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An hybrid approach based on neural networks and regression Tree Models for fast dynamic security assessment

This paper presents a new hybrid automatic learning approach, which combines artificial neural networks (ANN) and regression trees (RT), to perform on-line dynamic security assessment of power systems. In the proposed method, the RT is firstly used to split the vast amount of knowledge data that describes a security problem into several less spread and disjoint problems. Then, an ANN is trained for each of these new smaller problems, resulting in a tree structure with an ANN predicting function associated to each leaf. Moreover, the capability of the RT to perform feature subset selection before ANN training is also tested. With this new method, the advantages of the two techniques are exploited in order to obtained a more accurate model without compromising prediction time. The quality of the approach is illustrated through its application to a major security problem of the power system of Madeira Island (Portugal).