Dynamic Modeling Method for Power System Components Based on the MMOE-DAE Model

Abstract

The present invention discloses a method for dynamic modeling of power system components based on the MMOE-DAE model, which belongs to the field of electrical digital data processing technology, wherein high-precision and three-phase unified modeling of dynamic components of power systems: MMOE-DAE can achieve accurate modeling of complex high-order dynamic components in power systems in the absence of detailed physical models. By integrating neural ODE and automatic order promotion mechanism, the modeling capability of the dynamic behavior of complex components under various disturbance scenarios is effectively improved, ensuring high accuracy and robustness under strong nonlinear and high-order dynamic conditions. The unified modeling of three-phase circuits can simultaneously process the dynamic characteristics of each phase circuit under the same framework, and process the coupling relationship between phases through a multi-task learning mechanism, which significantly reduces the error caused by phase separation processing in traditional modeling methods and improves the overall simulation efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of electrical digital data processing, and in particular relates to a method for dynamic modeling of power system components based on a MMOE-DAE model. Background Art

[0002] Due to the integration of large-scale power electronic equipment, power systems exhibit complex dynamics, and there is an urgent need to develop highly reliable dynamic equivalent models using limited component measurement data. Electromagnetic transient simulation is based on the state of the system's instantaneous values and can describe microsecond-level processes involving switching devices. It is crucial for accurate stability analysis and control of modern power systems. Traditional electromagnetic transient simulation relies on detailed physical models of components (such as differential equations, algebraic equations, etc.), which usually require accurate physical parameters and operating conditions. However, in actual power systems, due to technical confidentiality, measurement limitations or component complexity, the physical models of some components may not be fully available, forming so-called "black box" or "grey box" models, making it difficult for traditional modeling methods to provide reliable dynamic predictions. Combining artificial intelligence technology, especially deep learning, with electromagnetic transient simulation is a hot research topic at present, but this technology still has many bottlenecks and challenges. First, the system measurement equipment can only capture low-dimensional discrete data samples with a limited frequency, resulting in traditional neural network models performing well under known conditions but performing poorly dynamically under unknown operating conditions; second, it is not clear whether data-driven models can fully utilize historical data and adaptively adjust parameters according to the system's operating conditions to improve the robustness and reliability of the simulation model; in addition, although some studies have combined the physical characteristics of power systems with deep learning, the ability of existing models to handle high-order differential dynamics and adapt to diverse scenarios still needs to be improved; finally, despite significant progress in data-driven modeling, the integration of deep learning models into unified simulations of electromagnetic transient simulations for commercial operations is still rare.

[0003] With the large-scale integration of renewable energy and power electronic loads, large-scale power systems are exhibiting increasingly complex nonlinear dynamic and coupled characteristics, placing increasing demands on accurate stability analysis and control of modern power systems. However, in the absence of prior knowledge, due to technical protection and measurement limitations, system access devices typically have black-box or gray-box models, where unmodeled components significantly undermine the reliability of operators' system operation decisions. Furthermore, even with comprehensive white-box models, achieving high-order dynamic responses in large-scale systems within acceptable time and computational resource constraints remains a significant challenge. Therefore, it is necessary to dynamically model black-box components and employ electromagnetic transient simulation for calculation and analysis.

[0004] Existing dynamic modeling technologies for electromagnetic transient (EMT) simulation face numerous challenges when addressing the dynamic behavior of complex components in modern power systems. First, there is a lack of physical models. Due to technical confidentiality, measurement limitations, and the complexity of system components, many power system components lack detailed internal models (i.e., "black box" or "gray box" models). This makes it difficult for traditional electromagnetic transient simulation to provide accurate dynamic predictions without a complete physical description, reducing the simulation accuracy and robustness of the system. Second, while deep learning technology holds great promise for application in simulation, it is highly dependent on high-quality measurement data. However, measurement equipment in power systems can typically only collect low-dimensional discrete data samples with a limited frequency, making it difficult for deep learning models developed from this data to generate reliable, continuous, and accurate dynamic equivalent models. Third, there is a lack of adaptive capabilities. While existing deep learning models perform well with known training datasets, their dynamic characteristics cannot adaptively adjust to unseen operating conditions or faults, resulting in poor simulation performance in complex and changing scenarios. Fourth, traditional deep learning models have limited ability to capture high-order dynamic characteristics. They struggle to maintain high dynamic tracking accuracy when dealing with strong coupling and nonlinear disturbances in the system. This is especially true when dealing with strong disturbances in large-scale power systems, where the models exhibit instability. Fifth, integrating deep learning models into existing commercial electromagnetic transient simulation platforms for unified simulation is rare. This is primarily due to the lack of effective data interfaces in existing technologies, making it difficult to seamlessly connect deep learning models with traditional simulation platforms, thus limiting their practical application in engineering projects.

