Machine Learning Techniques for Power System Security Assessment

Modern electricity grids continue to be vulnerable to large-scale blackouts. 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. The purpose of this paper is not to suggest that one particular kind of machine learning technique for security assessment would be more appropriate than others. We start from the premise that almost every method may be useful within some restricted context. Based on this idea, we developed an automated multi-model approach for on-line security assessment. The proposed method allows us to automatically test the different state-of-art techniques in order to find both the best algorithm and its top performance tuning for particular analyzed power system. A case study using the IEEE RTC-96 system demonstrates the effectiveness of the proposed approach. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.