# US 2023/0053431 A1 · Arc fault detection using machine learning

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2023/0053431 A1
- Document ID: US20230053431A1
- Status: Published application
- Kind: A1
- Application number: 17/778,524
- Filed: 2022-05-18
- Published: 2023-02-23
- Earliest claimed: 2021-06-15
- Assignees: Leviton Manufacturing Co., Inc.
- Applicants: Leviton Manufacturing Co., Inc.
- Inventors: Yazhou Zhang; Michael Ostrovsky; Ankit Sanghvi; Adam Kevelos
- Web page: https://patentgrep.com/patent/US20230053431A1

## CPC

- H02H 1/0015: {Using arc detectors}
- G01R 31/12: Testing dielectric strength or breakdown voltage {; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing (G01R31/08, G01R31/327 and G01R31/72 take precedence; measuring in plasmas G01R19/0061; measuring dielectric constants G01R27/2617; ESD, EMC or EMP testing of circuits G01R31/002)}
- G01R 31/3333: {Apparatus, systems or circuits therefor}
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 20/00: Machine learning
- H02H 1/0092: {concerning the data processing means, e.g. expert systems, neural networks}
- H02H 3/46: responsive to frequency deviations
- H02H 3/33: using summation current transformers (H02H3/347 takes precedence)
- H02H 3/52: responsive to the appearance of harmonics
- H02H: EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS (indicating or signalling undesired working conditions G01R, e.g. G01R31/00, G08B; locating faults along lines G01R31/08; emergency protective devices H01H)
- G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

In aspects of the present disclosure, a circuit interrupter includes a housing, a conductive path, a switch which selectively interrupts the conductive path, sensor(s), memory, and a controller within the housing. The sensor(s) measure electrical characteristic(s) of the conductive path. The memory stores an arc detection program that implements a machine learning model and includes a field-updatable program portion and a non-field-updatable program portion, where the field-updatable program portion includes program parameters used by the non-field-updatable program portion to decide between presence or absence of an arc fault. The controller executes the arc detection program to compute input data for the machine learning model based on the sensor measurements, decide between presence of an arc event or absence of an arc event based on the input data, and cause the switch to interrupt the conductive path when the decision indicates presence of an arc event.

## Claims

### Claim 1

A circuit interrupter comprising:
a housing configured to be installed in at least one of an electrical wall box or a load center;
a conductive path;
a switch configured to selectively interrupt the conductive path;
a sensor in electrical communication with the conductive path and configured to measure an electrical characteristic of the conductive path, the sensor configured to provide a plurality of sensor measurements;
a memory configured to store an arc detection program, the arc detection program configured to implement a machine learning model, the arc detection program including a field-updatable program portion configured to be field-updatable and a non-field-updatable program portion, the field-updatable program portion including a plurality of program parameters, wherein the non-field-updatable program portion is configured to use the plurality of program parameters to decide between presence of an arc event or absence of an arc event; and
a controller positioned within the housing and coupled to the memory, wherein the arc detection program, when executed by the controller, causes the controller to perform an operation comprising:
computing input data for the machine learning model based on the plurality of sensor measurements,
providing a decision between presence of an arc event or absence of an arc event, the decision based on the input data, and
causing the switch to interrupt the conductive path when the decision indicates presence of an arc event.

### Claim 2 (depends on 1)

The circuit interrupter of claim 1, further comprising a communication device,
wherein the controller is further configured to cause the communication device to communicate, with a remote device, at least one of: the plurality of sensor measurements or the input data computed based on the plurality of sensor measurements.

### Claim 3 (depends on 2)

The circuit interrupter of claim 2, further comprising a user-accessible actuator,
wherein the controller is configured to cause the communication device to communicate with the remote device when the user-accessible actuator is actuated.

### Claim 4 (depends on 2)

The circuit interrupter of claim 2, wherein the memory is further configured to store a firmware update program which, when executed by the controller, causes the controller to perform a further operation comprising:
causing the communication device to receive an updated program portion having reduced false positive arc event decisions; and
replacing the field-updatable program portion of the arc detection program with the updated program portion.

### Claim 5 (depends on 1)

The circuit interrupter of claim 1, wherein the arc detection program implements a machine learning model having nodes,
wherein the plurality of program parameters of the field-updatable program portion are node parameters of the nodes of the machine learning model, and
wherein the non-field-updatable program portion is configured to perform node operations for the nodes of the machine learning model.

### Claim 6 (depends on 5)

The circuit interrupter of claim 5, wherein the machine learning model is a decision tree,
wherein the node parameters include, for each node of the decision tree, one of: a branch criterion and destination nodes, or a decision tree decision, and
wherein the node operations comprise, for each node of the decision tree:
deciding whether the node parameters for the node include a decision tree decision or a branch criterion,
in the case the node parameters for the node include of a decision tree decision, providing the decision tree decision, and
in the case the node parameters for the node include a branch criterion and destination nodes, apply the branch criterion to the input data to select one of the destination nodes, and repeat the node operations for the selected destination node.

