Electromechanical transient simulation model parameter adaptive correction method and device

Abstract

The invention provides an electromechanical transient simulation model parameter adaptive correction method and device, and relates to the field of power system simulation. The method comprises the following steps: acquiring response data of a target power system in different operation states, and constructing an electromechanical transient observation response data set based on the response data; inputting the electromechanical transient observation response data set and the current electromechanical transient simulation model into a simulation engine to execute simulation operation, and generating a simulation response sequence corresponding to the disturbance process; performing difference analysis on the electromechanical transient observation response data set and the simulation response sequence, and constructing a target function for parameter correction based on a difference analysis result; based on the structure information of the current electromechanical transient simulation model, defining a corresponding to-be-corrected parameter set; and performing iterative optimization on the to-be-corrected parameter set based on the target function, and outputting a target corrected parameter set meeting an iteration condition. According to the method, the problem of low accuracy of model parameter correction in a dynamic scene in a traditional method is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system simulation, and in particular to a method and device for adaptive correction of parameters of an electromechanical transient simulation model. BACKGROUND

[0002] Nowadays, electromechanical transient simulation models, as the core tool for simulating the dynamic behavior of generators, grid components and control devices under electrical disturbances in power system analysis, are widely used in transient stability assessment, fault evolution deduction, control strategy verification and grid safety constraint checking. However, the continuous evolution of power system structure, dynamic adjustment of control strategy and continuous improvement of new energy penetration in actual operation lead to deviation between parameter configuration in the model and real system characteristics, and the conventional static modeling method cannot accurately reflect the real response process under disturbance, resulting in distorted simulation results and biased control judgment.

[0003] The existing method is to perform offline fitting correction on the static parameter set in the electromechanical transient model based on generalized least squares (GLS). This method minimizes the mean square error between the observed value and the simulation output, to some extent, improving the response consistency of the model under small disturbance conditions. However, in dynamic scenarios with high disturbance intensity, complex control path and strong frequency-voltage coupling, since this method cannot accurately reflect the dynamic response relationship between the electromagnetic torque of the generator and the active power exchange of the grid, there is a significant deviation between the frequency decline slope and the system inertia constant, which seriously affects the accuracy of model parameter correction in dynamic scenarios.

[0004] Therefore, there is an urgent need for a method and device for adaptive correction of parameters of an electromechanical transient simulation model. SUMMARY

[0005] The present application provides a method and device for adaptive correction of parameters of an electromechanical transient simulation model, which solves the problem that the existing method cannot accurately reflect the dynamic response relationship between the electromagnetic torque of the generator and the active power exchange of the grid, resulting in a significant deviation between the frequency decline slope and the system inertia constant, which seriously affects the accuracy of model parameter correction in dynamic scenarios.

[0006] The first aspect of the present application provides a method for adaptive correction of electromechanical transient simulation model parameters, the method comprising: obtaining response data of a target power system under different operating states, and constructing an electromechanical transient observation response data set based on the response data; inputting the electromechanical transient observation response data set and a current electromechanical transient simulation model into a simulation engine to perform simulation operation, and generating a simulation response sequence corresponding to the disturbance process; performing difference analysis on the electromechanical transient observation response data set and the simulation response sequence, and constructing a target function for parameter correction based on the difference analysis result; defining a corresponding parameter set to be corrected based on the structure information of the current electromechanical transient simulation model; performing iterative optimization on the parameter set to be corrected based on the target function, and outputting a target correction parameter set that meets the iteration condition.

[0007] Optionally, the response data of the target power system under different operating states is obtained, and an electromechanical transient observation response data set is constructed based on the response data: the response data corresponding to the steady-state operating state and the disturbance evolution operating state of the target power system is obtained; based on the response data, a time-consistent electromechanical transient observation response data set is constructed, which includes generator voltage data, grid-side bus voltage data, and transient response curve data.

[0008] Optionally, the electromechanical transient observation response data set and the current electromechanical transient simulation model are input into the simulation engine to perform simulation operation, and a simulation response sequence corresponding to the disturbance process is generated, specifically including: solving the transient response process of each electromechanical transient dynamic component in the target power system by numerical integration; based on the transient response process, a simulation response sequence corresponding to the disturbance process is generated, and the simulation response sequence is consistent with the electromechanical transient observation response data set in variable dimension.

[0009] Optionally, the electromechanical transient observation response data set and the simulation response sequence are subjected to difference analysis, and a target function for parameter correction is constructed based on the difference analysis result, specifically including: difference analysis is performed on the electromechanical transient observation response data set and the simulation response sequence, and a response residual time sequence is constructed, which is used to represent the transient response deviation characteristics of the electromechanical transient observation response data set and the simulation response sequence under the unified time scale; a target function with a weighting strategy is constructed based on the response residual time sequence, and the target function is used to measure the weighted aggregation degree of simulation errors in different response stages.

[0010] Optionally, based on the structure information of the current electromechanical transient simulation model, a corresponding to-be-corrected parameter set is defined, specifically including: obtaining the structure information of the current electromechanical transient simulation model based on the model parameters configured in the electromechanical transient simulation model, the structure information including generator electrical model parameter information, power grid topology and equivalent impedance information, and disturbance boundary and controller configuration information; based on the structure information, the to-be-corrected parameter set is defined by considering the influence of each to-be-corrected parameter on the transient response sensitivity, the physical coupling relationship between the parameters in the model structure, and the identifiability of the disturbance driving path.

