A Dynamic Behavior-Based Bulk Power System Event Signature Library with Empirical Clustering

Abstract

The grid reinforcement, advanced grid stabilizing systems, and inverter-interfaced loads have varied power system dynamics. The changing trends of various dynamic phenomena need to be scrutinized to ensure future grid reliability. A dynamic behavior-based event signature library of phasor measurement unit (PMU) data has great potential to discover new and unprecedented event signatures. This paper presents an event signature library design that further defines more granular event categories within the major event categories (e.g., frequency, voltage, and oscillation events) provided by electric utilities and regional transmission organizations. The proposed library design embraces a supervised machine learning approach with a deep neural network (DNN) model and manually-generated labels. The input of the model uses representative PMUs that evidently express dominant event signatures. The performance of the event categorization module was evaluated, via information entropy, against labels generated automatically from clustering analyses. We applied the event signature library design to two years of over 1000 actual events in the bulk U.S. power system. The module obtains remarkable event discrimination capability.