A multi-scenario adaptive dynamic carrier load regulation method and system

Abstract

The application relates to the technical field of power system monitoring, and particularly discloses a multi-scene adaptive dynamic carrier load regulation method and system, which comprises the following steps: acquiring a scene operation mode of a power line, and acquiring real-time operation data based on the scene operation mode, wherein the real-time operation data comprises DC bus voltage data, carrier state data, load power data and environment data; acquiring a real-time carrier operation state according to the DC bus voltage data and the load power data, and generating dynamic carrier adjustment demand data according to the real-time carrier operation state; acquiring carrier dynamic regulation parameters according to the dynamic carrier adjustment demand data, and acquiring an operation stability index according to the carrier dynamic regulation parameters. Through accurate identification and differential data acquisition of grid-connected and island scenes, the application realizes dynamic adaptation of a microgrid under different operation modes.

Description

Technical Field

[0001] This invention relates to the field of power system monitoring technology, and in particular to a dynamic carrier load regulation method and system with multi-scenario adaptive characteristics. Background Technology

[0002] Against the backdrop of the rapid development of the energy internet and distributed generation technology, microgrid systems that combine wind, solar, diesel, and energy storage have become an important form of achieving efficient energy utilization due to their combination of cleanliness and flexibility.

[0003] In existing technologies, traditional control strategies typically employ fixed parameters, such as moment of inertia and damping coefficient, which cannot be dynamically adjusted according to grid-connected and islanded scenarios. This leads to sudden changes in system frequency during mode switching, or even grid disconnection. For example, during islanded operation, fluctuations in wind and solar power output directly affect frequency stability, and fixed parameters cannot compensate for inertia deficits in real time. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic carrier load adjustment method and system that is adaptive in multiple scenarios, so as to solve the technical problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A multi-scenario adaptive dynamic carrier load adjustment method includes:

[0007] The scenario operation mode of the power line is obtained, and real-time operation data is obtained based on the scenario operation mode. The real-time operation data includes DC bus voltage data, carrier status data, load power data and environmental data.

[0008] The real-time carrier operating status is obtained based on the DC bus voltage data and load power data, and dynamic carrier adjustment requirement data is generated based on the real-time carrier operating status.

[0009] Based on the dynamic carrier adjustment demand data, obtain the carrier dynamic control parameters, and based on the carrier dynamic control parameters, obtain the operational stability index;

[0010] Based on the carrier state data and environmental data, obtain carrier-environment disturbance data;

[0011] The carrier load disturbance level is obtained based on the carrier-environment disturbance data and the operational stability index, and the carrier disturbance matching data is obtained based on the carrier load disturbance level.

[0012] The disturbance stage is obtained based on the carrier disturbance matching data, and it is determined whether the disturbance stage is within a preset threshold stage.

[0013] If not in the disturbance stage, the carrier frequency deviation is obtained based on the disturbance stage, and the carrier load adjustment data is obtained based on the carrier frequency deviation.

[0014] Preferably, the step of acquiring the scenario operation mode of the power line and acquiring real-time operation data based on the scenario operation mode includes:

[0015] The connection status signal between the power line and the external power grid is detected. The connection status signal includes the power grid disconnection signal and the power grid normal connection signal.

[0016] If a power grid disconnection signal is detected, the scenario is determined to be an isolated scene.

[0017] If a normal grid connection signal is detected, it is determined to be a grid-connected scenario mode;

[0018] Obtain the switching timestamp between the isolated scenario mode and the grid-connected scenario mode;

[0019] Obtain the data acquisition channels corresponding to the island scenario mode and the grid-connected scenario mode, and obtain real-time operating data based on the switching timestamp. The real-time operating data includes DC bus voltage data, frequency data, load power data, and environmental data.

[0020] Preferably, the step of obtaining real-time carrier operating status based on the DC bus voltage data and load power data, and generating dynamic carrier adjustment demand data based on the real-time carrier operating status, includes:

[0021] The degree of DC bus deviation is obtained based on the DC bus voltage data.

[0022] The load change amplitude is obtained based on the load power data;

[0023] Based on the degree of DC bus deviation and the magnitude of load change, carrier status data is generated;

[0024] The real-time carrier operating status is generated based on the carrier status data;

[0025] The carrier status operation trend is obtained based on the real-time carrier operation status, and the trend change rate within a preset time period is obtained based on the carrier status operation trend.

[0026] If the rate of change of the trend is greater than the preset trend threshold, it is marked as the first disturbance scenario;

[0027] If the rate of change of the trend is less than the preset trend threshold, it is marked as the second disturbance scenario;

[0028] The first parameter adjustment direction is obtained based on the first disturbance scenario, and the virtual inertia requirement is generated based on the island scenario mode and the first parameter adjustment direction.

[0029] The second parameter adjustment direction is obtained based on the second disturbance scenario, and the damping coefficient demand is generated based on the grid connection scenario mode and the second parameter adjustment direction.

[0030] Dynamic carrier adjustment requirement data is generated based on the virtual inertia requirement and damping coefficient requirement.

[0031] Preferably, the step of obtaining the carrier dynamic control parameters based on the dynamic carrier adjustment demand data, and obtaining the operational stability index based on the carrier dynamic control parameters, includes:

[0032] The virtual inertia requirement and damping coefficient requirement are obtained based on the dynamic carrier adjustment requirement data.

[0033] The differentiated enhancement adjustment amount is obtained based on the virtual inertia requirement;

[0034] The suppression adjustment amount is obtained based on the required damping coefficient.

[0035] Based on the distinction between enhancement adjustment amount and suppression adjustment amount, the dynamic control parameters of the carrier are obtained;

[0036] The frequency offset and frequency change rate are obtained based on the dynamic control parameters of the carrier.

[0037] The operational stability index is obtained based on the frequency offset and the frequency change rate.

[0038] Preferably, the step of obtaining carrier-environment disturbance data based on the carrier state data and environmental data includes:

[0039] Based on the carrier status data, obtain the power grid frequency fluctuation data and the fluctuation occurrence timestamp;

[0040] Based on the timestamp of the fluctuation and environmental data, obtain data on light intensity, ambient temperature, and wind speed;

[0041] A state-environment mapping relationship is established based on the fluctuation occurrence timestamp, the power grid frequency fluctuation data, and the corresponding light intensity, ambient temperature, and wind speed data.

[0042] The state-environment correlation coefficient between environmental variables and electrical quantities is obtained based on the state-environment mapping relationship.

[0043] Based on the state-environment correlation coefficient and power grid frequency fluctuation data, carrier-environment disturbance data are obtained.

