  As the intention is to reduce the amount of thermal generation and to
increase the share of clean energy, power systems are increasingly becoming
susceptible to frequency instability after outages due to reduced levels of
inertia. To address this issue frequency constraints are being included in the
scheduling process, which ensure a tolerable frequency deviation in case of any
contingencies. In this paper, a method is proposed to integrate the non-linear
frequency nadir constraint into the unit commitment problem, using machine
learning. First a synthetic training dataset is generated. Then two of the
available classic machine learning methods, namely logistic regression and
support vector machine, are proposed to predict the frequency nadir. To be able
to compare the machine learning methods to traditional frequency constrained
unit commitment approaches, simulations on the power system of La Palma island
are carried out for both proposed methods as well as an analytical linearized
formulation of the frequency nadir. Our results show that the unit commitment
problem with a machine learning based frequency nadir constraint is solved
considerably faster than with the analytical formulation, while still achieving
an acceptable frequency response quality after outages.
