Typhoon regional micro-grid regulation method and system considering power interaction between micro-grids

Abstract

This invention provides a method and system for microgrid regulation in typhoon areas, taking into account power interaction between microgrids. The method, relating to the field of microgrid regulation, includes: acquiring load data of a target microgrid in a typhoon area and identifying the degree of overload; acquiring historical interaction data of multiple supporting microgrids; extracting historical support periods, historical support frequency, and historical support power curves; calculating the support success rate and extracting support capacity data; determining the scheduling priority of supporting microgrids based on preset weights; acquiring the available power of a local energy storage system; generating a power support command based on the overload degree, support capacity data, scheduling priority, and the available power; and enabling the supporting microgrids to coordinate with the local energy storage system to output power to the distribution bus of the target microgrid, thereby achieving regulation of the target microgrid. This application can effectively improve the regulation resilience and power supply stability of the target microgrid in complex scenarios such as typhoon areas.

Description

Technical Field

[0001] This invention application relates to the field of microgrid regulation, and more particularly to a method and system for regulating microgrids in typhoon-prone areas that takes into account power interaction between microgrids. Background Technology

[0002] In some remote areas, microgrids are the core of distributed energy systems, typically providing power to users in isolated grid environments. However, in extreme scenarios such as direct impact from typhoon eyewalls, they often face complex system failures due to multiple influencing factors, affecting the stability of power supply.

[0003] Microgrids typically rely on satellite communication or terrestrial microwave links for data transmission and coordination. However, external factors such as typhoons often cause signal attenuation, leading to link loss and affecting data transmission quality. Furthermore, typhoons can cause sudden surges in load (e.g., increased electricity consumption in schools, clinics, or shelters during typhoons). Microgrids usually utilize local energy storage to ensure stable power supply when facing these impacts. However, this method is limited by the scale of local energy storage, resulting in insufficient resilience in complex scenarios such as typhoon-affected areas. There is still room for improvement in power supply stability. Summary of the Invention

[0004] This invention application provides a microgrid control method and system for typhoon areas that takes into account power interaction between microgrids, in order to solve the technical problem of how to improve the control resilience of target microgrids in typhoon areas and enhance the power supply stability of target microgrids.

[0005] To address the aforementioned technical problems, this invention provides a method and system for microgrid regulation in typhoon-prone areas, taking into account power interaction between microgrids, comprising:

[0006] Obtain load data of the target microgrid in the typhoon area, and identify the degree of overload based on the load data;

[0007] When the overload level exceeds a preset overload threshold, acquire historical interaction data of multiple supporting microgrids; extract historical support periods, historical support times, and historical support power curves based on the historical interaction data;

[0008] Calculate the support success rate based on the historical support periods and the number of historical support instances; extract support capacity data based on the historical support power curve;

[0009] Based on the support success rate and support capacity data, and combined with the preset weight of the support microgrid, the scheduling priority of the support microgrid is determined.

[0010] The system obtains the available power of the local energy storage system; generates a power support command based on the overload level, support capacity data, scheduling priority, and available power; and sends the power support command to each of the supporting microgrids, so that the supporting microgrids, in coordination with the local energy storage system, output power to the distribution bus of the target microgrid according to the power support command, thereby realizing the regulation of the target microgrid.

[0011] As a preferred embodiment, the step of generating a power support command based on the overload level, support capacity data, scheduling priority, and the available power of the local energy storage system includes:

[0012] The real-time overload power of the target microgrid is determined based on the degree of overload.

[0013] When the available power is less than the real-time overload power, the real-time overload power is used as the minuend and the available power is used as the subtrahend to calculate the total power gap that needs to be supplemented by the supporting microgrid.

[0014] Based on the scheduling priority and combined with the support capacity data of each supporting microgrid, the total power gap is allocated to each supporting microgrid in sequence according to the proportion and order corresponding to each scheduling priority, so as to obtain the target support power of each supporting microgrid; wherein, the proportion corresponding to each scheduling priority is pre-configured;

[0015] The power support command is generated based on the target support power.

[0016] As a preferred embodiment, generating the power support command based on the target support power includes:

[0017] For each supporting microgrid, generate basic instructions that include the target supporting power and the distribution bus identifier of the target microgrid;

[0018] Read the dynamic parameters of the energy storage unit supporting the microgrid; wherein, the dynamic parameters include the SOC value, charge / discharge power limit and current output power;

[0019] Based on the dynamic parameters, the power change rate is set to generate a power change curve that supports the microgrid;

[0020] A power balance curve is generated based on the power change curve, the real-time output power of the local energy storage system, and the load power of the target microgrid.

[0021] Based on the power balance curve and the total power gap, the target support power is updated to obtain the updated support power, and a power support command containing the updated support power and the target microgrid distribution bus identifier is generated.

[0022] As a preferred embodiment, obtaining the available power of the local energy storage system includes:

[0023] Acquire real-time meteorological data of the typhoon area, and calculate the signal attenuation parameters of satellite communication based on the real-time meteorological data and a preset signal attenuation model;

[0024] The communication interruption risk coefficient of the target microgrid is calculated based on the signal attenuation parameters.

[0025] Obtain vibration displacement data of the ground microwave link tower base; calculate the microwave link loss probability using the vibration displacement data; and monitor whether a double communication interruption state occurs based on the loss probability and the communication interruption risk coefficient.

[0026] When a dual communication interruption is detected, the status data of the local energy storage system is acquired, and the available power is obtained by analyzing the status data of the local energy storage system.

[0027] As a preferred embodiment, the step of calculating the microwave link loss probability using the vibration displacement data; and monitoring whether a dual communication interruption state occurs based on the loss probability and the communication interruption risk coefficient, including:

[0028] The displacement change rate is calculated based on the vibration displacement data; wind load data is collected by a wind speed sensor, and a correlation analysis is performed on the wind load data and the displacement change rate to obtain the correlation analysis results;

[0029] Based on the correlation analysis results, the offset exceeding the limit point is marked on the microwave antenna pointing angle curve to obtain the antenna loss of lock time point;

[0030] Based on the statistical interval of the loss of antenna lock at the moment of loss of lock, the transition probability matrix of antenna lock and loss of lock is established by a two-state Markov chain. The steady-state distribution is calculated by the transition probability matrix, and then the probability density function is generated.

[0031] Construct the state transition diagram of the target microgrid based on the probability density function;

[0032] According to the state transition graph, if the unlock probability value of the state transition graph exceeds the preset double interruption threshold and the communication interruption risk coefficient of the state transition graph is greater than the preset historical statistical benchmark value, then a double communication interruption state is determined to have occurred; otherwise, it is determined that a double communication interruption state has not occurred.

[0033] As a preferred embodiment, the step of calculating the displacement change rate based on the vibration displacement data includes:

[0034] The vibration displacement data is subjected to Butterworth low-pass filtering to obtain filtered data; vibration features are extracted based on the filtered data.

