60 results about "Transient stability assessment" patented technology

The invention discloses an electric power system transient stability evaluation sample enhancement method, system and device based on a timing constraint generative adversarial network and a medium, and belongs to the technical field of electric power system safety analysis, and the method comprises the steps: constructing a sample enhancement model, generating transient time sequence data based on random noise and system feature tags by a generator, the discriminator outputs a sample authenticity score and a time sequence consistency score through a time sequence neural network, the physical constraint module calculates physical constraint loss of the generated data, the model is alternately trained in combination with multi-dimensional loss, the trained generator is utilized to synthesize samples, and qualified samples are screened through physical verification. According to the invention, by introducing the LSTM time sequence constraint, the WGAN-GP stable training mechanism and the multi-dimensional physical consistency constraint, three core problems of poor time sequence coherence, modal collapse and physical law violation in the existing sample enhancement method are cooperatively solved, and high-quality transient scarce samples can be generated.

The invention discloses a CKM-CNN-based power system transient stability assessment method, system, equipment and medium, and relates to the technical field of power system assessment, and the method comprises the steps: collecting time sequence electrical quantity data after power system fault disturbance, constructing a transient sample set, carrying out the standardization processing and marking of a sample, and carrying out the calculation of the transient stability of the power system; a transient sample set is divided through a clustering algorithm, based on a sample cluster division result, an adaptive oversampling technology is adopted to carry out directional enhancement on an unstable sample, an equalized sample space is generated, a multilayer convolutional neural network is utilized to carry out hierarchical feature extraction on the equalized sample space, a high-dimensional feature vector is generated, and according to the high-dimensional feature vector, a high-dimensional feature vector is generated. And outputting a transient stable state probability evaluation result through the classification decision-making layer. According to the method, a generalization sample set is constructed through standardization and labeling, the scarce instability sample identification capability is improved by combining two-stage clustering and ADASYN oversampling, and the system dynamic state is accurately captured by utilizing 1D-CNN hierarchical feature extraction, so that efficient and robust evaluation of the transient stability of the power system is realized.

The invention discloses a power system multi-scene adaptive transient stability evaluation method based on active transfer learning, and the method comprises the following steps: constructing a transient stability evaluation model based on a Transform encoder, building a mapping relation between data and a stable state, and obtaining the source domain data of a power system; constructing a pre-training model based on the source domain data, and when it is detected that the pre-training model enters a new electrical system scene, performing short-term simulation to obtain an unlabeled target domain sample; calculating the information amount of each target domain sample through an active learning strategy; and carrying out fine adjustment on parameters of the model by utilizing transfer learning, selecting a transfer scheme by considering a difference degree between data distribution, enabling the transient stability evaluation model to quickly finish final updating training, evaluating the transient stability of the power system by using the updated model, and outputting a transient stability evaluation result. According to the method, the transient stable state of the power system can be accurately evaluated, and the model updating efficiency is higher.

The present invention provides a method, device, electronic device, and storage medium for evaluating transient stability of a power system. The method includes: obtaining power system flow information and power system topology information; determining a power system flow characteristic diagram based on the power system flow information and power system topology information; inputting node characteristics and topology characteristics of the power system flow characteristic diagram into a transient stability evaluation model to obtain the transient stability and transient stability index of the power system after each expected fault in the expected fault set output by the transient stability evaluation model; the transient stability evaluation model is used to perform coupled analysis of the transient stability and transient stability index of the power system by learning the characteristics of the power system flow characteristic diagram. The method, device, electronic device, and storage medium provided by the present invention enable rapid evaluation and prediction of the transient stability and transient stability index of the power system after each expected fault in the expected fault set.

The invention discloses a power system sample generation method based on a physical information generative adversarial network, and the method comprises the steps: 1), obtaining a large number of training samples through PSD-BPA simulation, extracting the characteristic quantity of each generator, and taking the stability result of the samples as original data; 2) training an original SSAE (Stacked Sparse Autoencoder) by using the original data, and screening out a critical sample according to a predicted value of the sample; 3) training a generative adversarial neural network fused with physical information according to the screened critical samples to obtain a critical sample generation model; 4) generating a critical sample set by using the trained critical sample generation model, and constructing an enhanced critical sample set based on the original sample set and the critical sample set; and 5) training a transient stability evaluation model based on data driving by using the enhanced critical sample set. According to the method, the data conforming to the dynamic equation of the power system can be generated, and the evaluation accuracy of the model can be effectively improved by training the model through the training set after critical sample enhancement.

