  Dynamic power system models are instrumental in real-time stability,
monitoring, and control. Such models are traditionally posed as systems of
nonlinear differential algebraic equations (DAEs): the dynamical part models
generator transients and the algebraic one captures network power flow. While
the literature on control and monitoring for ordinary differential equation
(ODE) models of power systems is indeed rich, that on DAE systems is
\textit{not}. DAE system theory is less understood in the context of power
system dynamics. To that end, this letter presents two new mathematical
transformations for nonlinear DAE models that yield nonlinear ODE models whilst
retaining the complete nonlinear DAE structure and algebraic variables. Such
transformations make (more accurate) power system DAE models more amenable to a
host of control and state estimation algorithms designed for ODE dynamical
systems. We showcase that the proposed models are effective, simple, and
computationally scalable.