[0011] Optionally, the to-be-corrected parameter set is iteratively optimized based on the objective function, and a target corrected parameter set satisfying an iteration condition is output, specifically including: inputting the to-be-corrected parameter set and the objective function into a Bayesian optimization engine to construct a Gaussian process proxy model; iteratively optimizing the to-be-corrected parameter set based on the Gaussian process proxy model, and outputting a first corrected electromechanical transient simulation model parameter set; based on the structural consistency constraint, performing physical consistency evaluation on the target power system through the first corrected electromechanical transient simulation model parameter set; based on the physical consistency evaluation, the first corrected electromechanical transient simulation model parameter set is modified to a second corrected electromechanical transient simulation model parameter set by introducing a multi-model consistency constraint; the second corrected electromechanical transient simulation model parameter set is re-input into the simulation engine to perform simulation operation, generating an updated simulation response sequence; the electromechanical transient observation response data set and the updated simulation response sequence are analyzed for differences, and the mean square error change rate before and after the current round of correction is calculated; based on the mean square error change rate and the maximum number of iterations, it is judged whether the second corrected electromechanical transient simulation model parameter set satisfies the convergence standard; if the second corrected electromechanical transient simulation model parameter set satisfies the convergence standard, the second corrected electromechanical transient simulation model parameter set is output as the target corrected parameter set.

[0012] Optionally, before inputting the to-be-corrected parameter set and the objective function into the Bayesian optimization engine to construct the Gaussian process proxy model, the method further includes: constructing a structural consistency constraint, the structural consistency constraint including a response symmetry constraint based on power changes between generator units, a node voltage dynamic range constraint based on voltage extreme intervals, and an inertia consistency constraint based on frequency response trends; constructing a multi-model consistency constraint, the multi-model consistency constraint including a consistency constraint on the output results of the same disturbance under different simulation model structures, a response trend coordination constraint based on an equivalent controller model, and a dynamic residual matching constraint combined with historical disturbance data.

[0013] In a second aspect of the present application, an electromechanical transient simulation model parameter adaptive correction device is provided, the device comprising an acquisition module and a processing module, wherein, The acquisition module is configured to acquire response data of the target power system under different operating states, and construct an electromechanical transient observation response data set based on the response data.

[0014] The processing module is configured to input the electromechanical transient observation response data set and the current electromechanical transient simulation model into a simulation engine to perform simulation operation, generate a simulation response sequence corresponding to the disturbance process, perform difference analysis on the electromechanical transient observation response data set and the simulation response sequence, and construct a target function for parameter correction based on the difference analysis result.

[0015] In a third aspect of the present application, an electronic device is provided, which includes a processor, a memory, a user interface and a network interface, the memory is configured to store instructions, the user interface and the network interface are configured to communicate with other devices, and the processor is configured to execute the instructions stored in the memory to enable the electronic device to perform the method of any one of the above aspects.

[0016] In a fourth aspect of the present application, a computer readable storage medium is provided, which stores a computer program, and the computer program is configured to enable a processor to perform the method of any one of the above aspects.

[0017] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By constructing the electromechanical transient observation response data set, performing difference analysis on the response sequence output by the current simulation model, constructing the weighted residual target function, extracting the parameter set in combination with the model structure information, performing parameter update based on the iterative mechanism such as Bayesian optimization, and outputting the corrected parameter set after convergence, the method realizes dynamic matching of the model parameters to the actual disturbance response, improves the consistency of the simulation results and the measured data, enhances the precision and physical credibility of the model in the strong disturbance scenario, and provides more reliable support for transient stability analysis and control decision.

[0018] 2. By comparing the electromechanical transient observation response data set and the simulation response sequence, constructing the response residual time sequence to depict the transient deviation characteristics thereof under the unified time scale, further introducing the stage weighting strategy to construct the target function, quantifying the aggregation degree of the error in different response stages, and then guiding the parameter optimization process, the method realizes fine identification and dynamic weight control of the response deviation in the whole disturbance process, significantly improves the pertinence and convergence efficiency of the parameter correction, and enhances the fitting ability and dynamic consistency of the model to the complex disturbance behavior.

[0019] 3, input the to-be-corrected parameter set and the objective function into the Bayesian optimization engine, construct a Gaussian process proxy model and perform iterative optimization to obtain a first corrected parameter set; introduce structure consistency and multi-model consistency constraints, perform physical evaluation and collaborative correction on the first corrected parameter set to obtain a second corrected parameter set; based on the parameter set, re-perform simulation, calculate the mean square error change rate before and after correction, and combine the number of iterations to determine whether the convergence condition is met, and finally output the target corrected parameter set meeting the convergence standard, so as to balance between simulation accuracy and physical consistency through joint optimization, physical verification and multi-model constraints, and effectively improve the stability fitting capability and dynamic response credibility of the model. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a flowchart of a method for adaptive correction of electromechanical transient simulation model parameters provided by an embodiment of the present application; Figure 2 is a module schematic diagram of an adaptive correction device for electromechanical transient simulation model parameters provided by an embodiment of the present application; Figure 3 is a structural schematic diagram of an electronic device provided by an embodiment of the present application.

[0021] Legend: 21, acquisition module; 22, processing module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0022] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be clearly and completely described below in conjunction with the drawings in the embodiments of the specification. Obviously, the described embodiments are only some of the embodiments of the present application, not all embodiments.

