Inertia distribution visualization method and equipment of offshore wind power grid-connected system, and medium

Abstract

The invention discloses an inertia distribution visualization method and device of an offshore wind power grid-connected system and a medium, and relates to the technical field of inertia analysis of power systems. The method comprises the following steps: when a typhoon disaster occurs, performing state analysis and output characteristic analysis according to obtained current first typhoon real-time data and offshore wind plant coordinates of an offshore wind power grid-connected system to determine a current operation state and an inertia support condition of an offshore wind plant group; modeling is carried out according to the inertia supporting condition, and a mathematical model of the offshore wind power grid-connected system is obtained; calculating to obtain a node inertia value of each network node; drawing according to the node inertia values and a system network topological graph to obtain a visual image main body frame, and performing spatial interpolation on the discrete node inertia values to obtain an interpolated data set; and according to the interpolated data set, visualizing drawing of the image main body frame to obtain a first inertia distribution thermodynamic diagram. The inertia distribution condition of the offshore wind power grid-connected system can be displayed in real time, and reliable reference is provided for dispatching management.

Description

Technical Field

[0001] The present application relates to the technical field of power system inertia analysis, and in particular to an inertia distribution visualization method, device, and medium for an offshore wind power grid-connected system. Background Art

[0002] In order to achieve green and low-carbon transformation, my country has accelerated the construction of new power systems. Offshore wind power has become a key construction target due to its mature technology and considerable development scale. In 2023, my country's new installed capacity of offshore wind power will be 6.3GW, a year-on-year increase of 25%, ranking first in the world in terms of new installed capacity and cumulative installed capacity of offshore wind power. As a non-synchronous power source, offshore wind power is connected to the system in large quantities through power electronic converters, decoupled from the grid frequency, and has an increasingly prominent impact on the frequency characteristics of the near-end coastal city grid. The dynamic process of the system frequency has undergone fundamental changes, and the system inertia has shown obvious spatiotemporal distribution characteristics.

[0003] At present, in the process of system inertia response of traditional power systems, due to the dominant position of synchronous machines, the traditional safety and stability analysis method is to analyze through the Center of Inertia (COI). This method aggregates the inertia of each generator in the traditional power system into the inertia of one generator, and uses the swing equation to reflect the average dynamic process of the frequency of the whole network unit after the traditional power system is disturbed. It can be seen that the COI analysis method can directly reflect the average dynamic process of the whole network frequency under active disturbance, but ignores the spatial scale characteristics of the system frequency and cannot characterize the distribution of system inertia. In the offshore wind power grid-connected system, the inertia support capacity of wind power is subject to the operating state of the unit, and can only provide inertia support for the system under the appropriate operating state. Under typhoon disasters, extreme wind speeds will cause the operating states of wind turbines to be different. When the wind speed is too large or too small, the wind turbine cannot provide inertia support. This causes the inertia distribution of wind turbines in various areas of the system to change significantly before and after the typhoon disaster. If the COI analysis method is still used at this time, it will be difficult to take into account the areas with weak inertia. When using power generation units or flexible energy storage to support the system's inertia, it is impossible to effectively increase the efficiency of the weak inertia areas, reducing the power reliability and safety of coastal power grids under typhoon disasters. It can be seen that with the large-scale grid connection of offshore wind power, the inertia characteristics of the system have undergone tremendous changes. Under typhoon disasters, the inertia support role of offshore wind power changes with the different stages of the typhoon's passage, and the inertia time and space characteristics are obvious. However, the existing commonly used inertia analysis method is mainly COI, which cannot reflect the spatial distribution of inertia, is not suitable for the inertia analysis of offshore wind power grid-connected systems, and is difficult to provide a reliable reference for dispatchers to conduct dispatching and control. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a method, device and medium for visualizing the inertia distribution of an offshore wind power grid-connected system, which can display the inertia distribution of the offshore wind power grid-connected system in real time when a typhoon disaster occurs, and provide a reliable reference for dispatchers to perform dispatch management.

[0005] In a first aspect, an embodiment of the present application provides a method for visualizing inertia distribution of an offshore wind power grid-connected system, wherein the offshore wind power grid-connected system comprises: an offshore wind farm group consisting of a plurality of offshore wind turbines; the method comprises:

[0006] When a typhoon disaster occurs, a state analysis process is performed based on the acquired real-time data of the first typhoon and the offshore wind farm coordinates of the offshore wind power grid-connected system to determine the current operating state of the offshore wind farm group;

[0007] Perform output characteristic analysis and processing according to the current operating state to determine the inertia support status of the offshore wind farm group;

[0008] Modeling the offshore wind power grid-connected system according to the inertia support condition to obtain a mathematical model of the offshore wind power grid-connected system;

[0009] Performing node inertia calculation processing according to the mathematical model to obtain the node inertia value of each network node in the mathematical model;

[0010] Performing a first drawing process according to the node inertia value and the system network topology diagram of the offshore wind power grid-connected system to obtain a visualization image main frame;

[0011] Performing spatial interpolation processing on the discrete node inertia values ​​to obtain an interpolated data set;

[0012] A second drawing process is performed based on the interpolated data set and the visualization image main frame to obtain a first inertia distribution heat map.

[0013] In a second aspect, an embodiment of the present application 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 computer program, the method for visualizing the inertia distribution of an offshore wind power grid-connected system as described in any one of the embodiments of the first aspect is implemented.

[0014] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the method for visualizing the inertia distribution of an offshore wind power grid-connected system as described in any one of the embodiments of the first aspect.