[0044] Preferably, the step of obtaining the carrier load disturbance level based on the carrier-environment disturbance data and the operational stability index, and obtaining carrier disturbance matching data based on the carrier load disturbance level, includes:

[0045] The disturbance type information is obtained based on the carrier-environment disturbance data, wherein the disturbance type information includes input power disturbance and energy storage response disturbance;

[0046] Obtain the occurrence time and load impact degree of the input power disturbance and load response disturbance;

[0047] The load disturbance intensity is obtained based on the occurrence time and the degree of load impact.

[0048] The load disturbance level of the carrier is obtained based on the load disturbance intensity.

[0049] Obtain the disturbance type label based on the carrier load disturbance level;

[0050] Obtain the input power information, grid frequency information, and energy storage charging and discharging information corresponding to the disturbance type label;

[0051] Based on the input power information, grid frequency information, and energy storage charging and discharging information, carrier disturbance matching data is obtained.

[0052] Preferably, the step of obtaining the carrier frequency deviation based on the disturbance stage and obtaining carrier load adjustment data based on the carrier frequency deviation includes:

[0053] Obtain the disturbance stage type corresponding to the disturbance stage, and obtain the virtual carrier output frequency according to the disturbance stage type;

[0054] The carrier frequency deviation is obtained based on the output frequency of the virtual carrier and the power grid frequency, and the trend of the deviation change is obtained.

[0055] The maximum frequency offset value is obtained based on the disturbance stage and the deviation change trend;

[0056] The maximum frequency offset direction is obtained based on the maximum frequency offset value;

[0057] The carrier load adjustment data is obtained based on the maximum frequency offset direction and the maximum frequency offset value.

[0058] This invention also provides a multi-scenario adaptive dynamic carrier load adjustment system, comprising:

[0059] The data acquisition module is used to acquire the scenario operation mode of the power line and acquire real-time operation data based on the scenario operation mode. The real-time operation data includes DC bus voltage data, carrier status data, load power data and environmental data.

[0060] The adjustment demand generation module is used to obtain the real-time carrier operating status based on the DC bus voltage data and load power data, and generate dynamic carrier adjustment demand data based on the real-time carrier operating status.

[0061] The stability assessment module is used to obtain dynamic control parameters of the carrier based on the dynamic carrier adjustment demand data, and to obtain the operational stability index based on the dynamic carrier control parameters.

[0062] The environmental disturbance correlation analysis module is used to obtain carrier-environmental disturbance data based on the carrier state data and environmental data;

[0063] The feature matching module is used to obtain the carrier load disturbance level based on the carrier-environment disturbance data and the operational stability index, and to obtain carrier disturbance matching data based on the carrier load disturbance level;

[0064] The load adjustment module is used to obtain the disturbance stage based on the carrier disturbance matching data and determine whether the disturbance stage is within a preset threshold stage.

[0065] If not in the disturbance stage, the carrier frequency deviation is obtained based on the disturbance stage, and the carrier load adjustment data is obtained based on the carrier frequency deviation.

[0066] Preferably, the data acquisition module includes:

[0067] The connection status detection unit is used to detect the connection status signal between the power line and the external power grid, wherein the connection status signal includes the power grid disconnection signal and the power grid normal access signal;

[0068] If a power grid disconnection signal is detected, the scenario is determined to be an isolated scene.

[0069] If a normal grid connection signal is detected, it is determined to be a grid-connected scenario mode;

[0070] The mode switching timestamp recording unit is used to obtain the switching timestamp of the island scenario mode and the grid-connected scenario mode;

[0071] The scenario data acquisition channel management unit is used to acquire the data acquisition channels corresponding to the island scenario mode and the grid-connected scenario mode, and to acquire real-time operating data based on the switching timestamp. The real-time operating data includes DC bus voltage data, frequency data, load power data and environmental data.

[0072] Preferably, the adjustment requirement generation module includes:

[0073] A voltage deviation calculation unit is used to obtain the degree of DC bus deviation based on the DC bus voltage data.

[0074] A load change calculation unit is used to obtain the load change amplitude based on the load power data;

[0075] A status data generation unit is used to generate carrier status data based on the degree of DC bus deviation and the magnitude of load change.

[0076] The operation status determination unit is used to generate the real-time operation status of the carrier based on the carrier status data.

[0077] The operation trend analysis unit is used to obtain the operation trend of the carrier status based on the real-time carrier operation status, and to obtain the rate of change of the trend within a preset time based on the operation trend of the carrier status.

[0078] If the rate of change of the trend is greater than the preset trend threshold, it is marked as the first disturbance scenario;

[0079] If the rate of change of the trend is less than the preset trend threshold, it is marked as the second disturbance scenario;

[0080] The isolated virtual inertia demand generation unit is used to obtain the first parameter adjustment direction according to the first disturbance scenario, and generate the virtual inertia demand based on the isolated scenario mode and the first parameter adjustment direction.

[0081] The grid-connected damping coefficient demand generation unit is used to obtain the second parameter adjustment direction according to the second disturbance scenario, and generate the damping coefficient demand based on the grid-connected scenario mode and the second parameter adjustment direction.

[0082] The adjustment demand integration unit is used to generate dynamic carrier adjustment demand data based on the virtual inertia demand and damping coefficient demand.

[0083] The beneficial effects of this application are as follows: This invention achieves dynamic adaptation of microgrids under different operating modes through accurate identification and differentiated data acquisition in grid-connected and islanded scenarios. This method generates adaptive adjustment requirements for virtual inertia and damping coefficients through real-time analysis of DC bus voltage and load power, and quantifies the operational stability index by combining frequency offset and rate of change, thereby improving the system's response accuracy to load mutations and environmental disturbances. By establishing a state-environment mapping relationship through cross-domain correlation between carrier state and environmental data, environmental factors such as illumination and temperature are transformed into quantifiable electrical disturbance types, solving the problem of missing environmental factor impact analysis in traditional methods. Based on a disturbance intensity grading mechanism and dynamic evaluation of disturbance stages, differentiated adjustment from "second disturbance scenario parameter fine-tuning" to "first disturbance scenario emergency load shedding" is achieved. Combined with multi-dimensional information generation, this significantly improves system frequency stability and fault early warning capabilities. Attached Figure Description

[0084] Figure 1 This is a schematic diagram of a method flow according to an embodiment of this application.

[0085] Figure 2 This is a schematic diagram of the system structure according to an embodiment of this application.

