Parameter optimization of each of the supervised learning algorithm is performed through cross-validation of varying parameters with optimal accuracy. The figures show the accuracy of each of the three algorithms
with varying parameters on the IEEE 14-bus system. SVM is cross-validated for varying kernel coefficient and penalty parameter,
gamma and
C respectively, KNN is cross-validated for varying number of neighbours,
K,
and ANN is
cross-validated for varying learning rate,
alpha. The data used for this cross-validation consists of all the measurements of the system. Optimal parameters of each learning algorithms are selected based on the maximum accuracy
achieved on the IEEE 14-bus system with no FS.