[0005] Therefore, at this stage, it is necessary to design a dynamic modeling method, system and storage medium for power system components based on the MMOE-DAE model to solve the above problems. Summary of the Invention

[0006] The purpose of the present invention is to provide a method, system and storage medium for dynamic modeling of power system components based on the MMOE-DAE model, so as to solve the technical problems existing in the above-mentioned prior art.

[0007] To achieve the above object, the technical solution of the present invention is:

[0008] The dynamic modeling method of power system components based on the MMOE-DAE model includes the following steps:

[0009] S1: We propose a multi-gate hybrid differential-algebraic equation expert network (MMOE-DAE) that uses multiple gated neural differential-algebraic equation (DAE) networks to simulate the dynamic characteristics of three-phase circuits and establish the relationship between the phases.

[0010] S2: An automatic order raising module is proposed, which uses multiple independent continuous numerical integration channels and related state variable storage to automatically infer the optimal order for dynamic component modeling through a fully gated network;

[0011] S3: MMOE-DAE accurately simulates the dynamic relationship between different phases in the same model, achieving unified modeling of three-phase circuits and addressing the shortcomings of traditional models in handling coupling relationships between phases. MMOE-DAE is seamlessly integrated into the electromagnetic transient simulation platform, enabling real-time simulation and dynamic analysis in actual industrial application scenarios, thereby improving the practicality and reliability of electromagnetic transient simulation in power systems.

[0012] Furthermore, in step S1:

[0013] The architecture of the MMOE-DAE network for modeling three-phase circuits with dynamic components consists of three parts: the input part, the underlying model, the tower model, and the gated network module;

[0014] The input part receives the historical current signal i of each phase circuit A (t),i B (t),i C (t), historical voltage signal u of each phase circuit A (t),u B (t),u C (t), and external signals; the above signals are transmitted to the underlying neural ODE expert module through the gating network to predict the current and voltage changes in the next time step;

[0015] The underlying model includes multiple neural ODE expert modules, each of which is responsible for processing different dynamic characteristics and deciding which modules to participate in the current modeling task through the scheduling of the gate network; the neural ODE expert module is used to approximate the differential equations of the dynamic components and model the relationship between the current, voltage and time of each phase circuit; the input signal i A (t),i B (t),i C (t) After the neural ODE, the current i of each phase circuit in the next time step is obtained A (t+1),i B (t+1),i C (t+1);

[0016] The gating network module is responsible for selecting and scheduling neural ODE expert modules. It selects the neural ODE expert suitable for the current task based on the characteristics of the input signal, assigns weights through the softmax function, and schedules the neural ODE expert to participate in modeling. Its output is then transmitted to the corresponding DAE tower based on the conditions of each phase circuit.

[0017] The tower model is divided into multiple DAE towers, each tower is used to process the corresponding phase current in the three-phase circuit, i.e. A (t+1),i B (t+1),i C (t+1); Based on the DAE tower, the MMOE-DAE model can simultaneously handle the multi-task modeling of three-phase circuits and establish the relationship between the currents of different phases.

[0018] Furthermore, in step S2, the automatic order improvement module is specifically as follows:

[0019] By automatically inferring the optimal dynamic order of components through multiple numerical integration channels, the Neural ODE Expert can handle high-order dynamic behaviors. This allows MMOE-DAE to dynamically adjust the modeling order based on data to ensure modeling accuracy even when faced with components lacking detailed physical information.

[0020] The automatic order raising module processes dynamic behaviors of different orders through multiple independent integration channels and automatically selects the optimal output through a gating network and the backward Euler method;

[0021] The multi-channel integration module has multiple independent integration channels, each used for derivative processing of different orders. Each channel numerically integrates the previous state to calculate the derivative value of the corresponding order. The first-order channel calculates the rate of change of the system state, that is, the derivative, to predict the change of the system state in the next time step. The second-order channel processes the first-order and second-order derivatives, and calculates the acceleration or higher-order changes of the system through the transformation of the state space. The third-order channel processes the rate of change of higher orders to capture the high-order dynamic behavior in complex systems.