[0023] The terms used in the following embodiments of the present application are only for the purpose of describing the specific embodiments, and are not intended to be limiting to the present application. As used in the specification of the present application, the singular expression "one", "a", "said", "the above", "the" and "this" are intended to also include the plural expression, unless there is clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application means and includes any or all possible combinations of one or more listed items.

[0024] Hereinafter, the terms "first", "second", "third", "fourth", "fifth", "sixth", "seventh" and "eighth" are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implying the number of the indicated technical features. Therefore, the features defined with "first", "second", "third", "fourth", "fifth", "sixth", "seventh" and "eighth" can explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise specified.

[0025] In order for those skilled in the art to better understand the technical solutions of the present application, the present application will be further described in detail below with reference to the drawings.

[0026] Please refer to Figure 1 which shows a flowchart of a method for adaptive correction of electromechanical transient simulation model parameters provided by the embodiments of the present application, and the flowchart mainly includes the following steps: S101 to S105.

[0027] Step S101, obtaining response data of a target power system under different operating states, and constructing an electromechanical transient observation response data set based on the response data.

[0028] Specifically, in the receiving end regional power grid with high proportion of new energy access, due to the significant decrease in system inertia and the high sensitivity of power electronic interface equipment to transient disturbance, the traditional electromechanical transient simulation model based on static parameter configuration has been difficult to truly restore the system dynamic behavior under large disturbance, and cannot accurately evaluate the disturbance propagation path and local control response characteristics, which is easy to lead to misjudgment of stability margin or failure of regulation scheme. The present application is suitable for the high-precision model dynamic correction demand based on disturbance process in the above-mentioned scenario, which can dynamically correct the model parameters through the measured response information, improve the simulation accuracy and evaluation reliability, and thus provide accurate support for online regulation strategy. Based on the above purpose, the embodiments in the present application first obtain the typical response data of the target power system under the steady-state operating state and the disturbance evolution state through the real-time monitoring system or the event recording device of the dispatching center, including the time series of physical quantities such as generator voltage, bus voltage, current and frequency, and perform time series alignment, variable classification and format standardization processing on the response data, to construct an electromechanical transient observation response data set with unified time reference and clear structure as the reference input in the subsequent parameter correction process.

[0029] In one possible implementation, step S101 further includes: obtaining response data corresponding to the target power system under the steady-state operating state and the disturbance evolution operating state, respectively; based on the response data, constructing an electromechanical transient observation response data set with consistent time sequence, the electromechanical transient observation response data set including generator voltage data, grid-side bus voltage data and transient response curve data.

[0030] Specifically, multi-point synchronous measurement data of the target power system in a steady-state operating state is acquired, the steady-state data being used to represent the stable electrical operating state of each generator unit and key bus before a disturbance, including basic parameters such as steady-state voltage amplitude, frequency value, and active and reactive power; secondly, after a disturbance event is triggered, response data in a disturbance evolution operating state is acquired through a wide-area measurement system, a synchronous phasor measurement device, or a disturbance recording system, the disturbance data including a generator terminal voltage response sequence, a power grid side bus voltage change trajectory, a key channel current response sequence, and a frequency fluctuation process curve, etc., to ensure time stamp consistency and synchronization.

[0031] In the process of constructing the time-consistent electromechanical transient observation response data set, first, a unified time reference framework is established, linear interpolation or synchronization alignment processing is performed according to the time stamps of each response data, to ensure that all response variables have corresponding values on the same time sequence; then, the response variables of different sources are uniformly mapped to a standard variable set, including but not limited to: generator voltage data, power grid side bus voltage data, transient response curve data, etc. Among them, the generator voltage data refers to the voltage change sequence of the generator terminal during the disturbance, reflecting its direct electrical response to the system transient fluctuation; the power grid side bus voltage data includes the voltage dynamic change of multiple key nodes during the disturbance process, for representing voltage stability and system coupling characteristics; the transient response curve data is the complete response trajectory of each physical variable in the whole disturbance process, including but not limited to current response curve, frequency response curve, and node voltage evolution trajectory, for comprehensively reflecting the overall dynamic response mode of the system.

[0032] Step S102, inputting the electromechanical transient observation response data set and the current electromechanical transient simulation model to a simulation engine to perform simulation operation, to generate a simulation response sequence corresponding to the disturbance process.

[0033] Specifically, the electromechanical transient observation response data set constructed in step S101 is input as an external comparison benchmark, and the structure configuration, initial parameters, and controller settings contained in the current electromechanical transient simulation model are also input to the simulation engine; on this basis, the simulation model is driven to run according to the disturbance type, disturbance location, and start time recorded in the observation data, the transient response process of the generator, grid element, and controller is solved through numerical integration, to generate a simulation response sequence corresponding to the whole disturbance process, the simulation response sequence being consistent with the electromechanical transient observation response data set in variable dimension, covering the dynamic change trajectory of the generator terminal voltage, power grid bus voltage, current, and frequency, and being output at a unified time resolution, for subsequent residual construction and objective function evaluation.

[0034] In a possible implementation, step S102 further includes: solving transient response processes of each electromechanical transient dynamics constituent element in the target power system through numerical integration; and generating a simulation response sequence corresponding to the disturbance process based on the transient response processes, the simulation response sequence being consistent with the electromechanical transient observation response dataset in the variable dimension.

