Aquisição automática de conhecimento aplicada em avaliação de segurança de tensão

Abstract

One of the most challenging problems in real time operation of power systems is associated with voltage stability assessment (VSA). The problem of voltage stability is related to the ability of the power system to maintain an appropriate voltage profile on all its buses. Being directly associated with the reduction of the available reactive power reserve, the voltage instability phenomenon is characterized by a progressive reduction on the voltage magnitudes. With the goal of understanding and solving the voltage stability problem, several methodologies have been proposed during the last few years. However, most of them require a high computational burden. Analytical techniques for solving the VSA problem are infeasible to the operators to implement preventive or corrective control actions in due time. One possible solution to overcome this drawback is the application of automatic learning techniques associated with an efficient methodology for generating training patterns. Decision trees have been applied for on-line VSA. Nevertheless, decision trees are among the machine learning techniques with the highest variance. On the other hand, artificial neural networks (ANNs) have shown outstanding precision for classification and regression tasks. The major shortcoming of the ANN approach is its opacity, i.e., its low degree of human comprehensibility regarding its inference process and encoded knowledge (represented by the values of synaptic weights). This work tackles the mentioned above drawback of the ANN approach for VSA. An algorithm for qualitatively interpreting the knowledge base of a nonlinear feedforward ANN is employed. The work also proposes the application of a very fast training algorithm, Optimal Estimate Training 2 (OET2), which is fully compatible with the algorithm for rule extraction, Validity Interval Analysis (VIA), and suitable for dealing with very large databases. The main motivation for the proposed approach is to give the power system operator a set of the symbolic rules, which is consistent with the ANN inference process.