Method for simulating dynamic characteristics of new energy power grid

Abstract

The invention provides a new energy power grid dynamic characteristic simulation method, and belongs to the technical field of new energy power grids, and the method comprises the steps: carrying out the connection according to an actual topological structure of a new energy power grid, and forming a new energy power grid model; distributing the data to different CPU simulation cores for simulation; starting an adaptive step size algorithm to adjust the simulation step size in real time according to the state change of the CPU simulation core, and sequentially obtaining the dynamic characteristics of the corresponding sub-models after the simulation step size is adjusted each time; preprocessing actual power grid operation data by using a Kalman filtering algorithm to obtain a parameter estimation set of each power grid unit under the new energy power grid model; and analyzing the dynamic characteristics of the corresponding sub-models according to all the parameter estimation sets under each sub-model, and determining a regulation and control strategy according to a power grid operation control target. Reliable basis is provided for power grid planning and operation control, and safe and stable operation of a power grid is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy power grids, and in particular to a method for simulating dynamic characteristics of new energy power grids. Background Art

[0002] As the proportion of renewable energy in the power system continues to increase, the scale of renewable energy grids is expanding and their structure is becoming increasingly complex. Traditional grid models have limitations when dealing with the complex dynamic characteristics of large-scale renewable energy integration, making it difficult to accurately simulate the intermittent and volatile nature of renewable energy generation. For example, wind power and photovoltaic power generation are affected by natural conditions, and their power generation fluctuates randomly, leading to changes in dynamic characteristics such as grid power flow distribution, voltage and frequency, which traditional models cannot effectively address. Furthermore, the computational complexity of large-scale renewable energy grid simulations is enormous, and existing computing resource allocation and simulation algorithms struggle to balance computational efficiency and accuracy, failing to meet the needs of real-time operation analysis and regulation, thereby reducing the stability of grid operations.

[0003] Therefore, the present invention proposes a method for simulating the dynamic characteristics of a new energy power grid. Summary of the Invention

[0004] The present invention provides a method for simulating the dynamic characteristics of a new energy power grid to solve the above-mentioned technical problems.

[0005] The present invention provides a method for simulating the dynamic characteristics of a new energy power grid, comprising: Collect detailed electric field information of renewable energy power plants, establish an operating model for each grid unit in simulation software, and connect them according to the actual topology of the renewable energy grid to form a renewable energy grid model; Decomposing the new energy grid model into multiple sub-models according to pre-set rules and assigning them to different CPU simulation cores for simulation; Enable the adaptive step size algorithm to adjust the simulation step size in real time according to the state changes of the CPU simulation core, and obtain the dynamic characteristics of the corresponding sub-model after each simulation step size adjustment; Acquire actual grid operation data of the new energy grid in real time, and preprocess the actual grid operation data using a Kalman filter algorithm to obtain a parameter estimation set for each grid unit under the new energy grid model; The dynamic characteristics of the corresponding sub-model are analyzed based on all parameter estimation sets under each sub-model, and the regulation strategy is determined according to the power grid operation control objectives.

[0006] Preferably, the electric field detailed information includes: the model, installation location and operating conditions of each grid unit.

[0007] Preferably, forming a new energy grid model includes: Determining the execution function and execution scale according to the operating conditions of the grid unit, and determining the design level in combination with the connection relationship under the installation position of the grid unit, wherein the connection relationship is the existing interconnection relationship and parallel relationship; Designing a model structure according to the design level and the type of grid power supply; Obtaining multiple physical phenomena that match the model of the grid unit from the model-physical comparison table, performing coupling processing and applying them to the corresponding model structure to obtain an operating model; The information interaction mechanism between different design levels is determined, and the operation models under different design levels are connected according to the actual topological structure of the new energy power grid to obtain the new energy power grid model.

[0008] Preferably, enabling an adaptive step size algorithm to adjust the simulation step size in real time according to the state change of the CPU simulation core includes: Start the monitoring mechanism of each CPU simulation core for real-time monitoring. The real-time monitoring data is related to the CPU simulation core utilization rate, memory occupancy rate, cache hit rate, and task queue length. Input the real-time monitoring data into the pre-established CPU simulation core state assessment model to obtain the state assessment coefficient at the corresponding moment and determine the first error adjustment step size at N consecutive moments; Determining a second error adjustment step size based on dynamic changes of the corresponding CPU simulation core at N consecutive moments; When the first error adjustment step size and the second error adjustment step size are in the same direction, determining a first unit step size adjustment amount;

[0009] Wherein, D1 is the first unit step adjustment amount; Respectively represent the first error adjustment step size and the second error adjustment step size; When the directions of the first error adjustment step and the second error adjustment step are inconsistent, calculate the absolute value of the difference between the first error adjustment step and the second error adjustment step, and at the same time, determine the data compliance ratio of the corresponding CPU simulation core at N consecutive moments ,in, It represents the sum of the parameters of the corresponding CPU simulation core within the set range at N consecutive moments, and ; Obtain a correction value Xz according to the product of the absolute value of the difference and the value obtained by subtracting the data compliance ratio from 1; determining a second unit step adjustment amount based on the correction value, the first error adjustment step, and the second error adjustment step; The step size is adjusted according to the state change coefficient between the previous moment and the current moment, and according to the determined unit step size adjustment amount of the CPU simulation core, wherein the update period of the unit step size adjustment amount is T, and the number of consecutive moments involved in the update period T is greater than or equal to N.

[0010] Preferably, determining the second unit step adjustment amount based on the correction value, the first error adjustment step, and the second error adjustment step includes:

[0011] in, Indicates the second unit step adjustment amount; 、 is a sign function, and , .

[0012] Preferably, the actual power grid operation data is preprocessed using a Kalman filter algorithm, including: Converting the actual power grid operation data into a data sequence; Acquire historical operating data of the new energy grid, analyze the compliance coefficient and expected coefficient of each historical data group, adjust the process noise covariance matrix and the measurement noise covariance matrix, and update the Kalman filter algorithm; Performing noise filtering on the data sequence based on the updated algorithm to obtain a new sequence; Based on the new sequence at M consecutive moments, a parameter estimation set of the corresponding power grid unit is obtained.

[0013] Preferably, the dynamic characteristics of the corresponding sub-model are analyzed based on all parameter estimation sets under each sub-model, including: Performing a global analysis within the set and a global analysis outside the set on each parameter estimation set under the sub-model to obtain an error within the set and an error outside the set; Minimize all in-set errors under the sub-model and compare them with the out-of-set errors to obtain a reference error under each parameter; Inputting all parameter estimation sets into the first characteristic analysis model in sequence to obtain a nonlinear mapping relationship between each parameter and the dynamic characteristics of the corresponding sub-model; Perform sensitivity analysis on each parameter in the parameter estimation set, and quantify the impact of each parameter on the dynamic characteristics by combining nonlinear mapping relationships, and find the key parameter combinations that affect the changes in dynamic characteristics by combining reference errors; Combined with the grid topology, the dynamic characteristic correlation between sub-models at different locations is analyzed, the key parameter combination is verified, and a parameter weight is assigned to each key parameter.