The invention provides an electric power system transient stability evaluation method based on a space-time diagram convolutional network, and belongs to the technical field of electric power system transient stability evaluation. In order to solve the technical problem of low efficiency and performance of an evaluation model caused by high-dimensional redundancy of transient stability characteristics of an existing power system, the technical scheme adopted by the invention is as follows: generating an operation scene as a simulation sample by utilizing a time domain simulation function of power system simulation software, and incorporating the simulation sample into a sample data set; processing the original data of the simulation sample by adopting a two-stage screening hybrid intelligent feature selection method; a subset of final data features of the power system is converted into graph structure data, training is performed based on a learning framework of a space-time graph convolution network, topological features of the graph structure data of the power system are captured by using a space-time graph convolution encoder, and transient stability of the power system is evaluated based on a multi-outlet classification mechanism. Establishing a transient stability evaluation model; the method is applied to transient stability evaluation of the power system.

The invention belongs to the technical field of inverter transient stability evaluation, and provides a power amplitude limiting setting method and system for improving the transient stability of a network construction device in current limiting. The method comprises the steps of triggering and controlling a grid-forming inverter to enter a current limiting mode when the grid-forming inverter grid-connected system has voltage drop at a power grid side, and limiting the increase of the power difference when the power difference exceeds the maximum value of the set power difference; the switching conditions are analyzed into corresponding power angle switching conditions; according to the power vacancy at the fault moment of the network-building type inverter, calculating the acceleration area of the network-building type inverter under the action of power amplitude limiting; on the basis of the relation between the fault clearing angle and the power angle switching condition of the network building type inverter, the deceleration area of the network building type inverter under the power amplitude limiting effect is obtained; and based on an equal area rule, solving a setting value range of a maximum value of a power difference value of power amplitude limiting under the condition of transient stability of the single-machine grid-connected system of the grid-forming inverter.

The invention relates to the technical field of power system transient stability assessment, and discloses a power system transient stability assessment method based on FTF-ACGAN and space-time attention. According to the method, an original data set reflecting different power flow operation modes and fault conditions is generated, and transient electrical quantities are extracted as input characteristics; generating an adversarial network through a fuzzy transfer field-auxiliary classifier to extract modals with different characteristics of a sample, and revealing transient characteristics of the electrical signal; and establishing a mapping relation between original high-dimensional input and a system stability or instability label according to the processed transient measurement data of the power system by considering respective capturing capabilities of a spatial feature extraction module and a time feature extraction module in the dual-module multi-head space-time attention network on space-time local association and global dependency features. The method can consider the time-frequency domain information of the electric power system sample, construct a data set with moderate and abundant sample characteristics, and accurately judge the actual operation condition of the electric power system.

The invention discloses a power system transient stability evaluation scene expansion method. The method comprises the following steps: fusing a non-equilibrium thermodynamic sampling framework with a dynamic equation of a power system to obtain a physical constraint diffusion generation model; establishing a transient differential equation embedding mechanism of the power system; aiming at the space-time correlation characteristics of the dynamic trajectory of the power system, designing a space-time decoupling self-attention coupling mechanism based on a physical constraint diffusion generation model, deeply fusing the attention mechanism with the physical law of the power system, and establishing an interaction relationship through a dynamic equation; through a hierarchical decoupling strategy of system-level diffusion, node-level inverse diffusion and dynamic projection, a space-time decoupling self-attention coupling mechanism is fused to capture complex association, the rationality of a dynamic trajectory of a power system is guaranteed by relying on physical constraints, and scene expansion of global-local coordination and a physical constraint closed loop is realized. According to the method, the original data distribution characteristics can be effectively learned, the high-quality instability scene is synthesized, the original data set is balanced, and the transient stability evaluation performance is improved.

The invention discloses a power system transient stability assessment method, which comprises the steps of constructing a training sample set for transient stability assessment of a target power system, and marking real sample tags of the training sample set to generate an original sample set with tags; dividing samples in the original sample set with the labels into boundary samples, edge samples and internal samples; respectively executing differential weight extraction operation on the boundary sample, the edge sample and the internal sample to generate a model training subset; training the initial transient stability evaluation model based on the model training subset until a trained transient stability evaluation model is obtained; and inputting the collected real-time operation data of the target power system into the trained transient stability evaluation model to output a transient stability evaluation result of the power system. According to the invention, through sample classification and differential weighted training, the evaluation precision, generalization ability and decision reliability of the model in a critical state are significantly improved.

