176 results about "Load model" patented technology

The invention relates to a dual-resource constraint flexible job shop scheduling method for reducing worker load, which comprises the following steps: S1, construction of a worker load model: dividing the worker load model into four conditions of light work, moderate work, micro-severe work and rest according to daily work arrangement of workers, and setting the maximum working time length for each worker, the calculation module is used for calculating extra workloads; in the scheduling method provided by the invention, three different initialization strategies are combined, and the proportion of the initialization strategies in a population is set, so that the diversity and quality of an initial solution are ensured, specifically, the used initialization strategies comprise random initialization, initialization according to the process completion time and initialization according to the process remaining time; under the random initialization strategy, the initial solution of the population has great diversity, which is helpful for avoiding the trouble of a local optimal solution.

The invention relates to the technical field of industrial robot fault diagnosis, and discloses a fault early warning and remote diagnosis method for an industrial robot electrical control system, and the method comprises the steps: obtaining a mechanical load parameter through a potential load model, an inertia load model and a basic dynamics model, obtaining an electrical state parameter through an electrical efficiency model, and carrying out the fault early warning and remote diagnosis. A correction factor is generated through a correction model in combination with the environment temperature, a dynamic current threshold value is calculated through a threshold value model based on the correction factor and a basic current threshold value, and finally health scores and fault early warning information are output through a diagnosis model in combination with the vibration acceleration and servo ring following errors. According to the method, mechanical, electrical and environmental multi-source parameters are fused, dynamic threshold adjustment and multi-dimensional health assessment are realized, normal working conditions and abnormal states are effectively distinguished, the fault early warning accuracy is remarkably improved, the false alarm rate is reduced, and a reliable remote diagnosis scheme is provided for the industrial robot.

The invention discloses a power grid dispatching method in an energy storage participation scene. The method comprises the following steps: establishing a master-slave game system taking an energy storage end of a demand side of a power distribution network as an upper layer and an electrical load end of the demand side as a lower layer, wherein the master-slave game system comprises an upper-layer energy storage model and a lower-layer electrical load model; inputting the scheduling capacity and the scheduling ratio of the energy storage end into the upper-layer model, outputting electric quantity transmission cost parameters of the energy storage end and the electric load end, inputting the electric quantity transmission cost parameters into the lower-layer model, outputting the transmission electric quantity of the energy storage end and the electric load end after processing, and inputting the transmission electric quantity into the upper-layer model for circulation until a variable reaches a steady-state value, and obtaining the scheduling amount to realize the scheduling of the power grid. According to the method, dynamic collaborative optimization of energy storage and power utilization load side resources can be realized, the problems of low resource utilization rate and insufficient model precision in the prior art are solved, the overall optimization and scheduling flexibility of an energy system is remarkably improved, and the technical blank in the field of collaborative optimization and dynamic interaction in the prior art is filled.

The invention relates to the technical field of engineering monitoring, in particular to a bridge structure state monitoring method and system based on digital twinning, and the method comprises the steps: building a multi-source monitoring data matrix based on a data timestamp; establishing a geometric model, a material attribute model and a load model of a bridge structure based on the multi-source monitoring data matrix, and fusing to obtain a bridge digital twin initial model; and carrying out structural mechanical analysis based on the bridge digital twin initial model to obtain stress distribution data of each region of the bridge. Through real-time acquisition and analysis of multi-source monitoring data, the method can dynamically evaluate the structural state of the bridge and timely identify potential safety hazards, so that the safety of the bridge is improved; and through calibration of the initial model, the obtained digital twin precise model reflects the real state of the bridge, and a more accurate data basis is provided for subsequent structural analysis.

The invention discloses a power distribution network operation parameter optimization identification method based on optimal power flow calculation, and relates to the technical field of power distribution systems, and the method comprises the steps: collecting historical operation parameters of each node of a power distribution network, screening to obtain a data collection priority of an optimal power flow output index, and reasonably distributing node sampling frequencies according to the data collection priority; measuring and collecting operation parameters of the power distribution network according to the distributed sampling frequency, carrying out pre-detection on the collected data, and meanwhile, carrying out adaptive anomaly judgment and sampling frequency dynamic adjustment and carrying out multi-scale decomposition and sensitivity analysis on node residual errors, so as to realize optimal power flow calculation time window adjustment driven by dynamic parameters; and the load model parameters are verified to ensure the accuracy and reliability of the parameter identification result. Through combination of historical data, real-time measurement and residual sensitivity information, transient disturbance, medium-term fluctuation and long-term trend can be considered in the operation parameter identification of the power distribution network, and the online precision of a load model and line parameters is improved.

