# US 2026/0099641 A1 · Online calibration of power system model using time series measurement data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0099641 A1 - Document ID: US20260099641A1 - Status: Published application - Kind: A1 - Application number: 19/112,738 - Filed: 2023-06-13 - Published: 2026-04-09 - Earliest claimed: 2022-09-23 - Assignees: Siemens Aktiengesellschaft - Applicants: Siemens Corporation - Inventors: Xiaofan Wu; Murat Kaan Erdal; Suat Gumussoy; Ulrich Muenz - Web page: https://patentgrep.com/patent/US20260099641A1 ## CPC - G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) - H02J 13/12: Monitoring network conditions, e.g. electrical magnitudes or operational status - G06F 2113/04: Power grid distribution networks - H02J 2103/30: Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY ## Abstract A computer-implemented method for online calibration of power system model against a power system includes iteratively approximating the power system model to a time-domain system model, at sequential optimization steps, as a function of parameter values of a set of system parameters. At each optimization step, an error is measured between a time series of a model output in response to the dynamic input signal and a time series of measurement signals obtained from the measurement devices defining an actual power system response to the dynamic input signal, summed over a number of discretized points in time with a defined sampling interval. A sequential optimizer is used to adjust parameter values of the calibration parameters and a system state to minimize the measured error, constrained by a discretization of the time-domain system model based on the sampling interval, to thereby determine optimal values of the calibration parameters. ## Claims ### Claim 1 A computer-implemented method for online calibration of a power system model against a power system having a number of active generator subsystems connected to a power network and a number of measurement devices installed in the power network to measure electrical quantities associated with each of the active generator subsystems, the method comprising: obtaining measurement signals from the measurement devices as time series data that defines a response of the power system to a dynamic input signal, determining initial parameter values of a set of system parameters of the power system model, performing, over a series of iterations: executing a model approximator to generate a time-domain system model that at least locally approximates the power system model around a specified operating point, based on the initial parameter values and current parameter values of the set of system parameters, wherein a model output of the time-domain system model is a function of a system state and an input signal and wherein the system state is a function of the input signal, executing an objective evaluator to measure an error between a time series of the model output in response to the dynamic input signal and the time series of the measurement signals obtained from the measurement devices, summed over a number of discretized points in time with a defined sampling interval, executing a sequential optimizer to adjust parameter values of at least a subset of the system parameters and the system state in a direction to minimize the measured error, wherein the sequential optimizer is constrained based on a discretization of the time-domain system model using the defined sampling interval, whereby, the power system model is calibrated against the power system based on final values of the system parameters after the series of iterations. ### Claim 2 (depends on 1) The method according to claim 1, further comprising, at each iteration, executing a sensitivity analyzer to select the subset of the system parameters to be adjusted by the sequential optimizer in that iteration keeping the remaining system parameters in the set of system parameters fixed. ### Claim 3 (depends on 2) The method according to claim 2, wherein the sensitivity analyzer selects the subset of the system parameters at each iteration by measuring a derivative of the error with respect to each system parameter in the set of system parameters and selecting a predefined number of system parameters which provide the largest derivatives. ### Claim 4 (depends on 1) The method according to claim 1, wherein the error is measured at each iteration as a norm between the time series of the model output and the time series of the measurement signals for individual active generator subsystems, summed over the number of active generator subsystems. ### Claim 5 (depends on 1) The method according to claim 1, wherein the power system model comprises a non-linear system model, wherein the time-domain system model generated by the model approximator at each iteration comprises a time-domain linear system model, and wherein the sequential optimizer is constrained at each iteration by discretization of the time-domain linear system model using a bilinear transformation based on the defined sampling interval. ### Claim 6 (depends on 1) The method according to claim 1, wherein each active generator subsystem of the power system comprises a generator and one or more controllers, and wherein the system parameters comprise physical parameters of the generator subsystems and controller parameters of the controllers of the generator subsystems. ### Claim 7 (depends on 1) The method according to claim 1, wherein the dynamic input signal comprises one or more of: reference values, loads and disturbances. ### Claim 8 (depends on 1) The method according to claim 1, wherein the model output signal and the actual power system output signal are each mapped to a multi-dimensional output space, wherein the output space is defined by quantities selected from the group consisting of: frequency, voltage, active power and reactive power. ### Claim 9 (depends on 1) A non-transitory computer-readable storage medium including instructions that, when processed by a computing system, configure the computing system to perform the method according to claim 1. ### Claim 10 A power system comprising: one or more active generator subsystems connected to a power network, a number of measurement devices installed in the power network to dynamically measure electrical quantities associated with each of the active generator subsystems, and an online model calibration system for calibrating a power system model against the power system, the online model calibration system comprising: one or more processors, and a memory storing algorithmic modules executable by the one or more processors, the algorithmic modules comprising: a model approximator configured to, at each iteration in a series of iterations, generate a time-domain system model that at least locally approximates the power system model around a specified operating point, based on initial parameter values and current parameter values of a set of system parameters, wherein a model output of the time-domain system model is a function of a system state and an input signal and the system state is a function of the input signal, an objective evaluator configured to, at each iteration, to measure an error between a time series of the model output in response to the dynamic input signal and a time series of measurement signals obtained from the measurement devices that defines a response of the power system to the dynamic input signal, summed over a number of discretized points in time with a defined sampling interval, and a sequential optimizer configured to, at each iteration, adjust parameter values of at least a subset of the system parameters and the system state in a direction to minimize the measured error, wherein the sequential optimizer is constrained based on a discretization of the time-domain system model using the defined sampling interval, whereby, the power system model is calibrated against the power system based on final values of the system parameters after the series of iterations.