This work focuses on the design of modern power system controllers for
automatic voltage regulators (AVR) and the applications of machine learning
(ML) algorithms to correctly classify the stability of the IEEE 14 bus system.
The LQG controller performs the best time domain characteristics compared to
PID and LQG, while the sensor and amplifier gain is changed in a dynamic
passion. After that, the IEEE 14 bus system is modeled, and contingency
scenarios are simulated in the System Modelica Dymola environment. Application
of the Monte Carlo principle with modified Poissons probability distribution
principle is reviewed from the literature that reduces the total contingency
from 1000k to 20k. The damping ratio of the contingency is then extracted,
pre-processed, and fed to ML algorithms, such as logistic regression, support
vector machine, decision trees, random forests, Naive Bayes, and k-nearest
neighbor. A neural network (NN) of one, two, three, five, seven, and ten hidden
layers with 25%, 50%, 75%, and 100% data size is considered to observe and
compare the prediction time, accuracy, precision, and recall value. At lower
data size, 25%, in the neural network with two-hidden layers and a single
hidden layer, the accuracy becomes 95.70% and 97.38%, respectively. Increasing
the hidden layer of NN beyond a second does not increase the overall score and
takes a much longer prediction time; thus could be discarded for similar
analysis. Moreover, when five, seven, and ten hidden layers are used, the F1
score reduces. However, in practical scenarios, where the data set contains
more features and a variety of classes, higher data size is required for NN for
proper training. This research will provide more insight into the damping
ratio-based system stability prediction with traditional ML algorithms and
neural networks.Comment: Masters Thesis Dissertatio