[0022] The automatic order raising module also introduces a state storage unit to store the state information calculated for each channel. This ensures that when performing multiple integrations, the system can access previous states and derivative values, ensuring the continuity and accuracy of each channel.

[0023] The gating network is used to select the best order output from the output of multiple integration channels; through a trainable weight matrix W go , use the softmax function to determine which channel's output is most appropriate;

[0024] Among them, the principle of the multi-channel backward Euler upgrade module with dynamic component order identification is:

[0025] The input initial state h0, first-order derivative h0′ and second-order derivative h0″ are subjected to backward Euler order upgrading to obtain the first-order, second-order and third-order prediction values; the backward Euler method is used to perform numerical integration of each order derivative to deduce the state change at each time step, and finally the state of the next time step is obtained.

[0026] Furthermore, step S3 is specifically as follows:

[0027] Electromagnetic transient simulation replaces each component with a Norton equivalent circuit and constructs network equations based on the equivalent circuit, avoiding analyzing all DAEs of the system as discrete entities.

[0028] The component simulation interface includes the following four steps:

[0029] (1) Calculate the state variable x(t+Δt)

[0030] Measurements based on component ports and To infer the state variable x(t+Δt); use the trapezoidal integration method to calculate x(t+Δt):

[0031]

[0032] Among them, the variable x(t) is the implicit function of the port measurement value, which is constructed by the ODE expert neuron, that is,

[0033] (2) Derivation of the Norton equivalent current i(t+Δt) of the component

[0034] Based on the formula

[0035]

[0036] Where i is the component injection current, g(·) is the algebraic equation describing the component current; the port current of the dynamic component is calculated using the AE tower module

[0037]

[0038] Steps (1) and (2) are completely executed within the MMOE-DAE network, and their functions are consistent with the external characteristics of general components;

[0039] (3) Calculate the voltage vector V(t+Δt): According to the node connection relationship, the current I of the data-driven model component is converted to eql (t+Δt) and the current I of conventional components gen (t+Δt) is combined to obtain

[0040] I(t+Δt)=[I eql (t+Δt)+I gen (t+Δt)]

[0041] Substituting into the formula GV=I, we can get

[0042] GV(t+Δt)=I(t+Δt)

[0043] (4) Calculate the branch current I ij (t+Δt): To complete the historical current iteration process of conventional components, it is necessary to calculate I ij (t+Δt), the current passes through V(t+Δt) and the corresponding admittance G ij Calculated

[0044] I ij (t+Δt)=G ij (V i (t+Δt)-V j (t+Δt)).

[0045] A power system component dynamic modeling system based on the MMOE-DAE model adopts the power system component dynamic modeling method based on the MMOE-DAE model as described above to perform power system component dynamic modeling.

[0046] A storage medium stores a computer program, which, when executed, executes the above-mentioned method for dynamic modeling of power system components based on the MMOE-DAE model.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] High-precision and three-phase unified modeling of power system dynamic components: MMOE-DAE can accurately model complex high-order dynamic components in power systems in the absence of detailed physical models. By integrating neural ODEs with an automatic order-raising mechanism, the modeling capability of the dynamic behavior of complex components under various disturbance scenarios is effectively improved, ensuring high accuracy and robustness under strong nonlinear and high-order dynamic conditions. The unified modeling of three-phase circuits can simultaneously process the dynamic characteristics of each phase circuit under the same framework, and handle the coupling relationship between phases through a multi-task learning mechanism, significantly reducing the errors caused by phase separation processing in traditional modeling methods and improving overall simulation efficiency.

[0049] The GNN model is used to aggregate information between adjacent nodes and combine it with system topology information to achieve better performance.

[0050] Comparing the model parameters and performance when using multidimensional data matrix and oscillation current time series data as model input, the results show that using multidimensional data matrix as model input not only has better timeliness but also better positioning performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Diagram of the architecture used to model the three-phase circuit with dynamic components for the MMOE-DAE network.

