Development of Rule-Based Classifiers for Rapid Stability Assessment of Wide-Area Post-Disturbance Records

Abstract

The paper proposes a systematic scheme for building compact and transparent fuzzy rule-based classifiers for rapid stability assessment; the classifiers are initialized by large accurate decision trees (DTs). The approach starts by selecting strategic monitoring buses where phasor measurement units (PMUs) are placed to capture wide-area response signals in real-time operation. These measurements are processed in the time and frequency domains for extracting selected decision features such as the peak spectral density of the angle, frequency and their dot product evaluated over the grid areas. These so-called wide-area severity indices (WASI) are reliable time-varying stability indicators that form the basis of an effective classification system. Large-size DTs are used to generate initial accurate classification boundaries for decision making as early as 1 s or 2 s after fault clearing. From the DT classification boundaries, fuzzy membership functions (MFs) are developed and the corresponding fuzzy rule base is formulated parsimoniously by eliminating redundant MFs and rules using a similarity measure. The resulting fuzzy-rule classifiers are successfully tested for system-wise and area-wise contingencies based on a large database of detailed simulations of the Hydro-Québec grid and are further confirmed on actual measurements recorded with existing wide-area measurements (WAMS).