A method and system for resampling time-domain data in electromechanical-electromagnetic simulations that preserves time-frequency domain characteristics.

Abstract

This invention discloses a method and system for resampling time-domain data of electromechanical-electromagnetic simulations while preserving time-frequency domain features. It includes: constructing electromechanical and electromagnetic transient time-series trajectory samples of a new energy power grid system; processing the trajectory samples separately using an improved Proni method to obtain frequency domain response features; reconstructing the electromechanical and electromagnetic transient time-series trajectories separately using linear interpolation and adaptive resampling techniques based on the frequency domain response features; and fusing the time-domain and frequency-domain features in the reconstructed time-series trajectory data to form a comprehensive feature vector, thereby obtaining electromechanical-electromagnetic simulation time-domain data that preserves time-frequency domain features. This invention solves the asynchronous sampling problem of electromechanical-electromagnetic time-domain simulation data and can be used to support the feature analysis of inconsistent data at different time scales in electromechanical and electromagnetic systems. It has broad application prospects and significant practical implications in fields such as scheduling and control of new energy power grids.

Description

Technical Field

[0001] This invention belongs to the field of energy technology and relates to a method and system for resampling time-domain data in electromechanical-electromagnetic simulation, particularly a method and system for resampling time-domain data in electromechanical-electromagnetic simulation that preserves time-frequency domain characteristics. Background Technology

[0002] Time-domain simulation of power systems with a high proportion of renewable energy integration (hereinafter referred to as renewable energy systems) includes electromechanical transient simulation and electromagnetic transient simulation. Electromechanical transient simulation supports rapid simulation of power grids with tens of thousands of nodes, with step sizes in the millisecond range, suitable for stability analysis of the grid's fundamental frequency characteristics, but it is difficult to reflect the high-frequency characteristics of renewable energy and power electronic equipment. Electromagnetic transient simulation supports accurate characterization of the high-frequency characteristics of power electronic equipment, with step sizes in the microsecond range, but its high-order nature makes it difficult to adapt to full-network simulation of systems of a certain scale. Combining the advantages of electromechanical and electromagnetic simulation data can improve the applicability of existing advanced applications based on electromechanical transient platforms, enabling more refined safety and stability assessments of power grids with a high proportion of renewable energy integration.

[0003] In practice, due to differences in time scales and iterative solution algorithms, even with the same simulation duration, the resolution and sampling windows of the time-series data generated by electromagnetic and electromechanical simulations vary significantly. This makes it impossible to directly compare the time-series trajectories of electromagnetic and electromechanical simulations, affecting subsequent modeling and research. Furthermore, current resampling techniques based on interpolation algorithms are prone to losing important information and fail to reflect the key time-frequency domain characteristics of the data, thus exhibiting certain limitations. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a method and system for resampling time-domain data from electromechanical-electromagnetic simulations while preserving time-frequency domain characteristics. This method effectively unifies the sampling of time series from electromagnetic and electromechanical simulations, while retaining the time-frequency domain characteristics of the time series trajectories generated by both simulations. This invention can support the preprocessing and feature analysis of low-consistency data at different time scales in electromechanical and electromagnetic simulations. It also has broad application prospects and significant practical implications in fields such as the scheduling and control of new energy power grids.

[0005] The technical solution adopted in this invention is as follows:

[0006] A method for resampling time-domain data in electromechanical-electromagnetic simulations that preserves time-frequency domain characteristics includes the following steps:

[0007] S1. Based on the electromagnetic simulation platform and the electromechanical simulation platform, construct electromechanical and electromagnetic transient time-series trajectory samples of the new energy power grid system;

[0008] S2. The improved Proni method was used to process the electromechanical and electromagnetic transient time-series trajectory samples respectively to obtain the frequency domain response characteristics of the electromechanical and electromagnetic transient time-series trajectory samples;

[0009] S3. Based on the frequency domain response characteristics, linear interpolation and adaptive resampling techniques are used to reconstruct the electromechanical and electromagnetic transient time-series trajectories respectively, to obtain electromechanical and electromagnetic transient reconstructed time-series trajectory data containing frequency domain characteristics;

[0010] S4. The time-domain and frequency-domain features in the electromechanical and electromagnetic transient reconstructed time-series trajectory data are fused to form a comprehensive feature vector, resulting in electromechanical-electromagnetic simulation time-domain data that retains the time-frequency domain features.

