Feature Extraction-Based Real-Time Transient Stability Analysis

With increasing amounts of asynchronous generation being deployed to meet system energy demands, many transmission corridors may become constrained by angular stability criteria rather than steady-state thermal limitations. In such a system, it is paramount to have the capability to rapidly evaluate the stability margins of the system, particularly in a threatened post-fault state. The use of single machine equivalents (SIME) has been shown to be a powerful and flexible hybrid stability analysis method which can be computed directly from measured PMU data. However, due to the nature of the system reduction employed by SIME, as well as the method of extrapolation to estimate stability margins, there are many cases where the swing of the system is not accurately modelled by the traditional methods until a significant amount of data has been collected, at which point it may be too late to respond to the threat. In this paper, we address some of the limitations imposed by the traditional methods of reduction and prediction. We propose a method where rather than identifying a single critical SIME model for stability prediction, a spectrum of SIME representations of the post-fault system is developed, yielding a more timely and accurate estimate of post-fault stability conditions, through observation and feature extraction.