular frequency deviation according to the aforementioned influence law.

[0068] This invention utilizes a dynamic adjustment mechanism for the arctangent function based on angular frequency deviation design to achieve nonlinear perception and response to frequency disturbance intensity. Through a strategy of dividing the system into three and two characteristic intervals, the virtual inertia and damping coefficient can be adaptively adjusted according to the frequency change stage, effectively improving the system's dynamic response capability throughout the entire frequency fluctuation cycle.

[0069] Step 3: Based on the dynamic adjustment mechanism of the arctangent function, the first correlation model between angular frequency deviation and virtual inertia and the second correlation model between angular frequency deviation and damping coefficient are established using the arctangent function as the dynamic adjustment coefficient, so that the virtual inertia and damping coefficient are adaptively matched according to the intensity of frequency disturbance.

[0070] This invention establishes a precise mapping relationship between angular frequency deviation and control parameters by constructing a correlation model between virtual inertia and damping coefficient using the arctangent function. This enables virtual inertia and damping coefficient to achieve adaptive matching according to the intensity of frequency disturbance, solving the problem of response lag or overshoot under traditional fixed parameter configuration and significantly enhancing the system's anti-interference capability.

[0071] Step 4: Use a genetic algorithm to simultaneously optimize the dynamic adjustment coefficients to be optimized in the first association model and the second association model to obtain a set of optimal dynamic adjustment coefficient combinations.

[0072] This invention employs a genetic algorithm to simultaneously optimize multiple dynamic adjustment coefficients. Combined with ITAE index evaluation and binary encoding evolution strategy, it achieves rapid convergence of the globally optimal coefficient combination within a preset frequency perturbation range. This effectively improves the stability and robustness of the system under complex frequency perturbation scenarios and avoids the trap of local optima.

[0073] Step 5: Configure the control parameters of the grid-type energy storage converter according to the optimal dynamic adjustment coefficient combination to optimize the active frequency of the grid-type energy storage converter.

[0074] This invention configures control parameters based on the optimal dynamic adjustment coefficient combination, achieving precise optimization of the active frequency support process of grid-connected energy storage converters. While ensuring rapid response, it avoids over-adjustment, significantly improving the dynamic performance and stability of the system under frequency disturbances, and providing reliable technical support for the frequency support of new energy grid-connected systems.

[0075] Example 2

[0076] Similar to the inventive concept of Embodiment 1, this embodiment introduces the implementation steps of an active frequency support method for a grid-type energy storage converter based on genetic algorithm optimization, including:

[0077] Step 1: Analyze the influence of virtual inertia and damping coefficient on the stability of the grid-type energy storage converter by constructing the second-order motion equation of the virtual synchronous machine.

[0078] In this embodiment, the influence of the virtual inertia and damping coefficient on the stability of the network system is characterized by the input power and output power response characteristics of the network system, which are expressed based on a second-order transfer function as follows:

[0079] ;

[0080] In the formula, This represents the input power and output power response characteristics of the network system. This represents the per-unit value of synchronous power. Represents a variable in the complex frequency domain. Indicates the rated power.

[0081] analyze Figure 3 It can be seen that the system provided in this embodiment contains a pair of conjugate eigenvalues. and When the damping coefficient As it gradually increases from zero, and The trajectory moves from the imaginary axis to the left complex plane, the system enters an underdamped state and the overshoot gradually decreases; as the damping coefficient... The value of ... Approaching zero will gradually decrease the stability margin. When the moment of inertia... As the value increases continuously from 0.2, the characteristic roots generally show a tendency to shift towards the imaginary axis, and the system stability weakens accordingly. Analysis shows that the moment of inertia of the virtual synchronous generator determines the oscillation frequency during its dynamic response, while the damping determines the rate of oscillation decay.