The invention discloses a key transient sample enhancement method and system based on a diffusion model, and the method comprises the steps: generating a transient sample through a diffusion process and a reverse process based on the diffusion model, adding a constraint condition into the reverse process based on a classifier in the reverse process, and generating a transient sample meeting the constraint condition; determining key samples in the transient samples based on a sensitive area of a preset transient stability evaluation classification boundary; and enhancing the key sample based on a classifier-guided diffusion model to generate an enhanced sample. According to the invention, through an innovative model design and data processing method, high-fidelity transient state scene data meeting multi-scene and diversity requirements are generated through an intelligent algorithm, so that powerful support is provided for transient state stability evaluation and optimization of a power system.

Disclosed in the present disclosure is a method for analyzing the safety and stability of a new-energy power system on the basis of a Lyapunov function. The method comprises the following steps: acquiring the voltage of a new-energy grid connection point, the voltage phase angle of the new-energy grid connection point, the internal electromotive force of a synchronous machine, a line susceptance, and the rotor position angle of the synchronous machine in a new-energy power system, so as to obtain the output power of the synchronous machine; on the basis of the new-energy power system and the output power of the synchronous machine, obtaining a rotor motion equation of the synchronous machine; on the basis of the rotor motion equation of the synchronous machine, constructing a Lyapunov function based on the synchronous machine in the new-energy power system; on the basis of the Lyapunov function, obtaining a stability region boundary of the synchronous machine with respect to state variables; and on the basis of the stability region boundary and real-time values of post‑fault state variables, determining the transient stability of the synchronous machine. The method for analyzing the safety and stability of a new-energy power system on the basis of a Lyapunov function provided in the present disclosure features low construction difficulty, is universally applicable to nonlinear systems of different forms, and enables fast and accurate transient stability assessment.

The invention discloses a transient stability evaluation method and device for a multi-VSG parallel system. The method comprises the following steps: obtaining a power system model and a transient energy function according to the system; the transient energy function comprises a kinetic energy function and a potential energy function; obtaining a critical stable energy value according to the power system model and the transient energy function in combination with a BCU method; constructing a first model for obtaining a dynamic coupling relationship between VSGs based on a PINN network; training the first model according to a preset data set to obtain a second model; solving a transient energy function according to the real-time state vector of the system, and solving a path related item in the transient energy function in combination with a second model to obtain a real-time energy value; and if the real-time energy value is greater than the critical stable energy value, determining that the system is unstable, otherwise, determining that the system is stable.

This invention relates to the field of power electronic converter control technology and discloses a method and apparatus for judging the transient stability of a hybrid system with a grid-connected converter. The method includes constructing an equivalent model of the hybrid system comprising a grid-connected converter and a network-connected converter, wherein a virtual impedance is connected in series in the branch of the network-connected converter; constructing a Lyapunov energy function based on the equivalent model, wherein the independent variables include the power angle and phase-locked loop frequency of the grid-connected converter, and the power angle, frequency, and voltage amplitude of the network-connected converter; determining the stability domain boundary of the hybrid system using the Lassel invariant set principle when the derivative of the energy function with respect to time is less than or equal to 0; calculating the critical range of the virtual impedance based on the changing trend of the stability domain boundary, and determining the target value of the virtual impedance to maintain the transient stability of the hybrid system. The above method achieves quantitative judgment and parameter tuning of the transient stability of the hybrid system.

The present invention discloses a transient stability assessment method, apparatus, terminal equipment and computer-readable storage medium for a grid-connected system of a new energy generator set and a grid-connected converter. The method comprises: obtaining system parameters after a fault occurs, and calculating the energy value of each time step after the fault occurs by using a dynamic equation and an approximate energy function; judging whether the energy value of each time step is less than a preset critical energy value, and if so, judging that the corresponding time step is in a stable state, and if not, judging that the corresponding time step is in an unstable state; taking the maximum time step corresponding to the stable state as the target maximum fault clearing time after the fault occurs, and performing transient stability assessment based on the target maximum fault clearing time; wherein the dynamic equation and the approximate energy function are determined in the following manner: constructing the dynamic equation and the energy function according to the structure of the grid-connected system of the new energy generator set and the grid-connected converter; and approximating the damping term in the energy function to obtain the approximate energy function.