[0015] The embodiment of the present application includes: in the process of supervising the offshore wind power grid-connected system, first, when a typhoon disaster occurs, state analysis processing is performed according to the current first typhoon real-time data obtained and the offshore wind farm coordinates of the offshore wind power grid-connected system to determine the current operating state of the offshore wind farm group; secondly, output characteristic analysis processing is performed according to the current operating state to determine the inertia support situation of the offshore wind farm group; then, modeling processing is performed on the offshore wind power grid-connected system according to the inertia support situation to obtain a mathematical model of the offshore wind power grid-connected system; then, node inertia calculation processing is performed according to the mathematical model to obtain the node inertia value of each network node in the mathematical model; then, according to the node inertia value and the offshore wind farm group, the inertia support situation of the offshore wind farm group is determined; The system network topology diagram of the wind power grid-connected system is first drawn to obtain the main framework of the visualization image; then, the discrete node inertia values ​​are spatially interpolated to obtain the interpolated data set; finally, the second drawing is performed based on the interpolated data set and the main framework of the visualization image to obtain the first inertia distribution heat map; the first inertia distribution heat map reflects the spatiotemporal characteristics of inertia, making up for the insufficient analysis ability of the COI method in terms of spatiotemporal characteristics of inertia; and the first inertia distribution heat map is visualized to display the change of inertia distribution of the offshore wind power grid-connected system under typhoon disasters in real time, which can assist dispatchers in real-time monitoring the inertia distribution status of the offshore wind power grid-connected system for management and decision-making. That is to say, the embodiment of the present application can display the inertia distribution of the offshore wind power grid-connected system in real time when a typhoon disaster occurs, providing a reliable reference for dispatchers to perform dispatch management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a schematic diagram of the steps of a method for visualizing the inertia distribution of an offshore wind power grid-connected system provided by an embodiment of the present application;

[0017] Figure 2 It is a flowchart of the specific steps of state analysis processing and output characteristic analysis processing provided by an embodiment of the present application;

[0018] Figure 3 This is a system network topology diagram of an offshore wind power grid-connected system provided by an embodiment of the present application;

[0019] Figure 4 This is a schematic diagram of the operating status of an offshore wind farm group under a typhoon provided by an embodiment of the present application;

[0020] Figure 5 This is an output power curve diagram of an offshore wind farm group under a typhoon provided by an embodiment of the present application;

[0021] Figure 6 is a schematic diagram of a formation structure of a node admittance matrix provided by an embodiment of the present application;

[0022] Figure 7 It is an inertia distribution thermal diagram of an offshore wind power grid-connected system provided by an embodiment of the present application;

[0023] Figure 8 It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0025] It should be noted that although a logical order is shown in the flowchart in the description of the present application, in some cases, the steps shown or described may be performed in an order different from that in the flowchart. In the description of the present application, a number of means one or more, and a plurality of means two or more. The description of "first" and "second" is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0027] First, some terms used in this application are explained:

[0028] Generator inertia: Inertia is an inherent property of an object (energy), which is manifested as the ability of an object to resist changes in its current state. Inertia is a measure of the size of inertia. For a generator in motion, its inertia is expressed as the ability to hinder changes in the rotor speed. The generator inertia refers to the rotational inertia of the rotor. When the system is disturbed, the generator releases the kinetic energy in the rotor to slow down the change in the rotor's angular velocity. The size of the inertia determines how fast the rotor's angular velocity decreases.

[0029] Inertia response: In the power system, after the system is subjected to power disturbance, the frequency response process of inertia response, primary frequency modulation, secondary frequency modulation, and tertiary frequency modulation will appear in sequence according to the time scale. The primary frequency modulation and the frequency modulation actions after it are active adjustments, the purpose of which is to ensure that the system frequency returns to the standard value. Inertia response is the process of suppressing frequency changes by relying on the rotational inertia of the synchronous machine rotor. It responds spontaneously in the frequency response process, and its speed is the fastest. It plays a leading role before active adjustment, and directly affects the maximum frequency change rate and the lowest frequency point of the system.

[0030] Swing equation: It is an equation that describes the "active power-frequency" process of the power system, and its expression is:

[0031] Among them, f is the frequency, H is the inertia constant, Pm is the mechanical power, Pe is the electromagnetic power, D is the damping coefficient, and t is the time.

[0032] Center of Inertia (COI): There are multiple generators in the power system with different capacities and inertia time constants. The COI is calculated by weighting the inertia time constants of all the generators in the system according to their capacities to obtain the equivalent inertia time constant of the system. This calculated value is substituted into the swing equation for calculation. The obtained frequency is the inertia center frequency of the system, which represents the average frequency response of the system.

[0033] Offshore wind power grid-connected system: In traditional power systems, the power generation units are mainly synchronous generators (thermal generators). With the grid connection of offshore wind farms, the power generation units of the system are synchronous generators and wind turbines. Since wind turbines are connected to the grid through converters, the system frequency is decoupled from the wind turbines.

[0034] Wind power inertia support: In offshore wind power grid-connected systems, wind turbines are decoupled from the grid frequency and cannot provide inertia support in normal operation. Inertia support can be achieved by adding relevant control methods to the converter. According to different control methods, it can be divided into voltage construction type and current following type. Its energy source is usually the kinetic energy of the wind turbine, which is affected by the actual operating state of the wind turbine.

[0035] Temporal and spatial distribution characteristics of inertia: The scale of offshore wind power connected to the grid through converters is constantly increasing. Compared with the traditional power system dominated by synchronous machines, its inertia characteristics will change significantly. On the one hand, due to the randomness, intermittency and volatility of offshore wind power output, the operation mode of the system is complex and changeable, and the inertia will show a large fluctuation characteristic on the time scale; on the other hand, the system inertia in areas with a large proportion of converters and areas with a large proportion of synchronous machines will show certain spatial distribution characteristics.

[0036] Kriging method: It is a regression algorithm based on covariance function, which is used for spatial modeling and prediction (interpolation) of random processes or random fields. Kriging method can provide optimal linear unbiased estimation, so it is also called spatial optimal unbiased estimator.

[0037] Kron simplification: is a method used to simplify Boolean expressions.