[0082] In this embodiment, the main circuit topology and control system structure of the grid-type energy storage converter are as follows: Figure 2 As shown in the figure. , These are the filter's inductance and capacitance parameters, respectively. DC side voltage Given the parameters of the parallel capacitor on the DC side, based on the basic principles of synchronous motors, the rotor mechanical equations for virtual synchronous control can be obtained, i.e., the second-order motion equations of the virtual synchronous machine of the grid-type energy storage converter, expressed as:

[0083] ;

[0084] In the formula, For virtual inertia, The rate of change of angular frequency, For angular frequency components, For time components, For mechanical torque, For electromagnetic torque, For damping torque, For mechanical power, Electromagnetic power, The damping coefficient is... Angular frequency, This refers to the grid synchronization angular velocity.

[0085] Step 2: Design a dynamic adjustment mechanism for the arctangent function based on the angular frequency deviation according to the aforementioned influence law.

[0086] Step 2.1: Based on the preset grid angular frequency, angular frequency and angular frequency change rate, divide a complete oscillation cycle of the angular frequency oscillation curve into four characteristic intervals.

[0087] In this embodiment, as Figure 4 As shown, the four feature intervals include the first feature interval, the second feature interval, the third feature interval, and the fourth feature interval.

[0088] If the preset grid angular frequency is greater than the angular frequency and the rate of change of the angular frequency is greater than or equal to 0, then it is divided into the first characteristic interval.

[0089] If the preset grid angular frequency is greater than the angular frequency and the rate of change of the angular frequency is less than 0, then it is divided into the second characteristic interval;

[0090] If the preset power grid angular frequency is less than or equal to the angular frequency and the rate of change of the angular frequency is less than 0, then it is classified as the third characteristic interval;

[0091] If the preset grid angular frequency is less than or equal to the angular frequency and the rate of change of the angular frequency is greater than 0, then it is classified as the fourth characteristic interval.

[0092] Step 2.2: Dynamically adjust the virtual inertia based on the angular frequency deviation and angular frequency change rate within the first and second characteristic intervals.

[0093] Step 2.3: Dynamically adjust the damping coefficient based on the angular frequency deviation within the first and second characteristic intervals.

[0094] Step 3: Based on the dynamic adjustment mechanism of the arctangent function, the first correlation model between angular frequency deviation and virtual inertia and the second correlation model between angular frequency deviation and damping coefficient are established using the arctangent function as the dynamic adjustment coefficient, so that the virtual inertia and damping coefficient are adaptively matched according to the intensity of frequency disturbance.

[0095] Based on the mathematical properties of the arctangent function and the requirements for system stability, this embodiment uses the arctangent function as the virtual inertia. and damping coefficient The dynamic adjustment coefficient. This function has strict range constraints and nonlinear sensitivity characteristics: within the angular frequency deviation... When the value is small, its derivative reaches its peak, causing the virtual inertia to... and damping coefficient Capable of quickly responding to minute frequency fluctuations; with As the value increases, the derivative asymptotically converges to zero, automatically reducing the adjustment strength to avoid overshoot oscillations.

[0096] This embodiment is based on the principles of virtual inertia and damping coefficient variation and their relationship with the rate of change of angular frequency. and angular frequency offset Based on the correlation between them, design the first association model and the second association model:

[0097] In this embodiment, the first association model is represented as:

[0098] ;

[0099] In the formula, This is the initial value of the virtual inertia. For angular frequency deviation, For adaptive startup threshold, This is the virtual inertia increment gain coefficient, used to determine the rate of change of virtual inertia with angular frequency. The magnitude of the increase, It is the arctangent function. The first input scaling factor is used to adjust the sensitivity of the virtual inertia increment of the arctangent function. This is the virtual inertia decrement gain coefficient, used to determine the rate of change of virtual inertia with angular frequency. The magnitude of the reduction The second input scaling factor is used to adjust the sensitivity of the virtual inertia reduction of the current arctangent function.

