GENERATOR DYNAMIC MODEL PARAMETER ESTIMATION AND TUNING USING ONLINE DATA AND SUBSPACE STATE SPACE MODEL

• US 20150100282A1
• Filed: 10/03/2013
• Published: 04/09/2015
• Est. Priority Date: 10/03/2012
• Status: Active Grant

• Current Assignee: Operation Technology, Inc. (Schneider Electric SE)
• Original Assignee: Operation Technology, Inc. (Schneider Electric SE)
• Inventors: Shokooh, Farrokh, Khandelwal, Tanuj, Saber, Ahmed Yousuf, Dai, J.J.
• Granted Patent: US 9,864,820 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30

Abstract

Generator dynamic model parameter estimation and tuning using online data and subspace state space models are disclosed. According to one embodiment, a system comprises a sensor, a data acquisition network in communication with the sensor; a user console and an identification and tuning engine in communication with the data acquisition network, the user console, and a database. The database comprises one or more generator models, and the identification and tuning engine identifies and tunes parameters associated with a selected generator model.

Claims

1. A system, comprising:

a sensor;

a data acquisition network in communication with the sensor;

a user console; and

an identification and tuning engine in communication with the data acquisition network, the user console, and a database, the database comprising one or more generator models;

wherein the identification and tuning engine identifies and tunes parameters associated with a selected generator model.

2. The system of claim 1, wherein the tuning engine identifies and tunes parameters by:

measuring current event data using the sensor;

upon detecting the current event data comprises bad data, one of rejecting or filtering the bad data;

receiving a machine model including model controls;

identifying and tuning model parameters based on the current event data and the machine model;

comparing the identified and tuned model parameters associated with the current event to a predefined threshold; and

selecting one of the identified and tuned model parameters associated with the current event or identified and tuned parameters associated with a previous event based on the comparing.

3. The system of claim 1, wherein the sensor is a PMU.

4. The system of claim 1, further comprising a plurality of sensors.

5. The system of claim 1, wherein the user console comprises a graphic user interface, and generates one or more of a report, a log, and an alert based on received parameters.

6. The system of claim 2, wherein the detecting of bad data and one of rejecting or filtering the bad data comprises:

receiving signals of the current event data, the signals comprising abc signals and field signals;

transforming the abc signals into 0dq signals by using Park'"'"'s transformation;

filtering the 0dq signals and the field signals to remove noise from the current event data, wherein a measurement associated with a signal having noise is removed from the current event data.

7. The system of claim 6, wherein the filtering is performed by using one or more of butterworth and adaptive noise filters.

10. The method of claim 1, wherein current event data is measured by using a plurality of sensors.

8. A method of identifying and tuning model parameters, comprising:

measuring current event data using a sensor;

upon detecting the current event data comprises bad data, one of rejecting or filtering the bad data;

receiving a machine model including model controls;

identifying and tuning model parameters based on the current event data and the machine model;

comparing the identified and tuned model parameters associated with the current event to a predefined threshold; and

selecting one of the identified and tuned model parameters associated with the current event or identified and tuned parameters associated with a previous event based on the comparing.

9. The method of claim 8, wherein the sensor is a PMU.

11. The method of claim 8, wherein a user console comprising a graphic user interface generates one or more of a report, a log, and an alert based on received parameters.

12. The method of claim 8, wherein the detecting of bad data and one of rejecting or filtering the bad data comprises:

receiving signals of the current event data, the signals comprising abc signals and field signals;

transforming the abc signals into 0dq signals by using Park'"'"'s transformation;

filtering the 0dq signals and the field signals to remove noise from the current event data, wherein a measurement associated with a signal having noise is removed from the current event data.

13. The system of claim 12, wherein the filtering is performed by using one or more of butterworth and adaptive noise filters.