[0035] Based on the vibration characteristics, the spectral density distribution is calculated using fast Fourier transform, and the main frequency component value is extracted based on the spectral density distribution.

[0036] Based on the dominant frequency component value, record vibration frequency data points within the antenna azimuth deviation range to obtain the frequency characteristic curve;

[0037] Based on the frequency characteristic curve and the vibration characteristics, the displacement change rate is calculated.

[0038] As a preferred embodiment, the step of determining the scheduling priority of the support microgrid based on the support success rate and the support capacity data, combined with the preset weight of the support microgrid, includes:

[0039] Read the power transmission loss data and response time data between each supporting microgrid and the target microgrid respectively; perform a comprehensive evaluation on the power transmission loss data and response time data to obtain the support evaluation result;

[0040] Configure basic weights based on power transmission loss data, configure time weights based on response time data, and configure historical weights based on support success rate and support capacity data; update the preset weights based on the basic weights, time weights, and historical weights to obtain optimized weights;

[0041] The support evaluation results are superimposed with the optimization weights to obtain the priority quantization value of each support micronetwork, and then the scheduling priority of the support micronetwork is determined based on the priority quantization value.

[0042] As a preferred embodiment, the load data is represented by equipment load; the step of identifying the overload level based on the load data includes:

[0043] Obtain historical operating condition data for the typhoon area; wherein, the historical operating condition data includes deviation values ​​of the impact of typhoon operating conditions on the load;

[0044] The phase difference of the equipment load is recorded at a fixed sampling interval; the power factor curve and voltage fluctuation curve are extracted from the equipment load.

[0045] Based on the power factor curve, calculate the active power and reactive power of the equipment; based on the voltage fluctuation curve, extract the fluctuation amplitude points;

[0046] Based on the phase difference, fluctuation amplitude points, active power of the equipment, and reactive power of the equipment, a load fluctuation dataset is constructed.

[0047] By comparing and analyzing the load fluctuation dataset with the historical operating condition data, the degree of overload can be identified.

[0048] Accordingly, this invention application also provides a typhoon-affected microgrid control system that considers power interaction between microgrids, including an overload identification module, a historical data extraction module, a support data extraction module, a scheduling priority determination module, and a control module; wherein,

[0049] The overload identification module is used to acquire load data of the target microgrid in the typhoon area and identify the degree of overload based on the load data.

[0050] The historical data extraction module is used to acquire historical interaction data of multiple supporting microgrids when the overload level is greater than a preset overload threshold; and to extract historical support periods, historical support times, and historical support power curves based on the historical interaction data.

[0051] The support data extraction module is used to calculate the support success rate based on the historical support period and the number of historical support sessions; and to extract support capacity data based on the historical support power curve.

[0052] The scheduling priority determination module is used to determine the scheduling priority of the support microgrid based on the support success rate and the support capacity data, combined with the preset weight of the support microgrid.

[0053] The control module is used to obtain the available power of the local energy storage system; generate a power support command based on the overload level, support capacity data, scheduling priority, and available power; and send the power support command to each of the supporting microgrids, so that the supporting microgrids, in accordance with the power support command, coordinate with the local energy storage system to output power to the distribution bus of the target microgrid, thereby realizing the control of the target microgrid.

[0054] As a preferred embodiment, the control module generates a power support command based on the overload level, support capacity data, scheduling priority, and the available power of the local energy storage system, including:

[0055] The control module determines the real-time overload power of the target microgrid based on the degree of overload.

[0056] When the available power is less than the real-time overload power, the real-time overload power is used as the minuend and the available power is used as the subtrahend to calculate the total power gap that needs to be supplemented by the supporting microgrid.

[0057] Based on the scheduling priority and combined with the support capacity data of each supporting microgrid, the total power gap is allocated to each supporting microgrid in sequence according to the proportion and order corresponding to each scheduling priority, so as to obtain the target support power of each supporting microgrid; wherein, the proportion corresponding to each scheduling priority is pre-configured;

[0058] The power support command is generated based on the target support power.

[0059] As a preferred embodiment, the control module generates the power support command based on the target support power, including:

[0060] The control module generates basic instructions for each supporting microgrid, including the target supporting power and the distribution bus identifier of the target microgrid.

[0061] Read the dynamic parameters of the energy storage unit supporting the microgrid; wherein, the dynamic parameters include the SOC value, charge / discharge power limit and current output power;

[0062] Based on the dynamic parameters, the power change rate is set to generate a power change curve that supports the microgrid;

[0063] A power balance curve is generated based on the power change curve, the real-time output power of the local energy storage system, and the load power of the target microgrid.

[0064] Based on the power balance curve and the total power gap, the target support power is updated to obtain the updated support power, and a power support command containing the updated support power and the target microgrid distribution bus identifier is generated.

[0065] As a preferred embodiment, the control module acquires the available power of the local energy storage system, including:

[0066] The control module acquires real-time meteorological data of the typhoon area and calculates the signal attenuation parameters of satellite communication based on the real-time meteorological data and a preset signal attenuation model.

[0067] The communication interruption risk coefficient of the target microgrid is calculated based on the signal attenuation parameters.

[0068] Obtain vibration displacement data of the ground microwave link tower base; calculate the microwave link loss probability using the vibration displacement data; and monitor whether a double communication interruption state occurs based on the loss probability and the communication interruption risk coefficient.

[0069] When a dual communication interruption is detected, the status data of the local energy storage system is acquired, and the available power is obtained by analyzing the status data of the local energy storage system.

[0070] As a preferred embodiment, the control module calculates the microwave link loss probability using the vibration displacement data; and monitors whether a dual communication interruption state occurs based on the loss probability and the communication interruption risk coefficient, including:

[0071] The control module calculates the displacement change rate based on the vibration displacement data; it collects wind load data through a wind speed sensor and performs correlation analysis on the wind load data and the displacement change rate to obtain the correlation analysis results.

[0072] Based on the correlation analysis results, the offset exceeding the limit point is marked on the microwave antenna pointing angle curve to obtain the antenna loss of lock time point;

[0073] Based on the statistical interval of the loss of antenna lock at the moment of loss of lock, the transition probability matrix of antenna lock and loss of lock is established by a two-state Markov chain. The steady-state distribution is calculated by the transition probability matrix, and then the probability density function is generated.

[0074] Construct the state transition diagram of the target microgrid based on the probability density function;

[0075] According to the state transition graph, if the unlock probability value of the state transition graph exceeds the preset double interruption threshold and the communication interruption risk coefficient of the state transition graph is greater than the preset historical statistical benchmark value, then a double communication interruption state is determined to have occurred; otherwise, it is determined that a double communication interruption state has not occurred.

