Resumo:
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.
