Control method of virtual inertia and damping of mesh-type wind turbine based on moss growth

Abstract

The present invention provides a control method for the virtual inertia and damping of a meshed wind turbine based on moss growth, comprising determining a frequency deviation based on a real-time frequency of a power grid system in which the meshed wind turbine is located and a preset frequency reference value, and determining a frequency change rate based on the real-time frequency of the power grid; inputting the frequency deviation, the frequency change rate, a preset virtual inertia initial value, and a virtual damping initial value into a network controller pre-constructed based on moss growth; determining a target damping coefficient, a target virtual rotational inertia coefficient, and a target proportional coefficient of a power tracking curve of the power grid system based on the network controller; determining a first reference value of the active input of the meshed wind turbine and a second reference value of the active input of an energy storage system in the power grid system based on the target proportional coefficient; and controlling the virtual inertia and damping of the meshed wind turbine based on the first reference value, the second reference value, the target damping coefficient, and the target virtual rotational inertia coefficient to achieve dynamic adjustment of the inertia and damping coefficients.

Description

Technical Field

[0001] The invention relates to the technical field of wind power grid-connected control, and in particular to a method for controlling virtual inertia and damping of a moss-growth-based meshed wind turbine. Background Art

[0002] With the development of renewable energy, the proportion of wind power generation in the power system continues to increase. However, due to the volatility and intermittency of wind energy, the stability and reliability of wind power systems face severe challenges. Traditional wind turbine control methods show low inertia support capabilities when dealing with frequent power fluctuations and grid failures, and it is difficult to meet the requirements of modern power systems for high dynamic performance and high stability. At present, grid-type wind turbines are gradually becoming an effective solution to improve grid stability. Grid-type wind turbines can provide virtual inertia and damping through upper-level control strategies, enhance the regulation capabilities of wind turbines in the grid, and alleviate frequency fluctuations in the grid system.

[0003] However, the dynamic interaction between grid-connected wind turbines and power grids still faces many technical challenges, especially in the accurate calculation of wind turbine inertia and damping coefficient. Existing technologies usually rely on fixed parameter settings and lack dynamic adjustment and optimization capabilities, resulting in the inability of wind turbines and energy storage systems to fully exert their due inertia response and frequency regulation functions. Summary of the invention

[0004] The present invention provides a method for controlling virtual inertia and damping of a moss-growing mesh-type fan, which is used to solve the technical problem that the fan inertia and damping coefficient cannot be accurately calculated in the prior art.

[0005] In one aspect, the present invention provides a method for controlling virtual inertia and damping of a moss-growing mesh-type wind turbine, comprising:

[0006] Determine a frequency deviation based on a real-time frequency of a power grid system where the grid-type wind turbine is located and a preset frequency reference value, and determine a frequency change rate based on the real-time frequency of the power grid;

[0007] Inputting the frequency deviation, the frequency change rate, the preset virtual inertia initial value and the virtual damping initial value into a network controller pre-constructed based on moss growth;

[0008] Based on the network controller, determining a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportional coefficient of a power tracking curve of the power grid system;

[0009] Based on the target proportionality coefficient, determining a first reference value of the active input of the grid-forming wind turbine and a second reference value of the active input of the energy storage system in the grid system;

[0010] Based on the first reference value, the second reference value, the target damping coefficient, and the target virtual rotational inertia coefficient, the virtual inertia and damping of the grid-type wind turbine are controlled.

[0011] According to a method for controlling virtual inertia and damping of a moss-growing grid-type wind turbine provided by the present invention, the target damping coefficient, target virtual rotation inertia coefficient and target proportional coefficient of a power tracking curve of the power grid system are determined based on the network controller, including:

[0012] Determine an initial population; wherein each individual in the initial population represents a combination of a set of inertia coefficients and damping coefficients;

[0013] Determine respective functions of a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportionality coefficient of a power tracking curve of the power grid system;

[0014] Based on each of the functions, a combination of inertia coefficient and damping coefficient that meets a preset rule is selected;

[0015] Based on a combination of inertia coefficients and damping coefficients that conform to preset rules, a target damping coefficient, a target virtual rotational inertia coefficient, and a target proportional coefficient are determined.