The invention discloses a bidirectional time sequence feature enhanced adaptive transient stability evaluation method and system, and aims to construct a bidirectional time sequence feature enhanced evaluation model BTFE combined by a bidirectional long-short-term memory network and a Kolmogorov-Arnold network in order to overcome the defect of feature extraction capability of an existing deep learning model, and integrate bidirectional time sequence features in a transient process, so that the adaptive transient stability evaluation method and system based on bidirectional time sequence feature enhancement are obtained. The capturing capability of the model on a complex dynamic process is effectively improved; the model updating method based on the adaptive sample screening strategy is designed for the defect of adaptability of a transient stability evaluation model based on deep learning, samples required by model updating in different stages are adaptively screened out according to heterogeneity of a source domain and uncertainty of a target domain, and the labeling cost of the samples during model updating is reduced; based on a model migration strategy, the BTFE model is finely adjusted by using the screened samples, so that the adaptability of the model to the specific features of the target task is enhanced, and the model updating speed is increased.

The invention discloses a transient evaluation method based on SHAP, and relates to the field of transient stability of a power system, and the method comprises the steps: S1, constructing a prediction-interpretation synchronization framework: embedding a Shapley module supporting parallel calculation into a deep neural network, and achieving the synchronous execution of transient stability evaluation and feature attribution in forward propagation; s2, designing a spatio-temporal feature fusion architecture: adopting a time-space-feature three-dimensional tensor as input, constructing a deep SHAP network by referring to a convolutional neural network thought, and enhancing transient process dynamic feature modeling capability; and S3, establishing a quantitative decision support mechanism: based on the quantitative influence of the Shapley value analysis feature on the stability, realizing transparent explanation of an evaluation result, and providing a decision basis for beforehand prevention and control strategy making and post emergency control deployment. According to the method, the balance between the transparency and the accuracy of transient stability evaluation is realized, and a more accurate, quicker and interpretable evaluation result is provided for power grid dispatching.

This invention discloses a method and system for transient stability assessment of new energy systems based on machine learning and data-driven approaches. It includes: performing time-domain simulations of electromechanical and electromagnetic transient models of the new energy system to obtain a sample set; constructing a system stability classification model based on a long short-term memory network; training the system stability classification model using a data-driven method; and achieving the assessment of electromagnetic transient stability through monitoring curves from electromechanical transient simulations. This invention establishes a mapping relationship between electromechanical and electromagnetic time scales through data-driven approaches, enabling the prediction of system stability at the electromagnetic transient scale based solely on the system's operation at the electromechanical transient scale. This solves the dual problems of low accuracy in electromechanical transient modeling of high-proportion new energy systems, which fails to accurately reflect real instability scenarios, and slow and inefficient electromagnetic transient simulation, thus achieving transient stability assessment of new energy systems.

The invention relates to the technical field of power systems, and provides a transient stability evaluation method of a power system and related equipment, and the method comprises the steps: obtaining transient data of a plurality of branches of a target power system; calculating a heterogeneous mask of each branch of the target power system, and obtaining heterogeneous information of each branch based on the heterogeneous mask and the transient data of each branch; for each branch, performing feature extraction on the heterogeneous information of the branch to obtain heterogeneous features of the branch; performing weighted fusion on the heterogeneous features of all branches to obtain comprehensive features of the target power system; and based on the comprehensive characteristics of the target power system, evaluating the transient stability of the target power system to obtain a transient stability evaluation result of the target power system. According to the method, the stability degree of transient stability evaluation of the power system can be improved.

The invention discloses a wind power AC / DC system transient stability evaluation method based on a positive definite structure neural Lyapunov function. The method comprises the following steps: constructing a state space physical model containing a wind power AC / DC system, and collecting real-time operation data; determining a candidate Lyapunov function based on the neural network; updating parameters of the Lyapunov function based on a gradient method; generating a counter example set based on a projection gradient descent algorithm, and verifying a Lyapunov function based on an SMT solver; the method comprises the following steps: acquiring the operation state of a wind power-containing AC / DC system to obtain a training set, constructing a total training set based on the training set, a counter-example set and a verification set, and training based on the total training set to obtain a trained neural Lyapunov network; and evaluating the transient stability state of the wind power AC / DC system based on the trained neural Lyapunov network. According to the method, the local Lyapunov function of the complex power system can be effectively learned, and the transient stability of the alternating-current and direct-current hybrid system containing wind power can be quickly and accurately evaluated.

