96 results about "Load modeling" patented technology

The invention discloses a virtual power plant multi-objective optimization scheduling method, system and device, and a storage medium. The method comprises the following steps: constructing a virtual power plant architecture; the virtual power plant comprises internal resources and distributed power generation equipment; the internal resources comprise a base station standby energy storage system, an internet data center, an interruptible load and a gas turbine; the distributed power generation equipment comprises wind power generation and photovoltaic power generation; internal resources of the virtual power plant are modeled, specifically, base station standby energy storage system modeling, internet data center modeling, interruptible load modeling and gas turbine modeling are included; establishing an objective function of multi-objective optimization of the virtual power plant; establishing constraint conditions of multi-objective optimization of the virtual power plant; solving based on the objective function and the constraint condition, obtaining a scheduling instruction, and executing scheduling; according to the invention, the operation economy and scheduling flexibility of the virtual power plant system are improved.

The invention relates to the technical field of source network load storage intelligent scheduling, and discloses a source network load storage AI intelligent scheduling method and system, and the system comprises a data collection module, a fluctuation analysis module, a constraint calculation module, a load modeling module, an energy storage analysis module, a decision engine module, and a safety correction module. Output data of a photovoltaic power station, a wind power plant and a traditional power plant are collected in real time through an Internet of Things sensor, and a multi-source heterogeneous energy data pool is constructed; aligning the corrected source load data with the energy storage state and the power grid operation parameters according to a time sequence to generate a four-dimensional optimization combination matrix; matching an optimal scheduling algorithm through a bionic search strategy, and analyzing the data matrix through the deep reinforcement learning model to generate a three-section scheduling instruction; and finally, the power grid control system executes power generation adjustment, load regulation and control and energy storage charging and discharging instructions. According to the system, the edge computing gateway is adopted to realize data acquisition, the new energy consumption capability and the power grid stability are improved, and the risk of source-grid load-storage collaborative failure is reduced.

The invention discloses a micro-grid user load modeling method and system based on deep transfer learning, and relates to the technical field of smart grids, and the method comprises the steps: collecting micro-grid user data, and carrying out the data preprocessing and initialization; carrying out source domain pre-training, and constructing a deep transfer learning framework; carrying out feature extraction by using an improved deep convolutional neural network; carrying out domain adaptation loss calculation, introducing a dual attention mechanism, and constructing a load prediction model; and incremental learning optimization is set, and the strategy is optimized through an improved algorithm. According to the method, technologies such as field adaptation loss, incremental learning optimization and knowledge distillation are combined, and the problems of data scarcity, poor field adaptability and over-fitting in the prior art are effectively solved. According to the method, effective knowledge migration between the source domain and the target domain can be realized through the migration learning framework, the precision and the stability of load prediction are improved, and the method has relatively high adaptability when new data and changing scenes are processed.

The invention discloses an air-conditioning building system load modeling method and system based on a physical prior neural network, and relates to the field of air-conditioning building system loads, and the method comprises the steps: modeling a thermodynamic load dynamic evolution process of an air-conditioning building system as a discrete time Markov decision process; establishing a corresponding physical prior neural network architecture; simulating a room scene heated by a single heat source to generate training data, determining physical meanings of states and action spaces in the room scene, and updating a loss function form; training a physical prior neural network and carrying out hyper-parameter tuning, and verifying the prediction performance of the physical prior neural network on a test data set of a simulation scene and a test data set of an actual scene; according to the method, precise modeling and control over the temperature and power of the air conditioner building system are achieved, convergence is faster compared with a traditional neural network, the requirement for training data is lower, and better generalization performance and higher expandability deployment potential are achieved.

The invention discloses a novel power system satellite-ground converged communication load modeling and situation awareness method and system, and the method comprises the steps: carrying out the classification of communication loads based on the load feature information of a satellite-ground converged novel power system, and constructing a communication load model according to a load classification result, generating a load demand regulation and control strategy according to the response characteristic information of the communication load, aggregating the communication load based on the communication load model and the load demand regulation and control strategy, performing dependency analysis on a communication load aggregation result, and obtaining space-time dependency information among the aggregated loads; the load situation of the power system is predicted according to the space-time dependency information, the communication load is aggregated, so that the analysis efficiency is improved, the analysis complexity is effectively reduced, the communication load prediction accuracy of the satellite-ground fusion novel power system is greatly improved through space-time dependency analysis on the communication load, and the communication load prediction accuracy is improved. The timeliness of the prediction result is ensured, and the communication quality of the power business is effectively guaranteed.

