Hybrid expert system neural network hierarchical architecture for classifying power system contingencies

The authors present a hierarchical architecture which couples an expert system (ES) with multiple neural networks (NNs) for classifying power system contingencies. The ES performs the `coarse´ screening to decide if a contingency is potentially harmful and then determines its type of security limit violations. It uses a set of heuristic rules and a set of performance indicators to filter out the secure contingencies and direct the potentially harmful ones for further analysis in the appropriate NN. The NN´s take the coarse classification outcome from the ES and perform a `finer´ screening by classifying the contingencies according to the severity of limit violations