[0038] The present application discloses an inertia distribution visualization method of an offshore wind power grid-connected system, an electronic device, and a computer-readable storage medium, and relates to the technical field of inertia analysis of power systems. The method includes: when a typhoon disaster occurs, state analysis and output characteristic analysis are performed based on the current first typhoon real-time data obtained and the offshore wind farm coordinates of the offshore wind power grid-connected system, and the current operating state and inertia support of the offshore wind farm group are determined; modeling is performed based on the inertia support situation, and based on the obtained mathematical model of the offshore wind power grid-connected system; the node inertia value of each network node is calculated; the visualization image main frame is obtained based on the node inertia value and the system network topology diagram, and the discrete node inertia values ​​are spatially interpolated to obtain the interpolated data set; the first inertia distribution heat map is drawn based on the interpolated data set and the visualization image main frame. It can display the inertia distribution of the offshore wind power grid-connected system in real time, and provide a reliable reference for dispatching management.

[0039] The embodiments of the present application are further described below in conjunction with the accompanying drawings.

[0040] First, as Figure 1 As shown, the offshore wind power grid-connected system includes: an offshore wind farm group consisting of a plurality of offshore wind turbines. The inertia distribution visualization method of the offshore wind power grid-connected system may include but is not limited to steps S110 to S160.

[0041] Step S110: When a typhoon disaster occurs, status analysis is performed based on the acquired real-time data of the first typhoon and the offshore wind farm coordinates of the offshore wind power grid-connected system to determine the current operating status of the offshore wind farm group.

[0042] Step S120: Perform output characteristic analysis according to the current operating status to determine the inertia support status of the offshore wind farm group.

[0043] Step S130: Modeling the offshore wind power grid-connected system according to the inertia support conditions to obtain a mathematical model of the offshore wind power grid-connected system.

[0044] Step S140: performing node inertia calculation processing according to the mathematical model to obtain the node inertia value of each network node in the mathematical model.

[0045] Step S150: performing a first drawing process according to the node inertia value and the system network topology diagram of the offshore wind power grid-connected system to obtain a visualization image main frame.

[0046] Step S160: performing spatial interpolation processing on the discrete node inertia values ​​to obtain an interpolated data set.

[0047] Step S170: performing a second drawing process according to the interpolated data set and the visualization image main frame to obtain a first inertia distribution heat map.

[0048] It should be noted that when a typhoon disaster occurs, the current first typhoon real-time data can be obtained from the meteorological department or a professional meteorological service provider. Specifically, the real-time typhoon data such as the typhoon position, intensity, path, wind speed, and moving speed can be obtained through the typhoon information query interface. Therefore, the embodiment of the present application does not specifically limit the method of obtaining typhoon real-time data.

[0049] Specifically, the first typhoon real-time data includes but is not limited to: typhoon center position coordinates, wind circle information, moving speed, moving direction, and moving path; and the wind circle information includes: wind circle radius, wind circle speed, and wind circle level of each level of wind circles.

[0050] Through step S110 to step S160, in the process of supervising the offshore wind power grid-connected system, first, when a typhoon disaster occurs, a state analysis is performed based on the current first typhoon real-time data and the offshore wind farm coordinates of the offshore wind power grid-connected system to determine the current operating state of the offshore wind farm group; secondly, an output characteristic analysis is performed based on the current operating state to determine the inertia support situation of the offshore wind farm group; then, the offshore wind power grid-connected system is modeled based on the inertia support situation to obtain a mathematical model of the offshore wind power grid-connected system; then, node inertia calculation is performed based on the mathematical model to obtain the node inertia value of each network node in the mathematical model; then, based on the node inertia The first drawing process is performed on the system network topology diagram of the offshore wind power grid-connected system to obtain the main framework of the visualization image; then, the discrete node inertia values ​​are spatially interpolated to obtain the interpolated data set; finally, the second drawing process is performed based on the interpolated data set and the main framework of the visualization image to obtain the first inertia distribution heat map; the first inertia distribution heat map reflects the spatiotemporal characteristics of inertia, making up for the insufficient analysis ability of the COI method in terms of the spatiotemporal characteristics of inertia; and the first inertia distribution heat map is visualized to display the change of inertia distribution of the offshore wind power grid-connected system under typhoon disasters in real time, which can assist the dispatching personnel to monitor the inertia distribution status of the offshore wind power grid-connected system in real time for management and decision-making. Therefore, the embodiment of the present application can display the inertia distribution of the offshore wind power grid-connected system in real time when a typhoon disaster occurs, providing a reliable reference for dispatching personnel to perform dispatching management.

[0051] It should be emphasized that the existing COI method obtains the system equivalent inertia that can represent the average frequency dynamics of the system by equivalently aggregating the inertia of each unit in the system. During the dispatching process, the system equivalent inertia value will fluctuate with the time operation of the system. The dispatcher can judge whether the system operation is at risk based on whether the system equivalent inertia value is sufficient (whether it drops to the minimum allowed inertia value). If it exceeds the limit, the inertia can be supplemented by relevant means (such as starting the thermal power unit, configuring the energy storage unit, etc.). However, since COI cannot characterize the spatial distribution information, when supplementing, it may cause the area with high inertia level to be higher and the area with low level to be lower. Based on this, the embodiment of the present application proposes a method for visualizing the inertia distribution of the offshore wind power grid-connected system before and after the typhoon passes. The spatial strength distribution of the system inertia can be intuitively obtained from the inertia distribution heat map obtained by drawing. The dispatcher can increase the energy storage system configuration and other means for the weak areas of the system inertia when the typhoon passes according to the inertia distribution heat map to optimize the inertia level of the weak areas, thereby ensuring the inertia safety of the system when the typhoon passes.

[0052] According to some embodiments of the present application, step S110 is further described, wherein state analysis processing is performed based on the acquired current first typhoon real-time data and the offshore wind farm coordinates of the offshore wind power grid-connected system to determine the current operating state of the offshore wind farm group, including:

[0053] Step S111: Obtain the typhoon center position coordinates and wind circle information from the first typhoon real-time data.

[0054] Step S112: performing distance calculation processing according to the coordinates of the offshore wind farm and the coordinates of the typhoon center position, respectively, to obtain a straight-line reference distance between the offshore wind farm group and the typhoon center.