[0052] Figure 2 Schematic diagram of the automatic upgrade mechanism. DETAILED DESCRIPTION

[0053] A dynamic modeling method for power system components based on MMOE-DAE model is proposed.

[0054] First, the MMOE-DAE is a data-driven, multi-expert model that leverages measurement data at system ports (such as voltage and current) to infer the dynamic behavior of components. While lacking a complete physical model, the measured signals can capture the input-output relationship of the component, reflecting its dynamic characteristics under different operating conditions. Therefore, even with an incomplete physical model, the MMOE-DAE can still construct an equivalent dynamic model using the measured data, achieving highly accurate modeling of the component's dynamic characteristics.

[0055] Secondly, the dynamic behavior of components in modern power systems is usually expressed as high-order nonlinear differential equations. This patent introduces an automatic order improvement module that can automatically identify the dynamic order of components and thus enhance the simulation capability of high-order dynamic behavior of components.

[0056] Third, MMOE-DAE can accurately simulate the dynamic relationship between different phases in the same model, realizing unified modeling of three-phase circuits and making up for the shortcomings of traditional models in handling the coupling relationship between phases.

[0057] Fourth, MMOE-DAE can be seamlessly integrated into the existing electromagnetic transient simulation platform CloudPSS, ensuring efficient real-time simulation and dynamic analysis in actual industrial application scenarios, thereby improving the practicality and reliability of electromagnetic transient simulation in power systems.

[0058] Architecture of the MMOE-DAE network for modeling three-phase circuits with dynamic components:

[0059] The MMOE-DAE network effectively learns the dynamic characteristics of components by combining multiple neural differential equation (ODE) expert modules. These modules treat system states (such as current and voltage) as functions of time. By fitting the changing trends of these states, they approximate their differential evolution over time (i.e., approximating traditional differential equations). This effectively learns a differential equation that approximates the system's true dynamics, enabling high-precision approximation of complex nonlinear system behavior. Therefore, even without detailed physical equations, the input-output relationships of components can be learned from data to establish a dynamic equivalent model. Upon inputting historical current and voltage signals from each phase circuit, the MMOE-DAE automatically selects the most appropriate neural ODE expert module through a gating network to handle the current modeling task. Each expert module is assigned a specific task, focusing on a specific type of dynamic characteristics, ensuring robust model performance across diverse scenarios. Therefore, the MMOE-DAE can not only simulate a single component but also simultaneously address different components or dynamic characteristics through multiple expert modules, forming a unified multi-task learning framework. In this way, even if some components do not have complete physical models, the network can learn the complex dynamic behavior of components from limited port data by sharing the underlying neural network and collaboration between different tasks.

[0060] The architecture of MMOE-DAE network for dynamic component three-phase circuit modeling is as follows Figure 1 As shown:

[0061] The architecture is mainly divided into three parts: input part, underlying model (including neural ODE expert module), tower model (DAE tower) and gate network module. The input part receives the historical current signal i of each phase circuit. A (t),i B (t),i C (t), historical voltage signal u of each phase circuit A (t),u B (t),u C (t), as well as external signals. These input signals are transmitted to the underlying neural ODE expert module through the gating network to predict the current and voltage changes in the next time step. The underlying model consists of multiple neural ODE expert modules, each of which is responsible for processing different dynamic characteristics and determining which modules participate in the current modeling task through the scheduling of the gating network. The neural ODE module is used to approximate the differential equations of the dynamic components and model the relationship between the current, voltage and time of each phase circuit. The input signal i A (t),i B (t),i C (t) After the neural ODE, the current i of each phase circuit in the next time step is obtained A (t+1),iB (t+1),i C (t+1). The gating network is responsible for expert module selection and scheduling. It selects the neural ODE experts suitable for the current task according to the characteristics of the input signal, and assigns weights through the softmax function to schedule these experts to participate in modeling. Its output will be passed to the corresponding DAE tower according to the situation of each phase circuit. The tower model is divided into multiple DAE towers, each tower is used to process the corresponding phase current in the three-phase circuit, that is, i A (t+1),i B (t+1),i C (t+1). Through these DAE towers, the MMOE-DAE model is able to handle the multi-task modeling of three-phase circuits simultaneously and to establish the mutual relationship between the currents of different phases.