[0086] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0087] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0088] like Figure 1 As shown, this application provides a multi-scenario adaptive dynamic carrier load adjustment method, including:

[0089] S1. Obtain the scenario operation mode of the power line, and obtain real-time operation data based on the scenario operation mode, wherein the real-time operation data includes DC bus voltage data, carrier status data, load power data and environmental data;

[0090] S2. Obtain the real-time carrier operating status based on the DC bus voltage data and load power data, and generate dynamic carrier adjustment requirement data based on the real-time carrier operating status;

[0091] S3. Obtain the dynamic control parameters of the carrier based on the dynamic carrier adjustment demand data, and obtain the operation stability index based on the dynamic control parameters of the carrier.

[0092] S4. Obtain carrier-environmental disturbance data based on the carrier state data and environmental data;

[0093] S5. Obtain the carrier load disturbance level based on the carrier-environment disturbance data and the operational stability index, and obtain carrier disturbance matching data based on the carrier load disturbance level;

[0094] S6. Obtain the disturbance stage based on the carrier disturbance matching data, and determine whether the disturbance stage is within a preset threshold stage.

[0095] If not in the disturbance stage, the carrier frequency deviation is obtained based on the disturbance stage, and the carrier load adjustment data is obtained based on the carrier frequency deviation.

[0096] As described in steps S1-S6 above, this invention determines the system operating mode by detecting the grid-connected and islanded status signals of power lines, and records the mode switching timestamp to align the data acquisition sequence. The corresponding data acquisition channel is activated according to the scenario mode: in the grid-connected scenario, grid frequency, VSG output frequency, DC bus voltage, load power, and environmental data are acquired; in the islanded scenario, VSG angular velocity and electromagnetic power are additionally acquired.

[0097] Then, based on the degree of DC bus voltage deviation and the magnitude of load power change, the carrier's operating status is evaluated and disturbance scenarios are identified: if the voltage deviation exceeds the preset value and the load change exceeds the preset value and occurs within a short period of time, it is determined to be the first disturbance scenario, and a demand to increase the virtual inertia is generated; if the change magnitude is small and the duration is long, it is determined to be the second disturbance scenario, and a demand to increase the damping coefficient is generated.

[0098] Subsequently, the moment of inertia and damping coefficient are dynamically obtained based on the adjustment requirements: in the first disturbance scenario, the rate of change of the moment of inertia increases with the angular velocity, and in the second disturbance scenario, the damping coefficient increases with the angular velocity deviation. The system stability is quantified by an operational stability index that integrates frequency deviation and rate of change, and this index balances dynamic response and steady-state error with weighted coefficients.

[0099] Then, by correlating the timestamps of grid frequency fluctuations with corresponding environmental data (sunlight, temperature, wind speed), a mapping relationship between environmental variables and electrical quantities is established, and the degree of influence of environmental factors on frequency fluctuations is determined using correlation coefficients. For example, when sunlight intensity drops sharply by more than 20%, it is confirmed as a disturbance in photovoltaic input power through correlation coefficient calculations.

[0100] Next, the disturbances are classified according to their type (input power disturbance, energy storage response disturbance) and load disturbance intensity (integrated power change and operation stability index), and the disturbance type labels are matched with corresponding electrical information (such as power fluctuation curves and frequency change trends).

[0101] Finally, the system stability is determined based on the disturbance stage. If it is in a non-threshold stage, a load adjustment command is generated based on the frequency deviation, and an audible and visual alarm is triggered. This step combines the disturbance stage and frequency deviation to achieve graded control; for example, an emergency load shedding is initiated when there is a large frequency deviation to prevent system collapse.

[0102] In one embodiment, the step of acquiring the scenario operation mode of the power line and acquiring real-time operation data based on the scenario operation mode includes:

[0103] S101. Obtain the status signal indicating whether the power line is currently connected to the external power grid;

[0104] If a power grid disconnection signal is detected, the scenario is determined to be an isolated scene.

[0105] If a normal grid connection signal is detected, it is determined to be a grid-connected scenario mode;

[0106] S102. Obtain the switching timestamp between the isolated scene mode and the grid-connected scene mode;

[0107] S103. Obtain the data acquisition channels corresponding to the island scenario mode and the grid-connected scenario mode, and obtain real-time operating data based on the switching timestamp, wherein the real-time operating data includes DC bus voltage data, frequency data, load power data and environmental data.

[0108] As described in steps S101-S103 above, the present invention determines whether the system is currently in grid-connected or islanded mode by acquiring the auxiliary contact signal of the power line circuit breaker or the value of the grid connection status register. Specifically, when a circuit breaker tripping signal is detected, it is determined to be in islanded mode; when a circuit breaker closing signal is detected, it is determined to be in grid-connected mode.

[0109] Subsequently, a UTC timestamp is recorded at the instant the mode switching signal is detected, achieving a time resolution of milliseconds. This timestamp is used to bind the data acquisition sequence, achieving time alignment of data before and after the mode switch. Its physical significance lies in the fact that the moment of mode switching is a critical node in the system's dynamic response, and a precise timestamp can provide a time reference for analyzing the abrupt changes in parameters such as voltage and frequency.

[0110] Finally, based on the preset data acquisition channel table for grid-connected or islanded scenarios, the system dynamically switches the corresponding channels according to the mode switching timestamp. The data acquisition card then collects DC bus voltage, frequency, load power, and environmental data in real time according to the channel table. In grid-connected scenarios, the system focuses on collecting parameters such as grid frequency and DC bus voltage, while in islanded scenarios, it adds data acquisition of VSG output frequency, angular velocity, and illuminance. The physical significance of this design lies in the fact that the core monitoring parameters of the system differ in different scenarios. Dynamic channel switching enables targeted data acquisition. By dynamically switching acquisition channels, the system can accurately select the data type to be collected based on the current operating mode or environmental changes. For example, when the system enters islanded mode, the channel can switch to collect data on battery energy storage status, frequency fluctuations, and load changes, while in grid-connected mode, it may focus more on grid voltage and current data. This targeted acquisition avoids interference from irrelevant data, improves overall data accuracy, and by adjusting the data acquisition strategy in real time according to different scenarios, dynamic channel switching allows the system to react quickly to changes in the environment and operating mode. For example, when the system detects a frequency shift, it can immediately switch to the channel collecting frequency fluctuations and battery status to take timely adjustment measures. This enables rapid and precise adjustments, enhancing the stability and reliability of the system.

[0111] In one embodiment, the step of obtaining real-time carrier operating status based on the DC bus voltage data and load power data, and generating dynamic carrier adjustment demand data based on the real-time carrier operating status, includes:

[0112] S201. Obtain the degree of DC bus deviation based on the DC bus voltage data;

[0113] S202. Obtain the load change amplitude based on the load power data;

[0114] S203. Generate carrier status data based on the degree of DC bus deviation and the magnitude of load change;

[0115] S204. Generate real-time carrier operating status based on the carrier status data;

[0116] S205. Obtain the carrier status operation trend based on the real-time carrier operation status, and obtain the trend change rate within a preset time based on the carrier status operation trend.