The invention discloses a method for rapidly evaluating the fatigue life of a steel trestle for extreme heavy load impact, and relates to the technical field of bridge fatigue life evaluation. Establishing an extreme impact load probability model; multi-source bridge monitoring; establishing and correcting a finite element model; efficiently simulating a fatigue damage field; carrying out multi-stage modeling and prediction on crack propagation; performing multi-scale damage accumulation fusion analysis; and carrying out residual life evaluation and multi-dimensional risk grading. Through combination of extreme impact load modeling, multi-source data fusion and a physical constraint neural network technology, the impact load can be accurately identified and the stress response of the structure can be calculated under an extreme heavy load working condition, and meanwhile, a three-stage crack propagation model is introduced to comprehensively describe the whole process from microscopic damage to structure fracture. And the efficiency and reliability of fatigue life evaluation are obviously improved.

The invention relates to the technical field of mobile robot attitude control, and provides a mobile robot attitude control method, device and equipment and a medium, which can detect whether the load of a mobile robot changes in real time and perform reconfirmation based on a robot kinetic equation and real-time motion and attitude data, thereby improving the detection accuracy. Newly-added load modeling is carried out by utilizing an extended Kalman filtering algorithm and real-time motion and attitude data, and centroid reconstruction is carried out according to a newly-added load mass estimation value and a newly-added load position estimation value, so that the whole processing process does not need to stop and does not need to rearrange a sensor, and the compatibility is improved; and the model prediction control model is called, and the mobile robot is controlled to carry out attitude adjustment according to the reconstruction result and the real-time motion and attitude data, so that dynamic compensation for gravity center change can be realized, instability, slipping or falling caused by mass center drift is effectively prevented, and the task execution stability of the mobile robot in a changeable and unpredictable environment is improved.

The invention relates to the technical field of air conditioner load modeling and parameter identification, in particular to an air conditioner parameter identification and modeling method based on a multi-mechanism fusion grey wolf optimization algorithm. The method comprises the steps that firstly, air conditioner operation data are collected, preprocessed and normalized; then, on the basis of a traditional grey wolf algorithm, a composite chaos initialization strategy, a crisscross optimization strategy and a Cauchy inverse cumulative distribution variation strategy are fused, and a multi-mechanism fusion optimization algorithm is constructed; secondly, establishing a first-order equivalent thermal parameter model, identifying parameters of equivalent thermal resistance R and equivalent thermal capacity C by adopting the algorithm, and taking minimization of a mean square error of a predicted temperature and an actually measured temperature as a target; and finally, performing air conditioner load aggregation modeling based on the identification parameters, and analyzing the influence of diversity of the set temperature and the initial indoor temperature on aggregation power fluctuation. Through algorithm improvement and comprehensive aggregation characteristic analysis, theoretical and technical support is provided for flexible scheduling of integrating the air-conditioning load into a modern power system.

The invention discloses an internet-of-things central air conditioner control method based on a joint regulation and control algorithm model, which comprises the following steps: S1, periodically acquiring temperature, area and use purpose parameters through an internet-of-things temperature controller mounted in a tail end room, and uploading the parameters to an edge computing node; s2, collecting water temperature, water pressure and flow data in real time through an Internet of Things sensor arranged on a pipeline, and uploading the data to an edge computing node; s3, the edge calculation node calculates a load coefficient alpha and a water flow coefficient beta based on the collected information; s4, the control system takes the load coefficient alpha and the water flow coefficient beta as input, and determines a host outlet water temperature set value and a water pump frequency based on a host-water pump combined regulation and control algorithm model; and S5, the host and the water pump are controlled based on the host outlet water temperature set value and the water pump frequency, and dynamic regulation and control between cold and heat supply and energy consumption are achieved. According to the method, dynamic matching of cooling and heating loads and energy-saving optimized operation of the system are realized through accurate acquisition of terminal data and multi-dimensional load modeling.

The embodiment of the invention discloses a load modeling parameter identification method and a related device, and the method comprises the steps: obtaining a load data set of a power system, the load data set comprises load data samples of all time points, and the load data samples comprise current, voltage, active power and reactive power; a support vector machine is adopted to establish a load model, input data of the load model are voltage and current, and output data of the load model are active power and reactive power; and identifying model parameters of the load model based on a leave-one-out cross validation method and the load data set to obtain optimal load model parameters. Through the above mode, the method for optimizing the model parameters of the load model constructed by the support vector machine (SVM) based on the Leave-One-Out Cross Validation (LOCV) is adopted, so that the high-precision identification of the load model of the power system is realized.

