Comparação dos algoritmos C4.5 e MLP usados na avaliação da segurança dinâmica e no auxílio ao controle preventivo no contexto da estabilidade transitória de sistemas de potência

Abstract

This work compares the C4.5 and multilayer perceptron (MLP) algorithms applied for dynamic security assessment (DSA) and power system stability transient preventive control design. C4.5 is an algorithm of the decision tree (DT) technique and the MLP is a member of artificial neural network (ANNs) family. The advent of DTs and ANNs provides solution to real-time DSA issues in order to identify quickly when a power system is subjected to a critical disturbance (short-circuit) that may lead to transient instability. In addition, the knowledge obtained by both techniques can be utilized in the preventive control design to restore the power system security against critical disturbances. Based on the data base generation with exhaustive time-domain simulations, some specific critical disturbances are taken as examples to compare the C4.5 and MLP algorithms employed to DSA and guideline to preventive actions. The comparative study is tested on the New England power system. In the case studies, the knowledge database is generated by using PSTv3 (Power System Toolbox) software. The DTs and ANNs are trained and tested by the Rapidminer software. The obtained results have demonstrated a promising application of the C4.5 and MLP algorithms used in power system DSA and preventive control design.