[0016] According to a method for controlling virtual inertia and damping of a moss-growing grid-type wind turbine provided by the present invention, the target damping coefficient, target virtual rotation inertia coefficient and target proportional coefficient of a power tracking curve of the power grid system are determined based on the network controller, including:

[0017] Based on the network controller, spore propagation search and dual propagation search are adopted to determine the target damping coefficient, target virtual moment of inertia coefficient and target proportional coefficient of the power tracking curve of the power grid system.

[0018] According to a method for controlling virtual inertia and damping of a moss-growing grid-type wind turbine provided by the present invention, a spore propagation search and a dual propagation search are used to determine a target damping coefficient, a target virtual rotation inertia coefficient, and a target proportional coefficient of a power tracking curve of the power grid system, including:

[0019] Comparing the frequency deviation with a preset deviation threshold;

[0020] When the frequency deviation is greater than the preset deviation threshold, a spore propagation search is used to determine a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportional coefficient of a power tracking curve of the power grid system;

[0021] When the frequency deviation is less than or equal to the preset deviation threshold, a dual propagation search is used to determine a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportional coefficient of a power tracking curve of the power grid system.

[0022] According to a control method for virtual inertia and damping of a moss-growing mesh-type wind turbine provided by the present invention, the function of the target proportional coefficient includes:

[0023] ;in, P 0 k represents the k-th target proportional coefficient, P 0 k-1 represents the k-1th target proportional coefficient, Indicates the maximum value of frequency deviation. u represents the rate of change of frequency; e represents the base of natural logarithm;

[0024] The function of the target damping coefficient includes:

[0025] ;in, D 0 k represents the k-th target damping coefficient, D 0 k-1 represents the k-1th target damping coefficient, Indicates the frequency deviation;

[0026] The function of the target virtual moment of inertia coefficient includes:

[0027] ;in, J 0 k represents the k-th target virtual moment of inertia coefficient, J 0 k-1 represents the k-1th target virtual moment of inertia coefficient, and n represents the moment of inertia adjustment coefficient.

[0028] According to a method for controlling virtual inertia and damping of a moss-growing mesh-type wind turbine provided by the present invention, the preset rules include:

[0029] The absolute values ​​of the inertia coefficient and the damping coefficient in the combination are both smaller than a preset threshold.

[0030] According to a method for controlling virtual inertia and damping of a moss-growing grid-type wind turbine provided by the present invention, the method determines the frequency deviation based on the real-time frequency of the grid system where the grid-type wind turbine is located and a preset frequency reference value, and determines the frequency change rate based on the real-time frequency of the grid, including:

[0031] Determine the difference between the real-time frequency of the power grid system where the grid-type wind turbine is located and the preset frequency reference value as the frequency deviation;

[0032] The first-order derivative of the real-time frequency and time variable of the power grid is determined as the frequency change rate.

[0033] According to a method for controlling virtual inertia and damping of a moss-growing mesh-type wind turbine provided by the present invention, the combination of inertia coefficient and damping coefficient that meets preset rules is screened out based on each of the functions, including:

[0034] Using a resource competition mechanism, determining the fitness of each individual based on each of the functions;

[0035] Based on the fitness of each individual, the combination of inertia coefficient and damping coefficient that meets the preset rules is screened out.

[0036] On the other hand, the present invention also provides a control device for virtual inertia and damping of a moss-growing mesh-type fan, comprising:

[0037] A first calculation module is used to determine a frequency deviation based on a real-time frequency of a power grid system where the grid-type wind turbine is located and a preset frequency reference value, and to determine a frequency change rate based on the real-time frequency of the power grid;

[0038] An input module, used for inputting the frequency deviation, the frequency change rate, the preset virtual inertia initial value and the virtual damping initial value into a network controller pre-constructed based on moss growth;

[0039] A moss growth module, for determining a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportionality coefficient of a power tracking curve of the power grid system based on the network controller;

[0040] A second calculation module is used to determine a first reference value of the active input of the grid-forming wind turbine and a second reference value of the active input of the energy storage system in the grid system based on the target proportional coefficient;

[0041] A control module is used to control the virtual inertia and damping of the grid-type wind turbine based on the first reference value, the second reference value, the target damping coefficient and the target virtual rotational inertia coefficient.

[0042] On the other hand, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a control method for the virtual inertia and damping of a mesh-type fan based on moss growth as described above is implemented.