[0011] Furthermore, step S1 specifically includes:

[0012] Electromechanical transients of the new energy power grid system are modeled on an electromechanical transient simulation platform. The simulation step size and total simulation duration are set. The fundamental wave vector is used to describe the computational element model to obtain a system of differential algebraic equations. The system of differential algebraic equations is solved simultaneously to obtain the time-domain solutions of each physical quantity. Various typical operating conditions and typical faults of the new energy power grid system are traversed to obtain electromechanical transient time-series trajectory samples.

[0013] Electromagnetic transients of the new energy power grid system were modeled on an electromagnetic transient simulation platform. The simulation step size and total simulation duration were set. The three-phase instantaneous values ​​of a, b, and c were used to describe the computational element model, and a set of differential equations was obtained. The implicit integration method was used to solve the set of differential equations to obtain the time-domain solutions of each physical quantity. Various typical operating conditions and typical faults of the new energy power grid system were traversed to obtain electromagnetic transient time-series trajectory samples.

[0014] Furthermore, step S2 specifically involves:

[0015] For both electromechanical transient time-series trajectory samples and electromagnetic transient time-series trajectory samples, the following steps are performed:

[0016] S2.1. Based on the fault occurrence and duration set during simulation, extract data from the electromechanical transient time-series trajectory sample or the electromagnetic transient time-series trajectory sample for a period of time after the fault occurs to the steady state to obtain fault time-series trajectory samples. The aforementioned fault time-series trajectory samples form a fault time-series trajectory sample set.

[0017] S2.2. Initialize the time window and Proni order of the Proni method based on the fault time scale and oscillation frequency band;

[0018] S2.3. Divide the waveform data of the fault time-series trajectory sample in each time window into two segments, where the first segment of waveform data is the data from the occurrence of the fault to the fault recovery, and the second segment of waveform data is the data after the fault recovers to a steady state;

[0019] S2.4. Use the Proni method to analyze the second waveform data, identify the linear oscillation mode without resonance in the second waveform data, and obtain its frequency as the initial value of the frequency domain result;

[0020] S2.5. Fit the initial value of the frequency domain result to the first segment of waveform data, and use the Proni method to analyze the fitted first segment of waveform data to identify the second-order resonance mode caused by the nonlinear model after the fault occurs, and obtain the frequency domain response characteristics of each fault time-series trajectory sample.

[0021] Furthermore, step S3 specifically includes:

[0022] S3.1. Determine the resampling frequency for each time window based on the frequency domain response characteristics using the Nyquist sampling theorem;

[0023] S3.2. Calculate the complete sampling length K of the electromagnetic transient time-series trajectory based on the resampling frequency of each time window;

[0024] S3.3. Perform linear interpolation on the electromechanical transient time-series trajectory according to the sampling step size of each time window in the electromagnetic transient time-series trajectory to obtain the electromechanical transient time-series trajectory that corresponds one-to-one with the sampling points of the electromagnetic transient time-series trajectory.

[0025] S3.4. Using adaptive resampling technology, the electromagnetic transient time-series trajectory and the electromechanical transient time-series trajectory are resampled according to the resampling frequency and the complete sampling length to obtain an electromechanical transient sequence and an electromagnetic transient sequence, both of which have a length of K.

[0026] Furthermore, step S4 specifically involves:

[0027] S4.1. Delete redundant samples and abnormal data in the electromechanical transient sequence or the electromagnetic transient sequence;

[0028] S4.2 Perform Z-score normalization on the time-domain and frequency-domain characteristics of the electrical transient sequence or the electromagnetic transient sequence;

[0029] S4.3 The time-domain features and the frequency-domain features are weighted and fused to form a time-integrated feature vector, thereby obtaining electromechanical-electromagnetic simulation time-domain data that retains the time-frequency domain features.

[0030] Furthermore, the weighted fusion of the time-domain features and the frequency-domain features specifically involves: calculating the weights of the time-domain features based on the oscillation energy, calculating the weights of the frequency-domain features based on the spectral energy, and then summing the time-domain features and the frequency-domain features in a weighted manner to form a comprehensive feature vector.