[0100] In this embodiment, the second association model is represented as:

[0101] ;

[0102] In the formula, The initial value of the damping coefficient. This is the damping coefficient increment gain coefficient, used to measure the damping coefficient as a function of angular frequency deviation. The magnitude of the increase, The third input scaling factor is used to adjust the damping coefficient increment sensitivity of the current arctangent function.

[0103] Step 4: Use a genetic algorithm to simultaneously optimize the dynamic adjustment coefficients in the first and second association models to obtain a set of optimal dynamic adjustment coefficient combinations, such as... Figure 5 As shown.

[0104] Step 4.1: Use the dynamic adjustment coefficient to be optimized as the decision variable.

[0105] In this embodiment, the dynamic adjustment coefficient to be optimized includes the first input scaling coefficient. Second input scaling factor and the third input scaling factor ;

[0106] Step 4.2: Encode the data using binary encoding and randomly generate an initial population containing several individuals.

[0107] Step 4.3: Construct a fitness function based on system frequency deviation.

[0108] Repeat the following steps until the preset maximum number of generations is reached:

[0109] Step 4.3.1: Decode each individual in the population into a set of dynamic adjustment coefficients, substitute them into the first association model and the second association model respectively, perform simulation within the preset frequency intensity perturbation range, and calculate the fitness value of each individual based on the fitness function.

[0110] In this embodiment, the fitness function is expressed as:

[0111] ;

[0112] In the formula, The fitness value corresponding to the fitness function. For the current moment, This represents the system frequency deviation.

[0113] Step 4.3.2: Evaluate individuals whose fitness values ​​are within the preset range based on the ITAE index, and generate a new generation population through selection, crossover, and mutation operations.

[0114] Step 4.4: After the iteration is completed, the individual with the best fitness value in the population is decoded to obtain a set of optimal dynamic adjustment coefficient combinations.

[0115] Step 5: Configure the control parameters of the grid-type energy storage converter according to the optimal dynamic adjustment coefficient combination to optimize the active frequency of the grid-type energy storage converter.

[0116] In this embodiment, a grid-connected simulation model of a grid-connected energy storage converter is constructed for simulation verification. The genetic algorithm is used to optimize parameters with 30 iterations. After the fitness function converges, the optimal adjustment coefficient is determined. Subsequently, two sets of sudden event conditions are set, and simulation comparisons are conducted through three sets of control experiments. Experiment 1 uses a fixed virtual inertia. and damping coefficient ,in It is 0.5. The value is 30; Experiment 2 uses a set of optimal dynamic adjustment coefficient combinations obtained in this embodiment; Experiment 3 uses a fixed virtual inertia. and damping coefficient ,in It is 1.5. The control parameters of the grid-type energy storage converter are configured to 40 until the dynamic response test of all operating conditions is completed.

[0117] The parameters were tuned using a genetic optimization algorithm, and the fitness function curve during the algorithm's operation is shown in the figure. Figure 6 As shown, the algorithm's fitness began to decrease significantly at the 11th iteration and stabilized at the 13th iteration. After parameter tuning, the DC bus voltage of the simulation model was set to 1500V, the grid-side voltage to 380V, and the rated frequency to 50 Hz. Two sudden events were set in the simulation process: at t=0.5s, the system reduced active power by 20 kW; at t=1s, the system increased active power by 30 kW.

[0118] Among them, under the sudden event, the changes in the output frequency of the three control experiments are as follows: Figure 8 As shown.

[0119] from Figure 7As can be observed in the three sets of control experiments, the frequency deviations under sudden event 1 were 0.27 Hz, 0.17 Hz, and 0.17 Hz, respectively, and the settling times to return to steady state were 0.26 s, 0.25 s, and 0.41 s, respectively; under sudden event 2, the frequency deviations were 0.41 Hz, 0.26 Hz, and 0.27 Hz, respectively, and the settling times to return to steady state were 0.28 s, 0.27 s, and 0.42 s, respectively. Compared with the fixed parameter control strategy, the control strategy proposed in this paper not only reduces the frequency overshoot but also shortens the settling time, enabling the system to enter steady state more quickly under disturbance conditions.