[0076] As a preferred embodiment, the control module calculates the displacement change rate based on the vibration displacement data, including:

[0077] The control module performs Butterworth low-pass filtering on the vibration displacement data to obtain filtered data; based on the filtered data, vibration features are extracted.

[0078] Based on the vibration characteristics, the spectral density distribution is calculated using fast Fourier transform, and the main frequency component value is extracted based on the spectral density distribution.

[0079] Based on the dominant frequency component value, record vibration frequency data points within the antenna azimuth deviation range to obtain the frequency characteristic curve;

[0080] Based on the frequency characteristic curve and the vibration characteristics, the displacement change rate is calculated.

[0081] As a preferred embodiment, the step of determining the scheduling priority of the support microgrid based on the support success rate and the support capacity data, combined with the preset weight of the support microgrid, includes:

[0082] Read the power transmission loss data and response time data between each supporting microgrid and the target microgrid respectively; perform a comprehensive evaluation on the power transmission loss data and response time data to obtain the support evaluation result;

[0083] Configure basic weights based on power transmission loss data, configure time weights based on response time data, and configure historical weights based on support success rate and support capacity data; update the preset weights based on the basic weights, time weights, and historical weights to obtain optimized weights;

[0084] The support evaluation results are superimposed with the optimization weights to obtain the priority quantization value of each support micronetwork, and then the scheduling priority of the support micronetwork is determined based on the priority quantization value.

[0085] As a preferred embodiment, the load data is represented by equipment load; the overload identification module identifies the degree of overload based on the load data, including:

[0086] The overload identification module acquires historical operating condition data of the typhoon area; wherein, the historical operating condition data includes deviation values ​​of the impact of typhoon operating conditions on the load;

[0087] The phase difference of the equipment load is recorded at a fixed sampling interval; the power factor curve and voltage fluctuation curve are extracted from the equipment load.

[0088] Based on the power factor curve, calculate the active power and reactive power of the equipment; based on the voltage fluctuation curve, extract the fluctuation amplitude points;

[0089] Based on the phase difference, fluctuation amplitude points, active power of the equipment, and reactive power of the equipment, a load fluctuation dataset is constructed.

[0090] By comparing and analyzing the load fluctuation dataset with the historical operating condition data, the degree of overload can be identified.

[0091] Compared with the prior art, this invention application has the following beneficial effects:

[0092] This invention provides a method and system for regulating a microgrid in a typhoon area, taking into account power interaction between microgrids. This application acquires the available power of local energy storage and generates power support commands based on scheduling priorities. This enables the supporting microgrid to coordinate with local energy storage to output power to the distribution bus of the target microgrid, achieving a combination of power resources from local energy storage and external supporting microgrids. Compared to existing technologies that rely solely on local energy storage for regulation, this overcomes the bottleneck caused by the scale of local energy storage. Through the coordinated power output guarantee of both local energy storage and supporting microgrids, the regulation resilience and power supply stability of the target microgrid in complex scenarios such as typhoon areas can be effectively improved. It also reduces the risk of power outages due to insufficient power in the target microgrid by accurately filling load gaps. Furthermore, this application acquires historical interaction data from supporting microgrids, extracts historical support periods, frequency, and power curves, and analyzes the reliability (support success rate), actual support capabilities (support capacity data), and suitable scenarios (historical support periods) of each supporting microgrid. This avoids power mismatch caused by cognitive biases regarding the capabilities of supporting microgrids, providing data support for subsequent scheduling priority determination and improving the accuracy of support decisions. Attached Figure Description

[0093] Figure 1 This is a flowchart illustrating an embodiment of the typhoon-affected microgrid control method considering power interaction between microgrids provided in this invention application.

[0094] Figure 2 This is a flowchart illustrating a preferred embodiment of the typhoon-area microgrid control method considering power interaction between microgrids provided in this application.

[0095] Figure 3 This is a flowchart illustrating a preferred embodiment of the typhoon-affected microgrid control method considering power interaction between microgrids provided in this application.

[0096] Figure 4 This is a flowchart illustrating a preferred embodiment of the typhoon-area microgrid control method considering power interaction between microgrids provided in this application.

[0097] Figure 5 This is a flowchart illustrating a preferred embodiment of the typhoon-area microgrid control method considering power interaction between microgrids provided in this application.

[0098] Figure 6 This is a schematic diagram of an embodiment of a typhoon-area microgrid control system that takes into account power interaction between microgrids, provided in this application of the present invention. Detailed Implementation

[0099] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0100] Example 1

[0101] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the typhoon-area microgrid control method considering power interaction between microgrids provided in this invention application, including steps S101 to S105, each of which is described in detail below:

[0102] Step S101: Obtain the load data of the target microgrid in the typhoon area, and identify the overload level based on the load data.

[0103] In this embodiment, there may be multiple neighboring microgrids near the target microgrid. During a typhoon or when the target microgrid is located in a typhoon area, the typhoon's impact can lead to signal attenuation and affect communication quality. Furthermore, people in the area may seek refuge in locations such as schools, clinics, or shelters, causing a surge in electricity load. The target microgrid can maintain stable power supply by utilizing energy reserves in its local energy storage system. However, when the local energy storage system is insufficient (e.g., lack of capacity), this embodiment can leverage the support of neighboring microgrids (hereinafter referred to as supporting microgrids) to ensure power supply, thereby improving the resilience of the target microgrid in the typhoon area.

[0104] When ensuring the power supply to the target microgrid by supporting microgrids, the target microgrid can determine the supporting microgrid based on the power transmission distance.

[0105] When the supporting microgrid provides power support to the target microgrid, the methods adopted in this embodiment can be divided into two categories. One category is that the supporting microgrid outputs power to the distribution bus of the target microgrid, and then the distribution bus distributes it to the load. The other category is that the supporting microgrid outputs power to the local energy storage system of the target microgrid in an indirect support manner.

[0106] In some embodiments, the load data can be represented by equipment load, specifically, equipment load can be collected by an electricity meter.

[0107] like Figure 2 As shown, in some preferred embodiments, step S101, which identifies the degree of overload based on the load data, includes steps S201 to S205, each of which is detailed below:

[0108] Step S201: Obtain historical operating condition data of the typhoon area; wherein, the historical operating condition data includes deviation values ​​of the impact of typhoon operating conditions on the load;

[0109] Step S202: Record the phase difference value of the equipment load at a fixed sampling interval; extract the power factor curve and voltage fluctuation curve from the equipment load;

[0110] Step S203: Based on the power factor curve, calculate the active power and reactive power of the equipment; based on the voltage fluctuation curve, extract the fluctuation amplitude points;

[0111] Step S204: Construct a load fluctuation dataset based on the phase difference, fluctuation amplitude points, active power of the equipment, and reactive power of the equipment;

[0112] Step S205: Compare and analyze the load fluctuation dataset with the historical operating condition data to identify the degree of overload.

