System and Method to Detect and Characterize Power Quality Issues with Multi-Tier Ai-Enabled Models

A method for detecting and characterizing power quality issues, comprising:

• storing in a memory, power monitoring data on a plurality of different types of power quality issues, and executable code for executing actions by a processor;
• providing power monitoring data acquired from a plurality of different power sources associated with a particular facility or equipment;
• and
• creating a plurality of classified agents dedicated to a plurality of different types of power quality issues, the plurality of classified agents configured by the processor executing the executable code to identify instances of power quality issues of a particular type that occur in real-time at the particular facility or equipment and produce output data reflecting a critical power quality issue at the particular facility or equipment to generate an alert signal and prevent a disturbance at the particular facility or with the equipment, by further:
• training a normalizing flow model on feature vectors derived from normal-state high frequency waveform data to learn a probability distribution of normal behavior;
• calculating a negative log-likelihood of a new feature vector against said probability distribution;
• determining an anomaly score for the new feature based on an empirical cumulative distribution function of a plurality of negative log-likelihood values.