[0055] Step S113: performing relative position judgment processing according to the straight-line reference distance and the wind circle information to determine the wind circle grade and wind circle wind speed of the target wind circle where the offshore wind farm group is located.

[0056] Step S114: determining the current operating state of the offshore wind farm group according to the wind circle level and wind circle wind speed of the target wind circle in which the offshore wind farm group is located; wherein the current operating state includes: normal operating state, full power state and typhoon shutdown state.

[0057] Specifically, in step S111, the wind circle information includes: the wind circle radius, wind circle speed, and wind circle level of each level of wind circle.

[0058] It is understandable that when a typhoon disaster passes through, the operating state of the offshore wind turbines will switch between normal state, full power state and typhoon shutdown state according to the actual wind speed. In this regard, the operating state of the offshore wind farm group is divided into the following three categories according to the location of the offshore wind farm group in different typhoon wind circles.

[0059] The first category is: when the wind speed is lower than the maximum wind speed of 12m / s or near the maximum wind speed outside the typhoon level 7 wind circle, the offshore wind turbines are in normal operation, and the offshore wind farms can provide effective virtual inertia support and load reduction and frequency regulation. This stage is often in the early stage of the typhoon and the late stage of the typhoon.

[0060] The second category is: when the wind speed is significantly higher than 12m / s and lower than the wind speed of 25m / s, the offshore wind turbines are operating at full power, that is, the entire offshore wind farm group is outputting at full power. This state is usually called the golden power generation period. In order to meet the requirements of safe operation, the offshore wind farm group no longer provides auxiliary services such as inertia support, load reduction and frequency regulation. This process usually occurs when the typhoon level 10 wind circle approaches or just leaves.

[0061] The third category is: within the typhoon level 10 wind circle, that is, when the wind speed is higher than 25m / s, the offshore wind turbines in the same offshore wind farm group will be uniformly switched to the typhoon shutdown state, that is, all shut down. During the shutdown process, the offshore wind farm group will reduce the power output from the full power state to the zero power state, which usually takes only a few minutes. This process generally occurs during the typhoon transit.

[0062] It can be seen that the target wind circles of the offshore wind farm groups are different, and the current operating status of the offshore wind farm groups is also different. Therefore, it is necessary to determine the target wind circle of each offshore wind turbine according to the straight-line reference distance between the offshore wind farm coordinates and the typhoon center through steps S111 to S113.

[0063] According to some embodiments of the present application, step S114 is further described. Step S114: determining the current operating state of the offshore wind farm group according to the wind circle level and wind circle wind speed of the target wind circle where the offshore wind farm group is located, includes:

[0064] When the wind circle level of the target wind circle where the offshore wind farm group is located is less than level seven, or when the wind circle level of the target wind circle where the offshore wind farm group is located is equal to level seven and the wind circle wind speed is less than the first wind speed threshold, it is determined that the current operating state of the offshore wind farm group is a normal operating state.

[0065] When the wind circle level of the target wind circle where the offshore wind farm group is located is equal to level seven and the wind speed in the wind circle is greater than or equal to the first wind speed threshold, or, when the wind circle level of the target wind circle where the offshore wind farm group is located is greater than level seven and less than level ten, or, when the wind circle level of the target wind circle where the offshore wind farm group is located is equal to level ten and the wind speed in the wind circle is less than the second wind speed threshold, it is determined that the current operating state of the offshore wind farm group is in the full power state.

[0066] When the wind circle level of the target wind circle where the offshore wind farm group is located is equal to level ten and the wind speed in the wind circle is greater than or equal to the second wind speed threshold, it is determined that the current operating state of the offshore wind farm group is a typhoon shutdown state.

[0067] Specifically, the first wind speed threshold is 12 m / s, and the second wind speed threshold is 25 m / s.

[0068] Through steps S111 to S115, the current operating status of the offshore wind farm group is determined based on the real-time data of the first typhoon and the coordinates of the offshore wind farm, so as to facilitate the subsequent analysis of the output characteristics of the offshore wind farm group.

[0069] According to some embodiments of the present application, step S120 is further described. Step S120: Perform output characteristic analysis and processing according to the current operating state to determine the inertia support of the offshore wind farm group, including: when the current operating state is a normal operating state, determine the inertia support state as: can provide inertia support; when the current operating state is a full power state or a typhoon shutdown state, determine the inertia support state as: cannot provide inertia support. This lays a reference foundation for the subsequent establishment of a mathematical model of an offshore wind power grid-connected system.

[0070] Take an example, combined with Figure 2 As shown, the specific process of state analysis processing and output characteristic analysis provided in the embodiment of the present application is described.

[0071] Step S201, obtaining the coordinates of the offshore wind farm.

[0072] Step S202, obtaining the typhoon center position coordinates and wind circle information.

[0073] Step S203, calculating the straight-line reference distance between the offshore wind farm coordinates and the typhoon center position coordinates.

[0074] Step S204, judging whether the main circulation of the typhoon begins to cover the offshore wind farm according to the straight-line reference distance; if not, executing steps S205 to S206; if yes, executing step S207.

[0075] Step S205: Determine whether the system is in normal operation.

[0076] Step S206: judging the inertia support status as: being able to provide inertia support.

[0077] Step S207: Determine whether it is in the seventh-level wind circle and the wind speed in the wind circle is greater than 12m / s. If not, execute steps S205 to S206; if so, execute steps S208 to S209.

[0078] Step S208: Determine whether the power is in full-power state.

[0079] Step S209: judging the inertia support status: inertia support cannot be provided.

[0080] While executing step S204, execute step S210: determine whether the wind circle of level 10 or above covers the offshore wind farm; if not, execute steps S208 to S209; if yes, execute step S211.

[0081] Step S211: Determine whether the wind speed in the wind circle is greater than 25 m / s. If not, execute steps S208 to S209; if so, execute steps S212 and S209.