[0113] In some implementations, step S201 may involve obtaining historical operating condition data of the typhoon area or historical (typhoon) operating condition data of other areas. The aforementioned historical operating condition data can be obtained from a preset typhoon database.

[0114] In step S205, when comparing and analyzing the load fluctuation dataset with the historical operating condition data, the deviation values ​​of the typhoon conditions affecting the load in the historical operating condition data can be analyzed, such as fluctuation feature identification or fluctuation pattern extraction. Furthermore, the extracted fluctuation features or fluctuation patterns are used for comparison, matching, and analysis of the load fluctuation dataset. For example, when a certain time period in the load fluctuation dataset is identified as conforming to a certain fluctuation feature, the overload level of that time period can be identified.

[0115] Figure 2 Based on the embodiment shown in step S201, this embodiment calibrates and identifies equipment load using historical operating data. Through multi-dimensional analysis of equipment load parameters (phase, power factor, and voltage), it can accurately identify the degree of overload, avoiding errors in support power calculation caused by misjudgment of overload degree (reflected in generating power support commands in step S105) and thus preventing resource waste. Simultaneously, it helps step S102 accurately determine the timing of support activation, avoiding activation too early or too late. The dataset comparison method in this embodiment can adapt to load characteristics under different typhoon intensities, improving the adaptability and accuracy of overload identification.

[0116] Step S102: When the overload level is greater than the preset overload threshold, acquire historical interaction data of multiple supporting microgrids; extract historical support time periods, historical support times, and historical support power curves based on the historical interaction data.

[0117] In this embodiment, each supporting micronet can correspond to a historical interaction database, and this step obtains the historical interaction data from the aforementioned historical interaction database.

[0118] By analyzing the historical interaction data for each typhoon event, we can extract the historical support periods, the number of historical support events, and the historical support power curves.

[0119] Among them, the historical support period can reflect the characteristics of microgrid mutual assistance in different periods. For example, the number of support times during typhoons is significantly higher than during normal periods. The number of support times can be used in subsequent steps to analyze the support success rate in conjunction with the historical support period. The historical support power curve can be used in subsequent steps to extract support capacity data.

[0120] Step S103: Calculate the support success rate based on the historical support period and the number of historical support sessions; extract support capacity data based on the historical support power curve.

[0121] In this step, the support success rate can be calculated by counting the number of historical support requests within each historical support period. Generally, the support success rate is negatively correlated with the distance between micronets. For example, the support success rate of the near-range group reaches 95%, while that of the long-range group drops to 75%. Specifically, micronets within 2 kilometers can be classified as the near-range group, those between 2 and 5 kilometers can be classified as the medium-range group, and those more than 5 kilometers can be classified as the long-range group.

[0122] The aforementioned support capacity data can be obtained by marking the maximum output point at a certain point in the historical support power curve. On the one hand, it can reflect the maximum support capacity of the microgrid, and on the other hand, it can reflect the carrying capacity of the line.

[0123] This step analyzes the support capability and efficiency of the microgrid by calculating the support success rate and support capacity data, laying the foundation for accurately determining the scheduling priority of the support microgrid in the future.

[0124] Step S104: Based on the support success rate and support capacity data, and combined with the preset weight of the support microgrid, determine the scheduling priority of the support microgrid.

[0125] In this step, the preset weights can be pre-configured before step S104, and each supporting micronet is assigned a preset weight. In some embodiments, the scheduling priority of the supporting micronet can be calculated directly based on the support success rate and the support capacity data, combined with the aforementioned preset weights. In this way, when calculating the scheduling priority, part of the consideration is the support capability of the supporting micronet (for example, those with strong support capabilities can be scheduled first to avoid affecting the supporting micronet itself), and another part also considers the pre-assigned weights, which can ensure the rationality of the scheduling priority calculation.

[0126] Furthermore, such as Figure 3 As shown, step S104, which involves determining the scheduling priority of the support micronet based on the support success rate and the support capacity data and combined with the preset weight of the support micronet, includes steps S301 to S303; each step is detailed below:

[0127] Step S301: Read the power transmission loss data and response time data between each supporting microgrid and the target microgrid respectively; perform a comprehensive evaluation on the power transmission loss data and response time data to obtain the support evaluation result;

[0128] Step S302: Configure basic weights based on power transmission loss data, configure time weights based on response time data, and configure historical weights based on support success rate and support capacity data; update the preset weights based on the basic weights, time weights, and historical weights to obtain optimized weights;

[0129] Step S303: The support evaluation results are superimposed with the optimization weights to obtain the priority quantization value of each support micronet, and then the scheduling priority of the support micronet is determined based on the priority quantization value.

[0130] This implementation method reads the communication data between each supporting microgrid and the target microgrid, specifically power transmission loss data and response time data. It then comprehensively evaluates the power transmission loss data, response time data, and support success rate to obtain a support evaluation result. This evaluation result reflects the data transmission quality between the supporting microgrid and the target microgrid. Furthermore, by constructing a multi-dimensional system using time weights, historical weights, and basic weights, and updating preset weights, the scheduling priority can be made to perfectly match the actual support needs of the typhoon area (e.g., prioritizing microgrids with low losses, fast response times, and high success rates). This ensures more scientific decision-making and control of supporting microgrids based on scheduling priorities, further improving support response speed and power utilization efficiency, and reducing the risk of power outages.

[0131] Step S105: Obtain the available power of the local energy storage system; generate a power support command based on the overload level, support capacity data, scheduling priority, and available power; and send the power support command to each of the supporting microgrids, so that the supporting microgrids, in coordination with the local energy storage system, output power to the distribution bus of the target microgrid according to the power support command, thereby realizing the regulation of the target microgrid.

[0132] In existing technologies, when obtaining the available power of a local energy storage system, the impact of typhoons on the load is generally considered. At this time, the available power is usually estimated according to the normal communication scenario, which may ignore the communication anomalies caused by typhoons, leading to errors in the local energy storage call decision when communication is interrupted.

[0133] Therefore, in some preferred embodiments of step S105 of this embodiment, obtaining the available power of the local energy storage system includes: obtaining real-time meteorological data of the typhoon area; calculating the signal attenuation parameter of satellite communication based on the real-time meteorological data and a preset signal attenuation model; calculating the communication interruption risk coefficient of the target microgrid based on the signal attenuation parameter; obtaining vibration displacement data of the ground microwave link tower base; calculating the microwave link lockout probability through the vibration displacement data; and monitoring whether a dual communication interruption state occurs based on the lockout probability and the communication interruption risk coefficient; when a dual communication interruption state is determined to occur, obtaining the status data of the local energy storage system, and analyzing the available power based on the status data of the local energy storage system.