[0120] Figure 8 This is the adaptive waveform of a set of optimal dynamic adjustment coefficients obtained in this embodiment under a sudden event. It can be seen that the arctan function is used as the virtual inertia. and damping coefficient The dynamic adjustment coefficient can effectively constrain the upper and lower limits of the parameters, and when the angular frequency deviation is small, the virtual inertia... and damping coefficient It can quickly respond to minute frequency fluctuations.

[0121] Analysis of the simulation results shows that, compared with the traditional virtual synchronous control strategy, the method proposed in this embodiment can improve the dynamic characteristics of the system output response, reduce the maximum frequency overshoot, shorten the system adjustment time, and accelerate the stabilization adjustment speed. It can also provide more effective frequency support when dealing with load changes, thereby significantly enhancing the stability of the system.

[0122] Example 3

[0123] Based on the same inventive concept as Embodiment 1, this embodiment introduces an active frequency support system for a grid-type energy storage converter optimized by a genetic algorithm, comprising:

[0124] The modeling and analysis module is used to analyze the influence of virtual inertia and damping coefficient on the stability of the grid-type energy storage converter by constructing the second-order motion equation of the virtual synchronous machine.

[0125] The mechanism design module is used to design a dynamic adjustment mechanism based on the arctangent function of the angular frequency deviation according to the influence law;

[0126] The model building module is used to establish a first correlation model between angular frequency deviation and virtual inertia and a second correlation model between angular frequency deviation and damping coefficient based on the dynamic adjustment mechanism of the arctangent function, respectively using the arctangent function as the dynamic adjustment coefficient, so that the virtual inertia and damping coefficient are adaptively matched according to the intensity of frequency disturbance.

[0127] The optimization solution module is used to simultaneously optimize the dynamic adjustment coefficients to be optimized in the first association model and the second association model using a genetic algorithm, so as to obtain a set of optimal dynamic adjustment coefficient combinations.

[0128] The configuration application module is used to configure the control parameters of the grid-type energy storage converter according to the optimal dynamic adjustment coefficient combination, so as to optimize the active frequency of the grid-type energy storage converter.

[0129] For the specific functional implementation of each of the above modules, please refer to the relevant content in the method of Embodiment 1 or 2.

[0130] Example 4

[0131] Based on the same inventive concept as other embodiments, this embodiment describes a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the steps of the methods of Embodiment 1 or 2 described above.

[0132] Example 5

[0133] Based on the same inventive concept as other embodiments, this embodiment introduces a computer program product, including computer instructions that, when executed by a processor, implement the steps of the methods described in Embodiment 1 or 2 above.

[0134] In summary, this invention, by constructing a dynamic adjustment mechanism based on the arctangent function of angular frequency deviation and using a combination of dynamic adjustment coefficients optimized by a genetic algorithm, achieves adaptive matching of virtual inertia and damping coefficients to frequency disturbance intensity. This effectively improves the dynamic response capability and system stability of the grid-type energy storage converter during active frequency support, overcomes the problem of response lag or overshoot under traditional fixed parameter configurations, and solves the problem that virtual inertia and damping coefficients cannot be adaptively matched according to disturbance intensity in traditional virtual synchronous control strategies.

[0135] This invention achieves adaptive matching adjustment of virtual inertia and damping coefficient throughout the entire frequency disturbance cycle by using a dynamic adjustment mechanism of arctangent function driven by angular frequency deviation and accurately dividing four characteristic intervals. This significantly improves the dynamic response accuracy and anti-interference capability of grid-type energy storage converters to frequency disturbances and solves the problem of response lag or overshoot under traditional fixed parameter configuration.