The invention discloses a source load impact and system bearing life evaluation method and system, equipment and a medium, and relates to the technical field of traction power supply, and the method comprises the steps: collecting data to recognize impact, building a source load model, analyzing an impact diffusion path, evaluating system bearing, fusing multi-dimensional damage, calculating the residual life of the equipment, and automatically updating parameters. And generating a closed-loop regulation and control strategy. The system comprises a data analysis and dynamic modeling module, a bearing evaluation and conduction analysis module, a damage coupling and life prediction module and a parameter correction and strategy generation module. The traction impact time domain-frequency domain-topology integrated quantization is realized, and the problem of impact time-space diffusion assessment is solved. A life model is constructed, and multi-dimensional stress damage is accurately mapped. The micro-simulation and on-line filtering technology is utilized to ensure that model parameters are adaptively updated along with working conditions, and the problem of disjunction between evaluation and actual conditions is solved. The method supports side cloud cooperation and closed-loop pre-control, effectively improves the safety margin of the power grid, and delays the aging of equipment.

The invention belongs to the technical field of electromagnetic detection, and relates to an electromagnetic detection multi-level current emission system for a plateau environment and a control method. The control method comprises the steps of calculating load model impedance; analyzing an MFPWM control process, and determining an optimal control parameter by using an optimization target of transmitting spectrum intensity maximization; and MFPWM optimization control: realizing directional power distribution and independent regulation and control of each frequency component by adjusting the frequency and amplitude of each modulation signal component. A multi-level topological structure is adopted, high output voltage is achieved while switching voltage stress is reduced, and the problems that cost rises and system complexity is too high due to the fact that a traditional H-bridge inverter depends on high power supply voltage are solved; by matching with multi-frequency pulse width modulation optimization control, spectrum energy can be flexibly configured, high-frequency components of transmitted signals are enhanced, and the spectrum energy utilization rate and the signal-to-noise ratio are improved; grounding impedance changes in a high dynamic range can be accurately tracked, and the requirements of deep geological electromagnetic detection in an extreme environment are met.

The invention relates to the technical field of smart power grids, in particular to a detection signal design method and device for multi-injection-point and multi-load-node identification, and the method comprises the steps: calculating the static sensitivity of a plurality of to-be-identified load nodes corresponding to each target equipment injection node in a plurality of target equipment injection nodes based on steady-state operation data, establishing a detection signal optimization model taking a reactive detection signal as a decision variable by taking the total minimum load model parameter identification error of a plurality of to-be-identified load nodes as a target and combining static sensitivity, and introducing reactive energy constraints and signal structure constraints of a plurality of target equipment injection nodes and solving; generating an optimal reactive power detection signal waveform of each target equipment injection node, performing evaluation by using a target evaluation index, when an evaluation result meets a preset constraint condition, injecting the optimal reactive power detection signal waveform into the system, and after a response is generated, performing identification on a load model parameter by using the response, and obtaining an identification result of each load node to be identified.

The invention relates to the technical field of offshore wind power engineering, in particular to a collaborative optimization design method and system for an offshore wind turbine generator base, and the method comprises the steps: building an environment load model based on target sea area data, and generating a load working condition; a parameterized fan-base coupling model is constructed; aiming at minimizing the leveling cost per kilowatt-hour, and taking the structural strength and the inherent frequency as constraints; fan and base design variables are synchronously adjusted through multidisciplinary collaborative optimization, and candidate schemes are output; establishing a local high-precision model for the selected scheme to perform fatigue life verification, and if not, performing feedback optimization until a final scheme is obtained; and finally, based on the scheme, deploying the sensor and initializing the digital twin, realizing life prediction and graded early warning, and forming full life cycle closed-loop management.

