2.2. Dynamic Models
In stability simulations, a dynamic model is defined as any model that can be described by a set of differential-algebraic equations (DAE), commonly presented in the following form:
where x is a vector of state variables
y is a vector of algebraic variables
p is a vector of constant or controllable parameters
t is time (an independent variable)
f(.) are the differential equations
g(.) are the algebraic equations
The main goal of the PYPOWER-Dynamics simulation algorithm is to solve the DAE systems of each dynamic model for every time step in the simulation using a numerical integration method. Since a partitioned solution approach is adopted, the algorithm also needs to interface the dynamic models into the network and then solve the algebraic network equations.
Two types of dynamic models supported in PYPOWER-Dynamics:
- Built-in models
- User-defined controller models
Built-in models are hard-coded dynamic models of common network elements such as synchronous machines, external grids, induction machines, converters, etc. Machine parameters are specified in the machine definition files (*.mach).
See also the Machine Models reference page and Converter Models reference page for more details on the built-in models available.
User-defined controller models are the dynamic models of machine controllers such as AVRs, governors, etc that are created by the user in the form of controller definition files (*.dyn).
All dynamic models in PYPOWER-Dynamics share a common structure of signals, states and parameters:
- Signals are the algebraic variables within the model
- States are the state variables of the model
- Parameters are the constant parameters for the model
In PYPOWER-dynamics, signals, states and parameters are Python dictionaries that are formed for each model and updated during the simulation.