[0134] The signal attenuation model in this preferred embodiment can be pre-built and used to describe the mapping relationship between meteorological conditions and signal attenuation. This embodiment analyzes real-time meteorological data by calling the aforementioned signal attenuation model, calculates the signal attenuation parameters of satellite communication, and then monitors whether a dual communication interruption state occurs through the loss-of-lock probability and communication interruption risk coefficient. This allows for accurate determination of communication quality. Compared with existing technologies, which consider not only the impact of typhoons on the load but also the impact of typhoons on satellite communication quality and use the communication interruption state as a premise for calculating the available power of local energy storage, this ensures that in extreme scenarios where typhoons cause dual communication interruptions (satellite and microwave), the calculation of the available power of local energy storage can adapt to the special operating condition of "not being able to rely on external support microgrid real-time data." This avoids energy storage overload or insufficient power supply caused by calling energy storage according to the normal scenario during communication interruption, providing a guarantee for the coordinated output of local energy storage and support microgrid in extreme communication scenarios, and further improving the resilience of regulation.

[0135] Further, the step of calculating the microwave link's unlock probability using the vibration displacement data and monitoring whether a dual communication interruption state occurs based on the unlock probability and the communication interruption risk coefficient includes: calculating the displacement change rate based on the vibration displacement data; collecting wind load data using a wind speed sensor and performing correlation analysis on the wind load data and the displacement change rate to obtain the correlation analysis results; marking the offset exceeding the limit point on the microwave antenna pointing angle curve based on the correlation analysis results to obtain the antenna unlock time point; statistically analyzing the unlock duration interval based on the antenna unlock time point, establishing a transition probability matrix for antenna locking and unlocking using a two-state Markov chain, calculating the steady-state distribution using the transition probability matrix, and then generating a probability density function; constructing a state transition map of the target microgrid based on the probability density function; and determining that a dual communication interruption state has occurred when the unlock probability value of the state transition map exceeds a preset dual interruption threshold and the communication interruption risk coefficient of the state transition map is greater than a preset historical statistical benchmark value; otherwise, determining that a dual communication interruption state has not occurred.

[0136] For example, analysis of wind load data revealed that when wind speeds exceeded 12 m / s, antenna vibration amplitude increased significantly, showing a significant positive correlation between wind load and vibration amplitude. An antenna pointing angle deviation exceeding 1 degree could be marked as a point of loss of lock. The duration of antenna loss of lock typically ranges from 10 to 30 seconds. A two-state Markov chain is used to describe the antenna's locked-state transition process. In the state transition probability matrix, the probability of transitioning from a locked state to a unlocked state is 0.15, and the probability of recovering from an unlocked state to a locked state is 0.85. The long-term loss of lock probability is obtained by calculating the steady-state distribution of the Markov chain, and the probability density function is obtained using kernel density estimation within the loss of lock duration interval.

[0137] The state transition graph constructed in this implementation shows the distribution characteristics of the unlock probability value in the time dimension. The dual interruption threshold parameter can be set to an upper limit of 0.3 for the unlock probability. When the measured unlock probability exceeds this upper limit and the communication interruption risk coefficient is greater than the baseline value of 0.25 obtained from historical statistics, it indicates that the link is severely affected by both mechanical vibration and meteorological factors. The system judges this state as a dual communication interruption. In this case, emergency measures need to be taken to ensure communication.

[0138] Compared to existing technologies that rely solely on simple thresholds to determine communication interruptions (such as determining communication interruption upon signal disappearance), this preferred embodiment uses correlation analysis of wind load and displacement rate to determine the antenna loss-lock moment and thus statistically analyze the loss-lock duration interval. Combined with Markov chains and state graph visualization, it can significantly reduce the false positive and false negative rates of dual communication interruptions, avoid deviations in energy storage call timing due to incorrect interruption determination, and further improve the reliability of local energy storage regulation in extreme scenarios.

[0139] In some preferred embodiments, calculating the displacement change rate based on the vibration displacement data includes: performing Butterworth low-pass filtering on the vibration displacement data to obtain filtered data; extracting vibration features based on the filtered data; calculating the spectral density distribution using a fast Fourier transform based on the vibration features, and extracting the dominant frequency component value based on the spectral density distribution; recording vibration frequency data points within the antenna azimuth deviation range based on the dominant frequency component value to obtain a frequency characteristic curve; and calculating the displacement change rate based on the frequency characteristic curve and the vibration features.

[0140] In some implementations, considering that the sampling interval of the vibration sensor data acquisition is set to 5 milliseconds in the microwave link tower foundation vibration monitoring, and that the original vibration displacement data contains high-frequency noise components, a Butterworth low-pass filter with a cutoff frequency of 50 Hz is used for noise filtering.

[0141] Vibration characteristics can be represented in the form of a sequence. The time window length for vibration characteristic analysis is set to 1 minute, and the window sliding step is 10 seconds. In this embodiment, the vibration characteristic data in each time window are subjected to fast Fourier transform to obtain a spectral density distribution or curve with a frequency resolution of 0.1 Hz. The frequency component with the largest amplitude is extracted from the distribution or curve as the dominant frequency value. The allowable range of antenna azimuth deviation is ±0.5 degrees. The vibration frequency data points when the vibration causes the antenna azimuth to exceed this range are recorded.

[0142] This preferred embodiment uses Butterworth low-pass filtering for noise reduction and extracts the dominant frequency component using spectrum analysis. Combined with the constraint of the antenna azimuth angle, it can overcome the defects in the prior art that fail to denoise or extract key frequency features when processing vibration displacement data, resulting in deviations in the calculation of displacement change rate. At the same time, it can effectively eliminate interference from irrelevant vibrations in the typhoon environment (such as instantaneous equipment shaking), ensuring the accuracy of displacement change rate calculation, thereby reducing the error of dual communication interruption judgment and improving the overall monitoring accuracy.

[0143] For step S105, in this embodiment, the microgrid control center can generate a power support command for each supporting microgrid and send the power support command to each supporting microgrid. This allows the supporting microgrids to coordinate with their local energy storage systems to output power to the distribution bus of the target microgrid based on the information carried in the power support command (e.g., the power requirement of the target microgrid for a particular supporting microgrid), thereby achieving regulation of the target microgrid. Specifically, each supporting microgrid can be equipped with an energy storage unit, and the supporting microgrid can output power through this energy storage unit.

[0144] Preferably, such as Figure 4As shown, step S105, which generates a power support command based on the overload level, support capacity data, scheduling priority, and available power of the local energy storage system, includes steps S401 to S404, each of which is detailed below:

[0145] Step S401: Determine the real-time overload power of the target microgrid based on the overload level;

[0146] Step S402: When the available power is less than the real-time overload power, the real-time overload power is used as the minuend and the available power is used as the subtrahend to calculate the total power gap that needs to be supplemented by the supporting microgrid.

