Resumo:
The aim of this master thesis is to compare voltage and system loading mapping capabilities
of a variety of regression algorithms, such as Adaptive Network based Fuzzy Inference
System (ANFIS), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Support
Vector Regression (SVR), and Decision Tree (DT). A voltage sensitivity matrix is
generated from the power flow Jacobian matrix for a loading scenario near the unstable
point. Principal Component Analysis (PCA) is used to separate the system, close
to the critical point, in order to group the buses into coherent voltage controlling areas.
For different reactive power injection scenarios, we have different bus voltages that can
be mapped by the aforementioned regression algorithms. The algorithms are trained with
limited amounts of data, in order to establish a fair comparison between them. The present
work shows that ANFIS and KNN have a better performance in critical voltage and load
prediction when compared to the rest. The academic IEEE 14 and 118 bus systems are
employed with all its limits considered, so the results may be reproduced.
