A hybrid neural network model for fast voltage contingency screening and ranking
In this paper, a hybrid neural network based approach is proposed for fast voltage contingency screening and ranking. The developed hybrid neural network is a combination of a filter module and ranking modular neural network. All the selected contingency cases are applied to the filter module, which is trained to classify them either in critical contingency class or in non-critical contingency class using a modified BP algorithm. The screened critical contingencies are passed to the ranking modular neural network for their further ranking. The ranking modular neural network reduces a K-class problem to a set of K two-class problems with a separately trained network for each of the simpler problems. Total load demand, real and reactive pre-contingency line-flows and terminal voltages in the contingent element, along with a topology number corresponding to the contingent element, are selected as input features for the neural networks. The continuous values of voltage performance index are classified into four classes (levels) according to their severity, and the modular neural network is trained for this multi-class classification problem. The effectiveness of the proposed method is demonstrated by applying it for contingency screening and ranking at different loading conditions for IEEE 30-bus system and a practical 75-bus Indian system. Once trained, the hybrid neural network gives fast and accurate screening and ranking for unknown patterns and is found to be suitable for on-line applications at Energy Management Systems.
filter module modular neural network security analysis contingency selection and ranking subordinate class dominant class imbalanced training set