公开(公告)号：US20210399546A1

公开(公告)日：2021-12-23

申请号：US17288617

申请日：2018-11-05

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Anup MENON ,  Chaitanya Ashok BAONE ,  Honggang WANG ,  Mustafa Tekin DOKUCU

IPC: H02J3/00

Abstract: A dynamic simulation engine, having system parameters, may be provided for a component of an electrical power system (e.g., a generator, wind turbine, etc.). A model parameter tuning engine may receive, from a measurement data store, measurement data measured by an electrical power system measurement unit (e.g., a phasor measurement unit or digital fault recorder measuring a disturbance event). The model parameter tuning engine may then pre-condition the measurement data and set-up an optimization problem based on a result of the pre-conditioning. The system parameters of the dynamic simulation engine may be determined by solving the optimization problem with an iterative method until at least one convergence criteria is met. According to some embodiments, solving the optimization problem includes a Jacobian approximation that does not call the dynamic simulation engine if an improvement of residual meets a pre-defined criteria.

公开(公告)号：US20190129367A1

公开(公告)日：2019-05-02

申请号：US15794769

申请日：2017-10-26

Applicant: General Electric Company

Inventor： Chaitanya Ashok BAONE ,  Nan DUAN ,  Anup MENON ,  Mustafa Tekin DOKUCU

IPC: G05B17/02 ,  G06F17/50

Abstract: A power system model parameter conditioning tool including a server control processor in communication with phasor measurement unit monitored data records of multiple disturbance events, a model calibration unit providing event screening, power system model simulation, and simultaneous tuning of model parameters. The model calibration performing a simulation using default model parameters, the processor comparing the simulation results to the monitored data. If the prediction is within threshold, then terminating conditioning; else performing parameter identifiability analysis to determine differing effects of various model parameters on power system model accuracy, selecting a parameter set causing a degradation in power system model prediction, and updating the default model parameters corresponding to members of the parameter set with values selected to reduce the degradation. A method and a non-transitory computer readable medium are also disclosed.

公开(公告)号：US20180287382A1

公开(公告)日：2018-10-04

申请号：US15477696

申请日：2017-04-03

Applicant: General Electric Company

Inventor： Sahika GENC ,  Deepak ARAVIND ,  Yan PAN ,  Naresh ACHARYA ,  Chaitanya Ashok BAONE

IPC: H02J3/14 ,  G05B17/02

Abstract: The example embodiments are directed to a system and method for forecasting load flexibility of a power grid. In one example, the method includes receiving temperature values associated with temperature set points of a plurality of loads that are included on a power grid, forecasting a flexibility of the plurality of loads using a polynomial-time mixed-integer non-linear programming (MINLP) optimization based on the received temperature values for the plurality of loads, and outputting information about the forecasted flexibility for display to a display device. The MINLP optimization performs the forecasting of the load flexibility on a fine-grained basis in comparison to conventional methods and is still fast enough that it can be computed in real-time.

公开(公告)号：US20180260561A1

公开(公告)日：2018-09-13

申请号：US15453544

申请日：2017-03-08

Applicant: General Electric Company

Inventor： Lalit Keshav MESTHA ,  Santosh Sambamoorthy VEDA ,  Masoud ABBASZADEH ,  Chaitanya Ashok BAONE ,  Weizhong YAN ,  Saikat RAY MAJUMDER ,  Sumit BOSE ,  Annartia GIANI ,  Olugbenga ANUBI

IPC: G06F21/55

CPC classification number: G06F21/554 ,  G05B23/0275 ,  G06F2221/034 ,  Y04S10/522

Abstract: According to some embodiments, a plurality of heterogeneous data source nodes may each generate a series of current data source node values over time that represent a current operation of an electric power grid. A real-time threat detection computer, coupled to the plurality of heterogeneous data source nodes, may receive the series of current data source node values and generate a set of current feature vectors. The threat detection computer may then access an abnormal state detection model having at least one decision boundary created offline using at least one of normal and abnormal feature vectors. The abnormal state detection model may be executed, and a threat alert signal may be transmitted if appropriate based on the set of current feature vectors and the at least one decision boundary.