[0043] The present invention provides a method for controlling virtual inertia and damping of a grid-type wind turbine based on moss growth. The method determines a frequency deviation based on the real-time frequency of the grid system where the grid-type wind turbine is located and a preset frequency reference value, and determines a frequency change rate based on the real-time frequency of the grid; the frequency deviation, the frequency change rate, the preset virtual inertia initial value and the virtual damping initial value are input into a network controller pre-constructed based on moss growth; based on the network controller, a target damping coefficient, a target virtual rotational inertia coefficient and a target proportional coefficient of a power tracking curve of the grid system are determined; based on the target proportional coefficient, a first reference value of the active input of the grid-type wind turbine and a second reference value of the active input of an energy storage system in the grid system are determined; based on the first reference value, the second reference value, the target damping coefficient and the target virtual rotational inertia coefficient, the virtual inertia and damping of the grid-type wind turbine are controlled, and real-time dynamic adjustment of the inertia and damping coefficients is realized, that is, adaptive control of the virtual inertia and damping is realized, and the purpose of flexibly adjusting the inertia and damping is achieved, so that the frequency support capacity of the wind power grid-connected system is enhanced, and the frequency stability of the grid and the effectiveness and rapidity of the grid frequency response are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] 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.

[0045] Figure 1 It is a flow chart of a method for controlling virtual inertia and damping of a mesh-type fan based on moss growth provided by an embodiment of the present invention;

[0046] Figure 2 is a structural schematic diagram of a wind turbine grid-connected system equipped with a back-to-back converter and an energy storage system provided by an embodiment of the present invention;

[0047] Figure 3 yes Figure 2 Schematic diagram of the control architecture of the wind turbine grid-connected system;

[0048] Figure 4 It is a simulation comparison diagram of grid-side active power under unsupported control, fixed coefficient control, and virtual inertia and damping control implemented by the present invention, provided by an embodiment of the present invention;

[0049] Figure 5 It is a simulation comparison diagram of the change of the power grid frequency under the unsupported control, fixed coefficient control and the virtual inertia and damping control implemented by the present invention provided by the embodiment of the present invention;

[0050] Figure 6 It is a structural schematic diagram of a control device for virtual inertia and damping of a mesh-type fan based on moss growth provided by an embodiment of the present invention;

[0051] Figure 7 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] 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.

[0053] Figure 1 It is a flow chart of a control method for virtual inertia and damping of a moss-growth-based grid-type wind turbine provided in an embodiment of the present invention. The grid-type wind turbine may also be equipped with an energy storage system. The wind power control system mainly focuses on maximum power point tracking (MPPT), that is, finding and maintaining the wind speed at which the wind turbine outputs the maximum power. The control method of the present invention can be regarded as an optimization based on this control strategy, adding virtual inertia and damping control to improve the wind power system's support capacity for the power grid. The executor of the control method may be a computer, a mobile phone, a smart wearable device, and the like.

[0054] See also Figure 1 The control method of the virtual inertia and damping of the moss-growing mesh-type wind turbine may include the following steps 101 to 105 .

[0055] 101. Determine a frequency deviation based on a real-time frequency of a power grid system where a grid-type wind turbine is located and a preset frequency reference value, and determine a frequency change rate based on the real-time frequency of the power grid.

[0056] In this step, the frequency deviation is determined based on the real-time frequency of the power grid system where the grid-type wind turbine is located and a preset frequency reference value, and the frequency change rate is determined based on the real-time frequency of the power grid, including:

[0057] Determine the difference between the real-time frequency of the power grid system where the grid-type wind turbine is located and the preset frequency reference value as the frequency deviation;

[0058] The first-order derivative of the real-time frequency and time variable of the power grid is determined as the frequency change rate.

[0059] For example, the real-time frequency of the power grid is f The preset frequency reference value is f 0 , the frequency deviation is ,but .

[0060] For example, the frequency change rate is u , the time variable is t ,but u =df / dt.

[0061] 102. Input the frequency deviation, the frequency change rate, the preset virtual inertia initial value and the virtual damping initial value into a network controller pre-constructed based on moss growth.

[0062] In this step, the Moss Growth Optimization (MGO) algorithm is an emerging swarm intelligence optimization algorithm, which is inspired by the natural growth process of moss. It has the advantages of strong global optimization ability, fast calculation speed and good convergence performance. The algorithm simulates the growth pattern and resource competition mechanism of moss to find the optimal solution in the solution space, which can effectively solve the real-time dynamic adjustment problem of inertia and damping coefficient.