[0031] A time-domain data resampling system for electromechanical-electromagnetic simulation that preserves time-frequency domain characteristics includes:

[0032] Data acquisition module: used to obtain electromechanical and electromagnetic transient time-series trajectory samples of new energy power grid systems based on electromagnetic simulation platform and electromechanical simulation platform;

[0033] Feature acquisition module: used to process electromechanical and electromagnetic transient time-series trajectory samples using the improved Proni method to obtain the frequency domain response features of the electromechanical and electromagnetic transient time-series trajectory samples;

[0034] Trajectory Reconstruction Module: Based on frequency domain response characteristics, this module reconstructs electromechanical and electromagnetic transient time-series trajectories using linear interpolation and adaptive resampling techniques, respectively, to obtain electromechanical and electromagnetic transient reconstructed time-series trajectory data containing frequency domain characteristics.

[0035] Feature fusion module: used to fuse the time-domain and frequency-domain features in the electromechanical and electromagnetic transient reconstructed time-series trajectory data to form a comprehensive feature vector, thereby obtaining electromechanical-electromagnetic simulation time-domain data that retains the time-frequency domain features.

[0036] A computer device, the computer device comprising:

[0037] One or more processors;

[0038] Memory, used to store one or more programs;

[0039] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-described electromechanical-electromagnetic simulation time-domain data resampling method that preserves time-frequency domain characteristics.

[0040] A computer-readable storage medium storing computer instructions that, when executed by one or more processors, cause the one or more processors to perform the steps in the method described above.

[0041] The beneficial effects of this invention are:

[0042] This invention provides a resampling method for electromechanical-electromagnetic simulation time-domain data that preserves time-frequency domain features. It utilizes an improved Proni method to compute the frequency domain response characteristics of electromechanical and electromagnetic transient time-series trajectory samples. Based on these frequency domain response characteristics, linear interpolation and adaptive resampling techniques are used to reconstruct the electromechanical and electromagnetic transient time-series trajectories, respectively, resulting in reconstructed electromechanical and electromagnetic transient time-series trajectory data containing frequency domain features. This step focuses on analyzing the occurrence time of peak points in electromagnetic transient trajectories, supplementing the corresponding points of electromechanical transients through adaptive resampling to ensure that time-frequency domain information is not lost during abrupt changes after a fault. The time-domain and frequency-domain features in the reconstructed electromechanical and electromagnetic transient time-series trajectory data are fused to form a comprehensive feature vector, resulting in electromechanical-electromagnetic simulation time-domain data that preserves time-frequency domain features. This comprehensive feature vector can be directly used for subsequent analysis and processing such as mapping and stability analysis of electromechanical-electromagnetic trajectories, overcoming the problem of incomparability between electromagnetic and electromechanical simulation time-series trajectories caused by differences in time scales and iterative solution algorithms. This invention proposes a time-domain data resampling technique for electromechanical-electromagnetic simulations that preserves time-frequency domain characteristics, thus solving the asynchronous sampling and time-frequency domain feature analysis problems of electromechanical-electromagnetic time-domain sampled data. This technique can support the feature analysis of inconsistent data from electromechanical and electromagnetic systems at different time scales, and has broad application prospects and significant practical implications in fields such as the stability and control of new energy power grids. Attached Figure Description

[0043] Figure 1 This is a flowchart of the resampling method in an embodiment of the present invention.

[0044] Figure 2 This is a flowchart illustrating the analysis and calculation process of the improved Prony method in an embodiment of the present invention.

[0045] Figure 3 This is a graph showing the time-domain fitting results of the Prony method in an embodiment of the present invention.

[0046] Figure 4 This is a frequency domain feature map of the Prony method in an embodiment of the present invention.

[0047] Figure 5 This is a diagram showing the temporal resampling results based on the Prony method in an embodiment of the present invention. Detailed Implementation

[0048] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] This invention proposes a time-domain data resampling method for electromechanical-electromagnetic simulations that preserves time-frequency domain characteristics. This addresses the challenge of applying existing electromechanical-electromagnetic simulations to new energy power grids as the penetration rate of new energy sources continues to increase. The flowchart is shown below. Figure 1 As shown.