[0136] This invention uses a genetic algorithm to simultaneously optimize multiple dynamic adjustment coefficients, and combines ITAE index evaluation with a binary encoding evolution strategy to obtain a set of optimal dynamic adjustment coefficient combinations. This achieves the globally optimal configuration of control parameters and effectively enhances the stability and robustness of the system under complex frequency disturbance scenarios.

[0137] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0138] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0139] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0141] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for active frequency support of a grid-type energy storage converter based on genetic algorithm optimization, characterized in that, include: The influence of virtual inertia and damping coefficient on the stability of the grid-type energy storage converter is analyzed by constructing the second-order motion equation of the virtual synchronous machine. Based on the aforementioned influence law, a dynamic adjustment mechanism based on the arctangent function of angular frequency deviation is designed; Based on the dynamic adjustment mechanism of the arctangent function, the first correlation model between angular frequency deviation and virtual inertia and the second correlation model between angular frequency deviation and damping coefficient are established by using the arctangent function as the dynamic adjustment coefficient, so that the virtual inertia and damping coefficient are adaptively matched according to the intensity of frequency disturbance. A genetic algorithm is used to simultaneously optimize the dynamic adjustment coefficients to be optimized in the first association model and the second association model to obtain a set of optimal dynamic adjustment coefficient combinations; The control parameters of the grid-type energy storage converter are configured according to the optimal dynamic adjustment coefficient combination to optimize the active frequency of the grid-type energy storage converter.

2. The active frequency support method for grid-type energy storage converters based on genetic algorithm optimization according to claim 1, characterized in that, The second-order motion equation of the virtual synchronous machine of the grid-type energy storage converter is expressed as: ； In the formula, For virtual inertia, The rate of change of angular frequency, For angular frequency components, For time components, For mechanical torque, For electromagnetic torque, For damping torque, For mechanical power, Electromagnetic power, The damping coefficient is... Angular frequency, This refers to the grid synchronization angular velocity.

3. The active frequency support method for grid-type energy storage converters based on genetic algorithm optimization according to claim 1, characterized in that, Based on the aforementioned influence law, a dynamic adjustment mechanism for the arctangent function based on angular frequency deviation is designed, including: Based on the preset grid angular frequency, angular frequency and angular frequency change rate, a complete oscillation cycle of the angular frequency oscillation curve is divided into four characteristic intervals, including the first characteristic interval, the second characteristic interval, the third characteristic interval and the fourth characteristic interval. The virtual inertia is dynamically adjusted based on the angular frequency deviation and the rate of change of angular frequency within the first and second characteristic intervals. The damping coefficient is dynamically adjusted based on the angular frequency deviation within the first and second characteristic intervals.

4. The active frequency support method for grid-type energy storage converters based on genetic algorithm optimization according to claim 3, characterized in that, Based on preset grid angular frequency, angular frequency, and rate of change of angular frequency, a complete oscillation cycle of the angular frequency oscillation curve is divided into four characteristic intervals, including: If the preset grid angular frequency is greater than the angular frequency and the rate of change of the angular frequency is greater than or equal to 0, then it is divided into the first characteristic interval; If the preset grid angular frequency is greater than the angular frequency and the rate of change of angular frequency is less than 0, then it is divided into the second characteristic interval. If the preset power grid angular frequency is less than or equal to the angular frequency and the rate of change of the angular frequency is less than 0, then it is classified as the third characteristic interval; If the preset grid angular frequency is less than or equal to the angular frequency and the rate of change of the angular frequency is greater than 0, then it is classified as the fourth characteristic interval.

5. The active frequency support method for grid-type energy storage converters based on genetic algorithm optimization according to claim 2, characterized in that, The influence of the virtual inertia and damping coefficient on the stability of the network system is characterized by the input power and output power response characteristics of the network system, which are expressed based on a second-order transfer function as follows: ； In the formula, This represents the input power and output power response characteristics of the network system. This represents the per-unit value of synchronous power. Represents a variable in the complex frequency domain. This indicates the rated power.