[0035] Specifically, based on the structure information of the current electromechanical transient simulation model, a mathematical model including all electromechanical transient dynamics constituent elements is constructed, where the electromechanical transient dynamics constituent elements include generator electromagnetic and mechanical coupling equations, power grid branch equivalent π-type circuit models, various excitation systems, speed regulation systems, and power system stabilizer control models, and the model expresses the dynamic behavior in the form of differential-algebraic hybrid.

[0036] In the simulation process, a transient simulation engine is called, the state variables of the above-mentioned electromechanical transient dynamics constituent elements are integrated step by step based on a numerical integration method (such as the Euler method, the improved trapezoidal method, or the explicit Runge-Kutta method) by setting the disturbance start time, the disturbance type (such as short circuit, load shedding, or splitting), and the boundary conditions, and the state evolution trajectory of each constituent element during the disturbance is analyzed, so that the complete transient response process is obtained.

[0037] Based on the transient response process, the key variable outputs of the observation points are extracted, and a simulation response sequence is constructed, which has a one-to-one correspondence with the electromechanical transient observation response dataset in the variable dimension, including a generator terminal voltage time sequence, a power grid side bus voltage change sequence, a key branch current response curve, and a frequency change sequence, and ensuring uniform sampling frequency, variable naming, and timestamp alignment characteristics with the actual observation data.

[0038] In addition, considering the special scenario in the present application, the current electromechanical transient simulation model needs to have accurate modeling capabilities for high-penetration new energy access, power electronic interface device participation in regulation and control, and multi-source asynchronous disturbance response. Therefore, the current electromechanical transient simulation model should not only cover the electromagnetic and mechanical transient characteristics of traditional synchronous generators, but also include new energy generation unit modeling based on double-fed induction generators and full-power converter structures, power electronic control module modeling involving power balance control and reactive power support strategies, and dynamic network structure modeling with line protection action, node islanding, and topology reconstruction behavior description, so as to ensure the response integrity of the model structure and the identification sufficiency of the parameter space.

[0039] Step S103 performs difference analysis on the electromechanical transient observation response dataset and the simulation response sequence, and constructs a target function for parameter correction based on the difference analysis result.

[0040] Specifically, the electromechanical transient observation response dataset obtained in step S101 is compared variable-wise with the simulation response sequence generated in step S102 on a unified time scale. Residual functions are constructed according to dimensions such as generator voltage, grid-side bus voltage, current, and frequency. The transient deviations of each response variable throughout the entire disturbance process are extracted, forming a response residual time series covering multiple time points and multiple response variables. Based on this, a stage-weighted mechanism is introduced, assigning differentiated weights to different disturbance stages (such as the initial disturbance stage, transition response stage, and convergence recovery stage). A weighted objective function with time-series weights is constructed. This objective function quantifies the deviation of the current electromechanical transient simulation model from the actual system response under the parameter configuration and serves as a performance evaluation index in subsequent parameter correction and optimization, guiding the optimization algorithm to iteratively seek optimization in the multi-dimensional parameter space. This objective function ensures consistent variable dimensions and a unified time scale in its structure, while also possessing the ability to express the differences in response stages, ensuring the convergence and physical rationality of the parameter correction process.

[0041] In one possible implementation, step S103 further includes: performing difference analysis on the electromechanical transient observation response dataset and the simulation response sequence to construct a response residual time series, which is used to characterize the transient response deviation features of the electromechanical transient observation response dataset and the simulation response sequence at a unified time scale; and constructing an objective function with a weighted strategy based on the response residual time series, which is used to measure the weighted aggregation degree of simulation errors at different response stages.

[0042] Specifically, the electromechanical transient observation response dataset is first denoted as... The simulation response sequence is denoted as in, Indicates the first The response variable at the th ... Observations at each time point Indicates the first At a certain point in time, This indicates that at the same time point, based on the current parameter vector... The generated simulation results This is the set of parameters for the electromechanical transient simulation model to be corrected. Indicates the dimension of the response variable. This represents the total number of sampling times. Then, by constructing the response residual time series, it is represented as follows:

[0043] in, To characterize the transient response deviation between observation and simulation at a unified time scale, in the embodiments of this application, to ensure data rigor, three types of weighting factors are then introduced: Response variable importance weight vector :

[0044] in Indicates the first Error weighting factors for each response variable, used to control the contribution of different response variables to the overall objective function; time-segmented weighting function; Time-segmented weighted function :

[0045] in, Indicates the start time of the disturbance. The time window standardization factor is represented. This is the time-weighted decay parameter; Parameter regularization factor :

[0046] in Indicates the first The regularization constraint strength for each parameter of the model to be corrected is determined; this factor limits the degree to which the parameters deviate from their initial values. The final weighted residual objective function is constructed as follows:

[0047] in, The objective function value is used as a performance evaluation metric in the parameter optimization process. For the first One parameter to be corrected; For the first Initial reference values ​​for each parameter; Used to identify the first The importance of each response variable, such as the frequency residual, can be assigned a higher weight; As a perturbation weighting factor, it has a higher weight within the critical response window before and after the perturbation; The regularization coefficient is used to control the first... The degree to which each parameter deviates from its physical confidence interval is used to prevent overfitting.

[0048] Step S104: Based on the structural information of the current electromechanical transient simulation model, define the corresponding set of parameters to be corrected.