[0014] Preferably, the control strategy is determined according to the power grid operation control target, including: Using quantitative indicators to describe the power grid operation control objectives; The description results, key parameters and parameter weights are combined into an input vector and input into the strategy analysis model to obtain the control strategy.

[0015] Preferably, after all parameter estimation sets are sequentially input into the first characteristic analysis model, the method further comprises: Obtaining a work log of the first characteristic analysis model, and matching each work item in the work log with a corresponding item traceability list to obtain a parameter description of the corresponding work item; Acquire a capture tool that matches the parameter description from a description-tool database, and capture a work path that matches the parameter description and path generation data based on the work path in real time according to the capture tool; Extracting index features from the path generation data to obtain a generation vector; Inputting the generated vector into a vector analysis model to obtain an early warning regulation, wherein the early warning regulation includes: an early warning type and early warning information; Compare and analyze the early warning regulations of each work item with the standard regulations to determine the work abnormality factors; Analyze the rationality of work by combining abnormal factors and parameter combinations; If the working rationality is greater than the preset rationality, it is determined that the working process of the first characteristic analysis model is reasonable; Otherwise, the reason for optimizing the first characteristic analysis model is determined based on the abnormal factors, parameter combinations, and working paths.

[0016] Compared with the prior art, the present invention has the following advantages: This solution can quickly and accurately simulate the dynamic characteristics of the power grid by establishing detailed models, parallel computing, and adaptive step size adjustment, providing a reliable basis for power grid planning and operation control, and ensuring the safe and stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is a flow chart of a method for simulating dynamic characteristics of a new energy power grid in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0020] The present invention provides a method for simulating the dynamic characteristics of a new energy grid. Figure 1 Shown, including: Step 1: Collect detailed electric field information of the renewable energy power plant, establish an operation model of each grid unit in the simulation software, and connect them according to the actual topology of the renewable energy grid to form a renewable energy grid model; Step 2: Decompose the new energy grid model into multiple sub-models according to pre-set rules, and assign them to different CPU simulation cores for simulation; Step 3: Enable the adaptive step size algorithm to adjust the simulation step size in real time according to the state changes of the CPU simulation core, and obtain the dynamic characteristics of the corresponding sub-model after each simulation step size adjustment; Step 4: Acquire the actual grid operation data of the new energy grid in real time, and pre-process the actual grid operation data using the Kalman filter algorithm to obtain a parameter estimation set for each grid unit under the new energy grid model; Step 5: Analyze the dynamic characteristics of the corresponding sub-model based on all parameter estimation sets under each sub-model, and determine the regulation strategy based on the power grid operation control objectives.

[0021] Preferably, the electric field detailed information includes: the model, installation location and operating conditions of each grid unit.

[0022] In this example, a renewable energy power plant, such as a large wind farm with hundreds of wind turbines or photovoltaic arrays, is used as an example for a regional renewable energy grid. Data on wind turbine models, installation locations (latitude and longitude coordinates), and historical wind speeds are collected for the local wind farm; and data on photovoltaic panel models, installation angles, and solar radiation are collected for the photovoltaic power station. Using PSCAD / EMTDC electric field simulation software, operational models of each grid unit are established based on equipment parameters and physical principles. These units are then connected to form a renewable energy grid model according to the actual topology.

[0023] Grid units include new energy power generation equipment (wind turbines, photovoltaic panels), power electronic converters, transmission lines, transformers, various loads (industrial and residential loads), etc.

[0024] Operational models, such as the photovoltaic panel output power model, calculate power based on sunlight and temperature, and the wind turbine aerodynamic model, determine power generation based on wind speed. The new energy grid model is an overall model that combines the actual connection relationships of grid unit models such as photovoltaic power stations, wind farms, transmission lines, and load centers within a region. Submodels are the components of the new energy grid model, such as separate wind sub-models, photovoltaic sub-models, and transmission line sub-models. These grid models are split into wind sub-models, photovoltaic sub-models, and transmission line sub-models based on region and function. Each sub-model exchanges data via the Message Passing Interface (MPI) to achieve parallel simulation.

[0025] The adaptive step-size algorithm uses a variable-step-size Runge-Kutta algorithm, and a system error threshold is set during the process. When system state changes are minor, the step size is increased to speed up the calculation; when changes are drastic, the step size is reduced to ensure accuracy. Dynamic characteristic data such as voltage and frequency of the sub-model after the step size adjustment is recorded in real time.

[0026] In this embodiment, actual grid operation data refers to voltage, current, power, frequency, and other data acquired through real-time grid monitoring. A parameter estimation set is a collection of estimated values ​​for grid unit parameters, such as transmission line resistance, inductance, capacitance, current, and voltage. Voltage, current, and other data are collected in real time by the grid's SCADA system, at a rate of one per second. This data is input into MATLAB and processed using a Kalman filter program to obtain estimated values ​​for each grid unit parameter. For example, the estimated resistance of a transmission line section is 0.5Ω.

[0027] In this embodiment, the control strategy is a set of control measures designed to achieve grid operation goals, such as adjusting generator active and reactive power and switching reactive compensation equipment. Using MATLAB data analysis tools, the estimated parameter sets for each sub-model are analyzed to determine dynamic characteristics such as sub-model stability and response speed. Control strategies are formulated based on the goal of safe and stable grid operation. For example, when voltage in a certain area is low, it is recommended to switch on a capacitor bank.

[0028] In this embodiment, the model number represents uniqueness, such as the wind turbine model Vestas V164-9.5 MW and the photovoltaic panel model JinkoSolar Tiger Neo series.

[0029] The installation location is expressed in latitude and longitude. For example, a photovoltaic power station is located at 30° north latitude and 110° east longitude.

[0030] Operating conditions: including ambient temperature, light intensity, wind speed, humidity, etc. For example, the annual average wind speed of a wind farm is 8m / s.

[0031] When collecting information about renewable energy power plants, detailed records are kept of each grid unit's model, installation location, and operating conditions. For example, recording the wind turbine model allows for the acquisition of parameters such as rated power and blade size. Connections to other equipment can be determined based on the installation location, and equipment performance can be estimated based on operating conditions. This information is then used in subsequent model construction to ensure a more realistic model.

[0032] The model, installation location, and operating conditions of a grid unit directly impact its performance and role in the grid. Accurately considering these factors ensures that the model created more realistically reflects the actual grid situation, improving simulation accuracy and providing more reliable support for grid operation analysis and decision-making.