The invention discloses a reliability evaluation method for a power distribution network containing network construction type energy storage and flexible resources, and the method comprises the steps: firstly constructing a wind power and photovoltaic combined output probability distribution model considering the output uncertainty and spatial-temporal correlation of new energy, and a typical load scene; then establishing a'operation-fault 'two-state shutdown model, and generating a system operation state through sequential Monte Carlo simulation; simulating the power distribution network according to the fault information, and analyzing the power output characteristics of the constructed network type energy storage and the voltage support effect of the constructed network type energy storage on the near-area distributed new energy; and finally, constructing an optimal load shedding model considering the network-forming type energy storage power support effect, calculating the fault consequence of the system, and calculating the reliability index of the system according to the fault consequence. According to the method, the improvement effect of network construction type energy storage on the reliability of the power distribution network is analyzed; and meanwhile, the accurate reliability evaluation capability is still kept in a high-permeability scene of the distributed power supply, and a theoretical support is provided for network construction type energy storage planning investment for improving the reliability of the power distribution network.

The invention discloses a coagulation and bipolar coagulation integrated control system for a surgical operating instrument, and relates to the field of medical equipment. The system comprises an interface module, an energy detection module, a storage module, a load model matching module and an energy output control module. According to the system, an energy detection signal is output to a surgical instrument before energy output, and a real-time biological impedance spectrum of a target object is obtained; determining a target model from pre-stored electrical load models according to the impedance spectrum, and calling a corresponding adaptive energy output strategy; and in the energy output process, the impedance change of the target object is monitored, and the energy parameters are dynamically adjusted, so that the energy output is automatically stopped after the impedance change conforms to the preset stable target state. The self-adaptive precise energy control based on the real-time state of the tissue is realized, the problems of tissue carbonization, adhesion or incomplete coagulation and the like are effectively avoided, and the operation safety and effect are improved.

The invention is suitable for the technical field of traveling block hook testing, and provides a testing device for a traveling block hook and a testing method thereof.The testing device comprises a mounting assembly used for providing rigid support for the whole device and a testing assembly arranged on the mounting assembly, the test assembly is used for applying a simulation load to the hook assembly of the traveling block, the detection assembly is arranged on the mounting assembly and is used for carrying out image acquisition on the hook assembly of the traveling block, and the test assembly comprises a clamping unit and a load simulation unit which are oppositely arranged from top to bottom. The clamping unit comprises a suspension oil cylinder, a first hinge structure and a fixing seat, the load simulation unit comprises a load oil cylinder and a second hinge structure, a friction effect equivalent load is introduced through an equivalent load model, the defect that the friction force of a drilling tool is neglected in an existing test is overcome, the test load better fits the actual drilling working condition, and the test efficiency is improved. The accuracy of a detection result is improved.

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 an industrial and commercial time-of-use electricity price dynamic adjustment model considering an electric power spot market. The method comprises the steps of obtaining a historical load curve of a preset category; dividing the historical load curve into a first time period group based on a first preset quarter and a second time period group based on a second preset quarter based on a hierarchical clustering algorithm and by combining a load weighted Euclidean distance and a Ward connection method according to the historical load curve; obtaining a clustered load curve according to the first time period group and the second time period group; determining load characteristics according to the clustered load curve; adjusting a load elasticity coefficient according to the load characteristics and historical load data; constructing an optimized load model according to the load elastic coefficient; inputting the optimized load model into a single-target artificial hummingbird algorithm, and determining the electricity price of each time period; and determining a final electricity price scheme according to the electricity price of each time period. The invention solves the problem of insufficient seasonal load adaptability of the current power system.

An electric power and electric quantity balancing method for a clean energy base containing a high-energy load comprises the steps that firstly, the fluctuation rule of the high-energy load is recognized based on the historical operation condition of the high-energy load, and a high-energy load model is established; secondly, according to the operation mechanism of the clean energy base, establishing an output model for each new energy unit in the base, and predicting the output and load demand of the new energy units in combination with weather forecast and historical data; then, according to the characteristics of a high-energy load and a conventional load, establishing a load management system, introducing a demand response mechanism, constructing a flexible scheduling resource platform considering different time scales by relying on the load management system and an adjustable power supply in a base, and setting priorities and adjusting costs for all adjustable resources in the flexible scheduling resource platform; and different adjustment resources can be reasonably selected in the scheduling process of the power grid. And finally, establishing a high-energy load-containing optimal scheduling model considering electric power and electric quantity balance, and ensuring that the electric power and electric quantity balance on the two sides of the source load can be realized in the scheduling of the power grid on different time scales.