[0082] Step S212: Determine whether the system is in a typhoon shutdown state.

[0083] It should be noted that when analyzing the operating status, since the field-level switching method is usually used, offshore wind farms with similar geographical locations are equivalently aggregated. The IEEE39 node system is used here to illustrate this. The data of an offshore wind farm group actually built in a certain province is used to improve it. The aggregated wind turbines M4, M7, and M9 replace the corresponding positions of the original thermal power units G4, G7, and G9 in the system. The generated power is the actual generated power of the offshore wind power, and the virtual inertia provided by the offshore wind power is its set value under normal operating conditions. The geographical location data of the offshore wind turbines are recorded. The system network topology diagram of the offshore wind power grid-connected system is shown in the figure below: Figure 3 Then, the path of Typhoon Mina is used as an example to illustrate the operation status of a certain offshore wind farm group. Figure 4 The output power curve is shown in Figure 5 shown.

[0084] According to some embodiments of the present application, step S130 is further described. Step S130: modeling the offshore wind power grid-connected system according to the inertia support situation to obtain a mathematical model of the offshore wind power grid-connected system, including but not limited to steps S131 to S133.

[0085] Step S131: The offshore wind farm group that can provide inertia support is regarded as an equivalent synchronous machine.

[0086] Step S132: The offshore wind farm group whose inertia support condition is unable to provide inertia support is regarded as a constant power source.

[0087] Step S133: After determining the inertia support state of the offshore wind farm group, a simplified mathematical modeling process is performed based on the node admittance matrix to obtain a mathematical model of the offshore wind power grid-connected system.

[0088] Through steps S131 to S133, a mathematical model of the offshore wind power grid-connected system can be constructed based on the inertia support conditions of the offshore wind farm group, laying a data foundation for the subsequent calculation of node inertia values.

[0089] Further, the modeling process provided by the embodiment of the present application is explained.

[0090] When modeling, we only focus on the active power-frequency response process, and the generator motion process is described by the second-order equation. The second-order equation in the generator motion process is:

[0091]

[0092] Among them, δ g is the rotor power angle of the generator; t is time; ω g is the rotor speed; ω0 is the rated speed of the rotor; P m is the mechanical power; P e is the electromagnetic power; H is the inertia time constant of the generator; D is the damping coefficient.

[0093] For the dynamic process of offshore wind farms, when the offshore wind farms cannot provide inertia support for the system, the offshore wind farms are regarded as constant power sources; when the offshore wind farms can provide inertia support for the system, the offshore wind farms are regarded as equivalent synchronous units. When the offshore wind farms can provide inertia support for the system, the above control equations (i.e., the second-order equations in the generator movement process) are added to the converter control link during the modeling process, so that the converter control link has the grid-connected operation characteristics of communicating with the synchronous machine first, and the inertia response is similar to that of the synchronous machine.

[0094] like Figure 6 As shown in the figure, for system network modeling, the node admittance matrix is ​​used as the basis to describe the voltage and injection current of each node. The network equation of the mathematical model of the offshore wind power grid-connected system is established as follows:

[0095]

[0096] in, is the node voltage, is the node injection current, Y is the node admittance matrix, A is the matrix reflecting the network topology constraints, y b is the branch unit constraint; y lis the admittance of branch l, A l is the corresponding column vector in matrix A; A T is the transposed matrix of matrix A.

[0097] It should be noted that using the node admittance matrix Y to characterize the network state of the system is a common method for power system modeling. After processing and sorting the corresponding elements inside the node admittance matrix Y, we can get:

[0098]

[0099] According to some embodiments of the present application, step S140 is further described. Step S140: performing node inertia calculation processing according to a mathematical model to obtain a node inertia value of each network node in the mathematical model includes but is not limited to steps S141 to S142.

[0100] Step S141: Simplify the mathematical model to obtain the node inertia expression; the node inertia expression is: Where g represents the individual generator number, G represents the generator set in the offshore wind power grid-connected system, represents the element corresponding to the generator g and the network node n in the association matrix, Hg represents the inertia of the generator with the individual generator number g;

[0101] Step S142: Calculate the node inertia value of each network node according to the node inertia expression.

[0102] Through step S141 to step S142, the node inertia value of each network node is obtained, laying a foundation for subsequently drawing a reliable first inertia distribution heat map.

[0103] Furthermore, before describing the node inertia calculation process in detail, the basic theory of power system inertia is further described as follows.

[0104] In the power system, inertia is provided by the rotating motor directly coupled to the power grid, which is reflected in the ability of the synchronous machine rotor to suppress speed changes, and further reflected in the ability of the power system to suppress frequency changes. The inertia of the synchronous generator is defined by the following formula:

[0105] J = ∫r 2 dm=mr 2 ;

[0106] Among them, J is the moment of inertia of the synchronous machine rotor, r is the vertical distance between the mass point and the shaft, and m is the mass point.

[0107] The expression for the rotational kinetic energy stored in the rotor is:

[0108]

[0109] Among them, E k is the rotational kinetic energy stored in the rotor, and ω is the angular velocity of the shaft. The inertia of a single synchronous machine can be expressed by the inertia constant H, which is defined as the ratio of the machine kinetic energy to the rated power at rated speed:

[0110]

[0111] Among them, S r is the rated power of the synchronous machine, ω r is the rated speed of the synchronous machine.

[0112] In the traditional multi-machine system, the inertia constant of the entire power system is expressed as:

[0113]

[0114] Considering the virtual inertia H provided by the offshore wind farm group VI,j , the expression of the inertia constant of the entire power system can be modified as follows:

[0115]

[0116] Among them, H total is the inertia constant of the entire power system; H i is the inertia constant of the ith synchronous unit; S i is the rated capacity of the ith synchronous unit; S sys is the total installed capacity of the entire power system, that is, the sum of the rated capacities of all synchronous units; n is the number of synchronous units providing inertia support in the power system; H VI,j is the virtual inertia provided by the jth offshore wind turbine, S v,j is the rated capacity provided by the jth offshore wind turbine; m is the number of offshore wind turbines providing inertia support in the offshore wind power grid-connected system.

