Modeling Blocks

State Freeze

State freeze is used by converter controllers during fault transients to fix a variable at the pre-fault values. The concept of state freeze is applicable to both state or algebraic variables. For example, in the renewable energy electric control model (REECA), the proportional-integral controllers for reactive power error and voltage error are subject to state freeze when voltage dip is observed. The internal and output states should be frozen when the freeze signal turns one and freed when the signal turns back to zero.

Freezing a state variable can be easily implemented by multiplying the freeze signal with the right-hand side (RHS) of the differential equation:

\[T \dot{x} = (1 - z_f) \times f(x)\]

where \(f(x)\) is the original RHS of the differential equation, and \(z_f\) is the freeze signal. When \(z_f\) becomes zero the differential equation will evaluate to zero, making the increment zero.

Freezing an algebraic variable is more complicate to implement. One might consider a similar solution to freezing a differential variable by constructing a piecewise equation, for example,

\[0 = (1 - z_f)\times g(y)\]

where \(g(y)\) is the original RHS of the algebraic equation. One might also need to add a small value to the diagonals of dae.gy associated with the algebraic variable to avoid singularity. The rationale behind this implementation is to zero out the algebraic equation mismatch and thus stop incremental correction: in the frozen state, since \(z_f\) switches to zero, the algebraic increment should be forced to zero. This method, however, would not work when a dishonest Newton method is used.

If the Jacobian matrix is not updated after \(z_f\) switches to one, in the row associated with the equation, the derivatives will remain the same. For the algebraic equation of the PI controller given by

\[0 = (K_p u + x_i) - y\]

where \(K_p\) is the proportional gain, \(u\) is the input, \(x_I\) is the integrator output, and \(y\) is the PI controller output, the derivatives w.r.t \(u\), \(x_i\) and \(y\) are nonzero in the pre-frozen state. These derivative corrects \(y\) following the changes of \(u\) and \(x\). Although \(x\) has been frozen, if the Jacobian is not rebuilt, correction will still be made due to the change of \(u\). Since this equation is linear, only one iteration is needed to let \(y\) track the changes of \(u\). For nonlinear algebraic variables, this approach will likely give wrong results, since the residual is pegged at zero.

To correctly freeze an algebraic variable, the freezing signal needs to be passed to an EventFlag, which will set an custom_event flag if any input changes. EventFlag is a VarService that will be evaluated at each iteration after discrete components and before equations.

Profiling Import

To speed up the command-line program, import profiling is used to breakdown the program loading time.

With tool profimp, andes can be profiled with profimp "import andes" --html > andes_import.htm. The report can be viewed in any web browser.

What won't not work

You might have heard that PyPy is faster than CPython due to a built-in JIT compiler. Before you spend an hour compiling the dependencies, here is the fact: PyPy won't work for speeding up ANDES.

PyPy is often much slower than CPython when using CPython extension modules (see the PyPy_FAQ). Unfortunately, NumPy is one of the highly optimized libraries that heavily use CPython extension modules.