[0050] The specific steps of this method are as follows:

[0051] like Figure 1 As shown, a time-domain data resampling method for electromechanical-electromagnetic simulations that preserves time-frequency domain characteristics includes the following steps:

[0052] S1. Based on electromagnetic and electromechanical simulation platforms, construct electromechanical and electromagnetic transient time-series trajectory samples of new energy power grid systems to form an electromechanical-electromagnetic time-domain asynchronous dataset capable of characterizing the dynamic response characteristics of the system at different time scales; specifically:

[0053] Electromechanical transients of the new energy power grid system are modeled on an electromechanical transient simulation platform. The simulation step size and total simulation duration are set. The fundamental wave vector is used to describe the computational element model to obtain a system of differential algebraic equations. The system of differential algebraic equations is solved simultaneously to obtain the time-domain solutions of each physical quantity. Various typical operating conditions and typical faults of the new energy power grid system are traversed to obtain electromechanical transient time-series trajectory samples.

[0054] Electromagnetic transients of the new energy power grid system were modeled on an electromagnetic transient simulation platform. The simulation step size and total simulation duration were set. The three-phase instantaneous values ​​of a, b, and c were used to describe the computational element model, and a set of differential equations was obtained. The implicit integration method was used to solve the set of differential equations to obtain the time-domain solutions of each physical quantity. Various typical operating conditions and typical faults of the new energy power grid system were traversed to obtain electromagnetic transient time-series trajectory samples.

[0055] Assuming the effective simulation duration for electromechanical and electromagnetic transients is T, for the electromagnetic transient model, record the three-phase instantaneous values ​​v of voltage and current at any time at each new energy grid connection point. sabc i abc The effective values ​​of voltage and current V s emt (t), Including active and reactive power connected to the grid, for the electromechanical transient model, the amplitude V of voltage and current at any time at each new energy grid connection point is measured. s rms (t),

[0056]

[0057]

[0058] In the formula, Δt emt and Δt rms The simulation step size, V, represents the simulation step size for electromagnetic transients and electromechanical transients, respectively. s rms (k×Δt emt )and These are the k-th sampled values ​​in the electromagnetic transient voltage trajectory and the electromechanical transient current trajectory, respectively.

[0059] S2. The improved Prony method was used to process the electromechanical and electromagnetic transient time-series trajectory samples respectively to obtain the frequency domain response characteristics of the electromechanical and electromagnetic transient time-series trajectory samples;

[0060] The Prony method uses a complex exponentially decaying linear combination to fit equally spaced sampled data, assuming that the signal... It is a combination of a series of exponential functions with arbitrary amplitude, phase, frequency, and attenuation factor:

[0061]

[0062] In the formula, N is the number of attenuated sine and cosine signal components, and A i Let θ be the amplitude. i Let α be the phase (rad). i f is the attenuation factor. i Let be the oscillation frequency (Hz). Expanding the cosine using Euler's formula, the discrete form of the function is as follows:

[0063]

[0064] In the formula, Let p be the signal estimate at the nth sampling time point, where p is the number of complex exponential components. Let b be the i-th decaying complex exponential component. i Let z be the i-th complex linear coefficient. Fit a homogeneous solution to a linear difference equation with constant coefficients to obtain the Prony pole z. i The amplitude A is calculated using the following formula. i Phase θ i Attenuation factor α i Frequency f i And damping ratio ξ i :

[0065]

[0066] like Figure 2 As shown, the specific steps are as follows:

[0067] For both electromechanical transient time-series trajectory samples and electromagnetic transient time-series trajectory samples, the following steps are performed:

[0068] S2.1. Based on the fault occurrence and duration set during simulation, the initial time of the electromechanical and electromagnetic time series data is set as the fault time. Data from a period after the fault occurs to the steady state is extracted from the electromechanical transient time series trajectory sample or the electromagnetic transient time series trajectory sample to ensure that the key time nodes in the electromechanical and electromagnetic time series trajectories correspond one-to-one, and fault time series trajectory samples are obtained. The several fault time series trajectory samples form a fault time series trajectory sample set.

[0069] S2.2. Initialize the time window and Proni order of the Proni method based on the fault time scale and oscillation frequency band;

[0070] S2.3. Divide the waveform data of the fault time-series trajectory sample in each time window into two segments, where the first segment of waveform data is the data from the occurrence of the fault to the fault recovery, and the second segment of waveform data is the data after the fault recovers to a steady state;

[0071] S2.4. Use the Proni method to analyze the second waveform data, identify the linear oscillation mode without resonance in the second waveform data, and obtain its frequency as the initial value of the frequency domain result;

[0072] S2.5. Fit the initial value of the frequency domain result to the first segment of waveform data, and use the Proni method to analyze the fitted first segment of waveform data to identify the second-order resonance mode caused by the nonlinear model after the fault occurs, that is, the frequency domain response result of the large signal, thereby improving the fitting accuracy of the signal extreme point in the initial stage of the fault, and obtaining the frequency domain response characteristics of each fault time-series trajectory sample.