[0147] Step S403: Based on the scheduling priority and combined with the support capacity data of each supporting microgrid, the total power gap is allocated to each supporting microgrid in sequence according to the proportion and order corresponding to each scheduling priority, so as to obtain the target support power of each supporting microgrid; wherein, the proportion corresponding to each scheduling priority is pre-configured.

[0148] Step S404: Generate the power support command based on the target support power.

[0149] In this embodiment, since the overload level can be obtained by combining historical operating data of the typhoon area with comparative analysis of equipment load, this embodiment can determine the real-time overload power of the target microgrid by analyzing the overload level at the load end. Furthermore, by using the real-time overload power as the minuend and the available power as the subtrahend, the total power gap that needs to be supplemented by the supporting microgrids is calculated. Then, based on the scheduling priority and the support capacity data of each supporting microgrid, the total power gap is allocated to each supporting microgrid in the proportion and order corresponding to each scheduling priority. This ensures that the target support power of each supporting microgrid is completely matched with the total gap, avoiding both low support efficiency caused by insufficient power allocation to high-priority supporting microgrids and power or resource waste caused by over-allocation to low-priority supporting microgrids. This strengthens the coordinated output of the local energy storage system and the supporting microgrids, effectively reduces the risk of power fluctuations caused by power distribution imbalances, and improves the reliability of microgrid regulation in the typhoon area.

[0150] exist Figure 4 Based on the implementation method shown, as Figure 5 As shown, step S404, which generates the power support command based on the target support power, includes steps S501 to S505; each step is detailed below:

[0151] Step S501: For each supporting microgrid, generate a basic instruction containing the target supporting power and the distribution bus identifier of the target microgrid.

[0152] Step S502: Read the dynamic parameters of the energy storage unit supporting the microgrid; wherein, the dynamic parameters include SOC value, charge / discharge power limit and current output power;

[0153] Step S503: Based on the dynamic parameters, set the power change rate to generate a power change curve that supports the microgrid;

[0154] Step S504: Generate a power balance curve based on the power change curve, the real-time output power of the local energy storage system, and the load power of the target microgrid;

[0155] Step S505: Based on the power balance curve and the total power gap, update the target support power to obtain the updated support power, and generate a power support command containing the updated support power and the target microgrid distribution bus identifier.

[0156] As described above, microgrids can be equipped with energy storage units. For each energy storage unit, its dynamic parameters can be read in real time through an energy storage operation status monitor, thereby configuring a suitable power change rate and recording power control points within the allowable voltage range to obtain an accurate power change curve. For example, the power change rate can be set to 0.1 times the rated power per second, the allowable voltage deviation range can be ±7% of the rated value, and the power control point interval can be 1 second.

[0157] This implementation method updates the target support power by analyzing the dynamic parameters of the supporting microgrid energy storage unit (such as SOC value, charging and discharging power limits, and current output power). This ensures that the generated power support command meets the load demand of the target microgrid without exceeding the operating limits of the supporting microgrid. Simultaneously, the dynamic updating of the target power through the power balance curve can offset the deviation between local energy storage output fluctuations and target microgrid load fluctuations in real time. Figure 4 The implementation shown makes the target support power allocated on demand more feasible, avoids control interruptions caused by support microgrid failure or power imbalance, and further improves the stability of coordinated power supply.

[0158] Accordingly, such as Figure 6 As shown, this invention application also provides a typhoon-affected microgrid control system 600 that considers power interaction between microgrids, including an overload identification module 601, a historical data extraction module 602, a support data extraction module 603, a scheduling priority determination module 604, and a control module 605; wherein,

[0159] The overload identification module 601 is used to acquire load data of the target microgrid in the typhoon area and identify the degree of overload based on the load data;

[0160] The historical data extraction module 602 is used to acquire historical interaction data of multiple supporting microgrids when the overload level is greater than a preset overload threshold; and to extract historical support periods, historical support times, and historical support power curves based on the historical interaction data.

[0161] The support data extraction module 603 is used to calculate the support success rate based on the historical support period and the number of historical support sessions; and to extract support capacity data based on the historical support power curve.

[0162] The scheduling priority determination module 604 is used to determine the scheduling priority of the support microgrid based on the support success rate and the support capacity data and the preset weight of the support microgrid.

[0163] The control module 605 is used to obtain the available power of the local energy storage system; generate a power support command based on the overload level, support capacity data, scheduling priority, and available power; and send the power support command to each of the supporting microgrids, so that the supporting microgrids, in accordance with the power support command, coordinate with the local energy storage system to output power to the distribution bus of the target microgrid, thereby realizing the control of the target microgrid.

[0164] As a preferred embodiment, the control module 605 generates a power support command based on the overload level, support capacity data, scheduling priority, and the available power of the local energy storage system, including:

[0165] The control module 605 determines the real-time overload power of the target microgrid based on the overload level.

[0166] When the available power is less than the real-time overload power, the real-time overload power is used as the minuend and the available power is used as the subtrahend to calculate the total power gap that needs to be supplemented by the supporting microgrid.

[0167] Based on the scheduling priority and combined with the support capacity data of each supporting microgrid, the total power gap is allocated to each supporting microgrid in sequence according to the proportion and order corresponding to each scheduling priority, so as to obtain the target support power of each supporting microgrid; wherein, the proportion corresponding to each scheduling priority is pre-configured;

[0168] The power support command is generated based on the target support power.

[0169] As a preferred embodiment, the control module 605 generates the power support command based on the target support power, including:

[0170] The control module 605 generates basic instructions for each supporting microgrid, including the target supporting power and the distribution bus identifier of the target microgrid.

[0171] Read the dynamic parameters of the energy storage unit supporting the microgrid; wherein, the dynamic parameters include the SOC value, charge / discharge power limit and current output power;

[0172] Based on the dynamic parameters, the power change rate is set to generate a power change curve that supports the microgrid;

[0173] A power balance curve is generated based on the power change curve, the real-time output power of the local energy storage system, and the load power of the target microgrid.

[0174] Based on the power balance curve and the total power gap, the target support power is updated to obtain the updated support power, and a power support command containing the updated support power and the target microgrid distribution bus identifier is generated.

[0175] As a preferred embodiment, the control module 605 acquires the available power of the local energy storage system, including:

[0176] The control module 605 acquires real-time meteorological data of the typhoon area and calculates the signal attenuation parameters of satellite communication based on the real-time meteorological data and a preset signal attenuation model.

[0177] The communication interruption risk coefficient of the target microgrid is calculated based on the signal attenuation parameters.

[0178] Obtain vibration displacement data of the ground microwave link tower base; calculate the microwave link loss probability using the vibration displacement data; and monitor whether a double communication interruption state occurs based on the loss probability and the communication interruption risk coefficient.