[0063] 103. Based on the network controller, determine the target damping coefficient, target virtual moment of inertia coefficient and target proportional coefficient of the power tracking curve of the power grid system.

[0064] In this step, the target damping coefficient, target virtual moment of inertia coefficient and target proportional coefficient of the power tracking curve of the power grid system are determined based on the network controller, including:

[0065] Determine an initial population; wherein each individual in the initial population represents a combination of a set of inertia coefficients and damping coefficients;

[0066] Determine respective functions of a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportionality coefficient of a power tracking curve of the power grid system;

[0067] Based on each of the functions, a combination of inertia coefficient and damping coefficient that meets a preset rule is selected;

[0068] Based on a combination of inertia coefficients and damping coefficients that conform to preset rules, a target damping coefficient, a target virtual rotational inertia coefficient, and a target proportional coefficient are determined.

[0069] 104. Based on the target proportionality coefficient, determine a first reference value of the active input of the grid-connected wind turbine and a second reference value of the active input of the energy storage system in the grid system.

[0070] In this step, the target proportional coefficient can be used to indicate how the wind turbine and the energy storage system should distribute the total power output. After determining the total power demand of the power grid, the total power demand is distributed to the grid-forming wind turbine and the energy storage system using the target proportional coefficient. Based on the distribution results, a first reference value for the active input of the grid-forming wind turbine and a second reference value for the active input of the energy storage system are determined. The first reference value represents the active power that the wind turbine should provide under the current grid state to help maintain the stability of the grid. The second reference value represents the active power that the energy storage system should provide to assist the wind turbine in maintaining the stability of the grid. These two reference values ​​can meet the power demand of the power grid without exceeding the capacity of the system.

[0071] 105. Based on the first reference value, the second reference value, the target damping coefficient and the target virtual rotational inertia coefficient, control the virtual inertia and damping of the grid-type wind turbine.

[0072] In this step, a control signal can be generated based on the first reference value, the second reference value, the target damping coefficient, and the target virtual moment of inertia coefficient, and the output of the converter of the grid-type wind turbine can be controlled by the control signal to maintain the stability of the power grid system. The type of the control signal is an SPWM pulse modulation signal that can regulate the output of the converter, or other types of signals.

[0073] In this embodiment, the frequency deviation is determined based on the real-time frequency of the power grid system where the grid-type wind turbine is located and the preset frequency reference value, and the frequency change rate is determined based on the real-time frequency of the power grid; the frequency deviation, the frequency change rate, the preset virtual inertia initial value and the virtual damping initial value are input into a network controller pre-constructed based on moss growth; based on the network controller, the target damping coefficient, the target virtual rotational inertia coefficient and the target proportional coefficient of the power tracking curve of the power grid system are determined; based on the target proportional coefficient, the first reference value of the active input of the grid-type wind turbine and the second reference value of the active input of the energy storage system in the power grid system are determined; based on the first reference value, the second reference value, the target damping coefficient and the target virtual rotational inertia coefficient, the virtual inertia and damping of the grid-type wind turbine are controlled, and real-time dynamic adjustment of the inertia and damping coefficients is realized, that is, adaptive control of the virtual inertia and damping is realized, and the purpose of flexibly adjusting the inertia and damping is achieved, so that the frequency support capacity of the wind power grid-connected system is enhanced, and the frequency stability of the power grid and the effectiveness and rapidity of the power grid frequency response are improved.

[0074] In one embodiment of the present specification, determining a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportional coefficient of a power tracking curve of the power grid system based on the network controller includes:

[0075] Based on the network controller, spore propagation search and dual propagation search are adopted to determine the target damping coefficient, target virtual moment of inertia coefficient and target proportional coefficient of the power tracking curve of the power grid system.

[0076] In this embodiment, spore propagation search increases the algorithm's exploration ability through random diffusion, which helps to discover new, unexplored areas of the solution space; it can also increase the diversity of the population and avoid premature convergence of the algorithm. Dual propagation search can locally optimize the current solution through refined search to improve the quality of the solution; it focuses more on developing known good solutions, so it can converge to the optimal solution faster; through refined search of known solutions, the stability of the algorithm can be improved and fluctuations can be reduced.