The invention relates to a load model parameter identification method and system based on sample enhancement and reinforcement learning and a medium, and provides the load model parameter identification method based on sample enhancement and reinforcement learning for the problems in the prior art, and the method comprises the following steps: preprocessing transient data of a power system, and obtaining a preprocessed sample; inputting the preprocessed sample into a generative adversarial network for adversarial training to generate a new sample set; constructing a load parameter identification model based on a TD3 reinforcement learning algorithm, and importing the new sample set into the load parameter identification model for training to obtain a trained data model; and inputting online real-time data in the power system into the trained data model to output model parameters, and taking an output result as a final result of parameter identification. According to the invention, data enhancement is carried out by using the GAN, high-quality load data is generated, and the problem of insufficient samples is relieved. And the parameter identification process is optimized through reinforcement learning, so that the model can be dynamically adjusted, local optimum is avoided, and the identification precision is improved.

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 present disclosure provides a wind farm power dispatching method, a control device and a new energy station. The method comprises: generating power generation power and load models of each wind turbine in a wind farm under a plurality of state spaces according to design information of each wind turbine in the wind farm, wherein the design information comprises wind parameter information and a preset service life of each wind turbine; predicting power generation power and residual life of each wind turbine under a real-time state space according to real-time environmental data, real-time state data, the preset service life and the power generation power and load models under the plurality of state spaces; and adjusting an operation mode of each wind turbine according to the power generation power and the residual life of each wind turbine under the real-time state space and income-related data of the wind farm, so as to achieve a predetermined income target of the wind farm in a life cycle.

The invention provides a third industry user-oriented load adjustable characteristic evaluation and interactive regulation and control method, and aims to improve the demand side resource adjustable capability and support supply and demand coordinated regulation and control under a novel power system. The method comprises the following steps: firstly, collecting typical three-product user loads and multi-source heterogeneous data, after preprocessing and feature extraction, realizing feature dimension reduction by adopting kernel principal component analysis, and completing typical user load clustering through a K-Means clustering algorithm; and secondly, constructing a multi-dimensional user portrait evaluation system based on a clustering result, and quantitatively evaluating the adjustable potential of the typical user load in combination with a multi-dimensional time sequence load model. And finally, establishing a multi-constraint optimization scheduling model, introducing a particle swarm optimization algorithm for solving, and outputting targeted regulation and control strategies and regulation instructions of different time periods and different devices. According to the method, refined evaluation and rapid scheduling optimization of the three-production user load adjustable potential are effectively realized.

The invention relates to the field of energy scheduling, in particular to an off-network state-oriented service area integrated energy system energy scheduling method, which comprises the following steps of: acquiring load operation data of a service area in an off-network state, and establishing a load model of a road service area integrated energy system; obtaining a new energy supply type of the service area, and constructing various energy supply equipment models of the service area in an off-grid state so as to establish an energy supply model of the road service area integrated energy system; constructing a single-target optimization scheduling model; and performing a whale optimization algorithm on the single-target optimization scheduling model, and performing optimization according to a penalty term to obtain an energy scheduling scheme. According to the service area integrated energy system energy scheduling method oriented to the off-network state, power supply guarantee of the key load is maximized, normal operation of equipment with high priority level in the off-network state of the road service area is guaranteed, and the overall energy utilization efficiency is improved.

The invention discloses a power electronic load-containing load equivalence and parameter identification method, which comprises the following steps of: preprocessing original power load data, and extracting data required by subsequent parameter identification; determining a load model structure, and performing aggregation equivalence on the power distribution network, the static load, the dynamic load and the power electronic load according to a principle that system response before and after aggregation remains unchanged; the method comprises the following steps: firstly, constructing a load model, then determining parameters, needing to be identified, of each type of load model, distinguishing dominant parameters and non-dominant parameters by adopting trajectory sensitivity, setting values of the non-dominant parameters as typical values, then identifying the dominant parameters through an improved quantum particle swarm algorithm, determining the values of the dominant parameters, and finally, performing accuracy evaluation on the established load model. According to the invention, a more accurate load model can be established for a modern power system containing a power electronic load, the simulation precision of the power system is improved, and the analysis and control of a power department on the power system are facilitated.