[0117] If the rate of change of the trend is greater than the preset trend threshold, it is marked as the first disturbance scenario;

[0118] If the rate of change of the trend is less than the preset trend threshold, it is marked as the second disturbance scenario;

[0119] S206. Obtain the first parameter adjustment direction according to the first disturbance scenario, and generate the virtual inertia requirement based on the island scenario mode and the first parameter adjustment direction.

[0120] S207. Obtain the second parameter adjustment direction according to the second disturbance scenario, and generate the damping coefficient demand based on the grid connection scenario mode and the second parameter adjustment direction.

[0121] S208. Generate dynamic carrier adjustment requirement data based on the virtual inertia requirement and damping coefficient requirement.

[0122] As described in steps S201-S208 above, this invention calculates the voltage deviation degree by the ratio of the difference between the real-time DC bus voltage and the rated voltage, thereby quantifying the system power balance state. The collected DC bus voltage data, after normalization, reflects the power supply and demand relationship, providing basic electrical quantity indicators for subsequent disturbance analysis. Then, based on the ratio of the difference between the real-time load power and the reference load, the load change amplitude is calculated to characterize the intensity of load disturbance. The load power data originates from real-time sensor acquisition and is dynamically quantified by comparing with dynamic reference values ​​(such as preceding average power). This provides a core input for disturbance scene identification, effectively distinguishing between sudden load increases and slow changes, compensating for the shortcomings of traditional fixed thresholds that cannot adapt to dynamic load characteristics. Subsequently, the DC bus deviation degree and load change amplitude are encapsulated into structured data containing timestamps, forming carrier state data. By integrating the fluctuation characteristics of voltage and load, and synchronizing with the scene switching sequence through timestamps, a standardized disturbance characterization unit is provided for subsequent trend analysis, solving the problem of incomplete disturbance feature extraction caused by scattered analysis of multi-source data. Next, a sliding window algorithm is used to analyze the rate of change of the carrier state data. Combined with preset trend and time thresholds, the first and second disturbance scenarios are identified. Dynamic trend analysis is used to obtain the rate of change of the trend within a preset time period, distinguishing between sudden load changes (such as large fluctuations in a short period) and gradual changes (small fluctuations over a long period). Based on the rate of change of the trend, differentiated parameter adjustment strategies are matched for different disturbance types, solving the problem of high misjudgment rate in traditional single-threshold judgments. Based on the disturbance scenario label, a real-time operating status including the disturbance type, voltage, and load fluctuation is generated. Simultaneously, for the first disturbance scenario, the adjustment direction for increasing rotational inertia is determined, and a virtual inertia requirement is generated in conjunction with the islanded scenario mode. Through the correlation calculation between the load change amplitude and the inertia adjustment coefficient, dynamic matching of inertia compensation and load disturbance is achieved, solving the problem of insufficient inertia caused by sudden load increases in islanded mode, and providing immediate inertia support for frequency stability. For the second disturbance scenario, the adjustment direction for increasing the damping coefficient is determined, and a damping coefficient requirement is generated in conjunction with the grid-connected scenario mode. By calculating the correlation between voltage deviation and damping adjustment coefficient, the damping parameters are matched with the slow power imbalance, which solves the problem of low-frequency oscillation attenuation lag and optimizes the steady-state operation characteristics of the system.

[0123] Finally, the virtual inertia and damping coefficient requirements are integrated into structured data that includes scenario patterns to form dynamic adjustment requirements. For example, in islanded mode, the instability of wind and solar power generation may cause frequency fluctuations due to the interruption of external power supply, and virtual inertia needs to be increased to improve frequency stability. In grid-connected mode, the damping coefficient may need to be adjusted to cope with grid fluctuations.

[0124] In one embodiment, the step of obtaining dynamic control parameters of the carrier based on the dynamic carrier adjustment demand data, and obtaining an operational stability index based on the dynamic carrier control parameters, includes:

[0125] S301. Obtain the virtual inertia requirement and damping coefficient requirement based on the dynamic carrier adjustment requirement data.

[0126] S302. Obtain the differentiation enhancement adjustment amount based on the virtual inertia requirement;

[0127] S303. Obtain the suppression adjustment amount based on the required damping coefficient;

[0128] S304. Obtain the dynamic control parameters of the carrier based on the distinction between the enhancement adjustment amount and the suppression adjustment amount;

[0129] S305. Obtain the frequency offset and frequency change rate based on the dynamic control parameters of the carrier;

[0130] S306. Obtain the operating stability index based on the frequency offset and frequency change rate.

[0131] As described in steps S301-S306 above, this invention obtains a differentiated enhanced adjustment amount based on the virtual inertia demand, which is generated from the load surge scenario in the previous steps. The virtual inertia demand is weighted using empirical adjustment coefficients to form a targeted inertia compensation amount. Its physical significance lies in the fact that when a sudden increase in load leads to insufficient system inertia, the differentiated enhanced adjustment amount can quickly increase the rotational inertia and suppress the trend of sudden frequency drops. This solves the problem that traditional fixed inertia parameters cannot cope with dynamic load changes, providing instantaneous inertia support to the system and improving frequency stability by dynamically matching the intensity of load disturbances.

[0132] The suppression adjustment amount is obtained based on the damping coefficient demand, which originates from voltage deviation analysis under slow load fluctuation scenarios. The damping demand is amplified by an adjustment coefficient to generate a damping compensation amount suitable for low-frequency oscillation scenarios. Its physical meaning is that when system oscillations are triggered by gradual load changes, the suppression adjustment amount enhances the damping characteristics and accelerates oscillation decay. Compared to traditional fixed damping parameters, this step achieves dynamic matching between the damping coefficient and power fluctuations, effectively solving the problem of lag in system oscillation decay.

[0133] The system integrates enhanced and suppressed adjustment parameters, combined with rated parameters, to generate dynamic control parameters for the carrier. By synergistically adjusting the moment of inertia and damping coefficient, it balances the system's dynamic response speed and steady-state error. For example, in the first disturbance scenario, the damping coefficient is preferentially increased to suppress frequency abrupt changes, while in the second disturbance scenario, the focus is on increasing the moment of inertia to dampen oscillations. This step overcomes the limitations of traditional single-parameter adjustment, achieving precise adaptation to different types of load disturbances through dual-parameter joint optimization, thus improving the system's frequency control capability in multiple scenarios.