[0073] S3. Based on the frequency domain response characteristics, linear interpolation and adaptive resampling techniques are used to reconstruct the electromechanical and electromagnetic transient time-series trajectories, respectively. Fault critical nodes and extreme points are supplemented and corrected to obtain electromechanical and electromagnetic transient reconstructed time-series trajectory data containing frequency domain characteristics; specifically:

[0074] S3.1. Using the Nyquist sampling theorem, determine the resampling frequency for each time window based on the frequency domain response characteristics; for simulation steps with sizes Δt... emt and Δt rms Electromagnetic and electromechanical transient time series data can be regarded as two discrete signals sampled at equal intervals. The Prony algorithm described above is used to solve for a complex exponentially decaying linear combination that can fit the corresponding continuous signal, and the resampling frequency for electromechanical-electromagnetic time series data is determined based on the complex exponential frequency.

[0075] Since electromechanical transient timing data often does not contain high-frequency components, the maximum frequency component of the key oscillation mode of the electromagnetic simulation timing trajectory in the Prony window is set to f. m According to the Nyquist sampling theorem, the minimum sampling frequency f is obtained by preserving the complete frequency domain information of the original signal. s Must meet:

[0076] f s =2*f m

[0077] S3.2. Calculate the complete sampling length K of the electromagnetic transient time-series trajectory based on the resampling frequency of each time window; the complete sampling length K of the electromagnetic simulation time-series trajectory is calculated using the following formula:

[0078]

[0079] Among them, T prony For the time window of Prony calculations for electromagnetic and electromechanical sequences, f s (i) represents the sampling frequency of the i-th Prony sampling window. S3.3. Based on the sampling step size of each time window in the electromagnetic transient time trajectory, perform linear interpolation on the electromechanical transient time trajectory to obtain the electromechanical transient time trajectory that corresponds one-to-one with the sampling points of the electromagnetic transient time trajectory;

[0080] S3.4. Using adaptive resampling technology, the electromagnetic transient time-series trajectory and the electromechanical transient time-series trajectory are resampled according to the resampling frequency and the complete sampling length to obtain an electromechanical transient sequence X and an electromagnetic transient sequence Y, both of length K:

[0081] X = [x (1) ,…,x (K) ]

[0082] Y = [y (1) ,…,y (K) ].

[0083] S4. The time-domain and frequency-domain features of the electromechanical and electromagnetic transient reconstructed time-series trajectory data are fused to form a comprehensive feature vector, resulting in electromechanical-electromagnetic simulation time-domain data that retains both time and frequency domain features. Specifically:

[0084] S4.1. Delete redundant samples and abnormal data in the electromechanical transient sequence or the electromagnetic transient sequence;

[0085] S4.2 Perform Z-score normalization on the time-domain and frequency-domain features of the electrical transient sequence or the electromagnetic transient sequence to eliminate the differences between different feature dimensions;

[0086] S4.3 The time-domain features and the frequency-domain features are weighted and fused to form a time-comprehensive feature vector, resulting in electromechanical-electromagnetic simulation time-domain data that retains both time and frequency-domain features. Specifically, the weighted fusion of the time-domain features and the frequency-domain features involves: calculating the weights of the time-domain features based on the oscillation energy, calculating the weights of the frequency-domain features based on the spectral energy, and then summing the weighted sums of the time-domain features and the frequency-domain features to form a comprehensive feature vector.

[0087] A specific embodiment of the present invention is as follows:

[0088] The effectiveness of the proposed method was verified using electromechanical-electromagnetic simulation data from a high-proportion renewable energy power grid. Electromechanical and electromagnetic transient simulations were performed on the system under various operating conditions and fault scenarios. The fault was set as a three-phase ground fault, with the circuit breaker tripping 0.1 seconds after the fault to disconnect the faulty line. For the electromechanical simulation, the maximum power angle difference, minimum bus voltage, and maximum bus frequency of the system at each moment were recorded, with a sampling step size of ΔT = 10 ms. For the electromagnetic simulation, the sampling step size was ΔT = 10 ms, and the voltage, current, active power, and reactive power at each renewable energy grid connection point were recorded. The Prony algorithm parameter settings are shown in Table 1.

