On-line voltage stability monitoring using an Ensemble AdaBoost classifier

Predictive modeling in an electrical power systems is currently gaining momentum especially as Phasor Measurement Units (PMUs) are being deployed in modern electrical grids to replace the Supervisory Control and Data Acquisition System (SCADA). This paper evaluates machine learning algorithms for the task of monitoring voltage instability for online decision making. In particular the performance of the Naïve Bayesian, K-Nearest Neighbors, Decision Tree and Ensembles classifiers (XGBoost, Bagging, Random Forest, and AdaBoost) were compared. Performance evaluation measures of Precision, Recall, F1-score, and Accuracy were adopted to evaluate the performance of the classifiers. In this paper, a number of voltage stability operating points were generated with different variations of load/generation, using the PSSE Power-Voltage (PV) analysis tool. An IEEE 39 bus was used as a test system. Sufficient training patterns that captured different Operating Points (OPs) at base-case and at multiple contingencies (N-k) were gathered to train machine learning methods to identify acceptable operating conditions and near collapse situations. Experimental results show that AdaBoost achieved the highest classification accuracy, i.e. 96.02%, compared to the other classifiers.