[0134] Based on the carrier's dynamic control parameters, frequency offset and rate of change are extracted from the real-time acquired VSG output frequency and the grid frequency (or rated frequency). The frequency offset reflects the degree to which the frequency deviates from the rated value, while the rate of change characterizes the drastic trend of frequency fluctuation. Through high-frequency sampling and differential calculation, the real-time performance and accuracy of the extracted indicators are ensured, providing multi-dimensional dynamic data for system stability assessment and solving the problem that traditional single frequency deviation analysis cannot reflect the frequency change trend.

[0135] The operational stability index is generated by weighted summation of the frequency offset and the rate of change. A first weighting coefficient is obtained based on the frequency offset, and a second weighting coefficient is obtained based on the rate of change. The formula for calculating the operational stability index is as follows:

[0136] ;

[0137] in, Indicates the operational stability index. Indicates the frequency offset. This represents the first weighting coefficient. Indicates the rate of change. This represents the second weighting coefficient. This index quantifies the abstract system stability into a concrete numerical value, facilitating disturbance classification and automatic adjustment. For example, a higher index value indicates that the system is closer to instability, requiring the activation of emergency adjustment strategies.

[0138] In one embodiment, the step of obtaining carrier-environment disturbance data based on the carrier state data and environmental data includes:

[0139] S401. Obtain power grid frequency fluctuation data and fluctuation occurrence timestamp based on the carrier status data;

[0140] S402. Based on the timestamp of the fluctuation occurrence, obtain light intensity, ambient temperature, and wind speed data according to environmental data;

[0141] S403. Based on the fluctuation occurrence timestamp, establish a state-environment mapping relationship according to the power grid frequency fluctuation data and the corresponding light intensity, ambient temperature, and wind speed data:

[0142] S404. Obtain the state-environment correlation coefficient between environmental variables and electrical quantities based on the state-environment mapping relationship;

[0143] S405. Obtain carrier-environmental disturbance data based on the state-environment correlation coefficient and power grid frequency fluctuation data.

[0144] As described in steps S401-S405 above, this invention extracts power grid frequency fluctuation information from carrier state data and records the timestamp of the fluctuation occurrence. The frequency data originates from the real-time acquired VSG output frequency or power grid frequency, and the timestamp is generated by a high-precision clock module and synchronized with the timestamp of scene mode switching. This step provides a precise time reference for environmental data matching, ensuring the temporal consistency of frequency fluctuations and environmental factors, and solving the problem of misjudgment of disturbance sources caused by data timing misalignment in traditional methods.

[0145] Based on frequency fluctuation timestamps, environmental data such as light intensity, ambient temperature, and wind speed are acquired from a distributed sensor network at corresponding moments. Methods such as linear interpolation are used to match environmental data at different sampling rates to the fluctuation moments, achieving precise temporal alignment between environmental variables and electrical quantities. Its physical significance lies in establishing a potential correlation path between "environmental change" and "frequency fluctuation," solving the problem of disconnect between environmental data and electrical quantities. A time window method is used to establish a mapping relationship between frequency fluctuation data and corresponding environmental data, forming a triplet data structure of "timestamp-electrical quantity-environmental quantity." The mapping relationship is stored using data structures such as hash tables, supporting rapid retrieval and pattern recognition. This step constructs a causal relationship model between environmental variables and electrical quantities, solving the problem of lack of quantitative representation of the impact of environmental factors in traditional disturbance analysis, and providing structured data support for subsequent correlation coefficient calculations.

[0146] The Pearson correlation coefficient algorithm quantifies the correlation between environmental variables and electrical quantities, setting thresholds to distinguish between strong and weak correlations. The correlation coefficient reflects the significance of environmental factors on frequency fluctuations, such as the negative correlation between light intensity and frequency deviation. This step uses statistical methods to eliminate random correlations, achieving quantitative identification of environmental disturbance sources and solving the problem of insufficient accuracy in traditional qualitative analysis, providing a scientific basis for disturbance type judgment. Based on the correlation coefficient calculation results, strongly correlated environmental factors are mapped to specific disturbance types, such as photovoltaic input power disturbances and inverter efficiency disturbances, and encapsulated as structured data containing disturbance type, correlation coefficient, frequency deviation, and timestamp. This step transforms environmental factors into identifiable electrical disturbance labels, providing clear guidance for subsequent disturbance classification and regulation strategy generation, solving the problem of environmental disturbances being difficult to incorporate into the electrical control system, and improving the comprehensiveness of microgrid disturbance identification.

[0147] In one embodiment, the step of obtaining the carrier load disturbance level based on the carrier-environment disturbance data and the operational stability index, and obtaining carrier disturbance matching data based on the carrier load disturbance level, includes:

[0148] S501. Obtain disturbance type information based on the carrier-environment disturbance data, wherein the disturbance type information includes input power disturbance and energy storage response disturbance;

[0149] S502, Obtain the occurrence time and load impact degree of the input power disturbance and load response disturbance;

[0150] S503. Obtain the load disturbance intensity based on the occurrence time and the degree of load impact;

[0151] S504. Obtain the load disturbance level of the carrier based on the load disturbance intensity;

[0152] S505. Obtain the disturbance type label based on the carrier load disturbance level;

[0153] S506. Obtain the input power information, grid frequency information, and energy storage charging and discharging information corresponding to the disturbance type label;

[0154] S507. Obtain carrier disturbance matching data based on the input power information, grid frequency information and energy storage charging and discharging information.

[0155] As described in steps S501-S507 above, this invention analyzes disturbance types from carrier-environment disturbance data, mainly including input power disturbances (such as photovoltaic and wind turbine power fluctuations) and energy storage response disturbances (such as charging and discharging delays). By identifying the essential differences between disturbance sources, a classification basis is provided for subsequent intensity calculations. For example, input power disturbances require adjustment of the inertia parameters of the virtual synchronous generator, while energy storage response disturbances require optimization of the energy storage control strategy, solving the problem of unclear adjustment direction caused by fuzzy disturbance types in traditional methods.

[0156] The occurrence time (start time and duration) and load impact degree of the disturbance are obtained, the latter being calculated by weighting the load power deviation and the maximum frequency deviation. The occurrence time reflects the dynamic characteristics of the disturbance, while the impact degree quantifies its actual impact on the system. The combination of these two factors provides multidimensional data support for disturbance intensity assessment. This step addresses the shortcomings of traditional analysis that only focuses on the disturbance amplitude while neglecting the time dimension.

[0157] The impact, duration, and operational stability index of disturbances are integrated to calculate the load disturbance intensity using a weighted algorithm. This indicator comprehensively reflects the disturbance amplitude, duration, and system stability, providing a quantitative basis for disturbance classification. Compared to traditional single-indicator assessments, this multi-dimensional calculation method more accurately matches the actual severity of the disturbance.

