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. >