Arc fault detection using machine learning

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-updatable and a non-field-updatable program portion, the field-updatable program portion including a plurality of machine learning model 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.

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 machine learning model 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.

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 machine learning model 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.