Energy storage systems and methods for predictive health monitoring and fault detection for a power conversion system

A method for thermal health monitoring of an inverter in a battery energy storage system, the method comprising:

• determining, by one or more processors, a thermal response rate for operational data associated with the inverter using a multi-scale thermal response rate analysis;
• determining, by the one or more processors, one or more cross sensor correlations based on the thermal response rate using horizontal sensor-to-sensor analysis and vertical stack-to-stack analysis;
• determining, by the one or more processors, one or more temperature differentials based on the one or more cross sensor correlations;
• determining, by the one or more processors, a trend analysis for each of a plurality of sensors of the inverter based on the one or more temperature differentials;
• and
• generating, by the one or more processors, a thermal health index based on one or more of the thermal response rate, the one or more cross sensor correlations, the one or more temperature differentials, or the trend analysis associated with the inverter.

A method for training a machine learning model to predict a health of an inverter in a battery energy storage system, the method comprising:

• receiving, by one or more processors, first data regarding one or more element attributes associated with the inverter, wherein the first data includes a plurality of operational parameters;
• extracting, by the one or more processors, one or more first features from the first data based on analyzing processing algorithms identifying at least one of a thermal response pattern, an electrical parameter variation, or a component behavior signature;
• extracting, by the one or more processors, one or more second features from second data, wherein the second data includes one or more of an environmental condition, an operational loading pattern, a maintenance history record, or a component failure;
• training, by the one or more processors, the machine learning model to determine correlations between the one or more element attributes and one or more health assessment values based on the one or more first features and the one or more second features;
• and
• validating, by the one or more processors, the trained machine learning model using validation data to ensure accurate health predictions for operational inverter systems.