[0179] When a dual communication interruption is detected, the status data of the local energy storage system is acquired, and the available power is obtained by analyzing the status data of the local energy storage system.

[0180] As a preferred embodiment, the control module 605 calculates the microwave link loss probability using the vibration displacement data; and monitors whether a dual communication interruption state occurs based on the loss probability and the communication interruption risk coefficient, including:

[0181] The control module 605 calculates the displacement change rate based on the vibration displacement data; it collects wind load data through a wind speed sensor and performs correlation analysis on the wind load data and the displacement change rate to obtain the correlation analysis results.

[0182] Based on the correlation analysis results, the offset exceeding the limit point is marked on the microwave antenna pointing angle curve to obtain the antenna loss of lock time point;

[0183] Based on the statistical interval of the loss of antenna lock at the moment of loss of lock, the transition probability matrix of antenna lock and loss of lock is established by a two-state Markov chain. The steady-state distribution is calculated by the transition probability matrix, and then the probability density function is generated.

[0184] Construct the state transition diagram of the target microgrid based on the probability density function;

[0185] According to the state transition graph, if the unlock probability value of the state transition graph exceeds the preset double interruption threshold and the communication interruption risk coefficient of the state transition graph is greater than the preset historical statistical benchmark value, then a double communication interruption state is determined to have occurred; otherwise, it is determined that a double communication interruption state has not occurred.

[0186] As a preferred embodiment, the control module 605 calculates the displacement change rate based on the vibration displacement data, including:

[0187] The control module 605 performs Butterworth low-pass filtering on the vibration displacement data to obtain filtered data; and extracts vibration features based on the filtered data.

[0188] Based on the vibration characteristics, the spectral density distribution is calculated using fast Fourier transform, and the main frequency component value is extracted based on the spectral density distribution.

[0189] Based on the dominant frequency component value, record vibration frequency data points within the antenna azimuth deviation range to obtain the frequency characteristic curve;

[0190] Based on the frequency characteristic curve and the vibration characteristics, the displacement change rate is calculated.

[0191] As a preferred embodiment, the step of determining the scheduling priority of the support microgrid based on the support success rate and the support capacity data, combined with the preset weight of the support microgrid, includes:

[0192] Read the power transmission loss data and response time data between each supporting microgrid and the target microgrid respectively; perform a comprehensive evaluation on the power transmission loss data and response time data to obtain the support evaluation result;

[0193] Configure basic weights based on power transmission loss data, configure time weights based on response time data, and configure historical weights based on support success rate and support capacity data; update the preset weights based on the basic weights, time weights, and historical weights to obtain optimized weights;

[0194] The support evaluation results are superimposed with the optimization weights to obtain the priority quantization value of each support micronetwork, and then the scheduling priority of the support micronetwork is determined based on the priority quantization value.

[0195] As a preferred embodiment, the load data is represented by equipment load; the overload identification module 601 identifies the degree of overload based on the load data, including:

[0196] The overload identification module 601 acquires historical operating condition data of the typhoon area; wherein, the historical operating condition data includes deviation values ​​of the impact of typhoon operating conditions on the load;

[0197] The phase difference of the equipment load is recorded at a fixed sampling interval; the power factor curve and voltage fluctuation curve are extracted from the equipment load.

[0198] Based on the power factor curve, calculate the active power and reactive power of the equipment; based on the voltage fluctuation curve, extract the fluctuation amplitude points;

[0199] Based on the phase difference, fluctuation amplitude points, active power of the equipment, and reactive power of the equipment, a load fluctuation dataset is constructed.

[0200] By comparing and analyzing the load fluctuation dataset with the historical operating condition data, the degree of overload can be identified.

[0201] Compared with the prior art, this invention application has the following beneficial effects:

[0202] This invention provides a method and system for regulating a microgrid in a typhoon area, taking into account power interaction between microgrids. This application acquires the available power of local energy storage and generates power support commands based on scheduling priorities. This enables the supporting microgrid to coordinate with local energy storage to output power to the distribution bus of the target microgrid, achieving a combination of power resources from local energy storage and external supporting microgrids. Compared to existing technologies that rely solely on local energy storage for regulation, this overcomes the bottleneck caused by the scale of local energy storage. Through the coordinated power output guarantee of both local energy storage and supporting microgrids, the regulation resilience and power supply stability of the target microgrid in complex scenarios such as typhoon areas can be effectively improved. It also reduces the risk of power outages due to insufficient power in the target microgrid by accurately filling load gaps. Furthermore, this application acquires historical interaction data from supporting microgrids, extracts historical support periods, frequency, and power curves, and analyzes the reliability (support success rate), actual support capabilities (support capacity data), and suitable scenarios (historical support periods) of each supporting microgrid. This avoids power mismatch caused by cognitive biases regarding the capabilities of supporting microgrids, providing data support for subsequent scheduling priority determination and improving the accuracy of support decisions.

[0203] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A microgrid control method for typhoon-affected areas considering power interaction between microgrids, characterized in that, include: Obtain load data of the target microgrid in the typhoon area, and identify the degree of overload based on the load data; When the overload level exceeds a preset overload threshold, acquire historical interaction data of multiple supporting microgrids; extract historical support periods, historical support times, and historical support power curves based on the historical interaction data; The success rate of support is calculated based on the historical support periods and the number of historical support attempts. Based on the historical support power curve, extract support capacity data; Based on the support success rate and support capacity data, and combined with the preset weight of the support microgrid, the scheduling priority of the support microgrid is determined. Obtain the available power of the local energy storage system; Based on the overload level, support capacity data, scheduling priority, and available power, a power support command is generated; and the power support command is sent to each of the supporting microgrids, so that the supporting microgrids, in accordance with the power support command, coordinate with the local energy storage system to output power to the distribution bus of the target microgrid, thereby realizing the regulation of the target microgrid.

2. The microgrid control method for typhoon-affected areas considering power interaction between microgrids as described in claim 1, characterized in that, The step of generating a power support command based on the overload level, support capacity data, scheduling priority, and the available power of the local energy storage system includes: The real-time overload power of the target microgrid is determined based on the degree of overload. When the available power is less than the real-time overload power, the real-time overload power is used as the minuend and the available power is used as the subtrahend to calculate the total power gap that needs to be supplemented by the supporting microgrid. Based on the scheduling priority and combined with the support capacity data of each supporting microgrid, the total power gap is allocated to each supporting microgrid in sequence according to the proportion and order corresponding to each scheduling priority, so as to obtain the target support power of each supporting microgrid; wherein, the proportion corresponding to each scheduling priority is pre-configured; The power support command is generated based on the target support power.