[0077] In one embodiment of the present specification, spore propagation search and dual propagation search are used to determine the target damping coefficient, target virtual moment of inertia coefficient and target proportional coefficient of the power tracking curve of the power grid system, including:

[0078] Comparing the frequency deviation with a preset deviation threshold;

[0079] When the frequency deviation is greater than the preset deviation threshold, a spore propagation search is used to determine a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportional coefficient of a power tracking curve of the power grid system;

[0080] When the frequency deviation is less than or equal to the preset deviation threshold, a dual propagation search is used to determine a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportional coefficient of a power tracking curve of the power grid system.

[0081] In this embodiment, when the frequency deviation is large, that is, the deviation between the grid frequency and the preset frequency is large, it means that the system needs a larger adjustment to restore the balance. At this time, the spore propagation search can explore the solution space more widely and find possible solutions to quickly respond to larger deviations and enhance the stability of the system. When the frequency deviation is small or equal to the preset deviation threshold, the system is relatively stable. At this time, what is needed is not a large-scale search, but local optimization of the existing solution. The dual propagation search is used to improve the accuracy and efficiency of the control. Based on the size of the frequency deviation, the spore propagation search and the dual propagation search are selectively used, giving full play to the respective advantages of the spore propagation search and the dual propagation search to ensure the stability of the power grid system and the accuracy of control.

[0082] In one embodiment of the present specification, the function of the target proportionality coefficient includes the following function (1):

[0083] (1);

[0084] in, P 0 k represents the k-th target proportional coefficient, P 0 k-1 represents the k-1th target proportional coefficient, Indicates the maximum value of frequency deviation. u represents the rate of change of frequency; e represents the base of the natural logarithm.

[0085] The function of the target damping coefficient includes the following function (2):

[0086] (2);

[0087] in, D 0 k represents the k-th target damping coefficient, D 0 k-1 represents the k-1th target damping coefficient, Indicates the frequency deviation.

[0088] The function of the target virtual moment of inertia coefficient includes the following function (3):

[0089] (3);

[0090] in, J 0 k represents the k-th target virtual moment of inertia coefficient, J 0 k-1 represents the k-1th target virtual moment of inertia coefficient, and n represents the moment of inertia adjustment coefficient.

[0091] In this embodiment, through the above functions, a combination of inertia coefficient and damping coefficient that meets the preset rules can be obtained. Generally, after functions (1) to (3) converge or reach the maximum number of iterations, the calculation can be stopped to obtain the optimal combination of virtual inertia and damping coefficient.

[0092] In one embodiment of this specification, the preset rules include:

[0093] The absolute values ​​of the inertia coefficient and the damping coefficient in the combination are both smaller than a preset threshold.

[0094] In one embodiment of the present specification, the method of selecting a combination of inertia coefficient and damping coefficient that meets a preset rule based on each of the functions includes:

[0095] Using a resource competition mechanism, determining the fitness of each individual based on each of the functions;

[0096] Based on the fitness of each individual, the combination of inertia coefficient and damping coefficient that meets the preset rules is screened out.

[0097] In this embodiment, the resource competition mechanism can be understood as follows: the moss growth optimization algorithm optimizes inertia and damping coefficients by simulating resource competition; for example: system inertia resources (inertia support provided by wind turbines and energy storage systems to the power grid), solutions with high fitness (inertia parameters that provide better inertia response) will obtain more "resources", that is, they will be retained and preferentially expanded. The optimized inertia parameters help to improve system inertia, so that wind turbines and energy storage systems can more effectively support frequency stability. Fitness can be understood as follows: Fitness indicates the degree of superiority of the current value to the target expectation. For example, the fitness of the current value can be measured by the frequency deviation ∆f and the frequency change rate u.

[0098] The present invention is described below by means of specific examples. For example, the wind turbine grid-connected system constructed can be seen in Figure 2 and Figure 3 . The wind turbine grid-connected system can mainly include a permanent magnet synchronous wind turbine PMSG, a machine-side converter, a grid-side converter, an energy storage system, a transformer, an AC load, a Buck / Boost converter, a line and a weak power grid, etc. The parameters involved are shown in Table 1 below. The permanent magnet synchronous wind turbine PMSG is used to generate power. The back-to-back converter includes a machine-side converter close to the permanent magnet synchronous wind turbine PMSG and a grid-side converter close to the grid connection point. The machine-side converter is used for constant DC voltage control, and the grid-side converter is used to track the maximum power output of the permanent magnet synchronous wind turbine PMSG and realize the regulation of the grid frequency. The energy storage system is connected to the grid through the PCS converter after the DC / AC inverter, and finally connected to the grid through the step-up transformer and the AC bus.