This invention discloses a method for reliability assessment of distribution networks involving integrated energy parks in a market environment, belonging to the field of distribution network reliability assessment technology. The method includes: constructing constraints on multi-energy conversion equipment within the integrated energy park and quantity-price elasticity curves for end-user electricity in the integrated energy park; based on the constructed constraints on the multi-energy conversion equipment and the quantity-price elasticity curves for end-user electricity, changing the external electricity purchase price of the integrated energy park to obtain the integrated elastic quantity-price curve of the integrated energy park; constructing a two-state model of the probability distribution function of the electricity sales price and component failures according to the electricity sales price and the faults occurring in the distribution network, and generating typical distribution network operation scenarios; and simulating operation using an optimal load reduction model of the distribution network based on the electricity sales price and the electricity consumption of the integrated energy park, outputting evaluation indicators. The method described in this invention significantly improves the accuracy of the load model and achieves a complete and closed-loop quantitative reliability assessment.

This invention discloses a planning method and system for a hydrogen-containing integrated energy system. The method includes: constructing equipment instance models of various energy types in the system by configuring parameters; wherein the equipment includes electrolyzer equipment; constructing a load model in the system by configuring the load demand of the area to be planned, the load demand supporting configuration at multiple time scales; obtaining the energy price model of the area to be planned and completing the energy price configuration; constructing a system topology by graphically associating the equipment instance models, load models, and energy price models through a visual configuration interface, and configuring constraint rules on the system topology; wherein the constraint rules include start-up and shutdown optimization constraints for the electrolyzer; transforming the system topology with configured constraint rules into a mixed-integer linear programming model and performing optimization to obtain the configuration scheme and operation strategy of each device in the system. This invention solves the problems of insufficient characterization of equipment operating losses and low modeling efficiency in existing planning methods by introducing refined electrolyzer start-up and shutdown constraints and a visual modeling process, thus achieving efficient and accurate planning for hydrogen-containing integrated energy systems.

The application relates to a tunnel lining trolley framework deformation monitoring method and system, and belongs to the technical field of tunnel construction monitoring. The method comprises the following steps: determining key monitoring positions of a tunnel lining trolley framework and arranging sensors; arranging optical fiber strain sensors and displacement sensors; obtaining real-time structure parameters and pouring working condition parameters of the tunnel lining trolley based on the arranged sensor system; constructing a tunnel lining trolley framework mechanical model; correcting a load model and establishing a tunnel lining trolley framework moment equation; collecting strain data under each working condition, fitting unknown loads, and calculating tunnel lining trolley framework deformation values and stress values based on a deflection curve differential equation; and setting an early warning mechanism based on preset stress threshold values and deformation threshold values to realize real-time monitoring of tunnel lining trolley framework deformation. The application can improve the real-time performance and accuracy of tunnel lining trolley framework deformation monitoring.

The application provides an evaluation method based on an operation reliability intelligent evaluation model and related devices. System data of a power system is obtained, and after a minimum load shedding model is established according to the system data of the power system, the inherent mode of the minimum load shedding model is determined, and a robust learning loss function is constructed based on the inherent mode of the minimum load shedding model. Finally, strong generalization learning is performed according to the minimum load shedding neural network, the robust learning loss function and load disturbance, and an operation reliability intelligent evaluation model is obtained. The system data of the power system is input into the operation reliability intelligent evaluation model, and a result capable of evaluating the operation reliability of the system can be obtained, effectively improving the efficiency and accuracy of the evaluation result.

