Biblio

Dynamic security assessment provides system operators with vital information for possible preventive or emergency control to prevent security problems. In some cases, power system topology change deteriorates intelligent system-based online stability assessment performance. In this paper, we propose a new online assessment scheme to improve classification performance reliability of dynamic transient stability assessment. In the new scheme, we use an intelligent system consisting an ensemble of neural networks based on extreme learning machine. A new feature selection algorithm combining filter type method RRelief-F and wrapper type method Sequential Floating Forward Selection is proposed. Boosting learning algorithm is used in intelligent system training process which leads to higher classification accuracy. Moreover, we propose a new classification rule using weighted outputs of predictors in the ensemble helps to achieve 100% transient stability prediction in our case study.

Dynamic security assessment provides system operators with vital information for possible preventive or emergency control to prevent security problems. In some cases, power system topology change deteriorates intelligent system-based online stability assessment performance. In this paper, we propose a new online assessment scheme to improve classification performance reliability of dynamic transient stability assessment. In the new scheme, we use an intelligent system consisting an ensemble of neural networks based on extreme learning machine. A new feature selection algorithm combining filter type method RRelief-F and wrapper type method Sequential Floating Forward Selection is proposed. Boosting learning algorithm is used in intelligent system training process which leads to higher classification accuracy. Moreover, we propose a new classification rule using weighted outputs of predictors in the ensemble helps to achieve 100% transient stability prediction in our case study.