The invention belongs to the technical field of power electronics, and provides a power disturbance-based transient stability evaluation method and system for a grid-forming converter, which comprises the following steps of: applying disturbance to active power control of the grid-forming converter and acquiring corresponding power angle change to form a power angle difference; fitting a power angle curve based on the power angle difference, the active power reference, the reactive power reference and the disturbance quantity applied by the active power, and determining a transient stable transmission boundary of the reactive power or the active power under the current active power or reactive power reference condition according to the power angle curve; and determining the current transient stability of the network-forming converter based on the transient stability transmission boundary. According to the method, the transient stability boundary of the converter can be directly calculated, so that system parameters such as power grid voltage are prevented from being obtained, and the transient stability of the grid-forming converter is accurately evaluated.

The invention relates to the technical field of transient stability analysis of a power system, in particular to a transient stability evaluation method and device for a wind turbine generator, and the method comprises the steps: constructing a full-order model of a control loop of a to-be-evaluated wind turbine generator, and carrying out the transient stability analysis of the full-order model of the control loop; and performing simulation aiming at different voltage sags, different phase-locked loop control parameters, different power grid line impedances and different grid-connected point d-axis current instructions of the to-be-evaluated wind turbine generator, and performing simulation verification on the transient stability of the to-be-evaluated wind turbine generator. According to the derived maximum estimation attraction domain and the property thereof, the transient stability quantitative evaluation index capable of intuitively reflecting the initial state of the transient process of the new energy unit and the boundary of the maximum estimation attraction domain is constructed, and the relationship between the evaluation index value and the state quantity change of the transient process of the unit is qualitatively and quantitatively analyzed; and the transient stability margin of the unit is quickly and visually reflected.

The present invention discloses a method, system, terminal device and storage medium for transient stability assessment of a multi-machine system based on a hyper-tangent plane. The method constructs a differential algebraic equation group of the multi-machine system according to the state quantity of the multi-machine system, calculates the equilibrium point and Jacobian matrix of the multi-machine system according to the differential algebraic equation group of the multi-machine system, and then determines a class of unstable equilibrium points and Jacobian matrices thereof. The hyper-tangent plane equation is calculated according to the Jacobian matrix of the class of unstable equilibrium points. The nonlinear characteristics of the multi-machine system are analyzed by the hyper-tangent plane equation and the fault trajectory to calculate the critical fault removal time of the multi-machine system, and the transient stability of the multi-machine system is evaluated according to the critical fault removal time, thereby improving the accuracy of the transient stability assessment of the multi-machine system, ensuring the stable operation of the multi-machine system, and solving the problem that the transient stability assessment result of the multi-machine system is low due to the lack of analysis of the nonlinear characteristics of the multi-machine system in the time domain simulation method.

The invention provides a transient stability assessment method and system for a heterogeneous new energy grid-connected system. The method is applied to a heterogeneous new energy grid-connected system comprising a network-constructing type converter, a network-following type converter and a receiving end system, and comprises the following steps: establishing a power angle characteristic equation of the network-constructing type converter according to an electrical quantity relationship between the network-constructing type converter and the network-following type converter in combination with an electrical quantity rule of the heterogeneous new energy grid-connected system; determining an energy function of the heterogeneous new energy grid-connected system by using the power angle characteristic equation, and solving a critical energy value at the unstable equilibrium point in combination with the power angle characteristic equation and the energy function; calculating the system energy of the heterogeneous new energy grid-connected system at the fault clearing moment, and evaluating the transient stability of the system according to the comparison result of the system energy and the critical energy value. The invention provides a transient stability evaluation mode for the heterogeneous new energy grid-connected system, and the transient stability of the system can be evaluated at the fault clearing moment.