[0062] Automatic upgrade mechanism:

[0063] High-order dynamic behaviors (such as high-frequency oscillations, nonlinear perturbations, etc.) are generally difficult to capture for traditional neural network models. The automatic order improvement mechanism introduced in this patent can automatically infer the optimal dynamic order of the component through multiple numerical integration channels, thereby enabling neural ODE experts to handle high-order dynamic behaviors. This mechanism enables MMOE-DAE to dynamically adjust the order of modeling according to data when facing complex components that lack detailed physical information, ensuring high-precision modeling. Specifically, the module processes dynamic behaviors of different orders through multiple independent integration channels, and automatically selects the optimal output through gating networks and backward Euler method, which greatly improves the modeling accuracy in high-order dynamic systems, and is particularly suitable for processing complex, nonlinear and multi-path coupled systems.

[0064] The multi-channel integration module has multiple independent integration channels, each of which is used for derivative processing of different orders. Each channel performs numerical integration based on the previous state to calculate the derivative value of the corresponding order. The first-order channel calculates the rate of change of the system state, that is, the derivative, which is used to predict the change of the system state in the next time step; the second-order channel processes the first-order derivative and the second-order derivative, and calculates the acceleration or higher-order changes of the system through the transformation of the state space; the third-order channel processes higher-order rates of change, which is used to capture high-order dynamic behaviors in complex systems. The module also introduces a state storage unit to store the state information calculated by each channel, ensuring that the system can access the previous state and derivative values when performing multiple integrations, ensuring the continuity and accuracy of each channel.

[0065] The gating network is used to select the best order output from the output of multiple integration channels. Through a trainable weight matrix W go , use the softmax function to determine which channel's output is most appropriate.

[0066] The principle of the multi-channel backward Euler upgrade module with dynamic component order identification is as follows Figure 2 :

[0067] The input initial state h0, the first-order derivative h0′, and the second-order derivative h0″ are subjected to backward Euler order upgrading to obtain the first-order, second-order, and third-order prediction values. Specifically, the backward Euler method performs numerical integration of each order derivative to deduce the state change at each time step, and finally obtains the state of the next time step.

[0068] Dynamic simulation interface:

[0069] Electromagnetic transient simulation replaces each component with a Norton equivalent circuit and constructs network equations based on these equivalent circuits. This approach avoids analyzing all DAEs of the system as discrete entities, thereby improving modeling efficiency. The component simulation interface includes the following four steps:

[0070] (1) Calculate the state variable x(t+Δt)

[0071] Measurements based on component ports and To infer the state variable x(t+Δt). Use the trapezoidal integration method to calculate x(t+Δt):

[0072]

[0073] Among them, the variable x(t) is the implicit function of the port measurement value, which is constructed by the ODE expert neuron, that is,

[0074] (2) Derivation of the Norton equivalent current i(t+Δt) of the component

[0075] Based on the formula

[0076]

[0077] Where i is the component injection current and g(·) is the algebraic equation describing the component current. Use the AE tower module to calculate the port current of the dynamic component

[0078]

[0079] Steps (1) and (2) are completely executed within the MMOE-DAE network, and their functions are consistent with the external characteristics of general components.

[0080] (3) Calculate the voltage vector V(t+Δt): According to the node connection relationship, the current I of the data-driven model component is converted to eql (t+Δt) and the current I of conventional components gen (t+Δt) is combined to obtain

[0081] I(t+Δt)=[I eql (t+Δt)+I gen (t+Δt)]

[0082] Substituting into the formula GV=I we can get

[0083] GV(t+Δt)=I(t+Δt)

[0084] (4) Calculate the branch current I ij (t+Δt): In order to complete the historical current iteration process of conventional components, it is necessary to calculate I ij (t+Δt), the current passes through V(t+Δt) and the corresponding admittance G ij Calculated

[0085] I ij (t+Δt)=G ij (V i (t+Δt)-V j (t+Δt)).

[0086] Application links:

[0087] 1) Data preparation and processing

[0088] Data collection: Establish a simulation case, obtain the case topology, set the position disturbance source in the case, and collect the current timing operation data of each line during the system fault and the disturbance source location label.

[0089] Signal processing: Preprocess the collected time series signals, such as extracting discrete components, filtering, and denoising, to improve the accuracy and efficiency of model training. The preprocessed attenuated DC component is then extracted and Taylor expanded, and derivatives of various orders are extracted to construct the model input matrix.