[0158] Disturbances are categorized into three levels—small, medium, and large—based on their intensity, corresponding to different levels of system threat. The grading criteria are determined based on system stability analysis, providing clear thresholds for disturbance response: second-level disturbance scenarios can be handled through fine-tuning of VSG parameters, while first-level disturbance scenarios require emergency load shedding and other measures. This step addresses the problem of traditional, singular grading criteria. Based on the disturbance level, a pre-defined type tag library is matched, transforming abstract intensity values ​​into specific business tags (such as "sudden drop in photovoltaic power" or "energy storage system failure"). This tagging facilitates the rapid transmission and understanding of disturbance information.

[0159] By retrieving the database using disturbance type tags, corresponding input power curves, historical grid frequency deviations, and energy storage charging and discharging status are obtained. The disturbance type tags and associated electrical information are encapsulated into structured data, integrating the disturbance type, intensity, and multi-dimensional characteristics. This data provides complete input for subsequent disturbance stage judgment and adjustment strategy generation, supporting real-time analysis and response to disturbances. It significantly improves efficiency compared to traditional unstructured data processing, ensuring the targeted and timely nature of adjustment strategies.

[0160] In one embodiment, the step of obtaining carrier frequency deviation based on the disturbance stage and obtaining carrier load adjustment data based on the carrier frequency deviation includes:

[0161] S601. Obtain the disturbance stage type corresponding to the disturbance stage, and obtain the virtual carrier output frequency according to the disturbance stage type;

[0162] S602. Obtain the carrier frequency deviation based on the output frequency of the virtual carrier and the power grid frequency, and obtain the deviation change trend of the carrier frequency deviation;

[0163] S603. Obtain the maximum frequency offset based on the disturbance stage and the deviation change trend;

[0164] S604. Obtain the direction of the maximum frequency offset based on the maximum frequency offset;

[0165] S605. Obtain carrier load adjustment data based on the maximum frequency offset direction and the maximum frequency offset.

[0166] As described in steps S601-S605 above, this invention extracts the disturbance stage type from the carrier disturbance matching data and calculates the virtual carrier output frequency based on the stage characteristics. For example, the virtual frequency simulation frequency shows a continuous downward trend during the initial disturbance period, while the recovery period shows a frequency rebound characteristic. This step dynamically characterizes the frequency evolution trend through the disturbance stage, providing a predictive basis for subsequent deviation analysis. It solves the adjustment lag problem caused by traditional methods relying solely on real-time frequency data, enabling forward-looking analysis of dynamic frequency changes. The carrier frequency deviation is calculated based on the virtual carrier output frequency and the grid frequency (or rated frequency), and the deviation change trend is analyzed using a sliding window algorithm. Trend calculation can identify the rising or falling direction of the frequency, such as judging whether the deviation is aggravated or alleviated by combining the frequency change rate within the window. This step integrates the amplitude and change trend of the frequency deviation to form a multi-dimensional analysis dimension, solving the deficiency that traditional single deviation value analysis cannot reflect the frequency evolution trend, and providing data support for accurate judgment of the disturbance development trend. Combining the disturbance stage type and the deviation change trend, the maximum frequency offset in the future period is predicted. Through an inertial prediction model, the extreme value that the frequency may reach is predicted by combining the current deviation and the change rate. For example, during the period of heightened disturbance, the maximum frequency deviation is predicted by combining the downward trend, while during the recovery period, the impact of trend reversal on extreme values ​​is considered. This step enables forward-looking prediction of frequency extreme values, providing a limit reference for load adjustment strategies and solving the problem of insufficient or excessive adjustment caused by traditional methods that rely solely on current deviations.

[0167] The direction of the offset (rising or falling) is determined based on the sign of the maximum frequency offset, and the direction judgment is corrected by combining the characteristics of the disturbance stage (such as bidirectional fluctuations in the oscillation stage). The direction determination is directly related to the nature of the load adjustment; for example, a falling direction requires increased power injection, while a rising direction requires reduced load. This step provides clear directional guidance for load adjustment, avoiding reversed adjustment strategies due to misjudgment of direction, and improving the accuracy and effectiveness of the adjustment.

[0168] Based on the magnitude and direction of the maximum frequency deviation, differentiated load adjustment data is generated. For example, a large deviation triggers an emergency load shedding command, a medium deviation initiates energy storage charging and discharging regulation, and a small deviation is adjusted through controllable load adjustments. The adjustment strategy is strictly matched with the severity of the frequency deviation, achieving tiered and precise regulation. This step solves the problem of the coarseness of traditional fixed threshold regulation by dynamically matching deviation characteristics to generate targeted strategies, thereby improving the control accuracy and response efficiency of microgrid frequency stability.

[0169] like Figure 2 As shown, the present invention also provides a multi-scenario adaptive dynamic carrier load adjustment system, comprising:

[0170] The data acquisition module 1 is used to acquire the scenario operation mode of the power line and acquire real-time operation data based on the scenario operation mode. The real-time operation data includes DC bus voltage data, carrier status data, load power data and environmental data.

[0171] Adjustment demand generation module 2 is used to obtain the real-time carrier operating status based on the DC bus voltage data and load power data, and generate dynamic carrier adjustment demand data based on the real-time carrier operating status.

[0172] Stability assessment module 3 is used to obtain dynamic control parameters of the carrier based on the dynamic carrier adjustment demand data, and to obtain the operational stability index based on the dynamic control parameters of the carrier.

[0173] The environmental disturbance correlation analysis module 4 is used to obtain carrier-environmental disturbance data based on the carrier state data and environmental data;

[0174] Feature matching module 5 is used to obtain the carrier load disturbance level based on the carrier-environment disturbance data and the operation stability index, and to obtain carrier disturbance matching data based on the carrier load disturbance level;

[0175] The load adjustment module 6 is used to obtain the disturbance stage based on the carrier disturbance matching data and determine whether the disturbance stage is within a preset threshold stage.

[0176] If not in the disturbance stage, the carrier frequency deviation is obtained based on the disturbance stage, and the carrier load adjustment data is obtained based on the carrier frequency deviation.

[0177] In one embodiment, the data acquisition module 1 includes:

[0178] The connection status detection unit is used to detect the connection status signal between the power line and the external power grid, wherein the connection status signal includes the power grid disconnection signal and the power grid normal access signal;

[0179] If a power grid disconnection signal is detected, the scenario is determined to be an isolated scene.