The application discloses a kind of cylinder spinning machine detection control method and system, belong to spinning machine technical field, solve when high-precision thin-walled part batch production is carried out after spinning machine long time work, the dynamic gap change caused by the static compensation algorithm of numerical control system because unable to match guide rail wear, and the illusion of no-load detection qualified covers the commutation point trajectory step and force-position coupling effect caused by insufficient compensation under heavy load, finally lead to the problem of part because micron level profile distortion and scrap.By real-time acquisition of feed shaft operation data and observation external load force, residual gap error is captured when detecting commutation event, wear-load model parameters are identified online using the error, and compensation value is dynamically calculated based on the identification results and current load to correct the position command at commutation.Finally, the compensation effect is evaluated and new data is fed back to the model to realize adaptive closed-loop control.

The invention discloses an enterprise energy consumption management method based on data fluctuation load analysis. The method comprises the following steps: collecting multi-dimensional real-time operation data of a power enterprise production system; in a sliding time window, extracting fluctuation characteristics such as standard deviation, kurtosis and spectral entropy from the time sequence data of the key parameters, and converting the fluctuation characteristics into fluctuation characteristic vectors; constructing a data fluctuation load model, and learning a non-linear relationship between fluctuation feature vectors and system energy efficiency and carbon emission indexes; on the basis of a model prediction result, a fluctuation load index (FLBI) is calculated and used for quantitatively evaluating the health bearing capacity of the system for operation fluctuation; and finally, according to the FLBI value and the change trend thereof, proactive sub-health early warning, root cause diagnosis and accurate optimization control suggestions are generated. According to the invention, the information is used as core information for analyzing the dynamic health degree of the system, and the transformation from post-event alarm to pre-event early warning is realized.

The application discloses a two-stage distribution robust planning method of a comprehensive energy system considering flexibility, relates to the technical field of comprehensive energy system optimization planning and intelligent decision-making, and comprises the following steps: constructing a comprehensive energy system equipment and multi-network coupling model; establishing a random source and load model of photovoltaic available output, wind power available output, and electric load, heat load and gas load; extracting covariant information corresponding to a target scene, and constructing a conditional experience distribution and a Wasserstein conditional distribution fuzzy set based on historical error samples; establishing a two-stage distribution robust optimization model of one-stage capacity configuration and two-stage operation scheduling, which takes the minimum of annualized investment cost and expected operation cost under the worst distribution as a target and contains flexibility margin constraints; and obtaining an optimal capacity configuration scheme of the comprehensive energy system through a model reconstruction and decomposition algorithm. Compared with the prior art, the application can improve the flexibility, robustness and economy of the planning result of the comprehensive energy system under uncertain operation conditions.

The invention relates to a capacity evaluation method and system applied to grid connection of a new energy microgrid. The method comprises the following steps: firstly, describing source load uncertainty; determining constraint conditions of the power distribution network layer and the micro-grid layer by considering actual conditions of the micro-grid and the power distribution network; determining a target function of load capacity evaluation by combining constraint conditions of the micro-grid layer and the power distribution network layer and taking the optimal performance as an index; randomly sampling historical data and a probability source-load model by using Monte Carlo, and generating supply and demand scenes in batches; and 3, carrying out model capacity calculation on a supply and demand scene generated by Monte Carlo by applying an improved differential evolution and particle swarm hybrid algorithm, solving an optimal solution of the target function in the step 3, and determining the actual trusteeship capacity according to the minimum capacity under all conditions. The invention provides a comprehensive trusteeship capability evaluation method for integrating distributed renewable energy sources into a power distribution network through a micro-grid. By simultaneously considering internal micro-grid constraints, external power distribution network limitations and source-load uncertainty, the proposed model can effectively capture coupling dynamics among multiple system hierarchies.

The invention discloses a power system aggregation IBR and load parameter analysis method and device and a medium, and belongs to the technical field of power systems. The method comprises the following steps: acquiring power system operation data; identifying change modes of the operation data of the power system, and calculating voltage sensitivity values of the pre-constructed composite IBR and ZIP load models in each change mode; taking the deviation of the voltage sensitivity value as a steady-state index, and extracting a power system operation data window with a stable change mode from the power system operation data; and taking a power system operation data window with a stable change mode as a quasi-steady-state window, and performing parameter analysis on the composite IBR and ZIP load model according to the quasi-steady-state window to obtain a parameter analysis result. According to the method, the voltage-related behaviors of the load and the capacity of the adaptive mode to adapt to the IBR control mode change can be considered, and errors in parameter analysis are avoided.