[0117] From the swing equation, we can know that the frequency change rate of the system is inversely proportional to the inertia, that is, the larger the system inertia, the slower the frequency change rate when subjected to the same disturbance. Based on this concept, the node inertia is defined here, and the node inertia calculation process of the embodiment of the present application is further described below to explain the derivation process of the node inertia.

[0118] Based on the mathematical model of the offshore wind power grid-connected system established above, the transient reactance of the generator is further shrunk into the node, and then the load is regarded as an equivalent admittance and is also shrunk into the node. The mathematical expression of the internal potential of the generator and the network injection current is obtained by Kirchhoff's current law:

[0119]

[0120] in, is the potential inside the generator, are the other node potentials, is the generator injection current: The specific expression of the node admittance matrix Y is: In the dq coordinate system (direct axis-quadrature axis coordinate system) of the synchronous generator, Y x is the direct axis synchronous admittance; Y′ rx is the mutual admittance from the direct axis to the quadrature axis; Y′ xr is the mutual admittance from the quadrature axis to the direct axis; Y′ rr is the quadrature-axis synchronous admittance.

[0121] Furthermore, according to Kron simplification, the simplified formula of node potential is obtained:

[0122] Among them, C M Is: correlation matrix.

[0123] Based on the simplified formula of node potential, the potential is expressed in phasor form and the derivatives on both sides are derived. The specific process and derivative results are as follows:

[0124]

[0125] Considering that the potential amplitude change and phase angle deviation under the standard value are small, the above formula is further simplified:

[0126]

[0127] Reducing both sides yields:

[0128] Δf D =C M Δf E ;

[0129] Where Δf D represents the node frequency increment, Δf E is the frequency increment of the generator set. It can be seen that the node frequency is determined by the frequency of each generator. The change of the generator frequency leads to the change of the node frequency, which is expressed by the matrix C M Make an association.

[0130] Based on the expression of node frequency increment, for a specific node n, there is the following expression:

[0131]

[0132] In the formula, g represents the individual generator number, G represents the set of generators in the system, represents the element corresponding to generator g and node n in the association matrix, ΔP g The disturbance power obtained by the unit distribution, ignoring the disturbance power term, finally obtains the node inertia expression:

[0133] Where g represents the individual generator number, G represents the generator set in the offshore wind power grid-connected system, It represents the element corresponding to the generator g and the network node n in the association matrix, and Hg represents the inertia of the generator with the individual generator number g.

[0134] Step S150 is further described. Step S150: Perform a first drawing process according to the node inertia value and the system network topology diagram of the offshore wind power grid-connected system to obtain a visualization image main frame. Drawing the visualization image main frame through step S150 lays a data foundation for drawing the first inertia distribution heat map.

[0135] Specifically, the inertia support situation of the offshore wind farm group is judged according to its operating status before and after the typhoon passes. After the node inertia value of each node is calculated, the main framework of the visualization image is drawn according to the actual system network topology diagram. During the drawing process, circles or thicker line segments are used to represent nodes and draw nodes; thinner line segments are used to represent lines. Then, based on the node drawing and creation of the matrix, the spatial positions of the nodes and lines are mapped with the corresponding XY axis coordinates, and the Z axis is the calculated node inertia value.

[0136] Further explanation of step S160: Step S160: Perform spatial interpolation processing on the discrete node inertia values ​​to obtain an interpolated data set. Through step S160, the discrete node inertia values ​​are optimized to obtain an interpolated data set, which lays a data foundation for drawing the first inertia distribution heat map.

[0137] It is understandable that in the drawing process, the calculated node inertia value corresponds to the data of a single point in space, which is discrete and difficult to observe directly when displaying. Therefore, an interpolation network is created here to reasonably fill the discrete spatial data. Considering the spatial correlation, the Kriging method is used for interpolation, and the interpolation formula used is:

[0138] in, is the estimated value of the spatial point (X0, Y0); i is the weight coefficient, which is the optimal coefficient that satisfies the minimum variance between the estimated value and the true value of the point (X0, Y0): At the same time, it satisfies the unbiased estimation condition, that is, the expectation is 0; z i is the attribute value corresponding to the estimated spatial point (X0, Y0).

[0139] It is assumed here that in an intrinsically stationary random field, the mathematical expectation is independent of its position, that is, it has the same expectation and variance for any point in space:

[0140] E[Z]=c;var[Z]=σ 2 ; In mathematical terms, E[Z] = c means that the expected value of Z is c, and var[Z] = σ 2 The variance of Z is σ 2 .

[0141] Substituting the interpolation formula into the unbiased estimation condition, the Kriging problem and Kriging variance are expressed as follows:

[0142]

[0143] It is easy to prove from the best linear unbiased prediction theory that the unbiased estimation condition of Kriging is that the sum of all weight coefficients is 1:

[0144]

[0145] definition:

[0146] C ij = Cov(Z i ,Z j )=Cov(R i ,R j )

[0147] R i =Z i -C

[0149] r ij =δ 2 -C ij ;

[0150] Among them, C ij is the covariance between Zi and Zj, Zi is the spatial point (X i ,Y i ) corresponding to the attribute value, Zj is the spatial point (X j ,Y j ) corresponding attribute value, R i Represents a spatial point (X i ,Y i ) relative to the regional average, R j Represents a spatial point (X j ,Y j ) corresponds to the deviation of the attribute value relative to the regional average value, r ij is the semivariogram function, δ 2 is the variance.

[0151] Construct the cost function. Specifically, let the estimated error be the cost function J:

[0152]

[0153] in, is the estimated value, Z0 is the estimated spatial point (X0, Y0), λ i is the weight coefficient, λ j is the weight coefficient, according to the above formula C ij = Cov(Z i , Z j ) can know: C 00 is the covariance between Z0 and Z0, C i0 Z i and the covariance between Z0.

