Event analysis in an electric power system

1. A method comprising:

receiving, via a communication interface of an event detection and classification module comprising a processor, data from a plurality of sensors in a system, the data including a first data point and a second data point, wherein the event detection and classification module includes a detection module and a classification module;

determining an event occurred based on a change in measurements of the received first data point compared to the second data point and the change is within a pre-set magnitude and within a pre-defined amount of time;

applying a coherency similarity process to the received data via a classification module, wherein the coherency similarity process determines for the received data, which sensors of the plurality of sensors oscillate together before settling back to equilibrium and which sensors of the plurality of sensors oscillate out of phase before settling back to equilibrium, and the classification module determines whether the received data is similar to expected data for the event based on the applied coherency similarity process, and the similarity is within one of a threshold value and a range of values;

determining whether the event is an actual event or a mal-doer event based on an output of the classification module;

transmitting the determination of the event as the actual or the mal-doer event; and

modifying operation of the system based on the transmitted output.

2. The method of claim 1 , wherein the data is synchrophasor data collected from phasor measurement units or Phasor Data Concentrators.

3. The method of claim 2 , wherein the synchrophasor data includes at least one of voltage angle current angle, voltage magnitude, current magnitude, and active power.

4. The method of claim 2 , wherein determining whether the event is the actual event or mal-doer event further comprises:

calculating an event singular value decomposition for the received data based on the application of the coherency similarity process;

identifying a post-event right singular value based on the calculated post-event singular value decomposition;

comparing the post-event right singular value to a reference value for a same at least one pair of sensors of the plurality of sensors to output a classification threshold value; and

determining whether the classification threshold value is one of inside or outside a predetermined value or range of values.

5. The method of claim 4 , wherein the reference value is at least one of a synthetic right singular vector (RSV) and a historical RSV.

6. The method of claim 4 wherein the event singular value decomposition is based on a moving window data of an electrical signal.

7. The method of claim 6 , wherein a window size of the moving window of data is determined based on model analysis using at least one of a network admittance matrix, generator location and inertia, load location and magnitudes.

8. The method of claim 4 , wherein the coherency process outputs a right singular vector as a coherency value.

9. The method of claim 4 , wherein comparing the post-event right singular value to the reference value is a measure of one of: rotation angle, Manhattan distance, Euclidean distance, or Chebyshev distance.

10. The method of claim 4 , wherein classification threshold value is updated based on at least one of: an output of the event classification module, a user input, learning from historical data.

11. A system comprising:

an event detection and classification module, including an event detection module and a classification module;

an event detection and classification processor; and

a memory storing program instructions, the event detection and classification processor and the event detection and classification module operative with the program instructions to perform functions as follows:

receive data from a plurality of sensors in the system, the data including a first data point and a second data point;

determine an event occurred based on a change in measurements of the received first data point compared to the second data point and the change is within a pre-set magnitude and within a pre-defined amount of time;

apply a coherency similarity process to the received data, wherein the coherency similarity process determines for the received data, which of the plurality of sensors oscillate together before settling back to equilibrium and which sensors of the plurality of sensors oscillate out of phase before settling back to equilibrium, and the classification module determines whether the received data is similar to expected data for the event based on the applied coherency similarity process, and the similarity is within one of a threshold value and a range of values;

determine whether the event is an actual event or a mal-doer event based on an output of the classification module;

transmit the determination of the event as the actual or the mal-doer event; and

modify operation of the system based on the transmitted output.

12. The system of claim 11 , wherein the data is synchrophasor data collected from phasor measurement units or Phasor Data Concentrators.

13. The system of claim 12 , wherein the synchrophasor data includes at least one of voltage angle current angle, voltage magnitude, current magnitude, and active power.

14. The system of claim 11 , wherein determining whether the event is the actual event or mal-doer event further comprises program instructions to perform the functions as follows:

calculate an event singular value decomposition for the received data based on the application of the coherency similarity process;

identify a post-event right singular value based on the calculated post-event singular value decomposition;

compare the post-event right singular value to a reference value for a same at least one pair of sensors of the plurality of sensors to output a classification threshold value; and

determine whether the classification threshold value is one of inside or outside a predetermined value or range of values.

15. The system of claim 14 , wherein the reference value is at least one of a synthetic right singular vector (RSV) and a historical RSV.

16. The system of claim 14 , wherein the event singular value decomposition is based on a moving window data of an electrical signal.

17. A non-transitory computer-readable medium storing instructions that, when executed by a computer processor, cause the computer processor to perform a method comprising:

receiving, via a communication interface of an event detection and classification module comprising a processor, data from one or more sensors in a system;

determining an event occurred based on the received data based on a change in measurements of the received data within a pre-set magnitude and within a pre-defined amount of time;

applying a coherency similarity process to the received data via a classification module, wherein the coherency similarity process determines whether the received data is similar to expected data for the event, and the similarity is within one of a threshold value and a range of values;

determining whether the event is an actual event or a mal-doer event based on an output of the classification module, wherein the determination further comprises:

comparing a post-event right singular value to a reference value for a same at least one pair of nodes to output a classification threshold value; and

determining whether the classification threshold value is one of inside or outside a predetermined value or range of values;

transmitting the determination of the event as the actual or the mal-doer event; and

modifying operation of the system based on the transmitted output.

18. The medium of claim 17 , wherein the system is a power network and the one or more sensors are associated with a plurality of nodes in the power network.

19. The medium of claim 17 , wherein determining whether the event is the actual event or mal-doer event further comprises instructions to perform a method comprising:

calculating an event singular value decomposition for the received data based on the application of the coherency similarity process; and

identifying the post-event right singular value based on the calculated post-event singular value decomposition, wherein the identification is prior to the comparison of the post-event right singular value to the reference value.