[0049] Specifically, based on the structural information of the current electromechanical transient simulation model, the core parameters that significantly affect the transient response in its modeling units are identified. Combining the model's electrical structure, control path, and disturbance interface, a set of parameters to be corrected is constructed. This set includes the equivalent reactance and excitation parameters of the synchronous generator, the equivalent impedance and power flow distribution factor of the grid branches, and the gain coefficient and dynamic limits of key controllers. This ensures the set has good identifiability and sensitivity to response deviations, and is used for parameter updates and model corrections in subsequent optimization processes.

[0050] In one possible implementation, step S104 further includes: obtaining the structural information of the current electromechanical transient simulation model based on the configured model parameters in the electromechanical transient simulation model. The structural information includes generator electrical model parameter information, grid topology and equivalent impedance information, and disturbance boundary and controller configuration information. Based on the structural information, a set of parameters to be corrected is defined by considering the influence of each parameter to be corrected on the transient response sensitivity, the physical coupling relationship between parameters in the model structure, and the identifiability of the disturbance driving path.

[0051] Specifically, based on the model parameters already configured in the current electromechanical transient simulation model, its structural information is extracted. The structural information includes at least the following three categories: generator electrical model parameter information, referring to the core modeling parameters used to characterize the transient response characteristics of the synchronous generator, including stator reactance, transient reactance, inertial time constant, damping coefficient, excitation system gain, time constant, and initial excitation voltage, etc., to reflect the generator's response strength and decay rate under electromagnetic disturbances; grid topology and equivalent impedance information, including node connection relationships, branch equivalent reactance, resistance, transformer impedance ratio, system node voltage levels, and initial power flow distribution, etc., which determine the propagation path and amplitude decay characteristics of disturbances in the grid, and are key factors affecting the global response coupling structure; disturbance boundary and controller configuration information, referring to the disturbance triggering conditions, disturbance duration, load model dynamic characteristics, gain, response hysteresis, and upper and lower limit settings of control devices such as automatic voltage regulators (AVR), speed governors (GOV), and power system stabilizers (PSS) set in the simulation model, used to describe the disturbance source characteristics and the system's active adjustment behavior.

[0052] After extracting the structural information, sensitivity analysis and coupling evaluation are performed on the above parameters: First, through the response sensitivity function:

[0053] in, Indicates the first Model parameters For the overall objective function local sensitivity. The parameters with high response impact degree in the objective function are identified by the response sensitivity function; secondly, the explicit and implicit physical coupling relationship between parameters is analyzed based on the model equation structure, for example, the voltage rising edge is jointly influenced by the excitation gain and time constant, and the current peak value is jointly determined by the branch impedance and generator reactance; thirdly, the identifiability of parameters in the disturbance propagation chain is evaluated combined with the observable parameter structure on the disturbance driving path, and the low contribution parameters outside the observation window are removed.

[0054] Finally, the parameter set to be corrected is selected according to the above three types of criteria, which ensures that each parameter in the set not only has a direct dynamic effect in physical modeling, but also has sufficient identification ability and adjustable space in the objective function, providing a parameter correction entry with high convergence and physical consistency for the subsequent optimization process.

[0055] Step S105, based on the objective function, iteratively optimizing the parameter set to be corrected, and outputting the target correction parameter set meeting the iteration condition.

[0056] Specifically, the parameter set to be corrected determined in step S104 is input as an optimization variable into an optimization engine, and an iterative algorithm with global search ability and local convergence is used to solve the objective function multiple times. In each iteration, the parameter vector is updated to minimize the residual error between the simulation response and the observed response; in the optimization process, the sensitivity information and regularization constraints are combined to control the parameter search range, ensuring physical consistency and identifiability; when the error convergence rate of the objective function is lower than the preset threshold or the maximum number of iterations is reached, the optimization process is terminated, and the target correction parameter set meeting the convergence condition is output to update the electromechanical transient simulation model.

[0057] In a possible implementation, step S105 further includes: inputting the to-be-corrected parameter set and the objective function into a Bayesian optimization engine to construct a Gaussian process surrogate model; performing iterative optimization on the to-be-corrected parameter set based on the Gaussian process surrogate model, and outputting a first corrected electromechanical transient simulation model parameter set; performing physical consistency evaluation on the target power system by using the first corrected electromechanical transient simulation model parameter set based on a structural consistency constraint; the structural consistency constraint includes a response symmetry constraint based on power variation between generator units, a node voltage dynamic range constraint based on a voltage extreme interval, and an inertia consistency constraint based on a frequency response trend; based on the physical consistency evaluation, the first corrected electromechanical transient simulation model parameter set is corrected into a second corrected electromechanical transient simulation model parameter set by introducing a multi-model consistency constraint; the multi-model consistency constraint includes a consistency constraint on output results of the same disturbance under different simulation model structures, a response trend coordination constraint based on an equivalent controller model, and a dynamic residual error matching constraint combined with historical disturbance data; the second corrected electromechanical transient simulation model parameter set is re-input into the simulation engine to perform simulation operation, and an updated simulation response sequence is generated; difference analysis is performed on the electromechanical transient observation response data set and the updated simulation response sequence, and a mean square error change rate before and after correction of the current iteration is calculated; the mean square error change rate and the maximum number of iterations are used as limits to determine whether the second corrected electromechanical transient simulation model parameter set meets a convergence criterion; if the second corrected electromechanical transient simulation model parameter set meets the convergence criterion, the second corrected electromechanical transient simulation model parameter set is output as a target corrected parameter set.