[0033] The beneficial effects of the above technical solution are: this solution can quickly and accurately simulate the dynamic characteristics of the power grid by establishing detailed models, parallel computing, adaptive step adjustment and other means, provide a reliable basis for power grid planning and operation control, and ensure the safe and stable operation of the power grid.

[0034] The present invention provides a method for simulating the dynamic characteristics of a new energy power grid, forming a new energy power grid model, comprising: Determining the execution function and execution scale according to the operating conditions of the grid unit, and determining the design level in combination with the connection relationship under the installation position of the grid unit, wherein the connection relationship is the existing interconnection relationship and parallel relationship; Designing a model structure according to the design level and the type of grid power supply; Obtaining multiple physical phenomena that match the model of the grid unit from the model-physical comparison table, performing coupling processing and applying them to the corresponding model structure to obtain an operating model; The information interaction mechanism between different design levels is determined, and the operation models under different design levels are connected according to the actual topological structure of the new energy power grid to obtain the new energy power grid model.

[0035] In this embodiment, operating condition data for the grid unit is collected. For example, for photovoltaic panels, data such as light intensity, temperature, and humidity are collected; for wind turbines, data such as wind speed, wind direction, and air density are collected. Taking photovoltaic panels as an example, based on the light intensity-power characteristic curve (typically provided by the panel manufacturer or measured through experiments), the panel's function of converting light energy into electrical energy is determined when the light intensity reaches a certain threshold. Based on the panel's specifications (such as single-chip power and quantity) and operating conditions such as light intensity, the panel's generated power is calculated, thereby determining the implementation scale. The installation location of the grid unit is clearly defined, and its connection with surrounding equipment is analyzed. For example, in a new energy power generation park, photovoltaic panels and inverters are connected in series, and multiple such series combinations are connected in parallel. Based on these connection relationships, individual devices such as photovoltaic panels and inverters are considered at the component-level design level; the photovoltaic panel-inverter combination is considered at the subsystem-level design level; and the entire park's power generation, transmission, and substation systems are considered at the system-level design level. Operating conditions directly impact the functionality and scale of the grid unit. For example, insufficient light intensity can limit the power generated by photovoltaic panels. The installation location and interconnection relationships determine the grid unit's position and role within the overall system, which in turn influences the design hierarchy. Properly determining these elements provides an accurate foundation for subsequent model construction. By collecting and analyzing data on operating conditions and interconnection relationships, the grid unit's function, scale, and design hierarchy can be determined. For example, if a photovoltaic power station, under current sunlight conditions, converts solar energy into electricity and generates X megawatt-hours of electricity per day, has design hierarchies ranging from the component level (PV panels, inverters, etc.) to the subsystem level (PV panel-inverter combination), these findings provide a clear direction for subsequent model structure design.

[0036] In this embodiment, the grid power supply model is considered based on the design level determined in the first step. For example, for a wind farm using a certain type of wind turbine, if the design level is at the subsystem level, the connection relationship between the wind turbine and components such as the tower and foundation, as well as the interaction logic with the control system, needs to be considered. In simulation software (such as MATLAB / Simulink, PSCAD / EMTDC, etc.), the corresponding model structure is constructed using the modules and tools provided by the software. For example, in PSCAD, the electrical model of the wind turbine is constructed using the electrical component module, and the mechanical model is constructed using the mechanical module. These are then connected according to the actual physical connections. The design level determines the complexity and components of the model, while the grid power supply model influences the selection of specific modules and parameters. A properly designed model structure accurately reflects the actual operation of the grid unit, facilitating subsequent simulation and analysis. In the simulation software, the model structure is gradually constructed according to the design level and power supply model requirements. For example, the model structure of a wind turbine electronic system is completed, including the wind turbine body model, the control system model, and the wiring model connecting them. This model structure can initially simulate the operation of the wind turbine under different wind speed conditions, paving the way for further addition of physical phenomena and model refinement.

[0037] In this embodiment, a model-to-physical comparison table is established, which records the physical phenomena corresponding to different grid unit models, such as photovoltaic conversion and heat generation in photovoltaic panels, and aerodynamic and mechanical vibration in wind turbines. Based on the grid unit model, the corresponding physical phenomena are retrieved from the comparison table. Then, in simulation software, the obtained physical phenomena are coupled using the software's multi-physics coupling function (e.g., COMSOL Multiphysics, which can achieve multiple physical field coupling). For example, for photovoltaic panels, the current generated by photoelectric conversion is coupled with the temperature change caused by heat generation to account for the impact of temperature on photoelectric conversion efficiency. The coupled physical phenomena are then applied to the model structure designed in the second step to obtain an operational model. Different grid unit models have different physical characteristics and phenomena. Accurately capturing these physical phenomena through the model-to-physical comparison table and applying the coupled processing allows the model to more realistically reflect the actual operational behavior of the grid unit. In actual operation, multiple physical phenomena influence each other, such as increased photovoltaic panel temperature, which reduces power generation efficiency. By searching the comparison table and performing the coupled processing, multiple physical phenomena are integrated into the model structure to obtain an operational model. For example, a photovoltaic panel operational model is obtained that accounts for photoelectric conversion, heat generation, and the coupled relationship between them. This model can more accurately simulate the output power changes of photovoltaic panels under different lighting and temperature conditions, and the simulation accuracy is significantly improved compared with the model that does not consider the coupling relationship.

[0038] In this embodiment, the data flow and interaction requirements between different design levels are analyzed to determine an information exchange mechanism. For example, in a power grid system consisting of renewable energy power generation units, transmission lines, and loads, the component-level power generation unit model needs to transmit power data to the subsystem-level transmission line model, and the subsystem-level model needs to transmit data such as voltage and current to the system-level model. The communication protocol (such as Modbus, TCP / IP, etc.) and data format to be used for information exchange are determined. Then, based on the actual topology of the renewable energy power grid, the operation models at different design levels are connected in the simulation software. For example, in the power system simulation software DIgSILENT, the power generation unit model, transmission line model, and load model are connected according to the node connections of the actual power grid. Models at different design levels require information exchange to accurately simulate the operation of the entire power grid system. Determining an appropriate information exchange mechanism ensures accurate and timely data transmission. Connecting models according to the actual topology ensures that the constructed renewable energy power grid model is consistent with the actual power grid, improving the model's practicality and accuracy. By determining the information exchange mechanism and connecting the models, a renewable energy power grid model is constructed. For example, a regional renewable energy power grid model was constructed, encompassing multiple renewable energy generation units, transmission lines, and loads. This model can simulate the operation of the entire regional power grid under different operating conditions, such as changes in grid voltage and frequency when renewable energy generation power fluctuates. This provides an effective tool for analyzing grid operation and formulating control strategies.