### Claim 7 (depends on 1)

The circuit interrupter of claim 1, wherein the plurality of sensor measurements include high frequency component measurements relating to a high frequency characteristic of the conductive path,
wherein the input data includes high frequency component input data computed based on the high frequency component measurements, the high frequency component input data including at least one of:
(i) a close to low count computed as: a number of the high frequency component measurements in a first low measurement range when an average of the high frequency component measurements is in a first range, or a number of the high frequency component measurements in a second low measurement range when the average of the high frequency component measurements is in a second range,
(ii) a close to high count computed as: a number of the high frequency component measurements in a first high measurement range when the average of the high frequency component measurements is in the first range, or a number of the high frequency component measurements in a second high measurement range when the average of the high frequency component measurements is in the second range,
(iii) an index difference between a first index corresponding to a maximum increase between consecutive measurements of the high frequency component measurements and a second index corresponding to a maximum decrease between consecutive measurements of the high frequency component measurements,
(iv) a low count computed as a number of the high frequency component measurements in a third low measurement range, wherein the third low measurement range is below the second range,
(v) a first high count computed as a number of the high frequency component measurements in a third high measurement range, or
(vi) a second high count computed as a number of the high frequency component samples in a fourth high measurement range.

### Claim 8

A method in a circuit interrupter having a conductive path, a switch configured to selectively interrupt the conductive path, and at least one sensor in electrical communication with the conductive path and configured to measure at least one electrical characteristic of the conductive path to provide a plurality of sensor measurements, the method comprising:
executing an arc detection program in the circuit interrupter, the arc detection program implementing a machine learning model and including a field-updatable program portion configured to be field-up datable and a non-field-up datable program portion, the field-up datable program portion including a plurality of program parameters to be used by the non-field-updatable program portion for deciding between presence of an arc event or absence of an arc event,
wherein executing the arc detection program causes the circuit interrupter to perform an operation comprising:
computing input data for the machine learning model based on the plurality of sensor measurements,
deciding between presence of an arc event or absence of an arc event, based on the input data, to provide a decision, and
causing the switch to interrupt the conductive path when the decision indicates presence of an arc event.

### Claim 9 (depends on 8)

The method of claim 8, further comprising communicating, with a remote device, at least one of: the plurality of sensor measurements or the input data computed based on the plurality of sensor measurements, when the decision indicating presence of an arc event corresponds to a false positive arc event decision.

### Claim 10 (depends on 9)

The method of claim 9, further comprising:
receiving an updated program portion having reduced false positive arc event decisions; and
replacing the field-updatable program portion of the arc detection program with the updated program portion.

### Claim 11 (depends on 8)

The method of claim 8, wherein the arc detection program implements a machine learning model having nodes,
wherein the plurality of program parameters of the field-updatable program portion are node parameters of the nodes of the machine learning model, and
wherein the non-field-updatable program portion is configured to perform node operations for the nodes of the machine learning model.

### Claim 12 (depends on 11)

The method of claim 11, wherein the machine learning model is a decision tree,
wherein the node parameters include, for each node of the decision tree, one of: a branch criterion and destination nodes, or a decision tree decision, and
wherein the node operations comprise, for each node of the decision tree:
deciding whether the node parameters for the node include a decision tree decision or a branch criterion,
in the case the node parameters for the node include of a decision tree decision, providing the decision tree decision, and
in the case the node parameters for the node include a branch criterion and destination nodes, apply the branch criterion to the input data to select one of the destination nodes, and repeat the node operations for the selected destination node.

### Claim 13 (depends on 8)

The method of claim 8, wherein the plurality of sensor measurements include high frequency component measurements relating to a high frequency characteristic of the conductive path,
wherein the input data includes high frequency component input data computed based on the high frequency component measurements, the high frequency component input data including at least one of:
(i) a close to low count computed as: a number of the high frequency component measurements in a first low measurement range when an average of the high frequency component measurements is in a first range, or a number of the high frequency component measurements in a second low measurement range when the average of the high frequency component measurements is in a second range,
(ii) a close to high count computed as: a number of the high frequency component measurements in a first high measurement range when the average of the high frequency component measurements is in the first range, or a number of the high frequency component measurements in a second high measurement range when the average of the high frequency component measurements is in the second range,
(iii) an index difference between a first index corresponding to a maximum increase between consecutive measurements of the high frequency component measurements and a second index corresponding to a maximum decrease between consecutive measurements of the high frequency component measurements,
(iv) a low count computed as a number of the high frequency component measurements in a third low measurement range, wherein the third low measurement range is below the second range,
(v) a first high count computed as a number of the high frequency component measurements in a third high measurement range, or
(vi) a second high count computed as a number of the high frequency component samples in a fourth high measurement range.