[0090] (2) Model design and training

[0091] Model architecture design: Design the GNN model architecture and build a broadband oscillation disturbance source localization model.

[0092] Model training: Use the prepared dataset to train the model and adjust the model parameters to optimize performance.

[0093] Verification and testing: The model is verified and tested to evaluate its performance in locating broadband oscillation disturbance sources.

[0094] (3) Online applications and real-time performance

[0095] Deploy the model: Deploy the trained model to the actual power system monitoring platform.

[0096] Real-time performance optimization: Research and implement strategies to optimize the model's disturbance source localization performance, including reducing latency, improving accuracy, and improving response speed.

[0097] Continuous learning: The implementation mechanism enables the model to be continuously updated and optimized based on newly collected data, improving the generalization of the model.

[0098] (4) Evaluation and Iteration

[0099] Performance evaluation: Comprehensively evaluate the positioning performance of the model and establish a confusion matrix to evaluate the model performance.

[0100] Iterative optimization: Iteratively optimize the model based on the model performance evaluation results to improve its stability and reliability.

[0101] In summary: High-precision and three-phase unified modeling of power system dynamic components: MMOE-DAE can achieve accurate modeling of complex high-order dynamic components in power systems in the absence of detailed physical models. By integrating neural ODEs with an automatic order-raising mechanism, the modeling capability of the dynamic behavior of complex components under various disturbance scenarios is effectively improved, ensuring high accuracy and robustness under strong nonlinear and high-order dynamic conditions. The unified modeling of three-phase circuits can simultaneously process the dynamic characteristics of each phase circuit under the same framework, and handle the coupling relationship between phases through a multi-task learning mechanism, significantly reducing the errors caused by phase separation processing in traditional modeling methods and improving the overall simulation efficiency.

[0102] The GNN model is used to aggregate information between adjacent nodes and combine it with system topology information to achieve better performance.

[0103]

[0104] Comparing the model parameters and performance when using multidimensional data matrix and oscillation current time series data as model input, the results show that using multidimensional data matrix as model input not only has better timeliness but also better positioning performance.

[0105] index Multidimensional data matrix Oscillation current timing data Parameter quantity 27378 711828 Inference speed 2.429ms 4.318ms Accuracy 97.2％ 96.8％

Claims

1. A dynamic modeling method for power system components based on the MMOE-DAE model, characterized by: The following steps are involved: S1: We propose a multi-gate hybrid differential-algebraic equation expert network (MMOE-DAE) that uses multiple gated neural differential-algebraic equation (DAE) networks to simulate the dynamic characteristics of three-phase circuits and establish the relationship between the phases. S2: An automatic order raising module is proposed, which uses multiple independent continuous numerical integration channels and related state variable storage to automatically infer the optimal order for dynamic component modeling through a fully gated network; S3: MMOE-DAE accurately simulates the dynamic relationship between different phases within the same model, achieving unified modeling of three-phase circuits and addressing the shortcomings of traditional models in handling coupling relationships between phases. MMOE-DAE is seamlessly integrated into the electromagnetic transient simulation platform, enabling real-time simulation and dynamic analysis in actual industrial application scenarios, thereby improving the practicality and reliability of electromagnetic transient simulation in power systems. In step S1: The architecture of the MMOE-DAE network for modeling three-phase circuits with dynamic components consists of three parts: the input part, the underlying model, the tower model, and the gated network module; The input part receives the historical current signal i of each phase circuit A (t),i B (t),i C (t), historical voltage signal u of each phase circuit A (t),u B (t),u C (t), and external signals; the above signals are transmitted to the underlying neural ODE expert module through the gating network to predict the current and voltage changes in the next time step; The underlying model includes multiple neural ODE expert modules, each of which is responsible for processing different dynamic characteristics and deciding which modules to participate in the current modeling task through the scheduling of the gate network; the neural ODE expert module is used to approximate the differential equations of the dynamic components and model the relationship between the current, voltage and time of each phase circuit; the input signal i A (t),i B (t),i C (t) After the neural ODE, the current i of each phase circuit in the next time step is obtained A (t+1),i B (t+1),i C (t+1); The gating network module is responsible for selecting and scheduling the neural ODE expert module. It selects the neural ODE expert suitable for the current task based on the characteristics of the input signal, and assigns weights through the softmax function to schedule the neural ODE expert to participate in modeling. Its output will be transmitted to the corresponding DAE tower according to the situation of each phase circuit; The tower model is divided into multiple DAE towers, each tower is used to process the corresponding phase current in the three-phase circuit, i.e. A (t+1),i B (t+1),i C (t+1); Based on the DAE tower, the MMOE-DAE model can simultaneously handle the multi-task modeling of three-phase circuits and establish the relationship between the currents of different phases.