[0180] If a normal grid connection signal is detected, it is determined to be a grid-connected scenario mode;

[0181] The mode switching timestamp recording unit is used to obtain the switching timestamp of the island scenario mode and the grid-connected scenario mode;

[0182] The scenario data acquisition channel management unit is used to acquire the data acquisition channels corresponding to the island scenario mode and the grid-connected scenario mode, and to acquire real-time operating data based on the switching timestamp. The real-time operating data includes DC bus voltage data, frequency data, load power data and environmental data.

[0183] In one embodiment, the adjustment demand generation module 2 includes:

[0184] A voltage deviation calculation unit is used to obtain the degree of DC bus deviation based on the DC bus voltage data.

[0185] A load change calculation unit is used to obtain the load change amplitude based on the load power data;

[0186] A status data generation unit is used to generate carrier status data based on the degree of DC bus deviation and the magnitude of load change.

[0187] The operation status determination unit is used to generate the real-time operation status of the carrier based on the carrier status data.

[0188] The operation trend analysis unit is used to obtain the operation trend of the carrier status based on the real-time carrier operation status, and to obtain the rate of change of the trend within a preset time based on the operation trend of the carrier status.

[0189] If the rate of change of the trend is greater than the preset trend threshold, it is marked as the first disturbance scenario;

[0190] If the rate of change of the trend is less than the preset trend threshold, it is marked as the second disturbance scenario;

[0191] The isolated virtual inertia demand generation unit is used to obtain the first parameter adjustment direction according to the first disturbance scenario, and generate the virtual inertia demand based on the isolated scenario mode and the first parameter adjustment direction.

[0192] The grid-connected damping coefficient demand generation unit is used to obtain the second parameter adjustment direction according to the second disturbance scenario, and generate the damping coefficient demand based on the grid-connected scenario mode and the second parameter adjustment direction.

[0193] The adjustment demand integration unit is used to generate dynamic carrier adjustment demand data based on the virtual inertia demand and damping coefficient demand.

[0194] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0195] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0196] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent results or equivalent process transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A dynamic carrier load adjustment method with multi-scenario adaptive characteristics, characterized in that, include: The scenario operation mode of the power line is obtained, and real-time operation data is obtained based on the scenario operation mode. The real-time operation data includes DC bus voltage data, carrier status data, load power data and environmental data. The real-time carrier operating status is obtained based on the DC bus voltage data and load power data, and dynamic carrier adjustment requirement data is generated based on the real-time carrier operating status. Based on the dynamic carrier adjustment demand data, obtain the carrier dynamic control parameters, and based on the carrier dynamic control parameters, obtain the operational stability index; Based on the carrier state data and environmental data, obtain carrier-environment disturbance data; The carrier load disturbance level is obtained based on the carrier-environment disturbance data and the operational stability index, and the carrier disturbance matching data is obtained based on the carrier load disturbance level. The disturbance stage is obtained based on the carrier disturbance matching data, and it is determined whether the disturbance stage is within a preset threshold stage. If not in the disturbance stage, the carrier frequency deviation is obtained based on the disturbance stage, and the carrier load adjustment data is obtained based on the carrier frequency deviation. The step of obtaining real-time carrier operating status based on the DC bus voltage data and load power data, and generating dynamic carrier adjustment demand data based on the real-time carrier operating status, includes: obtaining the DC bus deviation degree based on the DC bus voltage data; obtaining the load change amplitude based on the load power data; generating carrier status data based on the DC bus deviation degree and load change amplitude; generating real-time carrier operating status based on the carrier status data; obtaining the carrier status operating trend based on the real-time carrier operating status, and obtaining the trend change rate within a preset time based on the carrier status operating trend; if the trend change rate is greater than a preset trend threshold, it is marked as a first disturbance scenario; if the trend change rate is less than the preset trend threshold, it is marked as a second disturbance scenario; obtaining a first parameter adjustment direction based on the first disturbance scenario, and generating a virtual inertia demand based on the islanded scenario mode and the first parameter adjustment direction; obtaining a second parameter adjustment direction based on the second disturbance scenario, and generating a damping coefficient demand based on the grid-connected scenario mode and the second parameter adjustment direction; generating dynamic carrier adjustment demand data based on the virtual inertia demand and the damping coefficient demand. The steps of obtaining dynamic control parameters of the carrier based on the dynamic carrier adjustment demand data and obtaining an operational stability index based on the dynamic carrier adjustment demand parameters include: obtaining virtual inertia demand and damping coefficient demand based on the dynamic carrier adjustment demand data; obtaining a differentiated enhancement adjustment amount based on the virtual inertia demand amount; obtaining a suppression adjustment amount based on the damping coefficient demand amount; obtaining the carrier dynamic control parameters based on the differentiated enhancement adjustment amount and the suppression adjustment amount; obtaining frequency offset and frequency change rate based on the carrier dynamic control parameters; and obtaining the operational stability index based on the frequency offset and frequency change rate. The step of obtaining carrier-environmental disturbance data based on the carrier state data and environmental data includes: obtaining power grid frequency fluctuation data and fluctuation occurrence timestamps based on the carrier state data; obtaining light intensity, ambient temperature, and wind speed data based on the fluctuation occurrence timestamps and environmental data; establishing a state-environment mapping relationship based on the fluctuation occurrence timestamps, the power grid frequency fluctuation data, and the corresponding light intensity, ambient temperature, and wind speed data; obtaining state-environment correlation coefficients between environmental variables and electrical quantities based on the state-environment mapping relationship; and obtaining carrier-environmental disturbance data based on the state-environment correlation coefficients and power grid frequency fluctuation data. The steps of obtaining the carrier load disturbance level based on the carrier-environment disturbance data and the operational stability index, and obtaining carrier disturbance matching data based on the carrier load disturbance level, include: obtaining disturbance type information based on the carrier-environment disturbance data, wherein the disturbance type information includes input power disturbance and energy storage response disturbance; obtaining the occurrence time and load impact degree of the input power disturbance and load response disturbance; obtaining the load disturbance intensity based on the occurrence time and load impact degree; obtaining the carrier load disturbance level based on the load disturbance intensity; obtaining a disturbance type label based on the carrier load disturbance level; obtaining the input power information, grid frequency information, and energy storage charging and discharging information corresponding to the disturbance type label; and obtaining carrier disturbance matching data based on the input power information, grid frequency information, and energy storage charging and discharging information.

2. The multi-scenario adaptive dynamic carrier load adjustment method according to claim 1, characterized in that, The step of acquiring the scenario operation mode of the power line and acquiring real-time operation data based on the scenario operation mode includes: The connection status signal between the power line and the external power grid is detected. The connection status signal includes the power grid disconnection signal and the power grid normal connection signal. If a power grid disconnection signal is detected, the scenario is determined to be an isolated scene. If a normal grid connection signal is detected, it is determined to be a grid-connected scenario mode; Obtain the switching timestamp between the isolated scenario mode and the grid-connected scenario mode; Obtain the data acquisition channels corresponding to the island scenario mode and the grid-connected scenario mode, and obtain real-time operating data based on the switching timestamp. The real-time operating data includes DC bus voltage data, frequency data, load power data, and environmental data.