### Claim 14

A processor-implemented method for updating installed arc fault circuit interrupt (AFCI) devices configured to execute an arc detection program implementing a machine learning model, the arc detection program including a field-updatable program portion configured to be field-updatable and a non-field-updatable program portion, the field-updatable program portion including a plurality of program parameters to be used by the non-field-updatable program portion for deciding between presence of an arc event or absence of an arc event, the method comprising:
receiving, from the installed AFCI devices, event data including at least one of: (i) a plurality of sensor measurements provided by sensors, in the installed AFCI devices, configured to measure at least one electrical characteristic of conductive paths in the AFCI devices, or (ii) input data for the machine learning model computed, in the installed AFCI devices, based on the plurality of sensor measurements;
preparing, based on the event data, an updated program portion having reduced false positive arc event decisions; and
communicating, to the installed AFCI devices, the updated program portion as a replacement for the field-updatable program portion of the arc detection program.

### Claim 15 (depends on 14)

The processor-implemented method of claim 14, wherein preparing the updated program portion having reduced false positive arc event decisions includes:
in case the event data is the plurality of sensor measurements, computing the input data for the machine learning model based on the plurality of sensor measurements;
associating the input data with a label indicating absence of an arc event;
training an updated machine learning model using the input data and the label; and
providing parameters of the updated machine learning model as the updated program portion.

### Claim 16 (depends on 14)

The processor-implemented method of claim 14, wherein the arc detection program implements a machine learning model having nodes,
wherein the plurality of program parameters of the field-updatable program portion are node parameters of the nodes of the machine learning model, and
wherein the non-field-updatable program portion is configured to perform node operations for the nodes of the machine learning model.

### Claim 17 (depends on 16)

The processor-implemented method of claim 16, wherein the machine learning model is a decision tree,
wherein the node parameters include, for each node of the decision tree, one of: a branch criterion and destination nodes, or a decision tree decision, and
wherein the node operations comprise, for each node of the decision tree:
deciding whether the node parameters for the node include a decision tree decision or a branch criterion,
in the case the node parameters for the node include of a decision tree decision, providing the decision tree decision, and
in the case the node parameters for the node include a branch criterion and destination nodes, apply the branch criterion to the input data to select one of the destination nodes, and repeat the node operations for the selected destination node.

### Claim 18

A system for updating installed arc fault circuit interrupt (AFCI) devices configured to execute an arc detection program implementing a machine learning model, the arc detection program including a field-updatable program portion configured to be field-updatable and a non-field-updatable program portion, the field-updatable program portion including a plurality of program parameters to be used by the non-field-updatable program portion for deciding between presence of an arc event or absence of an arc event, the system comprising:
at least one processor; and
one or more memory storing instructions which, when executed by the at least one processor, cause the system to perform an operation comprising:
receiving, from the installed AFCI devices, event data including at least one of: (i) a plurality of sensor measurements provided by sensors, in the installed AFCI devices, configured to measure at least one electrical characteristic conductive paths in the AFCI devices, or (ii) input data for the machine learning model computed, in the installed AFCI devices, based on the plurality of sensor measurements;
preparing, based on the event data, an updated program portion having reduced false positive arc event decisions; and
communicating, to the installed AFCI devices, the updated program portion as a replacement for the field-updatable program portion of the arc detection program.

### Claim 19 (depends on 18)

The system of claim 18, wherein in preparing the updated program portion having reduced false positive arc event decisions, the instructions, when executed by the at least one processor, cause the system to:
in case the event data is the plurality of sensor measurements, compute the input data for the machine learning model based on the plurality of sensor measurements;
associate the input data with a label indicating absence of an arc event;
train an updated machine learning model using the input data and the label; and
provide parameters of the updated machine learning model as the updated program portion.

### Claim 20 (depends on 18)

The system of claim 18, wherein the arc detection program implements a machine learning model having nodes,
wherein the plurality of program parameters of the field-updatable program portion are node parameters of the nodes of the machine learning model, and
wherein the non-field-updatable program portion is configured to perform node operations for the nodes of the machine learning model.

### Claim 21 (depends on 20)

The system of claim 20, wherein the machine learning model is a decision tree,
wherein the node parameters include, for each node of the decision tree, one of: a branch criterion and destination nodes, or a decision tree decision, and
wherein the node operations comprise, for each node of the decision tree:
deciding whether the node parameters for the node include a decision tree decision or a branch criterion,
in the case the node parameters for the node include of a decision tree decision, providing the decision tree decision, and
in the case the node parameters for the node include a branch criterion and destination nodes, apply the branch criterion to the input data to select one of the destination nodes, and repeat the node operations for the selected destination node.