2. The method for dynamic modeling of power system components based on the MMOE-DAE model according to claim 1, characterized in that: In step S2, the automatic order raising module is specifically as follows: By automatically inferring the optimal dynamic order of components through multiple numerical integration channels, the Neural ODE Expert can handle high-order dynamic behaviors. This allows MMOE-DAE to dynamically adjust the modeling order based on data to ensure modeling accuracy even when faced with components lacking detailed physical information. The automatic order raising module processes dynamic behaviors of different orders through multiple independent integration channels and automatically selects the optimal output through a gating network and the backward Euler method; The multi-channel integration module has multiple independent integration channels, each used for derivative processing of different orders. Each channel numerically integrates the previous state to calculate the derivative value of the corresponding order. The first-order channel calculates the rate of change of the system state, that is, the derivative, to predict the change of the system state in the next time step. The second-order channel processes the first-order and second-order derivatives, and calculates the acceleration or higher-order changes of the system through the transformation of the state space. The third-order channel processes the rate of change of higher orders to capture the high-order dynamic behavior in complex systems. The automatic order raising module also introduces a state storage unit to store the state information calculated for each channel. This ensures that when performing multiple integrations, the system can access previous states and derivative values, ensuring the continuity and accuracy of each channel. The gating network is used to select the best order output from the output of multiple integration channels; through a trainable weight matrix W go , use the softmax function to determine which channel's output is most appropriate; Among them, the principle of the multi-channel backward Euler upgrade module with dynamic component order identification is: The input initial state h0, first-order derivative h0′ and second-order derivative h0″ are subjected to backward Euler order upgrading to obtain the first-order, second-order and third-order prediction values; the backward Euler method is used to perform numerical integration of each order derivative to deduce the state change at each time step, and finally the state of the next time step is obtained.

3. The method for dynamic modeling of power system components based on the MMOE-DAE model according to claim 2, characterized in that: Step S3 is as follows: Electromagnetic transient simulation replaces each component with a Norton equivalent circuit and constructs network equations based on the equivalent circuit, avoiding analyzing all DAEs of the system as discrete entities. The component simulation interface includes the following four steps: (1) Calculate the state variable x(t+Δt) Measurements based on component ports and To infer the state variable x(t+Δt); use the trapezoidal integration method to calculate x(t+Δt): Among them, the variable x(t) is the implicit function of the port measurement value, which is constructed by the ODE expert neuron, that is, (2) Derivation of the Norton equivalent current i(t+Δt) of the component Based on the formula Where i is the component injection current, g(·) is the algebraic equation describing the component current; the port current of the dynamic component is calculated using the AE tower module Steps (1) and (2) are completely executed within the MMOE-DAE network, and their functions are consistent with the external characteristics of general components; (3) Calculate the voltage vector V(t+Δt): According to the node connection relationship, the current I of the data-driven model component is converted to eql (t+Δt) and the current I of conventional components gen (t+Δt) is combined to obtain I(t+Δt)=[I eql (t+Δt)+I gen (t+Δt)] Substituting into the formula GV=I, we can get GV(t+Δt)=I(t+Δt) (4) Calculate the branch current I ij (t+Δt): To complete the historical current iteration process of conventional components, it is necessary to calculate I ij (t+Δt), the current passes through V(t+Δt) and the corresponding admittance G ij Calculated I ij (t+Δt)=G ij (V i (t+Δt)-V j (t+Δt))。 4. A power system component dynamic modeling system based on the MMOE-DAE model, characterized by: Dynamic modeling of power system components is performed using the power system component dynamic modeling method based on the MMOE-DAE model as described in any one of claims 1 to 3.

5. A storage medium, characterized in that The storage medium stores a computer program, which, when executed, executes the method for dynamic modeling of power system components based on the MMOE-DAE model according to any one of claims 1 to 3.

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