Systems and methods for detecting intermittent loose connection in battery energy storage systems

A non-transitory computer readable medium for controlling a battery energy storage system, the non-transitory computer readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving a current delta for each battery rack of a plurality of battery racks, wherein the current delta is a first difference in current between each battery rack and a mean or median current of the plurality of battery racks;
• determining, via a machine learning model, a first battery rack of the plurality of battery racks by:
• comparing the current delta for each battery rack of the plurality of battery racks to a performance threshold, and
• based on the comparing, determining the first battery rack of the plurality of battery racks;
• determining, via the machine learning model, a rack deviation by determining a second difference between the mean or median current of each of the plurality of battery racks and a current of the first battery rack over a period of time, wherein the period of time includes ten to thirty second intervals over 24 hours, and wherein the machine learning model determines the first battery rack based on current data;
• and
• initiating an action based on the rack deviation, the action including one or more of:
• generating a notification based on the first battery rack and the rack deviation,
• deactivating the first battery rack, or
• discharging the first battery rack.

A battery energy storage system comprising:

• one or more processors;
• and
• one or more memories storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including:
• receiving a current delta for each battery rack of a plurality of battery racks, wherein the current delta is a first difference in current between each battery rack and a mean or median current of the plurality of battery racks;
• determining, via a machine learning model, a first battery rack of the plurality of battery racks by:
• comparing the current delta for each battery rack of the plurality of battery racks to a performance threshold, wherein the performance threshold is a scaled anomaly score from 0.2 to 0.4, and
• based on the comparing, determining the first battery rack of the plurality of battery racks;
• determining, via the machine learning model, a rack deviation by determining a second difference between the mean or median current of each of the plurality of battery racks and a current of the first battery rack over a period of time, wherein the period of time includes ten to thirty second intervals over 24 hours, and wherein the machine learning model determines the first battery rack based on current data;
• and
• initiating an action based on the rack deviation, the action including one or more of:
• generating a notification based on the first battery rack and the rack deviation,
• deactivating the first battery rack, or
• discharging the first battery rack.