The invention relates to the technical field of power system modeling and simulation, in particular to a load model identification error analysis method, device and equipment and a storage medium, and the method comprises the steps: obtaining the prediction output and the measurement output of a preset load model, and building a target function based on the error between the prediction output and the measurement output; decomposing the measurement output to obtain an actual output and a noise component of a preset load model; and based on the objective function and the actual output, establishing a linear model between the identification error of the preset load model and the noise component through a first-order approximation method, and performing linear regression according to the linear model to obtain an analysis result of the identification error. Therefore, by constructing the theoretical model of the load model identification error, the problems that the identification error is difficult to predict, the data processing strategy selection lacks theoretical guidance and the like in related technologies are solved, and a theoretical basis is provided for selecting an optimal load modeling data processing strategy.

The invention discloses a power distribution network comprehensive stability evaluation method and system based on load modeling, and the method comprises the steps: constructing a power distribution network differential load model with spatial-temporal characteristics according to the load classification and spatial-temporal change rule; based on the model, combining voltage stability, frequency stability, load fluctuation and external disturbance influence factors to determine a comprehensive system stability index of the power distribution network; establishing a relation model among the voltage, the load and the frequency; determining a sensitivity coefficient of load fluctuation to voltage and frequency based on the relation model; evaluating the stability of the power distribution network under the load fluctuation condition according to the sensitivity coefficient; determining a dynamic stability index of the power distribution network through the dynamic stability index; and performing comprehensive dynamic stability evaluation on the power distribution network through the comprehensive system stability index of the power distribution network, the stability of the power distribution network under the load fluctuation condition and the dynamic stability index of the power distribution network. And the stability of the power distribution network can be comprehensively evaluated.

The invention relates to the technical field of electric pile energy switching, and discloses a bidirectional charging pile energy switching control method and system supporting vehicle-network interaction, and the method comprises the steps: collecting the load state of a power grid, and constructing a load evaluation model in which a historical load is combined with real-time data; selecting a common charging mode or a V2G mode according to an evaluation result; switching the charging module according to the charging mode requirement; constructing a load attention model according to power grid dispatching and load distribution; optimal configuration is applied to charging control, and peak clipping and valley filling are carried out in cooperation with a power system; the system comprises a load acquisition module, a state judgment and mode driving module, a module interface switching control module, a local load modeling module, a multi-channel power optimization module, a strategy execution control module and a storage module. According to the method, a power grid load identification mechanism constructed by historical load statistics and a time period standard deviation model is introduced, and a power boundary threshold value is dynamically set through formula modeling, so that whether a load is in a tension state or not can be judged.

The invention relates to the technical field of electrical load analysis, and discloses an electrical load model modeling method and system based on time-sharing electric quantity. The method comprises the following steps: based on intelligent electric meter time-sharing electric quantity acquisition and equipment start-stop frequency identification, extracting peak-valley normal-period basic load characteristics by adopting a time-sharing electric quantity empirical mode decomposition algorithm, and obtaining equipment start-stop load correlation characteristics by combining a time-sharing variational mode decomposition algorithm with an equipment start-stop state matrix; a time-sharing hyper-parameter optimization algorithm is used to carry out load prediction in each time period, and finally a start-stop frequency influence weight mechanism is established through a time-sharing load state fusion algorithm to obtain a time-sharing electric quantity load modeling result. The method solves the problems that an existing time-sharing electric quantity load modeling method lacks equipment start-stop state sensing, modeling precision between time periods is unbalanced, and time period switching continuity is poor.

The invention discloses a power distribution system generalized comprehensive load modeling method based on a Bayesian estimation theory, and relates to the technical field of power distribution system modeling, and the method comprises the following steps: obtaining a photovoltaic grid-connected sub-model, an energy storage grid-connected sub-model, an induction motor sub-model and a static load sub-model of a power distribution system generalized comprehensive load; modeling the photovoltaic grid-connected sub-model and the energy storage grid-connected sub-model by adopting an RBF (Radial Basis Function) neural network, and modeling the induction motor sub-model and the static load sub-model by adopting a parameter identification method; and based on the modeling result, calculating the probability of occurrence of each sub-model in the generalized comprehensive load by using the Bayesian estimation theory, obtaining the output result of the generalized comprehensive load model of the power distribution system, and completing the modeling of the generalized comprehensive load of the power distribution system based on the Bayesian estimation theory. According to the method, the problem that complex dynamic response caused by photovoltaic and energy storage systems in a power distribution system cannot be processed by a traditional method is solved.

