Power system measurement based model calibration with enhanced optimization
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.
1. A system associated with an electrical power system, comprising:
a dynamic simulation engine, having system parameters, of a component of the electrical power system;
a measurement data store containing measurement data measured by an electrical power system measurement unit; and
a model parameter tuning engine, including:
a data connection to receive measurement data from the measurement data store,
at least one model parameter tuning engine computer processor coupled to the data connection, and
a computer memory coupled to the model parameter tuning engine computer processor and storing instructions that, when executed by the processor, cause the model parameter tuning engine to:
receive a model calibration command,
pre-condition the measurement data, including: a feature transformation on model output data, normalization of parameter and model output, and an identifiability assessment providing sensitivity and dependency,
set-up an optimization problem, including a weight set-up on an objective function based on a feature transformation result, based on a result of the pre-conditioning, and
determine the system parameters of the dynamic simulation engine by solving the optimization problem, including sending model input and model parameters into the dynamic simulation engine and obtaining predicted model output for calculation of residual, with an iterative method until at least one convergence criteria is met and solving the optimization problem includes a Jacobian approximation that;
responsive to a determination that an improvement of residual does not meet a pre-defined criteria, calls the dynamic simulation engine, and
responsive to a determination that an improvement of residual does meet a pre-defined criteria, does not call the dynamic simulation engine,
wherein model parameters of an active power system model are automatically updated based on the solution of the optimization problem.
2. The system of claim 1 , wherein the measurement data is associated with at least one of: (i) disturbance event data, and (ii) data from deliberately performed unit tests.
3. The system of claim 1 , wherein the electrical component is associated with at least one of: (i) a generator, (ii) a wind turbine, (iii) a solar panel, and (iv) a dynamic load.
4. The system of claim 1 , wherein the measurement unit is associated with at least one of: (i) a phasor measurement unit, (ii) a digital fault recorder, and (iii) other means of measuring frequency, voltage, current, or power phasors.
5. The system of claim 1 , wherein the calibration command is received via an automated process.
6. The system of claim 1 , wherein the calibration command is received via a user console interface display providing event data selection, simulation engine selection, and calibration algorithm configuration.
7. The system of claim 1 , wherein the measurement data includes at least one of: (i) device terminal real power, (ii) reactive power, (iii) voltage magnitude, (iv) frequency, and (v) phase angle data.
8. The system of claim 1 , wherein the model parameter tuning engine is associated with a graphical user interface display including validation information, identifiability information, and calibration information.
9. A computer-implemented method associated with an electrical power system, comprising:
receiving, by a computer processor of a model parameter turning engine from a measurement data store, measurement data measured by an electrical power system measurement unit;
receiving a model calibration command;
pre-conditioning the measurement data, including: a feature transformation on model output data, normalization of parameter and model output, and an identifiability assessment providing sensitivity and dependency;
setting-up an optimization problem, including a weight set-up on an objective function based on a feature transformation result, based on a result of the pre-conditioning;
determining system parameters, of a dynamic simulation engine for a component of the electrical power system, by solving the optimization problem, including sending model input and model parameters into the dynamic simulation engine and obtaining predicted model output for calculation of residual, with an iterative method until at least one convergence criteria is met;
solving the optimization problem by:
responsive to a determination that an improvement of residual does not meet a pre-defined criteria, calling the dynamic simulation engine by a Jacobian approximation, and
responsive to a determination that an improvement of residual does meet a pre-defined criteria, not calling the dynamic simulation engine by the Jacobian approximation; and
automatically updating model parameters of an active power system model based on the solution of the optimization problem.
10. The method of claim 9 , wherein the electrical component is associated with at least one of: (i) a generator, (ii) a wind turbine, (iii) a solar panel, and (iv) a dynamic load.
11. The method of claim 9 , wherein the measurement unit is associated with at least one of: (i) a phasor measurement unit, (ii) a digital fault recorder, and (iii) other means of measuring frequency, voltage, current, or power phasors.
12. The method of claim 9 , wherein the calibration command is received via an automated process.
13. The method of claim 9 , wherein the calibration command is received via a user console interface display providing event data selection, simulation engine selection, and calibration algorithm configuration.
14. The method of claim 9 , wherein the measurement data includes at least one of: (i) device terminal real power, (ii) reactive power, (iii) voltage magnitude, (iv) frequency, and (v) phase angle data.
15. A non-transitory, computer-readable medium storing program code, the program code executable by a computer processor to cause the processor to perform a method associated with an electrical power system, the method comprising:
receiving, by a computer processor of a model parameter turning engine from a measurement data store, measurement data measured by an electrical power system measurement unit;
receiving a model calibration command;
pre-conditioning the measurement data, including: a feature transformation on model output data, normalization of parameter and model output, and an identifiability assessment providing sensitivity and dependency;
setting-up an optimization problem, including a weight set-up on an objective function based on a feature transformation result, based on a result of the pre-conditioning; and
determining system parameters, of a dynamic simulation engine for a component of the electrical power system, by solving the optimization problem, including sending model input and model parameters into the dynamic simulation engine and obtaining predicted model output for calculation of residual, with an iterative method until at least one convergence criteria is met and solving the optimization problem by:
responsive to a determination that an improvement of residual does not meet a pre-defined criteria, calling the dynamic simulation engine by a Jacobian approximation, and
responsive to a determination that an improvement of residual does meet a pre-defined criteria, not calling the dynamic simulation engine by the Jacobian approximation; and
automatically updating model parameters of an active power system model based on the solution of the optimization problem.