Granted Patent B2

US 12,620,797 · App. 18/025,031 · Granted May 5, 2026

Inventors: Od Naidu (Karnataka, IN); Dinesh Patil (Karnataka, IN); Neethu George (Karnataka, IN); Vedanta Pradhan (Bhubaneswar, IN); Suresh Maturu (Pradesh, IN)

Assignee: HITACHI ENERGY LTD

H02H1/0092G01R31/088H02H7/26

Abstract

Claims (66)

1 . A computer-implemented method comprising:

computing features for use in generating a decision logic operative to generate a signal operative for use with a power system, wherein computing the features comprises

determining a fault inception time from at least one electric characteristics of data sampled for a given type of power system, and

determining rate of change values indicative of a rate of change of the at least one electric characteristics for at least one sample time, the at least one sample time being dependent on the fault inception time;

using the features to generate the decision logic, wherein using the features to generate the decision logic comprises performing a machine learning (ML) model training using the computed features to generate one or both of a phase classifier and a zone classifier, wherein the phase classifier receives rates of change of voltages for three phases and rates of change of currents for the three phases as inputs and outputs a fault type, and/or wherein the zone classifier receives rates of change of voltages for three phases and rates of change of currents for the three phases as inputs and outputs a zone; and

deploying the decision logic to an intelligent electronic device (IED) to generate the signal.

2 . The computer-implemented method of claim 1 , wherein the ML model has an input layer that receives the rate of change values.

3 . The computer-implemented method of claim 1 , wherein the decision logic is or comprises at least one of:

a protection function;

the phase classifier for fault classification; or

the zone classifier for faulted zone identification.

4 . The computer-implemented method of claim 1 , wherein the signal is at least one of:

a circuit breaker control signal;

a switch control signal;

an alarm;

a warning;

status information; or

output for outputting via a human machine interface (HMI).

5 . The computer-implemented method of claim 1 , wherein determining the rate of change values comprises, for each phase,

determining a filtered voltage,

determining a filtered current, and

calculating the rate of change values from the filtered voltage and from the filtered current.

6 . The computer-implemented method of claim 5 , wherein the filtered voltage is determined by averaging a number N>1 of sample values of the voltage and the filtered current is determined by averaging a number N>1 of sample values of the current.

7 . The computer-implemented method of claim 6 , wherein calculating the rate of change values comprises determining

a voltage difference between a sample value of the filtered voltage at a time k and a sample value of the filtered voltage at a time k−N, and

a current difference between a sample value of the filtered current at a time k and a sample value of the filtered current at a time k−N, and

wherein

the rate of change values for the voltage are determined as a change of the voltage difference between a sample time and a previous sample time, and

the rate of change values for the current are determined as a change of the current difference between the sample time and the previous sample time.

8 . The computer-implemented method of claim 1 , wherein deploying the decision logic comprises deploying the phase classifier and/or the zone classifier for execution by the IED for distance protection.

9 . The computer-implemented method of claim 1 , wherein generating the decision logic comprises using the computed features to generate one or both of the phase classifier and the zone classifier.

10 . The computer-implemented method of claim 9 ,

wherein using the computed features to generate the decision logic comprises generating the phase classifier and generating the zone classifier using an ensemble machine learning (ML) method, and/or

wherein generating the phase classifier and generating the zone classifier comprises a random forest training using the computed features to generate a random forest, and/or

wherein using the rate of change values to generate one or both of the phase classifier and the zone classifier comprises

training a first machine learning model, using the computed features, to generate the phase classifier, and

training a second machine learning model, using the computed features, to generate the zone classifier.

11 . The computer-implemented method of claim 1 , wherein the phase classifier and/or zone classifier are generated in a self-setting manner.

12 . The computer-implemented method of claim 1 , wherein the phase classifier and/or zone classifier are continually updated during field operation.

13 . The computer-implemented method of claim 1 ,

wherein the decision logic is a decision logic for transmission line protection, and/or

wherein generating the phase classifier and/or zone classifier is performed using a dataset including data for several distinct source to line impedances.

14 . An intelligent electronic device (IED) comprising:

an interface to receive a voltage and/or current for at least one phase of a power transmission line;

wherein the IED is operative to determine a rate of change of at least one electric characteristics and to input the determined rate of change of the at least one electric characteristics as input into a decision logic that has been generated and deployed to the IED according to the computer-implemented method of claim 1 .

15 . An electric power system, comprising:

a power transmission line; and

the IED of claim 14 operative to perform a distance protection function for the power transmission line.

16 . A computer-implemented method comprising:

computing features for use in generating a decision logic operative to generate a signal operative for use with a power system, wherein computing the features comprises

determining a fault inception time from at least one electric characteristics of data sampled for a given type of power system, wherein the fault inception time is identified as earlier one of

a time at which a modulus of a deviation of a voltage from a sliding window moving average of the voltage reaches or exceeds a phase-specific voltage threshold and

a time at which a modulus of a deviation of a current from a sliding window moving average of the current reaches or exceeds a phase-specific current threshold, and

determining rate of change values indicative of a rate of change of the at least one electric characteristics for at least one sample time, the at least one sample time being dependent on the fault inception time;

using the features to generate the decision logic; and

deploying the decision logic to an intelligent electronic device (IED) to generate the signal.

17 . A computer-implemented method comprising:

computing features for use in generating a decision logic operative to generate a signal operative for use with a power system, wherein computing the features comprises

determining a fault inception time from at least one electric characteristics of data sampled for a given type of power system, wherein determining the fault inception time comprises determining a sliding window moving average and comparing a modulus of a deviation of the at least one electric characteristic from the sliding window moving average to at least one threshold, and

determining rate of change values indicative of a rate of change of the at least one electric characteristics for at least one sample time, the at least one sample time being dependent on the fault inception time;

using the features to generate the decision logic; and

deploying the decision logic to an intelligent electronic device (IED) to generate the signal.

18 . The computer-implemented method of claim 17 , further including:

determining a sliding window standard deviation of the at least one electric characteristic, wherein the at least one threshold depends on the sliding window standard deviation, and/or wherein the fault inception time is identified as earlier one of

a time at which a modulus of a deviation of a voltage from a sliding window moving average of the voltage reaches or exceeds a phase-specific voltage threshold and

a time at which a modulus of a deviation of a current from a sliding window moving average of the current reaches or exceeds a phase-specific current threshold.

Assignments (2)

Priority Claims (2)

Continuity (1)

References Cited (11)