An ensemble based data mining model for contingency analysis of power system under STLO

In a large, interconnected power system, contingency analysis is a useful tool for pinpointing the potential consequences of post-event scenarios on the system's safety. In this work, the Newton-Raphson technique is applied to every single outage of a transmission line to compute the load flows. For the static security classification of the power system, the line voltage stability performance index (LVSI) is used. There are three levels of static security of power system namely: non-critical (the least severe), semi-critically insecure (the next lowest severe), and critical (the next highest severe). The various data mining techniques such as decision trees, bagging-based ensemble methods, and boosting-based ensemble methods were applied to assess the severity of the line under various loading and contingency conditions. Test systems based on the IEEE 30 bus system were used with the proposed machine learning classifiers. The experimental results proved that bagging based ensemble method provided better accuracy compared to the decision tree and the AdaBoost ensemble method for predicting the power system security assessment. The bagging-based ensemble method has a predictive accuracy of 85% and an AUC of 0.94.