Advanced Pattern Discovery-based Fuzzy Classification Method for Power System Dynamic Security Assessment

Dynamic security assessment (DSA) is an important issue in modern power system security analysis. This paper proposes a novel pattern discovery (PD)-based fuzzy classification scheme for the DSA. First, the PD algorithm is improved by integrating the proposed centroid deviation analysis technique and the prior knowledge of the training data set. This improvement can enhance the performance when it is applied to extract the patterns of data from a training data set. Secondly, based on the results of the improved PD algorithm, a fuzzy logic-based classification method is developed to predict the security index of a given power system operating point. In addition, the proposed scheme is tested on the IEEE 50-machine system and is compared with other state-of-the-art classification techniques. The comparison demonstrates that the proposed model is more effective in the DSA of a power system.

I. Introduction

Modern power system is one of the most complex artificial systems in the world. It is operated to maintain a continuous process of electricity generation, transmission, and distribution. However, the operation of a power system is inevitably exposed to all kinds of disturbances and faults, such as short-circuit of a transmission line or an unexpected generator outage. Depending on the severity of the disturbances and faults, the power system may lose its security, which can result in catastrophic consequences, such as wide-spread blackouts.