[0039] The beneficial effects of the above technical solution are: by clarifying the execution function and scale, designing a reasonable hierarchy and structure, coupling physical phenomena and establishing an interaction mechanism, a more accurate and comprehensive power grid model can be constructed, effectively simulating the dynamic characteristics of the power grid and meeting the power grid analysis and control needs.

[0040] The present invention provides a method for simulating the dynamic characteristics of a new energy power grid, which enables an adaptive step size algorithm to adjust the simulation step size in real time according to the state change of the CPU simulation core, including: Start the monitoring mechanism of each CPU simulation core for real-time monitoring. The real-time monitoring data is related to the CPU simulation core utilization rate, memory occupancy rate, cache hit rate, and task queue length. Input the real-time monitoring data into the pre-established CPU simulation core state assessment model to obtain the state assessment coefficient at the corresponding moment and determine the first error adjustment step size at N consecutive moments; Determining a second error adjustment step size based on dynamic changes of the corresponding CPU simulation core at N consecutive moments; When the first error adjustment step size and the second error adjustment step size are in the same direction, determining a first unit step size adjustment amount;

[0041] Wherein, D1 is the first unit step adjustment amount; Respectively represent the first error adjustment step size and the second error adjustment step size; When the directions of the first error adjustment step and the second error adjustment step are inconsistent, calculate the absolute value of the difference between the first error adjustment step and the second error adjustment step, and at the same time, determine the data compliance ratio of the corresponding CPU simulation core at N consecutive moments ,in, It represents the sum of the parameters of the corresponding CPU simulation core within the set range at N consecutive moments, and ; Obtain a correction value Xz according to the product of the absolute value of the difference and the value obtained by subtracting the data compliance ratio from 1; determining a second unit step adjustment amount based on the correction value, the first error adjustment step, and the second error adjustment step; The step size is adjusted according to the state change coefficient between the previous moment and the current moment, and according to the determined unit step size adjustment amount of the CPU simulation core, wherein the update period of the unit step size adjustment amount is T, and the number of consecutive moments involved in the update period T is greater than or equal to N.

[0042] In this embodiment, the CPU simulation core is monitored using tools or interfaces provided by the operating system. For example, in Linux, the top or ps command can be used to obtain information such as CPU usage and memory occupancy. In Windows, relevant data can be obtained through Performance Monitor. A script or program is written to periodically (e.g., once per second) collect data such as CPU simulation core usage, memory occupancy (the ratio of used memory to total memory), cache hit rate (the percentage of successful CPU reads from the cache), and task queue length (the number of tasks awaiting CPU processing). This monitoring data can reflect the real-time load and operating status of the CPU simulation core. CPU usage reflects the CPU's busyness; memory occupancy reflects memory resource usage; excessive occupancy may lead to system performance degradation; cache hit rate affects the speed at which the CPU retrieves data; a low hit rate may indicate frequent data reads from low-speed storage; and task queue length indicates a backlog of tasks awaiting processing. By monitoring this data in real time, the status of the CPU simulation core can be promptly understood, providing a basis for subsequent step size adjustments. The monitoring mechanism continuously collects data during program execution. For example, if data is acquired every second, a series of time series data may be obtained. For example, at the first second, the CPU usage is 30%, the memory usage is 60%, the cache hit rate is 70%, and the task queue length is 5. At the second second, these data will change based on the CPU's operating status. This real-time monitoring data will serve as input for subsequent steps.

[0043] In this embodiment, a CPU simulation core state assessment model is pre-established. This model can be based on machine learning algorithms (such as neural networks, decision trees, etc.) or traditional mathematical statistical methods (such as linear regression, multivariate regression, etc.). The real-time monitoring data collected in the first step is input into the model. Through internal calculations and processing, the model outputs a state assessment coefficient at the corresponding moment. This coefficient can be a numerical value that quantifies the state of the CPU simulation core. For example, a larger value indicates a worse state, which may indicate excessive load or resource constraints. Then, based on the state assessment coefficient and pre-set rules (for example, a calculation based on the comparison of the state assessment coefficient with a threshold and a proportional coefficient), the first error adjustment step size for N consecutive moments is determined. Assuming N is 10, the first error adjustment step size is calculated based on the state assessment coefficients for the past 10 moments. The CPU simulation core state assessment model is the core component, comprehensively considering multiple monitoring indicators and evaluating the CPU state using a specific algorithm. The state assessment coefficient provides a quantitative basis for subsequent step size adjustments. The determination of the first error adjustment step size is based on an analysis of the CPU state over a period of time, aiming to preliminarily determine the direction and magnitude of the step size adjustment based on the CPU load. After inputting real-time monitoring data into the model, the model calculates the state assessment coefficient. For example, the state assessment coefficient calculated at a certain moment is 0.8 (assuming it is between 0 and 1, with closer to 1 indicating worsening state). Based on pre-set rules, the first error adjustment step size is calculated to be 0.05 (assuming the step size adjustment unit is seconds). This calculates the first error adjustment step size for N consecutive moments and uses it for subsequent step size adjustment decisions.

[0044] In this embodiment, the dynamic changes of the corresponding CPU simulation core at N consecutive moments are analyzed. These dynamic changes can be reflected by calculating the difference or rate of change of monitoring data at adjacent moments. For example, the rate of change of CPU usage at adjacent moments, or the difference in memory usage, can be calculated. Based on these dynamic change data and combined with a pre-defined algorithm (for example, based on the slope of the change trend and an adjustment factor), a second error adjustment step size is determined. This step size reflects the step size adjustment amount determined based on the dynamic change trend of the CPU simulation core. Considering only the state assessment coefficient is not sufficient; the dynamic change trend of the CPU simulation core must also be analyzed. Even if the current state assessment coefficient is the same, if one is rapidly increasing and the other is slowly decreasing, the step size adjustment strategy should also be different. The second error adjustment step size is designed to capture the impact of this dynamic change on the step size adjustment. Dynamic change analysis is performed on monitoring data at N consecutive moments. Assume that the CPU usage is calculated to be increasing over these N moments, with a rate of change of 0.02 per second. According to the pre-set algorithm, the second error adjustment step size is calculated to be -0.03 (the negative sign indicates that the step size may need to be reduced, and the unit is also assumed to be seconds). The second error adjustment step size obtained in this step will be used together with the first error adjustment step size in subsequent step size adjustment decisions.

[0045] In this embodiment, a determination is made as to whether the directions of the first error adjustment step and the second error adjustment step are consistent. This determination can be made by comparing the signs of the two step sizes. If the two step sizes have the same signs, their adjustment directions are consistent. When the two error adjustment step sizes are consistent in direction, the direction of the step size adjustment is clear based on the CPU status assessment and dynamic change trend. This formula is used to calculate the first unit step size adjustment amount, comprehensively considering the sizes of the two step sizes and the time span (represented by N), making the step size adjustment more reasonable.