[0089] Table 1 Prony Algorithm Parameter Settings

[0090]

[0091] The Prony sliding window size was set to 0.5s, and the range of oscillation frequencies of interest was 0-100Hz. The Prony frequency domain calculation results of the electromagnetic transient time-series trajectory are shown in Table 2. The lowest oscillation frequency is 2.39Hz and the highest oscillation frequency is 77.06Hz.

[0092] Table 2 Prony frequency domain calculation results

[0093]

[0094]

[0095] Figure 3 The fitting effect of frequency domain analysis on time series data is presented. Figure 4 The frequency domain Bode plot and the frequency domain characteristics of the signal are given. The resampled time-series data are as follows: Figure 5 As shown in the figure, the results demonstrate that the method designed in this invention can accurately analyze the frequency domain characteristics of the system at different electromechanical and electromagnetic scales.

[0096] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0098] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0099] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0100] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0101] The above specific embodiments are used to explain and illustrate the present invention, but not to limit the present invention. Any modifications and changes made to the present invention within the spirit and scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A method for resampling time-domain data in electromechanical-electromagnetic simulations while preserving time-frequency domain characteristics, characterized in that, Includes the following steps: S1. Based on the electromagnetic simulation platform and the electromechanical simulation platform, construct electromechanical and electromagnetic transient time-series trajectory samples of the new energy power grid system; S2. The improved Proni method was used to process the electromechanical and electromagnetic transient time-series trajectory samples respectively to obtain the frequency domain response characteristics of the electromechanical and electromagnetic transient time-series trajectory samples; S3. Based on the frequency domain response characteristics, linear interpolation and adaptive resampling techniques are used to reconstruct the electromechanical and electromagnetic transient time-series trajectories respectively, to obtain electromechanical and electromagnetic transient reconstructed time-series trajectory data containing frequency domain characteristics; S4. The time-domain and frequency-domain features in the electromechanical and electromagnetic transient reconstructed time-series trajectory data are fused to form a comprehensive feature vector, thus obtaining electromechanical-electromagnetic simulation time-domain data that retains time-frequency domain features; Step S3 is as follows: S3.

1. Determine the resampling frequency for each time window based on the frequency domain response characteristics using the Nyquist sampling theorem; S3.

2. Calculate the complete sampling length of the electromagnetic transient time-series trajectory based on the resampling frequency of each time window. K ; S3.

3. Perform linear interpolation on the electromechanical transient time-series trajectory according to the sampling step size of each time window in the electromagnetic transient time-series trajectory to obtain the electromechanical transient time-series trajectory that corresponds one-to-one with the sampling points of the electromagnetic transient time-series trajectory. S3.

4. Using adaptive resampling technology, the electromagnetic transient time-series trajectory and the electromechanical transient time-series trajectory are resampled according to the resampling frequency and the complete sampling length, resulting in samples of length 1. K Electromechanical transient sequences and electromagnetic transient sequences.

2. The electromechanical-electromagnetic simulation time-domain data resampling method for preserving time-frequency domain characteristics according to claim 1, characterized in that, Step S1 is as follows: Electromechanical transients of a new energy power grid system are modeled on an electromechanical transient simulation platform. The simulation step size and total simulation time are set. The fundamental wave vector is used to describe the computational element model to obtain a system of differential algebraic equations. The system of differential algebraic equations is solved simultaneously to obtain the time-domain solutions of each physical quantity. By traversing various typical operating conditions and typical faults of the new energy power grid system, electromechanical transient time-series trajectory samples are obtained; Electromagnetic transient modeling of a new energy power grid system was performed on an electromagnetic transient simulation platform. The simulation step size and total simulation duration were set. abc The three-phase instantaneous value description calculation element model is used to obtain a set of differential equations. The implicit integration method is used to solve the set of differential equations to obtain the time-domain solutions of each physical quantity. By traversing various typical operating conditions and faults of the new energy power grid system, electromagnetic transient time-series trajectory samples are obtained.

3. The electromechanical-electromagnetic simulation time-domain data resampling method for preserving time-frequency domain characteristics according to claim 1, characterized in that, Step S2 is as follows: For both electromechanical transient time-series trajectory samples and electromagnetic transient time-series trajectory samples, the following steps are performed: S2.

