Machine learning for power grid

A machine learning system for determining propensity to failure metrics of like components within an electrical grid comprising:

• (a) a raw data assembly to provide raw data representative of the like components within the electrical grid;
• (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques;
• (c) a database, operatively coupled to the data processor, to store the more uniform data;
• (d) a machine learning engine, operatively coupled to the database, to provide:
• (i) a ranking of the collection of propensity to failure metrics for the like components, and
• (ii) absolute value of the propensity to failure metrics for the like components;
• (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics;
• and
• (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection of filtered propensity to failure metrics of like components within the electrical grid.