6. The active frequency support method for grid-type energy storage converters based on genetic algorithm optimization according to claim 5, characterized in that, The first association model is represented as: ； In the formula, This is the initial value of the virtual inertia. For angular frequency deviation, For adaptive startup threshold, This is the virtual inertia increment gain coefficient, used to determine the rate of change of virtual inertia with angular frequency. The magnitude of the increase, It is the arctangent function. The first input scaling factor is used to adjust the sensitivity of the virtual inertia increment of the arctangent function. This is the virtual inertia decrement gain coefficient, used to determine the rate of change of virtual inertia with angular frequency. The magnitude of the reduction The second input scaling factor is used to adjust the sensitivity of the virtual inertia reduction of the current arctangent function.

7. The active frequency support method for grid-type energy storage converters based on genetic algorithm optimization according to claim 5, characterized in that, The second association model is represented as follows: ； In the formula, The initial value of the damping coefficient. This is the damping coefficient increment gain coefficient, used to measure the damping coefficient as a function of angular frequency deviation. The magnitude of the increase, The third input scaling factor is used to adjust the damping coefficient increment sensitivity of the current arctangent function.

8. The active frequency support method for grid-type energy storage converters based on genetic algorithm optimization according to claim 7, characterized in that, A genetic algorithm is used to simultaneously optimize the dynamic adjustment coefficients in the first and second association models to obtain a set of optimal dynamic adjustment coefficient combinations, including: The dynamic adjustment coefficient to be optimized is used as the decision variable, and the dynamic adjustment coefficient to be optimized includes a first input scaling coefficient, a second input scaling coefficient, and a third input scaling coefficient. The encoding is performed using binary encoding, and an initial population containing a number of individuals is randomly generated. Construct a fitness function based on system frequency deviation; Repeat the following steps until the preset maximum number of generations is reached: Each individual in the population is decoded into a set of dynamic adjustment coefficients, which are then substituted into the first association model and the second association model respectively. Simulation is performed within a preset frequency intensity perturbation range, and the fitness value of each individual is calculated based on the fitness function. Individuals whose fitness values ​​are within a preset range are evaluated based on the ITAE index, and a new generation of population is generated through selection, crossover, and mutation operations. After the iteration is completed, the individual with the best fitness value in the population is decoded to obtain a set of optimal dynamic adjustment coefficient combinations.

9. The active frequency support method for grid-type energy storage converters based on genetic algorithm optimization according to claim 8, characterized in that, The fitness function is expressed as follows: ； In the formula, The fitness value corresponding to the fitness function. For the current moment, This represents the system frequency deviation.

10. An active frequency support system for a grid-type energy storage converter based on genetic algorithm optimization, characterized in that, include: The modeling and analysis module is used to analyze the influence of virtual inertia and damping coefficient on the stability of the grid-type energy storage converter by constructing the second-order motion equation of the virtual synchronous machine. The mechanism design module is used to design a dynamic adjustment mechanism based on the arctangent function of the angular frequency deviation according to the influence law; The model building module is used to establish a first correlation model between angular frequency deviation and virtual inertia and a second correlation model between angular frequency deviation and damping coefficient based on the dynamic adjustment mechanism of the arctangent function, respectively using the arctangent function as the dynamic adjustment coefficient, so that the virtual inertia and damping coefficient are adaptively matched according to the intensity of frequency disturbance. The optimization solution module is used to simultaneously optimize the dynamic adjustment coefficients to be optimized in the first association model and the second association model using a genetic algorithm, so as to obtain a set of optimal dynamic adjustment coefficient combinations. The configuration application module is used to configure the control parameters of the grid-type energy storage converter according to the optimal dynamic adjustment coefficient combination, so as to optimize the active frequency of the grid-type energy storage converter.

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