[0046] Assumptions is 0.05, is 0.03, N is 10, and D1 is 0.004. This result will be used for subsequent step size adjustment operations.

[0047] The setting range can be 0-100% CPU usage, 0-100% memory usage, etc., and the total sum of parameters Wn within these ranges is counted.

[0048] If the two error adjustment steps differ in direction, this indicates that the CPU's state assessment and dynamic change trends provide different signals for step adjustment. In this case, more factors must be considered to determine the step adjustment amount. The absolute value of the difference reflects the degree of discrepancy between the two step sizes, while the data compliance ratio reflects the CPU parameter compliance over N consecutive time periods. By calculating the correction value and combining it with the other two step sizes, the step adjustment amount can be more accurately determined.

[0049] In this embodiment, the state change coefficient between the previous moment and the current moment is calculated. This coefficient can be obtained by calculating the difference or rate of change of the state evaluation coefficient at adjacent moments. For example, the state evaluation coefficient at the current moment is subtracted from the state evaluation coefficient at the previous moment and then divided by the time interval. Then, a step size adjustment is performed based on the determined unit step size adjustment amount for the CPU simulation core (a first unit step size adjustment amount when the two error adjustment steps are in the same direction, and a second unit step size adjustment amount when they are not). The update period for the unit step size adjustment amount is T, and the number of consecutive moments involved in the update period T is greater than or equal to N. For example, T can be set to 10 seconds, meaning that the step size adjustment amount is updated and the step size adjustment operation is performed every 10 seconds based on the above calculation results. The state change coefficient reflects the changes in the CPU simulation core state over a short period of time. By combining the step size adjustment with the unit step size adjustment amount, the step size adjustment can more timely and accurately adapt to CPU state changes. The update period T is set to balance the computational effort and the timeliness of the step size adjustment, avoiding overly frequent or overly sparse step size adjustments. Assuming the state evaluation coefficient at the previous moment was 0.7, the current moment is 0.6, and the time interval is 1 second, the state change coefficient is (0.7 − 0.6) ÷ 1 = 0.1. If the previously determined unit step adjustment is 0.01 (assuming this is the second unit step adjustment), adjust the simulation step size accordingly. For example, increase the current simulation step size by 0.01 (assuming the step size is in seconds). After this adjustment, the simulation will continue at the new step size to better adapt to the state changes of the CPU simulation core.

[0050] The beneficial effects of the above technical solution are: by real-time monitoring of core status and dynamic adjustment of step size, computing resources can be fully utilized to avoid the impact of excessive core load on the overall simulation efficiency and accuracy, thereby ensuring the efficient and accurate operation of large-scale power grid simulation.

[0051] The present invention provides a method for simulating the dynamic characteristics of a new energy power grid, which determines a second unit step adjustment amount based on the correction value, the first error adjustment step, and the second error adjustment step, including:

[0052] in, Indicates the second unit step adjustment amount; 、 is a sign function, and , .

[0053] The beneficial effect of this technical solution is that it can more accurately adjust the step size in complex power grid dynamic simulations, improving the accuracy of step size adjustment by 20% compared to simple averaging or single judgment methods, further improving simulation efficiency and accuracy. When adjusting the step size based on the CPU core status and system dynamics, it is crucial to accurately determine the adjustment direction and amplitude. The introduction of symbolic functions and specific calculation formulas can make step size adjustment more scientific and reasonable, adapt to complex and changing simulation scenarios, and ensure the reliability of simulation results.

[0054] The present invention provides a method for simulating the dynamic characteristics of a new energy power grid, which uses a Kalman filter algorithm to preprocess actual power grid operation data, including: Converting the actual power grid operation data into a data sequence; Acquire historical operating data of the new energy grid, analyze the compliance coefficient and expected coefficient of each historical data group, adjust the process noise covariance matrix and the measurement noise covariance matrix, and update the Kalman filter algorithm; Performing noise filtering on the data sequence based on the updated algorithm to obtain a new sequence; Based on the new sequence at M consecutive moments, a parameter estimation set of the corresponding power grid unit is obtained.

[0055] In this embodiment, the actual power grid operation data collected in real time is sorted into a data sequence in chronological order.

[0056] The beneficial effect of this technical solution is that the Kalman filter algorithm can effectively remove noise and accurately estimate grid unit parameters by using historical data to update algorithm parameters. This provides an accurate data foundation for subsequent model analysis and control strategy formulation, which is a key step in improving simulation accuracy.

[0057] The present invention provides a method for simulating the dynamic characteristics of a new energy power grid, which analyzes the dynamic characteristics of a corresponding sub-model based on all parameter estimation sets under each sub-model, including: Performing a global analysis within the set and a global analysis outside the set on each parameter estimation set under the sub-model to obtain an error within the set and an error outside the set; Minimize all in-set errors under the sub-model and compare them with the out-of-set errors to obtain a reference error under each parameter; Inputting all parameter estimation sets into the first characteristic analysis model in sequence to obtain a nonlinear mapping relationship between each parameter and the dynamic characteristics of the corresponding sub-model; Perform sensitivity analysis on each parameter in the parameter estimation set, and quantify the impact of each parameter on the dynamic characteristics by combining nonlinear mapping relationships, and find the key parameter combinations that affect the changes in dynamic characteristics by combining reference errors; Combined with the grid topology, the dynamic characteristic correlation between sub-models at different locations is analyzed, the key parameter combination is verified, and a parameter weight is assigned to each key parameter.

[0058] In this embodiment, the actual power grid operation data usually comes from various monitoring devices, such as the measurement and control devices of substations, the power collection systems of power plants, etc. These data may be stored and transmitted in different formats, such as CSV, JSON, etc. First, it is necessary to read these data files and then sort the data in the order of timestamps. For example, using the pandas library in the Python language, the power grid operation data file in CSV format is read through the read_csv() function, and then the sort_values() function is used to sort the data in ascending order according to the time column, thereby organizing the data into a data sequence arranged in chronological order. Converting the actual power grid operation data into a data sequence is for the convenience of subsequent processing. The power grid operation data changes dynamically over time. After being arranged in chronological order, it can better reflect the chronological correlation of the data, meet the requirements of the Kalman filter algorithm for time series data processing, and facilitate the analysis of the data change pattern in the time dimension and the performance of noise filtering and parameter estimation. After reading and sorting operations, the originally disorganized data is organized into an orderly data sequence. For example, for a set of grid voltage data collected every 5 minutes, after processing, a data sequence is formed, arranged in order from the earliest collection time to the latest collection time. Each data point corresponds to a specific collection time, providing a standardized input data format for subsequent steps.

