Data-Driven Classification, Reduction, Parameter Identification and State Extension in Hybrid Power Systems

The paper describes a manifold learning-based algorithm for big data classification and reduction, as well as parameter identification in real-time operation of a power system. Both black-box and gray-box settings for SCADA- and PMU-based measurements are examined. Data classification is based on diffusion maps, where an improved data-informed metric construction for partition trees is used. Data classification and reduction is demonstrated on the measurement tensor example of calculated transient dynamics between two SCADA refreshing scans. Interpolation/extension schemes for state extension of restriction (from data to reduced space) and lifting (from reduced to data space) operators are proposed. The method is illustrated on the single-phase Motor D example from a very detailed WECC load model, connected to the single bus of a real-world 441-bus power system.

Automated summary

This paper presents a manifold learning-based algorithm for big data classification and reduction, as well as parameter identification in real-time operation of a power system. The algorithm examines both black-box and gray-box settings for SCADA- and PMU-based measurements, and uses improved data-informed metric construction for partition trees in data classification. Demonstrations are made on a measurement tensor example of calculated transient dynamics between two SCADA refreshing scans.