The invention discloses a distributed flexible load modeling method and system for reliability evaluation of a power distribution network, and the method comprises the steps: carrying out the classification of power consumption behaviors of residential users based on a parallel calculation and merging classification method, and converting a classification result into a gray image; constructing a demand curve function set based on multi-information fusion, and generating an image training sample; taking the image training sample as input, and training a nonlinear demand price elasticity evaluation model based on a deep learning method; inputting a grayscale image into the trained model, extracting polynomial representation of a demand curve, and outputting a demand price elastic curve polynomial set; a distributed flexible load model is constructed based on historical data and a demand price elastic curve polynomial set so as to evaluate the electricity consumption of a single user under a given electricity price, the method can be applied to personalized dynamic pricing and power distribution network reliability evaluation models and the like, and powerful support is provided for optimized operation and sustainable development of a power system.

The invention discloses a novel generalized comprehensive load modeling method. The method comprises the following steps of performing dimension reduction processing on power grid load data by utilizing a self-organizing mapping neural network; analyzing the load data of the power grid, and clustering the terminals with similar load characteristics to obtain subnet systems with different load characteristics; using the Elman neural network optimized by the grey wolf algorithm to carry out power system comprehensive load modeling; efficient and accurate modeling of power distribution network side loads is realized, the operation state of the power distribution network is accurately mastered, the observability of the power distribution network is improved, real-time calculation of the power distribution network loads is realized, the problem of limited data measurement configuration of the power distribution network is solved, and the observability of the system is improved; a subnet system formed by a neural network formed by power grid load data with different load characteristics can be obtained; the weight and the value domain of the Elman neural network are optimized through the grey wolf algorithm, so that the precision of the Elman neural network is improved, and the influence of an initial value is reduced.

The application discloses a kind of based on working condition division concentrator electric load modeling and production plan optimization method.The method first defines normal production, low production operation, three production states and corresponding load coefficients of shutdown maintenance, in combination with typical daily load curve, daily random fluctuation factor and seasonal variation factor, construct multi-layer composite annual load refinement model;Again, initial production plan is generated by state coding, and the scattered maintenance day and low production operation day are integrated into continuous period by using centralized scheduling optimization algorithm, to reduce the frequency of production state switching;Finally, the optimized production plan and load model are integrated to generate a 8760-hour refined load sequence throughout the year.The application realizes the deep coupling of production plan and electric load, significantly improves the stability and accuracy of load prediction, provides reliable data support and decision basis for power grid planning, demand side response and enterprise energy efficiency optimization, and is highly adaptable and easy to implement.

The invention relates to a main transformer thermal life prediction method based on multi-element load modeling, and relates to the technical field of transformer thermal life prediction, and the method comprises the steps: obtaining typical load data of a target main transformer; analyzing typical load data based on the multivariate load factors, and carrying out classified modeling to obtain a load mode set; traversing the load mode set, evaluating the physical property adaptation degree of a plurality of load modes, and dividing the load mode set into a first-class load mode group and a second-class load mode group; constructing a multivariate thermal life consumption rate evaluation model based on the first-class load mode group and the second-class load mode group respectively; and acquiring life cycle load data of the target main transformer, and predicting the thermal life of the target main transformer by combining the two load mode groups and the multivariate thermal life consumption rate evaluation model. The problems that in traditional main transformer thermal life prediction, the prediction result is not accurate enough, the applicability is limited, and actual application requirements are difficult to fully meet are solved.

The invention discloses a load modeling method and system for large power grid electromagnetic transient simulation, and belongs to the technical field of large power grid electromagnetic transient simulation. The method comprises the following steps: for a target load power grid, determining the types of basic units of an electromagnetic transient model of the target load power grid, and determining a connection relationship between different basic units; according to different basic units, different modeling modes are used for modeling, unit models corresponding to the different basic units are generated, and the unit models are connected through the connection relation; and obtaining model parameters of the electromagnetic transient model of the target load power grid, and performing parameter setting on the connected unit models according to the model parameters to generate an equivalent electromagnetic transient model of the target load power grid. According to the method, the external characteristics of the load power grid containing high-proportion power electronic equipment are accurately reflected, and compared with an electromagnetic transient simulation model of a detailed load power grid, hardware simulation resources can be effectively reduced, and the simulation efficiency is improved.