[0059] In this embodiment, historical operating data of the new energy grid is queried and extracted from the grid's data storage system, such as a database (MySQL, Oracle, etc.). SQL queries can be written to filter the required historical data based on time range, data type, and other criteria. For example, grid power, voltage, and current data for each day of the past year can be queried. For each historical data set, certain compliance criteria are defined, such as voltage within ±5% of the rated value being considered compliant. The proportion of compliant data in each data set is calculated and used as the compliance coefficient. The expected coefficient can be determined by calculating statistics such as the mean and median of the historical data, reflecting the average level or central tendency of the data. For example, the average value of the historical power data can be calculated as the expected coefficient for the power data. The process noise covariance matrix Q and the measurement noise covariance matrix R are adjusted based on the compliance coefficient and the expected coefficient. In the Kalman filter algorithm, these two matrices describe the statistical characteristics of system noise and measurement noise. The adjustment method can be determined based on empirical formulas or through machine learning algorithms. For example, a low compliance coefficient indicates high data noise, and the values ​​of the Q and R matrices can be increased accordingly to enhance the algorithm's noise handling capabilities. After adjusting the matrix, the new matrix parameters are substituted into the Kalman filter algorithm, completing the algorithm update. Historical operating data contains past operational characteristics and noise properties of the power grid. By analyzing the compliance coefficient and expected coefficient, we can understand the quality and distribution of the data. The process noise covariance matrix and the measurement noise covariance matrix are key parameters of the Kalman filter algorithm; they determine how the algorithm estimates and processes noise. Adjusting these two matrices based on the historical data analysis results allows the Kalman filter algorithm to better adapt to the characteristics of the power grid data, improving filtering effectiveness and parameter estimation accuracy. Through a series of operations, the Kalman filter algorithm is updated. For example, after analyzing historical voltage data, a low compliance coefficient was found, indicating high voltage data noise. Therefore, the voltage-related elements in the measurement noise covariance matrix R were increased. The updated Kalman filter algorithm can more effectively process noise in voltage data, providing a more reliable tool for subsequent data filtering and parameter estimation.

[0060] In this embodiment, the data sequence obtained in the first step is input into the updated Kalman filter algorithm. The Kalman filter algorithm, based on the system state equation and measurement equation, processes data through two steps: prediction and update. In the prediction step, the current state is predicted based on the state estimate at the previous moment and the system state transition matrix. In the update step, the predicted state is corrected using the current measurement value and the measurement matrix to obtain the optimal state estimate at the current moment. By continuously iterating these two steps, each data point in the data sequence is processed to remove the effects of noise, resulting in a new noise-filtered sequence. The Kalman filter algorithm can be implemented using relevant functions in MATLAB software, such as the kalman() function, passing the data sequence as an input parameter into the function for calculation. During the collection and transmission process, power grid operation data is inevitably subject to interference from various noises, such as electromagnetic interference and measurement equipment errors. The Kalman filter algorithm can utilize the system's dynamic model and the statistical characteristics of noise to estimate and compensate for these noises, thereby extracting a more realistic signal. By filtering the data sequence for noise, the data quality can be improved, laying the foundation for subsequent accurate estimation of grid unit parameters. After the data series is fed into the updated Kalman filter algorithm, it is processed to produce a new series. For example, for a noisy grid current data series, after Kalman filtering, the data fluctuations become smoother, outliers caused by noise are effectively removed, and a new series is produced that better reflects the actual current variation trend.

[0061] In this embodiment, data from M consecutive moments is selected from the new sequence after noise filtering. M here can be determined based on actual needs and data characteristics. For example, if the dynamic changes of the power grid unit are relatively slow, M can be larger; if the changes are rapid, M can be smaller. For each power grid unit, the parameters of these M consecutive moments are estimated based on its physical model and relevant electrical principles using the data from these M consecutive moments. For example, the parameters of a transmission line unit may include resistance, inductance, and capacitance. Based on Ohm's law, the law of electromagnetic induction, and other parameters, the values ​​of resistance, inductance, and capacitance can be calculated using parameter estimation methods such as least squares, combined with the voltage and current data from these M moments. This yields a parameter estimate set for the transmission line unit. Power grid unit parameters are key indicators that describe their electrical characteristics and operating behavior. By analyzing and processing the noise-filtered data, these parameters can be more accurately estimated. The selection of data from M consecutive moments comprehensively considers the operation of the power grid unit over a period of time, preventing the influence of randomness of data at a single moment on the parameter estimation results. Using appropriate parameter estimation methods, the data can be used to infer parameter values ​​that best reflect the actual operation of the power grid unit. By processing and calculating the new sequence data at M consecutive moments, we obtain the parameter estimates for the corresponding grid unit. For example, for a certain transmission line segment, the resistance estimate is 0.5Ω, the inductance estimate is 0.01H, and the capacitance estimate is 0.0001F. These parameter values ​​constitute the parameter estimate set for the transmission line unit, providing an important basis for subsequent analysis of the dynamic characteristics of the transmission line and the operating status of the entire power grid.

[0062] The beneficial effects of the above technical solution are: by comprehensively analyzing the parameter estimation set and determining the key parameters and weights, important factors can be highlighted, the analysis process can be simplified, the efficiency and accuracy of dynamic characteristic analysis can be improved, and strong support can be provided for the formulation of effective control strategies.

[0063] The present invention provides a method for simulating the dynamic characteristics of a new energy power grid, which determines a control strategy based on the power grid operation control target, including: Using quantitative indicators to describe the power grid operation control objectives; The description results, key parameters and parameter weights are combined into an input vector and input into the strategy analysis model to obtain the control strategy.

[0064] In this embodiment, based on the previous analysis of the dynamic characteristics of the power grid sub-model, key parameters that significantly impact the grid's operational control objectives are identified. For example, in renewable energy power generation units, light intensity, temperature, and inverter control parameters may be key parameters; in transmission lines, line impedance, transmission power, and reactive power compensation may be key parameters. Appropriate methods, such as the Analytic Hierarchy Process (AHP) or the Entropy Weight Method, are used to determine weights for each key parameter to represent their importance to the grid's operational control objectives. The quantitative indicator data obtained in the first step is combined with the key parameters and their weights to form an input vector. For example, assuming there are six quantitative indicators (voltage deviation, frequency, active power shortage ratio, power factor, grid loss rate, and renewable energy generation utilization rate), and four key parameters (light intensity, temperature, line impedance, and reactive power compensation) and their corresponding weights, the dimension of the input vector is 6 + 4 + 4 (parameter weights). These data are arranged in a certain order into a vector. The resulting input vector is input into the pre-established strategy analysis model. Policy analysis models can be based on artificial intelligence algorithms such as deep reinforcement learning (DRL) and neural networks (NN), or traditional optimization algorithms such as linear programming (LP) and integer programming (IP). The model learns and analyzes the input vector, utilizing its internal algorithms and rules to output a control strategy tailored to the current grid operating state. For example, a policy analysis model based on deep reinforcement learning will infer the input vector within its trained policy network and output control strategies such as adjusting generator active power and switching reactive power compensation equipment.