[0154] The following solution function is constructed using the Lagrange multiplier method, where φ is the Lagrange operator:

[0155]

[0156] And get the equation system of Kriging problem:

[0157]

[0158] Solving the equations of the Kriging problem gives the weight coefficient λ i , the weight coefficient λ i Substitute the interpolation formula to obtain the estimated value. Based on the calculated estimated value and the original discrete node inertia value, the interpolated data set is obtained.

[0159] According to some embodiments of the present application, step S170 is further described. Step S170: performing a second drawing process according to the interpolated data set and the main frame of the visualization image to obtain a first inertia distribution heat map, including but not limited to steps S171 to S176.

[0160] Step S171: Create a three-dimensional surface with solid color edges and solid color faces.

[0161] Step S172: On a first plane of the three-dimensional surface determined by the X-axis and the Y-axis, the value in the interpolated data set is determined as the grid height.

[0162] Step S173: Determine the numerical values ​​in the interpolated data set as grid height values ​​on a first plane of the three-dimensional surface; wherein the first plane is determined by an X-axis and a Y-axis of the three-dimensional surface.

[0163] Step S174: Determine the color of the three-dimensional surface according to the grid height value to obtain a colored three-dimensional surface.

[0164] Step S175: converting the colored three-dimensional surface into a two-dimensional plane image.

[0165] Step S176: Draw the main frame of the visualization image on the two-dimensional plane image to obtain a first inertia distribution heat map of the offshore wind power grid-connected system.

[0166] Specifically, after the spatial interpolation process is completed, a three-dimensional surface with solid color edges and solid color faces is created, and the values ​​in the matrix are plotted as the height values ​​above the grid on the xy axis plane. The surface color is determined by the height color specified by the value, and the time is rotated to the top to form a two-dimensional plane. Finally, the drawn system network main frame is overlaid on the image to form a Figure 7 Thermodynamic diagram of the inertia distribution of the system shown.

[0167] It is understandable that the existing inertia analysis method is mainly COI, which ignores the spatial characteristics of inertia distribution and cannot reflect the spatial distribution of inertia. With the large-scale grid connection of offshore wind power, the inertia characteristics of the system have undergone tremendous changes. Under typhoon disasters, the inertia support role of offshore wind power changes with the different stages of the typhoon's passage, and the inertia time-space characteristics are obvious. Based on this, the present application draws a first inertia distribution heat map through steps S171 to S176 to display the real-time distribution of inertia under typhoon disasters, which is convenient for dispatchers to intuitively understand the system's inertia time-space distribution, so as to make targeted dispatch instructions for regulation and control, and ensure the safe and stable operation of the system.

[0168] According to some embodiments of the present application, the method for visualizing the inertia distribution of an offshore wind power grid-connected system further includes but is not limited to steps S210 to S230.

[0169] Step S210: before performing state analysis processing based on the acquired real-time data of the current first typhoon and the offshore wind farm coordinates of the offshore wind power grid-connected system, the current time is obtained.

[0170] Step S220: After a preset execution cycle has passed at the current moment, after obtaining the first inertia distribution heat map, obtaining updated second typhoon real-time data.

[0171] Step S230: According to the real-time data of the second typhoon, state analysis processing, output characteristic analysis processing, modeling processing, node inertia calculation processing, first drawing processing, spatial interpolation processing, and second drawing processing are performed again in sequence to obtain an updated second inertia distribution thermal map.

[0172] It is understandable that in a typhoon disaster, the typhoon is constantly moving, and the inertia distribution heat map of the offshore wind power grid-connected system also needs to be continuously updated. Therefore, a preset execution cycle is set, and the preset execution cycle is equal to the execution time of step S110 to step S170; the specific preset execution cycle can be determined according to the actual processing situation. This application does not limit the specific value of the preset execution cycle.

[0173] Through steps S210 to S230, after obtaining the first inertia distribution heat map, the updated second typhoon real-time data is obtained, and then an analysis is performed based on the second typhoon real-time data to draw an updated second inertia distribution heat map to ensure the timeliness of the inertia distribution heat map, thereby providing a reliable reference for dispatchers to perform dispatch management.

[0174] like Figure 8 As shown, the present application also provides an electronic device, including:

[0175] The processor 801 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0176] The memory 802 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 802, and the processor 801 calls and executes the inertia distribution visualization method of the offshore wind power grid-connected system of the embodiment of this application;

[0177] Input / output interface 803, used to implement information input and output;

[0178] The communication interface 804 is used to realize the communication interaction between the present apparatus and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0179] A bus 805 that transmits information between the various components of the device (e.g., the processor 801, the memory 802, the input / output interface 803, and the communication interface 804);

[0180] The processor 801 , the memory 802 , the input / output interface 803 and the communication interface 804 are connected to each other in communication within the device via a bus 805 .

[0181] An embodiment of the present application further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the above-mentioned inertia distribution visualization method of the offshore wind power grid-connected system is implemented.

[0182] As a non-transient computer-readable storage medium, the memory can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and are implemented to be located in one place, or may also be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0183] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media generally include computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0184] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the above-mentioned implementation mode. Technical personnel familiar with the field can also make various equivalent deformations or substitutions without violating the spirit of the present application. These equivalent deformations or substitutions are all included in the scope defined by the present application.

Claims

1. A method for visualizing inertia distribution of an offshore wind power grid-connected system, characterized in that: The offshore wind power grid-connected system comprises: an offshore wind farm group consisting of a plurality of offshore wind turbines; the method comprises: When a typhoon disaster occurs, a state analysis process is performed based on the acquired real-time data of the first typhoon and the coordinates of the offshore wind farm of the offshore wind power grid-connected system to determine the current operating state of the offshore wind farm group; Perform output characteristic analysis and processing according to the current operating state to determine the inertia support status of the offshore wind farm group; Modeling the offshore wind power grid-connected system according to the inertia support condition to obtain a mathematical model of the offshore wind power grid-connected system; Performing node inertia calculation processing according to the mathematical model to obtain the node inertia value of each network node in the mathematical model; Performing a first drawing process according to the node inertia value and the system network topology diagram of the offshore wind power grid-connected system to obtain a visualization image main frame; Performing spatial interpolation processing on the discrete node inertia values ​​to obtain an interpolated data set; A second drawing process is performed based on the interpolated data set and the visualization image main frame to obtain a first inertia distribution heat map.