3. The multi-scenario adaptive dynamic carrier load adjustment method according to claim 1, characterized in that, The step of obtaining carrier frequency deviation based on the disturbance stage and obtaining carrier load adjustment data based on the carrier frequency deviation includes: Obtain the disturbance stage type corresponding to the disturbance stage, and obtain the virtual carrier output frequency according to the disturbance stage type; The carrier frequency deviation is obtained based on the output frequency of the virtual carrier and the power grid frequency, and the trend of the deviation change is obtained. The maximum frequency offset value is obtained based on the disturbance stage and the deviation change trend; The maximum frequency offset direction is obtained based on the maximum frequency offset value; The carrier load adjustment data is obtained based on the maximum frequency offset direction and the maximum frequency offset value.

4. A multi-scenario adaptive dynamic carrier load adjustment system, characterized in that, include: The data acquisition module is used to acquire the scenario operation mode of the power line and acquire real-time operation data based on the scenario operation mode. The real-time operation data includes DC bus voltage data, carrier status data, load power data and environmental data. The adjustment demand generation module is used to obtain the real-time carrier operating status based on the DC bus voltage data and load power data, and generate dynamic carrier adjustment demand data based on the real-time carrier operating status. The stability assessment module is used to obtain dynamic control parameters of the carrier based on the dynamic carrier adjustment demand data, and to obtain the operational stability index based on the dynamic carrier control parameters. The environmental disturbance correlation analysis module is used to obtain carrier-environmental disturbance data based on the carrier state data and environmental data; The feature matching module is used to obtain the carrier load disturbance level based on the carrier-environment disturbance data and the operational stability index, and to obtain carrier disturbance matching data based on the carrier load disturbance level; The load adjustment module is used to obtain the disturbance stage based on the carrier disturbance matching data and determine whether the disturbance stage is within a preset threshold stage. If not in the disturbance stage, the carrier frequency deviation is obtained based on the disturbance stage, and the carrier load adjustment data is obtained based on the carrier frequency deviation. The adjustment requirement generation module includes: a voltage deviation calculation unit, used to obtain the degree of DC bus deviation based on the DC bus voltage data; a load change calculation unit, used to obtain the load change amplitude based on the load power data; a status data generation unit, used to generate carrier status data based on the degree of DC bus deviation and the load change amplitude; an operation status determination unit, used to generate real-time carrier operation status based on the carrier status data; and an operation trend analysis unit, used to obtain the carrier status operation trend based on the real-time carrier operation status, and obtain the trend change rate within a preset time based on the carrier status operation trend; if the trend change rate is large... If the trend change rate is less than the preset trend threshold, it is marked as a first disturbance scenario; if the trend change rate is less than the preset trend threshold, it is marked as a second disturbance scenario; the island virtual inertia demand generation unit is used to obtain the first parameter adjustment direction according to the first disturbance scenario, and generate the virtual inertia demand based on the island scenario mode and the first parameter adjustment direction; the grid-connected damping coefficient demand generation unit is used to obtain the second parameter adjustment direction according to the second disturbance scenario, and generate the damping coefficient demand based on the grid-connected scenario mode and the second parameter adjustment direction; the adjustment demand integration unit is used to generate dynamic carrier adjustment demand data according to the virtual inertia demand and the damping coefficient demand. The stability assessment module obtains dynamic control parameters of the carrier based on the dynamic carrier adjustment demand data, and obtains an operational stability index based on the dynamic carrier adjustment parameters, including: obtaining virtual inertia demand and damping coefficient demand based on the dynamic carrier adjustment demand data; obtaining a differentiated enhancement adjustment amount based on the virtual inertia demand amount; obtaining a suppression adjustment amount based on the damping coefficient demand amount; obtaining the carrier dynamic control parameters based on the differentiated enhancement adjustment amount and suppression adjustment amount; obtaining frequency offset and frequency change rate based on the carrier dynamic control parameters; and obtaining the operational stability index based on the frequency offset and frequency change rate. The environmental disturbance correlation analysis module obtains carrier-environment disturbance data based on the carrier state data and environmental data, including: obtaining power grid frequency fluctuation data and fluctuation occurrence timestamps based on the carrier state data; obtaining light intensity, ambient temperature, and wind speed data based on the fluctuation occurrence timestamps and environmental data; establishing a state-environment mapping relationship based on the fluctuation occurrence timestamps, the power grid frequency fluctuation data, and the corresponding light intensity, ambient temperature, and wind speed data; obtaining the state-environment correlation coefficient between environmental variables and electrical quantities based on the state-environment mapping relationship; and obtaining carrier-environment disturbance data based on the state-environment correlation coefficients and power grid frequency fluctuation data. The feature matching module obtains the carrier load disturbance level based on the carrier-environment disturbance data and the operational stability index, and obtains carrier disturbance matching data based on the carrier load disturbance level, including: obtaining disturbance type information based on the carrier-environment disturbance data, wherein the disturbance type information includes input power disturbance and energy storage response disturbance; obtaining the occurrence time and load impact degree of the input power disturbance and load response disturbance; obtaining the load disturbance intensity based on the occurrence time and load impact degree; obtaining the carrier load disturbance level based on the load disturbance intensity; obtaining a disturbance type label based on the carrier load disturbance level; obtaining the input power information, grid frequency information, and energy storage charging and discharging information corresponding to the disturbance type label; and obtaining carrier disturbance matching data based on the input power information, grid frequency information, and energy storage charging and discharging information.

5. The multi-scenario adaptive dynamic carrier load adjustment system according to claim 4, characterized in that, The data acquisition module includes: The connection status detection unit is used to detect the connection status signal between the power line and the external power grid, wherein the connection status signal includes the power grid disconnection signal and the power grid normal access signal; If a power grid disconnection signal is detected, the scenario is determined to be an isolated scene. If a normal grid connection signal is detected, it is determined to be a grid-connected scenario mode; The mode switching timestamp recording unit is used to obtain the switching timestamp of the island scenario mode and the grid-connected scenario mode; The scenario data acquisition channel management unit is used to acquire the data acquisition channels corresponding to the island scenario mode and the grid-connected scenario mode, and to acquire real-time operating data based on the switching timestamp. The real-time operating data includes DC bus voltage data, frequency data, load power data and environmental data.

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