1. Based on the fault occurrence and duration set during simulation, extract data from the electromechanical transient time-series trajectory sample or the electromagnetic transient time-series trajectory sample for a period of time after the fault occurs to the steady state to obtain fault time-series trajectory samples. Several fault time-series trajectory samples form a fault time-series trajectory sample set. S2.

2. Initialize the time window and Proni order of the Proni method based on the fault time scale and oscillation frequency band; S2.

3. Divide the waveform data of the fault time-series trajectory sample in each time window into two segments, where the first segment of waveform data is the data from the occurrence of the fault to the fault recovery, and the second segment of waveform data is the data after the fault recovers to a steady state; S2.

4. Use the Proni method to analyze the second waveform data, identify the linear oscillation mode without resonance in the second waveform data, and obtain its frequency as the initial value of the frequency domain result; S2.

5. Fit the initial value of the frequency domain result to the first segment of waveform data, and use the Proni method to analyze the fitted first segment of waveform data to identify the second-order resonance mode caused by the nonlinear model after the fault occurs, and obtain the frequency domain response characteristics of each fault time-series trajectory sample.

4. The electromechanical-electromagnetic simulation time-domain data resampling method for preserving time-frequency domain characteristics according to claim 1, characterized in that, Step S4 is as follows: S4.

1. Delete redundant samples and abnormal data in the electromechanical transient sequence or the electromagnetic transient sequence; S4.2 Perform Z-score normalization on the time-domain and frequency-domain characteristics of the electrical transient sequence or the electromagnetic transient sequence; S4.3 The time-domain features and the frequency-domain features are weighted and fused to form a time-integrated feature vector, thereby obtaining electromechanical-electromagnetic simulation time-domain data that retains the time-frequency domain features.

5. The electromechanical-electromagnetic simulation time-domain data resampling method for preserving time-frequency domain characteristics according to claim 4, characterized in that, The weighted fusion of the time-domain features and the frequency-domain features is specifically performed as follows: the weight of the time-domain features is calculated based on the oscillation energy, the weight of the frequency-domain features is calculated based on the spectral energy, and the time-domain features and frequency-domain features are weighted and summed to form a comprehensive feature vector.

6. A time-domain data resampling system for electromechanical-electromagnetic simulation that preserves time-frequency domain characteristics, characterized in that, include: Data acquisition module: used to obtain electromechanical and electromagnetic transient time-series trajectory samples of new energy power grid systems based on electromagnetic simulation platform and electromechanical simulation platform; Feature acquisition module: used to process electromechanical and electromagnetic transient time-series trajectory samples using the improved Proni method to obtain the frequency domain response features of the electromechanical and electromagnetic transient time-series trajectory samples; The trajectory reconstruction module is used to reconstruct electromechanical and electromagnetic transient time-series trajectories based on frequency domain response characteristics, using linear interpolation and adaptive resampling techniques, respectively, to obtain electromechanical and electromagnetic transient reconstructed time-series trajectory data containing frequency domain characteristics. Specifically, it includes the following steps: determining the resampling frequency for each time window based on the frequency domain response characteristics using the Nyquist sampling theorem; and calculating the complete sampling length of the electromagnetic transient time-series trajectory based on the resampling frequency of each time window. K Linear interpolation is performed on the electromechanical transient time-series trajectory based on the sampling step size of each time window in the electromagnetic transient time-series trajectory to obtain an electromechanical transient time-series trajectory that corresponds one-to-one with the sampling points of the electromagnetic transient time-series trajectory. Using adaptive resampling technology, the electromagnetic transient time-series trajectory and the electromechanical transient time-series trajectory are resampled according to the resampling frequency and the complete sampling length to obtain trajectories of length [not specified in the original text]. K Electromechanical transient sequences and electromagnetic transient sequences; Feature fusion module: used to fuse the time-domain and frequency-domain features in the electromechanical and electromagnetic transient reconstructed time-series trajectory data to form a comprehensive feature vector, thereby obtaining electromechanical-electromagnetic simulation time-domain data that retains the time-frequency domain features.

7. A computer device, characterized in that, The computer device includes: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a time-domain data resampling method for electromechanical-electromagnetic simulation that preserves time-frequency domain characteristics as described in any one of claims 1-5.

8. A computer-readable storage medium storing computer instructions, characterized in that, When the computer instructions are executed by one or more processors, the one or more processors cause the processors to perform the steps of the method according to any one of claims 1-5.

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