Time Series-Analysis Based Engineering of High-Dimensional Wide-Area Stability Indices for Machine Learning

Information representative of actual power system dynamics is usually buried in masses of phasor measurement unit (PMU) data. To take full advantage of these data in early anticipation of stability loss, we propose to implement the high dimensional stability index (HDSI). This method allows the extraction of more than 500-labeled attributes describing generator response signals, such as speed and rate of change of transient energy function (RoCoTE). A combined 31 functions are computed from spectrum analysis based on the Periodogram and Welch methods, Lyapunov exponents, and wavelet transform approaches. The test databases are built by simulating faults on each line in the IEEE 39- and 68-bus networks. Applying comparative time-series analysis to such signal responses to disturbances then highlights the texture matrix of the stability attributes. A 10-fold support vector machine (SVM) is used to implement a HDSI-based stability prediction model, with its performance then compared to the artificial neural network (ANN), decision trees (DT), random forest (RF), and adaptive boosting (AdaBoost) models available in the statistical package R. While most methods performed similarly, with ~100% accuracy on test cases using the same set of HDSI-based attributes, the RF classifier with its associated Gini feature importance allows for explicit feature ranking and interpretation, which results in prioritization of frequency-domain over time-domain features.