Decision Tree Technologies to Power System Monitoring and Security Assessment

Abstract Majority of recent large-scale blackouts have been caused by voltage instability. As all states leading to large-scale blackouts are unique, there is no “algorithm” to identify pre-emergency states. Moreover, numerical conventional methods are computationally expensive, which makes it difficult to use for the on-line security assessment. Machine learning techniques with their pattern recognition, learning capabilities and high speed of identifying the potential security boundaries can offer an alternative approach. This paper proposes a novel semi-automated method based on ensemble decision trees (DTs) learning for on-line voltage security assessment. Operating conditions are randomly generated. Multiple DTs are first trained off-line using the resampling cross-validation method. The DT learning algorithm is implemented using C4.5 decision tree, Classification and Regression Tree (CART), bagged CART, Random Forest, Extra Trees and Stochastic Gradient Descent tree. The best model is selected based on its performance. The obtained security model is used on-line to classify the system operating states based on the patterns created in the off-line simulations. If required, the final DT model can produce an alarm for triggering emergency and protection systems. A case study using the IEEE 118-bus system demonstrates the effectiveness of the proposed approach. The results showed that ensemble DT learning approach can identify potentially dangerous states with higher accuracy than other learning techniques such as neural networks and support vector machine