Systems and methods for enhanced power system model calibration

Abstract

A system for enhanced power system model calibration is provided. The system is programmed to store a model of a device. The model includes a plurality of parameters. The system is also programmed to receive a plurality of events associated with the device, receive a first set of input calibration values for the plurality of parameters, sequentially analyze the plurality of events in a first sequence to determine a set of calibrated parameter values for the model, validate the set of calibrated parameter values for the model to determine fit, and perform Bayesian optimization on the determined fit, the set of calibrated parameter values for the model, and the plurality of events.

In the claims: 
     
         1 . A system for power system model calibration comprising a computing device comprising at least one processor in communication with at least one memory device, wherein said at least one processor is programmed to:
 store a model of a device, wherein the model includes a plurality of parameters;   receive a plurality of events associated with the device;   receive a first set of input calibration values for the plurality of parameters;   sequentially analyze the plurality of events in a first sequence to determine a set of calibrated parameter values for the model;   validate the set of calibrated parameter values for the model to determine a fit; and   perform Bayesian optimization on the determined fit, the set of calibrated parameter values for the model, and the plurality of events.   
     
     
         2 . The system in accordance with  claim 1 , wherein the model is a power system model, wherein the Bayesian optimization maintains a probabilistic surrogate model and an acquisition function, and wherein to perform Bayesian optimization said at least one processor is further programmed to:
 initialize the probabilistic surrogate model of an objective function using a plurality of initial parameter points, wherein the probabilistic surrogate model of the objective function comprises a stationary probabilistic model including a non-linear one-to-one mapping of values of the plurality of parameters from a first domain to a second domain;   generate a new set of parameter values corresponding to at least one parameter of the plurality of parameters by optimizing an acquisition function, wherein the acquisition function is based at least in part on the set of calibrated parameter values and the probabilistic surrogate model of the objective function;   evaluate the objective function using the power system model operated with the new set of parameter values;   update the probabilistic surrogate model of the objective function to obtain an updated probabilistic surrogate model of the objective function; and   repeat until reaching at least one of a predetermined number of iterations, a predetermined period of time, and a termination condition.   
     
     
         3 . The system in accordance with  claim 1 , wherein said at least one processor is further programmed to determine a second sequence of events based on the Bayesian optimization. 
     
     
         4 . The system in accordance with  claim 3 , wherein said at least one processor is further programmed to sequentially analyze the plurality of events based on the second sequence to determine a second fit. 
     
     
         5 . The system in accordance with  claim 4 , wherein said at least one processor is further programmed to:
 perform Bayesian optimization on the second fit, the set of calibrated parameter values for the model, and the plurality of events to determine a third sequence; and   sequentially analyze the plurality of events based on the third sequence.   
     
     
         6 . The system in accordance with  claim 1 , wherein said at least one processor is further programmed to determine a second set of input calibration values based on the Bayesian optimization. 
     
     
         7 . The system in accordance with  claim 6 , wherein said at least one processor is further programmed to sequentially analyze the plurality of events based on the second set of input calibration values to determine a second fit. 
     
     
         8 . The system in accordance with  claim 7 , wherein said at least one processor is further programmed to:
 perform Bayesian optimization on the second fit, the set of calibrated parameter values for the model, and the plurality of events to determine a third set of input calibration values; and   sequentially analyze the plurality of events based on the third set of input calibration values.   
     
     
         9 . The system in accordance with  claim 1 , wherein said at least one processor is further programmed to:
 compare the fit to a terminating condition; and   when the terminating condition is reached, update the model to include the set of calibrated parameter values.   
     
     
         10 . The system in accordance with  claim 1 , wherein the fit is based on one of an average fitting error of the set of calibrated parameter values across the plurality of events and a best fitting error of the set of calibrated parameter values across the plurality of events. 
     
     
         11 . A system for power system model calibration comprising a computing device comprising at least one processor in communication with at least one memory device, wherein said at least one processor is programmed to:
 store a model of a device, wherein the model includes a plurality of parameters;   receive a first event associated with the device;   analyze the first event to identify a subset of important parameters from the plurality of parameters; and   perform Bayesian optimization on the subset of important parameters to determine a set of calibrated parameter values for the model.   
     
     
         12 . The system in accordance with  claim 11 , wherein said at least one processor is further programmed to analyze the first event using at least one of a single value decomposition approach and a dot product angle approach. 
     
     
         13 . The system in accordance with  claim 11 , wherein the subset of important parameters includes less than ten parameters. 
     
     
         14 . The system in accordance with  claim 11 , wherein said at least one processor is further programmed to:
 receive a second event associated with the device;   analyze the second event to determine a second subset of important parameters from the plurality of parameters based on the set of calibrated parameter values; and   perform Bayesian optimization on the second subset of important parameters to determine a second set of calibrated parameter values for the model.   
     
     
         15 . A system for power system model calibration comprising a computing device comprising at least one processor in communication with at least one memory device, wherein said at least one processor is programmed to:
 store a model of a device, wherein the model includes a plurality of parameters;   receive a first event associated with the device;   analyze the first event to identify a subset of important parameters from the plurality of parameters;   determine at least one hyperparameter based on the analysis; and   perform Bayesian optimization on the hyperparameter.   
     
     
         16 . The system in accordance with  claim 15 , wherein said at least one processor is further programmed to analyze the first event using at least one of a single value decomposition approach and a dot product angle approach. 
     
     
         17 . The system in accordance with  claim 15 , wherein said at least one hyperparameter includes at least one of a maximum number of iterations, a residual tolerance, and one or more parameter weights. 
     
     
         18 . The system in accordance with  claim 15 , wherein said at least one processor is further programmed to:
 re-analyze the first event to identify the subset of important parameters from the plurality of parameters based on the hyperparameter; and   determine a set of calibrated parameter values for the model based on the subset of important parameters.   
     
     
         19 . The system in accordance with  claim 15 , wherein said at least one processor is further programmed to determine a set of calibrated parameter values for the model based on the subset of important parameters and the hyperparameter. 
     
     
         20 . The system in accordance with  claim 15 , wherein said at least one processor is further programmed to perform Bayesian optimization on the subset of important parameters to determine a set of calibrated parameter values for the model.