[0065] In this embodiment, key parameters and their weights reflect the factors that significantly impact the grid's operational control objectives and their degree of importance. Combining these with quantitative indicators to form an input vector allows comprehensive input of grid operation information into the strategy analysis model. By processing and analyzing the input vector, the strategy analysis model generates appropriate control strategies based on the learned knowledge and rules to achieve the grid's operational control objectives. Different strategy analysis models have different characteristics and advantages, and selecting the appropriate model is crucial for generating effective control strategies. After the combined input vectors are input into the strategy analysis model, the model performs calculations and reasoning to output a control strategy. For example, the strategy analysis model outputs a control strategy that increases the active power of a generator by 10 MW and implements a capacitor bank to compensate for reactive power. These control strategies are generated based on quantitative indicators of the current grid's operational status, as well as key parameters and weights, and are designed to optimize grid operation and align it toward the set control objectives.

[0066] The beneficial effects of the above technical solution are: the strategy analysis model can comprehensively consider key parameters and goals and automatically generate the optimal control strategy. Compared with formulating strategies based on manual experience, it is more scientific and efficient, and can achieve multi-objective optimization operation of the power grid, ensuring the safe, economical and stable operation of the power grid.

[0067] The present invention provides a method for simulating the dynamic characteristics of a new energy power grid, which includes, after all parameter estimation sets are sequentially input into a first characteristic analysis model, the following steps: Obtaining a work log of the first characteristic analysis model, and matching each work item in the work log with a corresponding item traceability list to obtain a parameter description of the corresponding work item; Acquire a capture tool that matches the parameter description from a description-tool database, and capture a work path that matches the parameter description and path generation data based on the work path in real time according to the capture tool; Extracting index features from the path generation data to obtain a generation vector; Inputting the generated vector into a vector analysis model to obtain an early warning regulation, wherein the early warning regulation includes: an early warning type and early warning information; Compare and analyze the early warning regulations of each work item with the standard regulations to determine the work abnormality factors; Analyze the rationality of work by combining abnormal factors and parameter combinations; If the working rationality is greater than the preset rationality, it is determined that the working process of the first characteristic analysis model is reasonable; Otherwise, the reason for optimizing the first characteristic analysis model is determined based on the abnormal factors, parameter combinations, and working paths.

[0068] In this embodiment, the work log documents the work performed by the first feature analysis model during operation, including a series of work items, and records information such as the start time, end time, performer, and execution process of each work item. For example, in a software development project, the work log of the first feature analysis model for code performance analysis will record the details of each code scan, performance test, and other work items.

[0069] A work item is the basic unit of a work log, and refers to a specific task or operation during the execution of a first-characteristic analysis model. Activities such as data collection, data cleaning, and model training can all be considered work items. An item traceability list is a pre-established list that stores standard information, historical data, and reference materials related to various work items. This list is used to trace and match detailed work item parameters. For example, in a logistics and transportation characteristics analysis model, the item traceability list would record parameters such as standard transportation time and cost for different transportation routes and transportation tools. Parameter descriptions describe the detailed characteristics and attributes of a work item by matching it with its source list. For example, for a data collection work item, the parameter description may include the type of data collected, the frequency of collection, and the source of the data. The description-tool database stores the mappings between various parameter descriptions and capture tools. A capture tool is software, a program, or a device used to acquire specific data or information. For example, in a meteorological data analysis model, the description-tool database would record the associations between parameter descriptions of temperature data and the corresponding meteorological monitoring equipment and data acquisition software. Capture tool: A tool used to acquire data or information that matches parameter descriptions in real time. For example, a network traffic monitoring tool can be used as a capture tool to capture network traffic data in real time to meet the parameter description requirements of related work items in the network characteristic analysis model. The work path is the process or sequence of steps followed when the work item is executed during the operation of the first characteristic analysis model.

[0070] Path-generated data is the data generated by a work item during its execution. For example, in the image recognition model's work path, intermediate data generated by work items such as image preprocessing and feature extraction, as well as the final recognition results, are all considered path-generated data. Indicator feature extraction is the process of extracting representative indicators from path-generated data that reflect the essential characteristics and patterns of the data. For example, in a user behavior analysis model, indicator features such as browsing time, number of clicks, and number of page jumps can be extracted from user browsing data.

[0071] The generated vector is the vector data formed by quantifying and combining the extracted indicator features.

[0072] A vector analysis model uses mathematical algorithms and machine learning techniques to analyze and process generated vectors. It can predict, judge, or output relevant results based on the input generated vectors. For example, in a risk prediction model, a vector analysis model generates vectors based on the input corporate financial data and outputs the company's risk level. Warning regulations are output by the vector analysis model and include rules or prompts for warning types and warning information. Warning types include data anomalies, performance degradation, and increased risk. Warning information provides a detailed description of the warning type, such as "sales data in a certain region has been below 70% of normal levels for three consecutive days, indicating a risk of sales decline." Standard regulations: Pre-set standards and specifications used to measure the proper functioning of the primary characteristic analysis model. For example, in a product quality inspection model, standard regulations define product dimensional error ranges and performance indicators.

[0073] Work anomaly factors are identified by comparing and analyzing each work item's warning regulations with the standard regulations. For example, in a manufacturing model, factors such as substandard raw material quality and equipment failure could be work anomaly factors. A parameter combination is a collection of multiple parameters related to a work item. For example, in a flight scheduling model, parameters such as flight departure time, arrival time, aircraft type, and passenger capacity constitute a parameter combination. The rationality of work is the evaluation result of the degree to which the working process of the first characteristic analysis model conforms to expectations and standards by combining abnormal factors and parameter combinations.

[0074] The preset rationality is a pre-set threshold or standard for judging whether the working process of the first characteristic analysis model is rational. When the working rationality is greater than the preset value, the working process is considered rational; otherwise, it is considered unreasonable. The optimization reason is when the work rationality is less than the preset rationality. Based on the abnormal factors, parameter combinations, and work paths, the reasons for optimizing and improving the first characteristic analysis model are analyzed. Examples include unreasonable model algorithms, data input errors, and unsmooth workflows.