[0058] Specifically, the to-be-corrected parameter set obtained in step S104 and the objective function constructed in step S103 are input into a Bayesian optimization engine. The Bayesian optimization process constructs a Gaussian process surrogate model to approximate the response characteristics of the objective function, where, represents the predicted mean of the objective function at the current parameter vector . represents the uncertainty (predicted variance) of the objective function at , which is used to measure the information deficiency of the sampling region. The Gaussian process covariance function adopts a Matern-5 / 2 kernel function form:

[0059] where, represents, , is the output variance of the kernel function, is the length scale hyperparameter. Bayesian optimization selects sampling points by maximizing an acquisition function (e.g., Upper Confidence Bound):

[0060] where, is the exploration-exploitation coefficient, the larger the more biased towards exploration. After each iteration, the Bayesian surrogate model is updated, and the current optimal solution is output as the first revised set of power system parameters, denoted as:

[0061] Then, the structural consistency constraints are applied to for physical plausibility verification. The structural consistency constraints include the following three: Symmetry constraint on the response of generator power variation: the variation amplitude of active power response rate of different generators during the disturbance process is constrained to be within a certain proportion:

[0062] where, denotes the active power output of the generator at time , and denotes the maximum allowable difference threshold of active power variation rate per unit time between different generators; Node voltage dynamic range constraint: during the disturbance process, the voltage of any node should satisfy:

[0063] where, denotes the minimum allowed node voltage amplitude, denotes the maximum allowed node voltage amplitude, denotes the voltage amplitude of the th node at time , and denotes the end time of the disturbance response, is the start time of the disturbance; Inertial consistency constraint on frequency response trend: the first derivative of the system frequency response is consistent with the model set inertia:

[0064] where, denotes the instantaneous value of the system frequency at time , and denotes the system inertia constant, denotes the active imbalance, This represents the maximum permissible deviation threshold between the frequency dynamic response and the theoretical inertial estimate. If the above constraints are not met, then based on the multi-model consistency constraint... Further refinement to a second set of modified parameters Multi-model consistency constraints include: Consistency constraints across different model structures: Construct simulation models with different modeling details, requiring that the responses of key variables under the same perturbation input conditions have a maximum deviation not exceeding a threshold.

[0065] in, This indicates the structural configuration of the first type of simulation model, such as a structure based on detailed excitation system modeling; This indicates the second type of simulation model structure configuration, such as using an equivalent simplified model or different controller modeling methods. Indicates key variables The maximum allowable response difference threshold; Equivalent controller cooperative response constraints: First derivative matching of the output trends of models with different controller modeling structures.

[0066] in, This indicates the control quantity output by the AVR structure used in the current model. This indicates the output trend of the corresponding variable under an alternative controller modeling strategy (such as a static function, a limiting structure, or an empirical modeling method). This represents the allowable threshold for the difference in first-order derivatives; Historical perturbation residual matching constraints: Constructing a reference residual function based on historical perturbation measured residuals The model residual function is required. satisfy:

[0067] in, This indicates that the current model parameters will be... Next The response error residual function of each variable. This represents the reference residual trajectory under actual perturbation. This represents the maximum permissible deviation threshold for residual matching. After correction, the parameter set of the second corrected electromechanical transient simulation model is obtained. This is then re-inputted into the simulation engine to generate an updated simulation response sequence. Based on this update response, calculate the rate of change of the mean squared error between the current round and the previous round. :

[0068] Then, set the error convergence threshold and the maximum number of iterations , determine whether the following conditions are met: , , represents the current number of iterations, , represents the maximum number of iterations; if any of the above conditions is met, it is determined that the parameter correction process converges, and the output is the final target correction parameter set; otherwise, continue to enter the next round of Bayesian optimization iteration with as the new starting point until the convergence criteria are met.

[0069] Please refer to Figure 2 , which shows a module schematic diagram of an electromechanical transient simulation model parameter adaptive correction device provided by an embodiment of the present application. The device comprises an acquisition module 21 and a processing module 22, wherein, The acquisition module 21 is configured to acquire response data of a target power system under different operating states, and construct an electromechanical transient observation response data set based on the response data.

[0070] The processing module 22 is configured to input the electromechanical transient observation response data set and the current electromechanical transient simulation model into a simulation engine to perform simulation calculation, generate a simulation response sequence corresponding to the perturbation process; perform difference analysis on the electromechanical transient observation response data set and the simulation response sequence, and construct a target function for parameter correction based on the difference analysis result; define a corresponding to-be-corrected parameter set based on the structure information of the current electromechanical transient simulation model; perform iterative optimization on the to-be-corrected parameter set based on the target function, and output a target correction parameter set that meets the iteration condition.

[0071] In a possible implementation, the acquisition module 21 is configured to acquire response data of a target power system under different operating states, and construct an electromechanical transient observation response data set based on the response data: acquire response data corresponding to the target power system under steady-state operating states and perturbation evolution operating states respectively; based on the response data, construct a time-consistent electromechanical transient observation response data set, which includes generator voltage data, grid-side bus voltage data, and transient response curve data.

[0072] In a possible implementation, the processing module 22 is configured to input the electromechanical transient observation response data set and the current electromechanical transient simulation model into a simulation engine to perform simulation calculation, generate a simulation response sequence corresponding to the perturbation process, specifically including: solving the transient response process of each electromechanical transient dynamic component in the target power system by numerical integration; based on the transient response process, generating a simulation response sequence corresponding to the perturbation process, the simulation response sequence being consistent with the electromechanical transient observation response data set in variable dimension.