The invention discloses a bypass operation drainage wire rapid lap joint tool structure force field analysis method, system and device and a medium, and belongs to the technical field of electrical tool design, and the method comprises the steps: automatically generating a tool geometric model through adaptive multi-resolution three-dimensional modeling, and carrying out local encryption of a grid for a mechanical key area; then integrating a multi-target material performance evaluation algorithm to realize material optimization, and applying a multi-physics coupling load based on a high-fidelity load modeling method; a high-risk area is dynamically corrected through a structure-interface self-correction stress simulation model, and a simulation drive design mechanism is introduced for iterative optimization until the overall performance is optimal; and verifying and solidifying the structure scheme through an extreme working condition inversion test. According to the method, the analysis and prediction precision of the tool under complex working conditions is improved through adaptive multi-resolution modeling and multi-physics field coupling simulation; and in combination with an evolutionary iterative optimization mechanism, collaborative spanning of mechanical, electrical and corrosion-resistant properties is realized, and the structural reliability is enhanced.

The invention discloses a BIM wide area network collaborative deepened design modeling method and system, and relates to the technical field of intelligent building construction design, and the method comprises the steps: collaborative preparation: selecting and defining a collaborative center, and loading a program at least capable of modeling in the collaborative center; determining a network environment to establish a central intranet, performing intranet penetration, and opening the permission of an extranet to access a specified central file; obtaining a central file based on the user behavior, and uploading the central file to a collaboration center; performing working set division on the central file through the loaded modeling program to generate working set data; s2, collaboration: identity information of a worker is acquired, and a matched work task is identified; allocating a work set according to the work task of each worker; and obtaining data updating of any worker on the working set, storing and synchronizing to the collaboration center. The method has the effect of improving convenience and efficiency during cooperative work of building design.

The invention belongs to the technical field of load modeling, and discloses a method, a system, equipment and a medium for identifying traction comprehensive load model parameters, and the method comprises the steps: building a simulation model according with a transient disturbance condition based on a traction load, and simulating and outputting the disturbance data of transient disturbance simulation through the simulation model; establishing a traction comprehensive load model of a three-order induction motor dynamic load model parallel power function static load model based on the disturbance data; and identifying unknown load parameters of the traction comprehensive load model by using an improved sparrow search algorithm, and optimizing the performance of the traction comprehensive load model according to an identification result of the unknown load parameters. According to the method, disturbance data are obtained from a simulation model, a traction comprehensive load model of a three-order induction motor parallel power function is established, and unknown load parameters are identified through an ISSA algorithm, so that an identification result meets a relative error of a dynamic load model.

The invention relates to a wide-load modeling method and device for a supercritical unit, electronic equipment and a storage medium, and the method comprises the steps: collecting the operation data of the supercritical unit, and carrying out the data preprocessing of the operation data, so as to generate data meeting a preset standardization condition; constructing a time sequence data sample of the supercritical unit based on the data meeting the preset standardization condition; constructing an attention residual error double-branch gating circulation unit model for the supercritical unit according to the time sequence data sample; and based on a preset two-stage plan sampling training strategy, training a pre-constructed attention residual error double-branch gating circulation unit model to generate a model autoregression prediction result of the supercritical unit. Therefore, the problems of low autoregressive prediction precision, poor long-term stability and limited generalization ability caused by factors such as exposure deviation and the like of an existing supercritical generator set data driving model under a wide load working condition are solved.

The invention discloses a novel power system load modeling method based on graph calculation, and belongs to the field of power systems. The method comprises the steps that a graph model of a power system of a researched area is built, a directed weighted graph is built according to a real geographical connection relation and load modeling data, nodes in the graph are main transformers or electrical loads of a transformer substation, and the direction of the edge is the power flow direction; performing parallel calculation of a data structure on the graph model, and updating an electrical quantity parameter value of the node; performing community division on the nodes based on the improved modularity; and according to the obtained community reconstruction network, carrying out statistics on various load types and composition proportions of the nodes, obtaining a comprehensive load model of each load node through a weighting method, and further obtaining a comprehensive load model of the transformer substation. According to the method, the structure of the load model can be rapidly summarized in a short time, various load model parameters can be calculated, the accuracy of the load model parameters is improved, and therefore the accuracy of a system simulation result is improved.