[0075] The beneficial effect of the above technical solution is that comprehensive monitoring, analysis, and evaluation of the first characteristic analysis model's operational process enables timely identification of anomalies and issues. By analyzing work logs, capturing data, and extracting indicators, combined with vector analysis and comparison standards, it accurately identifies anomaly factors and assesses the rationality of the work. If the work is not rational, the cause can be identified, providing a basis for optimization and improvement of the model. This ensures the efficient and accurate operation of the first characteristic analysis model, improves work quality and efficiency, reduces the probability of errors and risks, and enables the model to better serve business needs and decision-making.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for simulating the dynamic characteristics of a new energy power grid, characterized in that: include: Collect detailed electric field information of renewable energy power plants, establish an operating model for each grid unit in simulation software, and connect them according to the actual topology of the renewable energy grid to form a renewable energy grid model; Decomposing the new energy grid model into multiple sub-models according to pre-set rules and assigning them to different CPU simulation cores for simulation; Enable the adaptive step size algorithm to adjust the simulation step size in real time according to the state changes of the CPU simulation core, and obtain the dynamic characteristics of the corresponding sub-model after each simulation step size adjustment; Acquire actual grid operation data of the new energy grid in real time, and preprocess the actual grid operation data using a Kalman filter algorithm to obtain a parameter estimation set for each grid unit under the new energy grid model; The dynamic characteristics of the corresponding sub-model are analyzed based on all parameter estimation sets under each sub-model, and the regulation strategy is determined according to the power grid operation control objectives.

2. The method for simulating the dynamic characteristics of a new energy power grid according to claim 1, characterized in that: The electric field detailed information includes: the model, installation location and operating conditions of each grid unit.

3. The method for simulating the dynamic characteristics of a new energy power grid according to claim 1, characterized in that: Form a new energy grid model, including: Determining the execution function and execution scale according to the operating conditions of the grid unit, and determining the design level in combination with the connection relationship under the installation position of the grid unit, wherein the connection relationship is the existing interconnection relationship and parallel relationship; Designing a model structure according to the design level and the type of grid power supply; Obtaining multiple physical phenomena that match the model of the grid unit from the model-physical comparison table, performing coupling processing and applying them to the corresponding model structure to obtain an operating model; The information interaction mechanism between different design levels is determined, and the operation models under different design levels are connected according to the actual topological structure of the new energy power grid to obtain the new energy power grid model.

4. The method for simulating the dynamic characteristics of a new energy grid according to claim 1, characterized in that: Enable the adaptive step size algorithm to adjust the simulation step size in real time according to the state changes of the CPU simulation core, including: Start the monitoring mechanism of each CPU simulation core for real-time monitoring. The real-time monitoring data is related to the CPU simulation core utilization rate, memory occupancy rate, cache hit rate, and task queue length. Input the real-time monitoring data into the pre-established CPU simulation core state assessment model to obtain the state assessment coefficient at the corresponding moment and determine the first error adjustment step size at N consecutive moments; Determining a second error adjustment step size based on dynamic changes of the corresponding CPU simulation core at N consecutive moments; When the first error adjustment step size and the second error adjustment step size are in the same direction, determining a first unit step size adjustment amount; Wherein, D1 is the first unit step adjustment amount; Respectively represent the first error adjustment step size and the second error adjustment step size; When the directions of the first error adjustment step and the second error adjustment step are inconsistent, calculate the absolute value of the difference between the first error adjustment step and the second error adjustment step, and at the same time, determine the data compliance ratio of the corresponding CPU simulation core at N consecutive moments ,in, It represents the sum of the parameters of the corresponding CPU simulation core within the set range at N consecutive moments, and ; Obtain a correction value Xz according to the product of the absolute value of the difference and the value obtained by subtracting the data compliance ratio from 1; determining a second unit step adjustment amount based on the correction value, the first error adjustment step, and the second error adjustment step; The step size is adjusted according to the state change coefficient between the previous moment and the current moment, and according to the determined unit step size adjustment amount of the CPU simulation core, wherein the update period of the unit step size adjustment amount is T, and the number of consecutive moments involved in the update period T is greater than or equal to N.

5. The method for simulating the dynamic characteristics of a new energy power grid according to claim 4, characterized in that: Determining a second unit step adjustment amount based on the correction value, the first error adjustment step, and the second error adjustment step includes: in, Indicates the second unit step adjustment amount; 、 is a sign function, and , .

6. The method for simulating the dynamic characteristics of a new energy power grid according to claim 1, characterized in that: The Kalman filter algorithm is used to pre-process the actual power grid operation data, including: Converting the actual power grid operation data into a data sequence; Acquire historical operating data of the new energy grid, analyze the compliance coefficient and expected coefficient of each historical data group, adjust the process noise covariance matrix and the measurement noise covariance matrix, and update the Kalman filter algorithm; Performing noise filtering on the data sequence based on the updated algorithm to obtain a new sequence; Based on the new sequence at M consecutive moments, a parameter estimation set of the corresponding power grid unit is obtained.

7. The method for simulating the dynamic characteristics of a new energy power grid according to claim 1, characterized in that: The dynamic characteristics of each sub-model are analyzed based on all parameter estimation sets under each sub-model, including: Performing a global analysis within the set and a global analysis outside the set on each parameter estimation set under the sub-model to obtain an error within the set and an error outside the set; Minimize all in-set errors under the sub-model and compare them with the out-of-set errors to obtain a reference error under each parameter; Inputting all parameter estimation sets into the first characteristic analysis model in sequence to obtain a nonlinear mapping relationship between each parameter and the dynamic characteristics of the corresponding sub-model; Perform sensitivity analysis on each parameter in the parameter estimation set, and quantify the impact of each parameter on the dynamic characteristics by combining nonlinear mapping relationships, and find the key parameter combinations that affect the changes in dynamic characteristics by combining reference errors; Combined with the grid topology, the dynamic characteristic correlation between sub-models at different locations is analyzed, the key parameter combination is verified, and a parameter weight is assigned to each key parameter.

8. The method for simulating the dynamic characteristics of a new energy power grid according to claim 7, characterized in that: Determine the control strategy based on the grid operation control objectives, including: Using quantitative indicators to describe the power grid operation control objectives; The description results, key parameters and parameter weights are combined into an input vector and input into the strategy analysis model to obtain the control strategy.

9. The method for simulating the dynamic characteristics of a new energy power grid according to claim 7, characterized in that: After all parameter estimation sets are sequentially input into the first characteristic analysis model, the following steps are further included: Obtaining a work log of the first characteristic analysis model, and matching each work item in the work log with a corresponding item traceability list to obtain a parameter description of the corresponding work item; Acquire a capture tool that matches the parameter description from a description-tool database, and capture a work path that matches the parameter description and path generation data based on the work path in real time according to the capture tool; Extracting index features from the path generation data to obtain a generation vector; Inputting the generated vector into a vector analysis model to obtain an early warning regulation, wherein the early warning regulation includes: an early warning type and early warning information; Compare and analyze the early warning regulations of each work item with the standard regulations to determine the work abnormality factors; Analyze the rationality of work by combining abnormal factors and parameter combinations; If the working rationality is greater than the preset rationality, it is determined that the working process of the first characteristic analysis model is reasonable; Otherwise, the reason for optimizing the first characteristic analysis model is determined based on the abnormal factors, parameter combinations, and working paths.

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