[0099] Figure 3 The parameters involved are described as follows: U dc is the rectified DC voltage; U dcref is the reference value of the rectified DC voltage; P Hset It is the reference value of energy storage system output; J is the optimal solution of inertia obtained by the moss growth algorithm; D is the optimal damping solution obtained by the moss growth algorithm; U dref The output voltage is measured dAxis component reference value; U qref is the reference value of the q-axis component of the output voltage measurement value; is the rotor angular velocity of the permanent magnet synchronous wind generator PMSG; P set It is the reference value of active power output on the stator side; P ref Output active power to the stator side; i sq * For the stator q Nominal value of shaft current; i sd * For the stator d Nominal value of shaft current; is the system angular frequency reference value; U ref is the AC voltage reference value on the inverter side; E is the inverter output voltage amplitude; V dcset is the DC voltage reference value on the chopper circuit side of the energy storage system; is the system phase.

[0100] Table 1

[0101]

[0102] Based on the parameters in Table 1 above, the constructed wind turbine grid-connected system is simulated through simulation platforms such as Matlab or Simulink. The wind speed suddenly increases from 8m / s to 10m / s at the 2.5s of normal operation, and decreases from 10m / s to 8m / s at the 3.5s of operation. The simulation of the grid-side active power can be seen in Figure 4 , the simulation of grid frequency can be found in Figure 5 .

[0103] like Figure 4 As shown, curve R1 represents the change of active power P (MW) of the power grid system when it is unsupported, curve R2 represents the change of active power P (MW) of the power grid system when it is controlled by a fixed coefficient, and curve R3 represents the change of active power P (MW) of the power grid system under the virtual inertia and damping control based on the moss growth optimization algorithm of the present invention. When the power grid system is controlled by the virtual inertia and damping control method based on moss growth of the present invention, the active power P has the fastest convergence speed and the smallest oscillation amplitude.

[0104] like Figure 5As shown, when the power grid system suddenly increases its load, curve R4 represents the change of the frequency f (HZ) of the power grid system when it is unsupported. When there is no virtual inertia control, the system frequency drops significantly, and the frequency drops to about 49.73Hz. The fluctuation range is large, and there are small-range oscillations. Curve R5 represents the change of the frequency f (HZ) of the power grid system when the fixed coefficient control is in place. When the power grid system is in fixed coefficient inertia control, the system frequency also drops, but compared with the non-virtual inertia control mode, the frequency drop range is significantly reduced, and it only drops to 49.86Hz. Curve R6 represents the change of the frequency f (HZ) of the power grid system when it is controlled by the present invention. When the power grid system is controlled by the virtual inertia and damping control method based on the moss growth optimization algorithm of the present invention, the minimum value of the system frequency is 49.92Hz, the drop range is the smallest, and the subsequent oscillation is the least. It can be seen that the present invention can significantly improve the frequency support capacity of the power grid system and enhance the stability of the power grid system.

[0105] Based on the same general inventive concept, the present invention also protects a control device for virtual inertia and damping of a moss-growing mesh-type fan, such as Figure 6 As shown, Figure 6 1 is a schematic diagram of the structure of the control device for virtual inertia and damping of a mesh-type fan based on moss growth provided by an embodiment of the present invention. The control device for virtual inertia and damping of a mesh-type fan based on moss growth provided by the present invention is described below. The control device for virtual inertia and damping of a mesh-type fan based on moss growth described below and the control method for virtual inertia and damping of a mesh-type fan based on moss growth described above can be referred to each other.

[0106] The control device for virtual inertia and damping of a lattice-type fan based on moss growth includes a first calculation module 601 , an input module 602 , a moss growth module 603 , a second calculation module 604 and a control module 605 .