The invention relates to a method for realizing a resource adaptive pressure test based on load scaling. The method comprises the following steps: load modeling; constructing a distributed test cluster; service simulation and Mock construction are carried out; load prediction; self-adaptive pressurization is carried out; and evaluating a result. The invention also relates to a system and a device for realizing the resource adaptive pressure test based on load scaling, a processor and a computer readable storage medium thereof. By adopting the method, the system and the device for realizing the resource adaptive pressure test based on the load scaling, the processor and the computer readable storage medium thereof, the resource utilization rate is greatly improved, the test efficiency is greatly improved, manual intervention is reduced through an automatic test process, and the test period is shortened. The environment adaptability is enhanced, and the method has good universality for different types of service systems. The cost effectiveness is outstanding, the dependence on a special high-performance test environment is greatly reduced, the existing low-configuration equipment is fully utilized, and the hardware investment and the maintenance cost are saved.

The invention discloses a main and distribution network fusion method and system based on active power distribution network modular modeling, and the system comprises an active power distribution network modular modeling module which is used for carrying out the topology analysis of a power supply network of an active power distribution network, and automatically generating a detailed power supply network small system simulation model and an equivalent load simulation model of a 220kV transformer substation below a 110kV bus side; the main and distribution network model mapping relation dictionary management module is used for establishing a mapping relation dictionary among a transformer substation name in an offline simulation calculation model adopted by main network simulation, a transformer substation name in an online simulation calculation model adopted by load modeling, a 110 kV / 35 kV transformer substation, a distribution line and a load node name; the main distribution network model fusion module is used for automatically splicing the detailed power supply network small system simulation model or the equivalent load simulation model of the active power distribution network with the main network simulation calculation model; the main and distribution network joint simulation solving module is used for performing main and distribution network integrated joint simulation analysis; and the precision, efficiency and flexibility of joint simulation of the main network and the distribution network are improved.

The present invention proposes a method for predicting the safety performance and lifespan of a new energy vehicle charging pile, including: collecting data from real-time operation monitoring devices of the new energy vehicle charging pile; constructing a degradation model of key components to evaluate the lifespan degradation trend of the current charging pile; adopting a time decay model based on the remaining lifespan of the current charging pile; constructing a group lifespan prediction model to predict the overall remaining lifespan of the charging pile group in the micro-region according to the failure risk probability of the current charging pile; using micro-region environmental data combined with dynamic load characteristics to evaluate the current load optimization strategy; designing a load optimization strategy to maximize the overall lifespan of the charging pile group in the micro-region, and generating an active maintenance plan based on the priority maintenance list. The present invention realizes the full-process closed-loop management from data collection to load modeling, lifespan prediction and active optimization, and provides an efficient and accurate solution for the safety performance and lifespan management of charging piles.

The invention provides a longitudinal assessment and early warning method, device and equipment for high-order thinking ability of students and a medium, and relates to the technical field of education, and the method comprises the steps: improving a DINA model through employing a joint-cross load modeling method, constructing a longitudinal cognitive assessment model suitable for multi-source and multi-frequency features, and carrying out the longitudinal assessment and early warning of the high-order thinking ability of students. Processing the multi-source feature data of the student according to a longitudinal cognitive evaluation model to obtain a longitudinal evaluation result of the high-order thinking ability of the student; and constructing a prediction model suitable for multi-source and multi-frequency features based on a long-short term memory neural network, and processing a longitudinal evaluation result according to the prediction model to obtain a student high-order thinking ability prediction result at a future time point. According to the invention, comprehensive, objective and accurate evaluation and prediction of high-order thinking ability of students can be realized; potential problems can be recognized in advance, educators are helped to adjust teaching strategies in time, and therefore the teaching effect and the learning efficiency of students are remarkably improved.

The application relates to the field of power distribution system simulation, in particular to a distributed power distribution system unified scale load modeling simulation method and system, which comprises the following steps: denoising and fitting a wideband signal to obtain a multiscale signal; obtaining a Hankel matrix time-varying extended model based on the multiscale signal and historical modal parameters and solving to obtain real-time modal parameters; constructing a coupling matrix based on the real-time modal parameters, fitting a fast action layer state equation and a slow action layer state equation by using the coupling matrix to obtain a unified coupling equation; transforming and decoupling the unified coupling equation to obtain a mixed differential-algebraic equation group; and solving the mixed differential-algebraic equation group by using a preset step length switching rule based on a dominant dynamic type to obtain full-time domain simulation results of the distributed power distribution system. The application can effectively solve the problems of time scale fragmentation and insufficient parameter identification accuracy in traditional load modeling.