[0073] In a possible implementation, the processing module 22 is configured to perform difference analysis on the electromechanical transient observation response data set and the simulation response sequence, and construct a target function for parameter correction based on the difference analysis result, specifically including: performing difference analysis on the electromechanical transient observation response data set and the simulation response sequence, and constructing a response residual time sequence, the response residual time sequence being used to represent transient response deviation characteristics of the electromechanical transient observation response data set and the simulation response sequence in a unified time scale; and constructing a target function with a weighting strategy based on the response residual time sequence, the target function being used to measure the weighted aggregation degree of simulation errors in different response stages.

[0074] In a possible implementation, the processing module 22 is configured to define a corresponding parameter set to be corrected based on the structure information of the current electromechanical transient simulation model, specifically including: obtaining the structure information of the current electromechanical transient simulation model based on the model parameters configured in the electromechanical transient simulation model, the structure information including generator electrical model parameter information, power grid topology and equivalent impedance information, and disturbance boundary and controller configuration information; and defining the parameter set to be corrected based on the structure information by considering the influence of each parameter to be corrected on transient response sensitivity, the physical coupling relationship between parameters in the model structure, and the identifiability of the disturbance driving path.

[0075] In a possible implementation, the processing module 22 is configured to perform iterative optimization on the parameter set to be corrected based on the target function, and output a target correction parameter set that meets an iteration condition, specifically including: inputting the parameter set to be corrected and the target function into a Bayesian optimization engine together to construct a Gaussian process proxy model; performing iterative optimization on the parameter set to be corrected based on the Gaussian process proxy model, and outputting a first correction electromechanical transient simulation model parameter set; performing physical consistency evaluation on the target power system by the first correction electromechanical transient simulation model parameter set based on a structure consistency constraint; modifying the first correction electromechanical transient simulation model parameter set to a second correction electromechanical transient simulation model parameter set by introducing a multi-model consistency constraint based on the physical consistency evaluation; inputting the second correction electromechanical transient simulation model parameter set into the simulation engine again to perform simulation operation, and generating an updated simulation response sequence; performing difference analysis on the electromechanical transient observation response data set and the updated simulation response sequence, and calculating the mean square error change rate before and after correction in the current iteration; judging whether the second correction electromechanical transient simulation model parameter set meets a convergence standard by taking the mean square error change rate and the maximum number of iterations as limits; and outputting the second correction electromechanical transient simulation model parameter set as the target correction parameter set if the second correction electromechanical transient simulation model parameter set meets the convergence standard.

[0076] In a possible implementation, the processing module 22 is configured to, before inputting the parameter set to be corrected and the target function into the Bayesian optimization engine to construct the Gaussian process proxy model, the method further includes: constructing a structural consistency constraint, the structural consistency constraint including a response symmetry constraint based on power change between generator groups, a node voltage dynamic range constraint based on a voltage extreme interval, and an inertia consistency constraint based on a frequency response trend; constructing a multi-model consistency constraint, the multi-model consistency constraint including a consistency constraint on the same disturbance output result under different simulation model structures, a response trend coordination constraint based on an equivalent controller model, and a dynamic residual matching constraint combined with historical disturbance data.

[0077] It should be noted that the apparatus provided in the above embodiments is only used as an example to divide the above functional modules to achieve the functions thereof. In actual applications, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the apparatus is divided into different functional modules to complete all or part of the above described functions. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be described here.

[0078] The present application also provides an electronic device. Referring to Figure 3 , Figure 3 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. The electronic device can include at least one processor 301, at least one communication bus 302, a user interface 303, at least one network interface 304, and a memory 305.

[0079] The communication bus 302 is configured to realize the connection and communication between the components.

[0080] The user interface 303 can include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 can further include a standard wired interface and a wireless interface.

[0081] The network interface 304 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0082] The processor 301 can include one or more processing cores. The processor 301 connects various parts within the server through various interfaces and lines, performs various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Alternatively, the processor 301 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 301 can integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes operating systems, user interfaces, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 301, but can be realized by a separate chip.

[0083] The memory 305 can include a random access memory (RAM) and a read-only memory (ROM). Alternatively, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 305 can also be at least one storage device located away from the aforementioned processor 301. Referring to Figure 3 The memory 305 as a computer storage medium can include an operating system, a network communication module, a user interface module, and an electromechanical transient simulation model parameter adaptive correction application program.

[0084] In Figure 3In the electronic device shown, the user interface 303 is mainly used to provide an interface for the user to input, and obtain data input by the user; and the processor 301 can be used to invoke the parameter adaptive correction application program of the electromechanical transient simulation model stored in the memory 305, and when executed by one or more processors 301, causes the electronic device to perform the method described in one or more of the above embodiments. It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a combination of a series of actions, but those skilled in the art should know that the present application is not limited to the order of the actions described, because according to the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0085] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, causes the electronic device to perform the method described in one or more of the above embodiments.

[0086] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0087] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other manners. For example, the described device embodiments are merely schematic. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical or other forms.

[0088] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e. may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0089] In addition, each functional unit in the embodiments of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware, or in the form of software functional units.

[0090] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned memory includes: a U disk, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0091] The above is only exemplary embodiments of the present application, and cannot limit the scope of the present application. That is, any equivalent changes and modifications made in accordance with the teachings of the present application are still within the scope of the present application.