3. The microgrid control method for typhoon-affected areas considering power interaction between microgrids as described in claim 2, characterized in that, The step of generating the power support command based on the target support power includes: For each supporting microgrid, generate basic instructions that include the target supporting power and the distribution bus identifier of the target microgrid; Read the dynamic parameters of the energy storage unit supporting the microgrid; wherein, the dynamic parameters include the SOC value, charge / discharge power limit and current output power; Based on the dynamic parameters, the power change rate is set to generate a power change curve that supports the microgrid; A power balance curve is generated based on the power change curve, the real-time output power of the local energy storage system, and the load power of the target microgrid. Based on the power balance curve and the total power gap, the target support power is updated to obtain the updated support power, and a power support command containing the updated support power and the target microgrid distribution bus identifier is generated.

4. The microgrid control method for typhoon-affected areas considering power interaction between microgrids as described in claim 1, characterized in that, The process of obtaining the available power of the local energy storage system includes: Acquire real-time meteorological data of the typhoon area, and calculate the signal attenuation parameters of satellite communication based on the real-time meteorological data and a preset signal attenuation model; The communication interruption risk coefficient of the target microgrid is calculated based on the signal attenuation parameters. Obtain vibration displacement data of the ground microwave link tower base; calculate the microwave link loss probability using the vibration displacement data; and monitor whether a double communication interruption state occurs based on the loss probability and the communication interruption risk coefficient. When a dual communication interruption is detected, the status data of the local energy storage system is acquired, and the available power is obtained by analyzing the status data of the local energy storage system.

5. A microgrid control method for typhoon-affected areas considering power interaction between microgrids as described in claim 4, characterized in that, The probability of microwave link loss is calculated using the vibration displacement data. And based on the probability of loss of lock and the communication interruption risk coefficient, monitor whether a double communication interruption state occurs, including: The displacement change rate is calculated based on the vibration displacement data; wind load data is collected by a wind speed sensor, and a correlation analysis is performed on the wind load data and the displacement change rate to obtain the correlation analysis results; Based on the correlation analysis results, the offset exceeding the limit point is marked on the microwave antenna pointing angle curve to obtain the antenna loss of lock time point; Based on the statistical interval of the loss of antenna lock at the moment of loss of lock, the transition probability matrix of antenna lock and loss of lock is established by a two-state Markov chain. The steady-state distribution is calculated by the transition probability matrix, and then the probability density function is generated. Construct the state transition diagram of the target microgrid based on the probability density function; According to the state transition graph, if the unlock probability value of the state transition graph exceeds the preset double interruption threshold and the communication interruption risk coefficient of the state transition graph is greater than the preset historical statistical benchmark value, then a double communication interruption state is determined to have occurred; otherwise, it is determined that a double communication interruption state has not occurred.

6. A microgrid control method for typhoon-affected areas considering power interaction between microgrids as described in claim 5, characterized in that, The calculation of the displacement change rate based on the vibration displacement data includes: The vibration displacement data is subjected to Butterworth low-pass filtering to obtain filtered data; vibration features are extracted based on the filtered data. Based on the vibration characteristics, the spectral density distribution is calculated using fast Fourier transform, and the main frequency component value is extracted based on the spectral density distribution. Based on the dominant frequency component value, record vibration frequency data points within the antenna azimuth deviation range to obtain the frequency characteristic curve; Based on the frequency characteristic curve and the vibration characteristics, the displacement change rate is calculated.

7. A microgrid control method for typhoon-affected areas considering power interaction between microgrids as described in claim 1, characterized in that, The step of determining the scheduling priority of the support microgrid based on the support success rate and support capacity data, combined with the preset weight of the support microgrid, includes: Read the power transmission loss data and response time data between each supporting microgrid and the target microgrid respectively; perform a comprehensive evaluation on the power transmission loss data and response time data to obtain the support evaluation result; Configure basic weights based on power transmission loss data, configure time weights based on response time data, and configure historical weights based on support success rate and support capacity data; update the preset weights based on the basic weights, time weights, and historical weights to obtain optimized weights; The support evaluation results are superimposed with the optimization weights to obtain the priority quantization value of each support micronetwork, and then the scheduling priority of the support micronetwork is determined based on the priority quantization value.

8. A microgrid control method for typhoon-affected areas considering power interaction between microgrids as described in claim 1, characterized in that, The load data is represented by equipment load; the identification of the overload level based on the load data includes: Obtain historical operating condition data for the typhoon area; wherein, the historical operating condition data includes deviation values ​​of the impact of typhoon operating conditions on the load; The phase difference of the equipment load is recorded at a fixed sampling interval; the power factor curve and voltage fluctuation curve are extracted from the equipment load. Based on the power factor curve, calculate the active power and reactive power of the equipment; based on the voltage fluctuation curve, extract the fluctuation amplitude points; Based on the phase difference, fluctuation amplitude points, active power of the equipment, and reactive power of the equipment, a load fluctuation dataset is constructed. By comparing and analyzing the load fluctuation dataset with the historical operating condition data, the degree of overload can be identified.

9. A microgrid control system for typhoon-affected areas that considers power interaction between microgrids, characterized in that, It includes an overload identification module, a historical data extraction module, a support data extraction module, a scheduling priority determination module, and a control module; among which, The overload identification module is used to acquire load data of the target microgrid in the typhoon area and identify the degree of overload based on the load data; The historical data extraction module is used to acquire historical interaction data of multiple supporting microgrids when the overload level is greater than a preset overload threshold; and to extract historical support periods, historical support times, and historical support power curves based on the historical interaction data. The support data extraction module is used to calculate the support success rate based on the historical support period and the number of historical support sessions; and to extract support capacity data based on the historical support power curve. The scheduling priority determination module is used to determine the scheduling priority of the support microgrid based on the support success rate and the support capacity data, combined with the preset weight of the support microgrid. The control module is used to obtain the available power of the local energy storage system; generate a power support command based on the overload level, support capacity data, scheduling priority, and available power; and send the power support command to each of the supporting microgrids, so that the supporting microgrids, in accordance with the power support command, coordinate with the local energy storage system to output power to the distribution bus of the target microgrid, thereby realizing the control of the target microgrid.

10. A typhoon-area microgrid control system considering power interaction between microgrids as described in claim 9, characterized in that, The control module generates power support commands based on the overload level, support capacity data, scheduling priority, and available power of the local energy storage system, including: The control module determines the real-time overload power of the target microgrid based on the degree of overload. When the available power is less than the real-time overload power, the real-time overload power is used as the minuend and the available power is used as the subtrahend to calculate the total power gap that needs to be supplemented by the supporting microgrid. Based on the scheduling priority and combined with the support capacity data of each supporting microgrid, the total power gap is allocated to each supporting microgrid in sequence according to the proportion and order corresponding to each scheduling priority, so as to obtain the target support power of each supporting microgrid; wherein, the proportion corresponding to each scheduling priority is pre-configured; The power support command is generated based on the target support power.

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