This application belongs to the field of transient stability assessment of new energy power systems, and specifically discloses a method and system for calculating the critical short-circuit ratio of a grid-connected converter system. Through this application, the dominant links of the grid-connected converter system, including the terminal voltage link and the synchronization link, are simplified and reduced in order, and the reactance value of the grid line is calculated according to the Routh criterion, thereby obtaining the critical short-circuit ratio for small disturbances, which can effectively evaluate the grid strength for stable operation of the system; and the difference between the critical short-circuit ratio for large disturbances and the critical short-circuit ratio for small disturbances obtained by a large number of simulations can be used to obtain the critical short-circuit ratio for large disturbances of the system, which can be used to estimate the critical short-circuit ratio for large disturbances of the system, thereby increasing the reference value for the system to be able to operate safely and stably after a large disturbance, thereby improving the grid connection stability.

A kind of transient stability evaluation method for misjudgment graph convolution network, comprising the following steps: step 1: different operating scenarios of power system are simulated to construct sample set;Step 2: obtain improved adaptive graph convolution network GCN;Step 3: based on the improved adaptive graph convolution network GCN, an evaluation model is established, and offline training is carried out;Step 4: in the process of online application, the stability class and stability level obtained by the model are coordinated by constructing an auxiliary decision module, to judge the real operating state of power system.

The application discloses a power system transient stability evaluation method and system, and belongs to the technical field of power systems. The power system transient stability evaluation method comprises the following steps: iteratively training a first deep neural network according to an initial sample set to obtain an initial evaluation model; determining an importance index according to the weight of the first deep neural network in each iteration training process, the prediction result of sample operation data and the label of the sample operation data; determining a stability parameter based on the initial evaluation model; determining the stability score of sample data according to the importance index and the stability parameter; removing sample data with a stability score greater than a maximum threshold value or less than a minimum threshold value in the initial sample set, and iteratively training a second deep neural network; and determining a transient stability state based on real-time operation data of the power system according to an optimal evaluation model. The application saves training time, improves the generalization ability of the evaluation model and the accuracy of transient stability evaluation.

The application relates to the technical field of power system automation, and discloses a power system transient stability evaluation method based on deep Bayesian active learning, which comprises the following steps: S1, in an offline stage, for a target power system, power grid power flow mode data sets are generated by randomly sampling the power grid power flow mode of the target power system; S2, power grid power flow mode sample sets containing target attributes are generated through transient stability simulation analysis; S3, a transient stability evaluation model based on a deep Bayesian neural network is constructed; S4, a minimum credibility-based active learning method is used to obtain samples to be labeled from the power grid power flow mode sample sets; and S5, in a real-time operation stage of the power system, the transient stability of the power system is predicted by using the transient stability evaluation model. The transient stability evaluation model based on the deep Bayesian neural network can give a prediction result to evaluate the transient stability of the power system.

The invention provides a model building method, an evaluation method and a system for evaluating the time sequence stability of a power grid with high precision and strong interpretability. The model building method comprises the following steps: initializing an Informer encoder; initializing a multivariable decision tree improved model; obtaining multi-dimensional time sequence data after power grid disturbance and system static characteristic data at the same moment, and respectively processing the data to obtain a transient time sequence characteristic vector and a static characteristic vector of a fixed dimension; splicing and fusing the transient time sequence feature vector of the fixed dimension and the static feature vector by adopting a dimension superposition mode to form a fused feature vector; and inputting the fusion feature vector into the initialized multivariable decision tree improved model for iterative training to obtain a power grid time sequence stability evaluation result and a decision path result. According to the method, a power grid time sequence stability evaluation tool with high accuracy and a trusted decision process is generated, and an efficient and accurate guidance decision is provided for power grid time sequence stability evaluation. The method is suitable for the technical field of transient stability evaluation.

This application provides a model building method, evaluation method, and system for power grid time-series stability assessment with high accuracy and strong interpretability. The model building method includes: initializing an Informer encoder; initializing a multivariate decision tree improved model; acquiring multidimensional time-series data after power grid disturbances and simultaneous system static feature data, processing them separately to obtain fixed-dimensional transient time-series feature vectors and static feature vectors; concatenating and fusing the fixed-dimensional transient time-series feature vectors and static feature vectors using a dimension superposition method to form a fused feature vector; inputting the fused feature vector into the initialized multivariate decision tree improved model for iterative training to obtain the power grid time-series stability assessment results and decision path results. This application generates a highly accurate power grid time-series stability assessment tool with a trustworthy decision-making process, providing efficient and accurate guidance for power grid time-series stability assessment. It is applicable to the technical field of transient stability assessment.