[0107] The first calculation module 601 is used to determine the frequency deviation based on the real-time frequency of the power grid system where the grid-type wind turbine is located and a preset frequency reference value, and to determine the frequency change rate based on the real-time frequency of the power grid;

[0108] The input module 602 is used to input the frequency deviation, the frequency change rate, the preset virtual inertia initial value and the virtual damping initial value into a network controller pre-constructed based on moss growth;

[0109] The moss growth module 603 is used to determine the target damping coefficient, the target virtual moment of inertia coefficient and the target proportional coefficient of the power tracking curve of the power grid system based on the network controller;

[0110] The second calculation module 604 is used to determine a first reference value of the active input of the grid-type wind turbine and a second reference value of the active input of the energy storage system in the grid system based on the target proportional coefficient;

[0111] The control module 605 is used to control the virtual inertia and damping of the grid-type wind turbine based on the first reference value, the second reference value, the target damping coefficient and the target virtual rotational inertia coefficient.

[0112] Figure 7 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0113] like Figure 7 As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730 and a communication bus 740, wherein the processor 710, the communication interface 720 and the memory 730 communicate with each other through the communication bus 740. The processor 710 may call the logic instructions in the memory 730 to execute the control method of the virtual inertia and damping of the mesh-type fan based on moss growth.

[0114] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0115] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the control methods of the virtual inertia and damping of the moss growth-based grid-type fan provided by the above-mentioned methods.

[0116] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the control method of the virtual inertia and damping of the moss growth-based mesh fan provided by the above-mentioned methods.

[0117] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0118] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0119] 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 method for controlling virtual inertia and damping of a moss-growing mesh-type fan, characterized in that: include: Determine a frequency deviation based on a real-time frequency of a power grid system where the grid-type wind turbine is located and a preset frequency reference value, and determine a frequency change rate based on the real-time frequency of the power grid; Inputting the frequency deviation, the frequency change rate, the preset virtual inertia initial value and the virtual damping initial value into a network controller pre-constructed based on moss growth; Based on the network controller, determining a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportional coefficient of a power tracking curve of the power grid system; Based on the target proportionality coefficient, determining a first reference value of the active input of the grid-forming wind turbine and a second reference value of the active input of the energy storage system in the grid system; Controlling the virtual inertia and damping of the grid-type wind turbine based on the first reference value, the second reference value, the target damping coefficient, and the target virtual rotational inertia coefficient; Wherein, determining the target damping coefficient, target virtual moment of inertia coefficient and target proportional coefficient of the power tracking curve of the power grid system based on the network controller includes: Determine an initial population; wherein each individual in the initial population represents a combination of a set of inertia coefficients and damping coefficients; Determine respective functions of a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportionality coefficient of a power tracking curve of the power grid system; Based on each of the functions, a combination of inertia coefficient and damping coefficient that meets a preset rule is selected; Determining a target damping coefficient, a target virtual rotational inertia coefficient, and a target proportional coefficient based on a combination of inertia coefficients and damping coefficients that conform to a preset rule; The determining, based on the network controller, a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportional coefficient of a power tracking curve of the power grid system comprises: Based on the network controller, a target damping coefficient, a target virtual moment of inertia coefficient and a target proportional coefficient of a power tracking curve of the power grid system are determined by using spore propagation search and dual propagation search; The target damping coefficient, target virtual moment of inertia coefficient and target proportional coefficient of the power tracking curve of the power grid system are determined by using spore propagation search and dual propagation search, including: Comparing the frequency deviation with a preset deviation threshold; When the frequency deviation is greater than the preset deviation threshold, a spore propagation search is used to determine a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportional coefficient of a power tracking curve of the power grid system; When the frequency deviation is less than or equal to the preset deviation threshold, a dual propagation search is used to determine a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportional coefficient of a power tracking curve of the power grid system; The function of the target scale factor includes: ;in, P 0 k represents the k-th target proportional coefficient, P 0 k-1 represents the k-1th target proportional coefficient, Indicates the maximum value of frequency deviation. u represents the rate of change of frequency; e represents the base of natural logarithm; The function of the target damping coefficient includes: ;in, D 0 k represents the k-th target damping coefficient, D 0 k-1 represents the k-1th target damping coefficient, Indicates the frequency deviation; The function of the target virtual moment of inertia coefficient includes: ;in, J 0 k represents the k-th target virtual moment of inertia coefficient, J 0 k-1 represents the k-1th target virtual moment of inertia coefficient, and n represents the moment of inertia adjustment coefficient.