[0092] The present application is intended to cover any variations, uses or adaptive changes of the present application, which follow the general principles of the present application and include common knowledge or conventional technical means in the technical field not disclosed in the present application.

Claims

1. A method for adaptive correction of parameters of an electromechanical transient simulation model, characterized in that, The method comprises: acquiring response data of a target power system under different operating states, and constructing an electromechanical transient observation response data set based on the response data; inputting the electromechanical transient observation response data set and a current electromechanical transient simulation model into a simulation engine to perform simulation operation, and generating a simulation response sequence corresponding to a disturbance process; performing difference analysis on the electromechanical transient observation response data set and the simulation response sequence, and constructing a target function for parameter correction based on the difference analysis result; defining a corresponding to-be-corrected parameter set based on the structure information of the current electromechanical transient simulation model; iteratively optimizing the to-be-corrected parameter set based on the target function, and outputting a target correction parameter set that meets an iteration condition.

2. The method of claim 1, wherein, The method comprises: acquiring the response data of the target power system under steady-state operating states and disturbance evolution operating states respectively; based on the response data, constructing the electromechanical transient observation response data set in time sequence consistency, which includes generator voltage data, grid-side bus voltage data, and transient response curve data.

3. The method of claim 1, wherein, The method comprises: solving the transient response process of each electromechanical transient dynamic component in the target power system through numerical integration; based on the transient response process, generating a simulation response sequence corresponding to the disturbance process, which is consistent with the electromechanical transient observation response data set in variable dimension.

4. The method of claim 1, wherein, The method comprises: performing difference analysis on the electromechanical transient observation response data set and the simulation response sequence, and constructing a response residual time sequence based on the difference analysis result, which is used to represent the transient response deviation characteristics of the electromechanical transient observation response data set and the simulation response sequence under the unified time scale; constructing the target function with a weighting strategy based on the response residual time sequence, which is used to measure the weighted aggregation degree of simulation errors in different response stages.

5. The method of claim 1, wherein, The method comprises: based on the model parameters configured in the electromechanical transient simulation model, acquiring the structure information of the current electromechanical transient simulation model, which includes generator electrical model parameter information, grid topology and equivalent impedance information, and disturbance boundary and controller configuration information; based on the structure information, defining the to-be-corrected parameter set by considering the influence of each to-be-corrected parameter on transient response sensitivity, the physical coupling relationship between parameters in the model structure, and the identifiability of the disturbance driving path.

6. The method of claim 1, wherein, The method comprises the following steps: The target function is input into a Bayesian optimization engine together with the parameter set to be corrected to construct a Gaussian process surrogate model; The parameter set to be corrected is iteratively optimized based on the Gaussian process surrogate model, and a first corrected electromechanical transient simulation model parameter set is output; The target power system is physically consistent evaluated based on the structural consistency constraint and the first corrected electromechanical transient simulation model parameter set; The first corrected electromechanical transient simulation model parameter set is modified into a second corrected electromechanical transient simulation model parameter set by introducing a multi-model consistency constraint based on the physical consistency evaluation; The second corrected electromechanical transient simulation model parameter set is re-input into the simulation engine to perform simulation operation, and an updated simulation response sequence is generated; The electromechanical transient observation response data set and the updated simulation response sequence are analyzed for difference, and a mean square error change rate before and after correction of the current round is calculated; The second corrected electromechanical transient simulation model parameter set is judged whether it meets the convergence criteria based on the mean square error change rate and the maximum number of iterations; If the second corrected electromechanical transient simulation model parameter set meets the convergence criteria, the second corrected electromechanical transient simulation model parameter set is output as the target correction parameter set.

7. The method of claim 6, wherein, Before the parameter set to be corrected and the target function are input into the Bayesian optimization engine to construct the Gaussian process surrogate model, the method further comprises the following steps: The structural consistency constraint is constructed, which comprises a response symmetry constraint based on power variation between generator units, a node voltage dynamic range constraint based on voltage extreme interval, and an inertia consistency constraint based on frequency response trend; The multi-model consistency constraint is constructed, which comprises a consistency constraint of output results of the same disturbance under different simulation model structures, a response trend coordination constraint based on equivalent controller model, and a dynamic residual matching constraint combined with historical disturbance data.

8. An apparatus for adaptive correction of parameters of an electromechanical transient simulation model, characterized by The device comprises an acquisition module and a processing module, wherein: The acquisition module is configured to acquire response data of a target power system under different operating states, and construct an electromechanical transient observation response data set based on the response data; The processing module is configured to input the electromechanical transient observation response data set and a current electromechanical transient simulation model into a simulation engine to perform simulation operation, and generate a simulation response sequence corresponding to a disturbance process; analyze the electromechanical transient observation response data set and the simulation response sequence for difference, and construct a target function for parameter correction based on the difference analysis result; define a parameter set to be corrected based on structural information of the current electromechanical transient simulation model; iteratively optimize the parameter set to be corrected based on the target function, and output a target correction parameter set meeting an iteration condition.

9. An electronic device, comprising: The electronic device comprises a processor, a communication bus, a user interface, a network interface, and a memory for storing instructions, the user interface and the network interface being configured to communicate with other devices, and the processor being configured to execute the instructions stored in the memory to cause the electronic device to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method of any one of claims 1 to 7.

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