2. The inertia distribution visualization method of the offshore wind power grid-connected system according to claim 1 is characterized in that: The state analysis and processing is performed based on the acquired real-time data of the current first typhoon and the offshore wind farm coordinates of the offshore wind power grid-connected system to determine the current operating state of the offshore wind farm group, including: Obtaining the typhoon center position coordinates and wind circle information from the first typhoon real-time data; Perform distance calculation processing according to the offshore wind farm coordinates and the typhoon center position coordinates to obtain a straight-line reference distance between the offshore wind farm group and the typhoon center; Perform relative position judgment processing according to the straight-line reference distance and the wind circle information to determine the wind circle grade and wind circle wind speed of the target wind circle where the offshore wind farm group is located; The current operating state of the offshore wind farm group is determined according to the wind circle level and the wind speed of the target wind circle in which the offshore wind farm group is located; wherein the current operating state includes: normal operating state, full power state and typhoon shutdown state.

3. The inertia distribution visualization method of the offshore wind power grid-connected system according to claim 2 is characterized in that: Determining the current operating state of the offshore wind farm group according to the wind circle level and the wind circle wind speed of the target wind circle in which the offshore wind farm group is located includes: When the wind circle level of the target wind circle where the offshore wind farm group is located is less than level seven, or when the wind circle level of the target wind circle where the offshore wind farm group is located is equal to level seven and the wind speed of the wind circle is less than a first wind speed threshold, it is determined that the current operating state of the offshore wind farm group is a normal operating state; When the wind circle level of the target wind circle where the offshore wind farm group is located is equal to level seven and the wind speed of the wind circle is greater than or equal to the first wind speed threshold, or, when the wind circle level of the target wind circle where the offshore wind farm group is located is greater than level seven and less than level ten, or, when the wind circle level of the target wind circle where the offshore wind farm group is located is equal to level ten and the wind speed of the wind circle is less than the second wind speed threshold, it is determined that the current operating state of the offshore wind farm group is a full power state; When the wind circle level of the target wind circle where the offshore wind farm group is located is equal to level ten and the wind speed of the wind circle is greater than or equal to a second wind speed threshold, it is determined that the current operating state of the offshore wind farm group is a typhoon shutdown state.

4. The inertia distribution visualization method of the offshore wind power grid-connected system according to claim 2, characterized in that: The performing output characteristic analysis and processing according to the current operating state to determine the inertia support status of the offshore wind farm group includes: When the current operating state is the normal operating state, determining the inertia support condition as: being able to provide inertia support; When the current operating state is the full power state or the typhoon shutdown state, the inertia support condition is determined as: inertia support cannot be provided.

5. The inertia distribution visualization method of the offshore wind power grid-connected system according to claim 4, characterized in that: The modeling process of the offshore wind power grid-connected system according to the inertia support condition to obtain a mathematical model of the offshore wind power grid-connected system includes: The offshore wind farm group that can provide inertia support is regarded as an equivalent synchronous unit; The offshore wind farm group that cannot provide inertia support is regarded as a constant power source; After the inertial support state of the offshore wind farm group is determined, a simplified mathematical modeling process is performed based on a node admittance matrix to obtain a mathematical model of the offshore wind power grid-connected system.

6. The inertia distribution visualization method of the offshore wind power grid-connected system according to claim 1, characterized in that: The node inertia calculation process is performed according to the mathematical model to obtain the node inertia value of each network node in the mathematical model, including: The mathematical model is simplified and calculated to obtain the node inertia expression; the node inertia expression is: Where g represents the individual generator number, G represents the generator set in the offshore wind power grid-connected system, represents the element corresponding to the generator g and the network node n in the association matrix, Hg represents the inertia of the generator with the individual generator number g; The node inertia value of each network node is calculated according to the node inertia expression.

7. The inertia distribution visualization method of the offshore wind power grid-connected system according to claim 1, characterized in that: The second drawing process is performed according to the interpolated data set and the visualization image main frame to obtain a first inertia distribution heat map, including: Create a 3D surface with solid edges and solid faces; On a first plane of the three-dimensional curved surface determined by an X-axis and a Y-axis, determining a value in the interpolated data set as a grid height; Determine the values ​​in the interpolated data set as grid height values ​​on a first plane of the three-dimensional surface; wherein the first plane is determined by an X-axis and a Y-axis of the three-dimensional surface; Determine the color of the three-dimensional surface according to the grid height value to obtain a colored three-dimensional surface; Performing two-dimensional processing on the three-dimensional curved surface after coloring to obtain a two-dimensional plane image; The main frame of the visualization image is correspondingly drawn on the two-dimensional plane image to obtain a first inertia distribution thermal map of the offshore wind power grid-connected system.

8. The inertia distribution visualization method of the offshore wind power grid-connected system according to claim 1, characterized in that: The method further comprises: Before performing state analysis processing based on the acquired current first typhoon real-time data and the offshore wind farm coordinates of the offshore wind power grid-connected system, obtaining the current time; After the first inertia distribution heat map is obtained after a preset execution cycle at the current moment, updated second typhoon real-time data is obtained; According to the second typhoon real-time data, the state analysis processing, the output characteristic analysis processing, the modeling processing, the node inertia calculation processing, the first drawing processing, the spatial interpolation processing, and the second drawing processing are re-performed in sequence to obtain an updated second inertia distribution thermal map.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for visualizing the inertia distribution of an offshore wind power grid-connected system as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the inertia distribution visualization method for an offshore wind power grid-connected system according to any one of claims 1 to 8.

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