A Real-Time Self-Healing Methodology Using Model- and Measurement-Based Islanding Algorithms

In this paper, a new real-time defensive islanding method, which is adaptive to the operating conditions is proposed. In the method, a number of candidate islanding schemes are generated using both model- and measurement-based islanding algorithms after detecting a severe fault in the system by means of a new severity index based on generator bus voltage frequency measurements. Of model-based algorithms, slow coherency-based islanding, in which the prefault measurements are utilized, is adopted. On the other hand, K-means, hierarchical, and fuzzy relational eigenvector centrality-based clustering are employed as measurement-based islanding algorithms, where the postfault measurements of the evolving dynamics after the severe fault are utilized. A faster-than-real-time software platform is, then, employed to validate the success of the candidate schemes in healing the system. Among the successful schemes, the one resulting in the least load imbalance is chosen to be applied. All the computations from the detection of the fault to the application of islanding are performed in real-time, directly after the occurrence of the fault. The proposed method is demonstrated on the 37-generator 127-bus WSCC power test system, and on a model of the Turkish power system to assess the method's performance.