2. The method for controlling virtual inertia and damping of a moss-growing mesh-type fan according to claim 1, characterized in that: The preset rules include: The absolute values ​​of the inertia coefficient and the damping coefficient in the combination are both smaller than a preset threshold.

3. The method for controlling virtual inertia and damping of a moss-growing mesh-type fan according to claim 1, characterized in that: The method of determining the frequency deviation based on the real-time frequency of the power grid system where the grid-type wind turbine is located and a preset frequency reference value, and determining the frequency change rate based on the real-time frequency of the power grid includes: Determine the difference between the real-time frequency of the power grid system where the grid-type wind turbine is located and the preset frequency reference value as the frequency deviation; The first-order derivative of the real-time frequency and time variable of the power grid is determined as the frequency change rate.

4. The method for controlling virtual inertia and damping of a moss-growing mesh-type fan according to claim 1, characterized in that: The step of selecting a combination of inertia coefficient and damping coefficient that meets a preset rule based on each of the functions includes: Using a resource competition mechanism, determining the fitness of each individual based on each of the functions; Based on the fitness of each individual, the combination of inertia coefficient and damping coefficient that meets the preset rules is screened out.

5. A control device for virtual inertia and damping of a moss-growing net-type fan, characterized in that: include: A first calculation module is used to determine a frequency deviation based on a real-time frequency of a power grid system where the grid-type wind turbine is located and a preset frequency reference value, and to determine a frequency change rate based on the real-time frequency of the power grid; An input module, used for inputting the frequency deviation, the frequency change rate, the preset virtual inertia initial value and the virtual damping initial value into a network controller pre-constructed based on moss growth; A moss growth module, for determining a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportionality coefficient of a power tracking curve of the power grid system based on the network controller; A second calculation module is used to determine a first reference value of the active input of the grid-forming wind turbine and a second reference value of the active input of the energy storage system in the grid system based on the target proportional coefficient; A control module, configured to control the virtual inertia and damping of the grid-type wind turbine based on the first reference value, the second reference value, the target damping coefficient, and the target virtual rotational inertia coefficient; Based on the network controller, determining a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportional coefficient of a power tracking curve of the power grid system includes: Determine an initial population; wherein each individual in the initial population represents a combination of a set of inertia coefficients and damping coefficients; Determine respective functions of a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportionality coefficient of a power tracking curve of the power grid system; Based on each of the functions, a combination of inertia coefficient and damping coefficient that meets a preset rule is selected; Determining a target damping coefficient, a target virtual rotational inertia coefficient, and a target proportional coefficient based on a combination of inertia coefficients and damping coefficients that conform to a preset rule; Based on the network controller, determining a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportional coefficient of a power tracking curve of the power grid system includes: Based on the network controller, a target damping coefficient, a target virtual moment of inertia coefficient and a target proportional coefficient of a power tracking curve of the power grid system are determined by using spore propagation search and dual propagation search; The target damping coefficient, target virtual moment of inertia coefficient and target proportional coefficient of the power tracking curve of the power grid system are determined by using spore propagation search and dual propagation search, including: Comparing the frequency deviation with a preset deviation threshold; When the frequency deviation is greater than the preset deviation threshold, a spore propagation search is used to determine a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportional coefficient of a power tracking curve of the power grid system; When the frequency deviation is less than or equal to the preset deviation threshold, a dual propagation search is used to determine a target damping coefficient, a target virtual moment of inertia coefficient, and a target proportional coefficient of a power tracking curve of the power grid system; Functions of the target scale factor include: ;in, P 0 k represents the k-th target proportional coefficient, P 0 k-1 represents the k-1th target proportional coefficient, Indicates the maximum value of frequency deviation. u represents the rate of change of frequency; e represents the base of natural logarithm; The function of the target damping coefficient includes: ;in, D 0 k represents the k-th target damping coefficient, D 0 k-1 represents the k-1th target damping coefficient, Indicates the frequency deviation; The function of the target virtual moment of inertia coefficient includes: ;in, J 0 k represents the k-th target virtual moment of inertia coefficient, J 0 k-1 represents the k-1th target virtual moment of inertia coefficient, and n represents the moment of inertia adjustment coefficient.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method for controlling the virtual inertia and damping of the moss growth-based mesh-type fan as described in any one of claims 1 to 4 above is implemented.

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