def first_time_step(system):
"""Compute the first time step"""
settings = system.TDS
if not system.DAE.n:
freq = 1.0
elif system.DAE.n == 1:
B = matrix(system.DAE.Gx)
linsolve(system.DAE.Gy, B)
As = system.DAE.Fx - system.DAE.Fy * B
freq = abs(As[0, 0])
else:
freq = 20.0
if freq > system.Settings.freq:
freq = float(system.Settings.freq)
tspan = abs(settings.tf - settings.t0)
tcycle = 1 / freq
settings.deltatmax = min(5 * tcycle, tspan / 100.0)
settings.deltat = min(tcycle, tspan / 100.0)
settings.deltatmin = min(tcycle / 64, settings.deltatmax / 20)
if settings.fixt:
if settings.tstep <= 0:
system.Log.warning('Fixed time step is negative or zero')
system.Log.warning('Switching to automatic time step')
settings.fixt = False
else:
settings.deltat = settings.tstep
if settings.tstep < settings.deltatmin:
system.Log.warning(
'Fixed time step is below the estimated minimum')
return settings.deltat
def linsolve(self, A, b):
"""
Solve linear equation set ``Ax = b`` and returns the solutions in a 1-D array.
This function performs both symbolic and numeric factorizations every time, and can be slower than
``Solver.solve``.
Parameters
----------
A
Sparse matrix
b
RHS of the equation
Returns
-------
The solution in a 1-D np array.
"""
if self.sparselib == 'umfpack':
try:
umfpack.linsolve(A, b)
except ArithmeticError:
logger.error('Singular matrix. Case is not solvable')
return np.ravel(b)
elif self.sparselib == 'klu':
try:
klu.linsolve(A, b)
except ArithmeticError:
logger.error('Singular matrix. Case is not solvable')
return np.ravel(b)
elif self.sparselib in ('spsolve', 'cupy'):
ccs = A.CCS
size = A.size
data = np.array(ccs[2]).reshape((-1, ))
indices = np.array(ccs[1]).reshape((-1, ))
indptr = np.array(ccs[0]).reshape((-1, ))
A = csc_matrix((data, indices, indptr), shape=size)
if self.sparselib == 'spsolve':
x = spsolve(A, b)
return np.ravel(x)
elif self.sparselib == 'cupy':
# delayed import for startup speed
import cupy as cp # NOQA
from cupyx.scipy.sparse import csc_matrix as csc_cu # NOQA
from cupyx.scipy.sparse.linalg.solve import lsqr as cu_lsqr # NOQA
cu_A = csc_cu(A)
cu_b = cp.array(np.array(b).reshape((-1, )))
x = cu_lsqr(cu_A, cu_b)
return np.ravel(cp.asnumpy(x[0]))
def klu_linsolve(A, b):
"""
KLU wrapper function for linear system solve A x = b
:param A: System matrix
:param b: right hand side
:return: solution
"""
A2 = A.tocoo()
A_cvxopt = cvxopt.spmatrix(A2.data, A2.row, A2.col, A2.shape, 'd')
x = cvxopt.matrix(b)
klu.linsolve(A_cvxopt, x)
return np.array(x)[:, 0]
def linsolve(self, A, b):
"""
Solve linear equation set ``Ax = b`` and store the solutions in ``b``.
Parameters
----------
A
Sparse matrix
b
RHS of the equation
Returns
-------
None
"""
if self.sparselib == 'umfpack':
umfpack.linsolve(A, b)
return b
elif self.sparselib == 'klu':
klu.linsolve(A, b)
return b
elif self.sparselib in ('spsolve', 'cupy'):
ccs = A.CCS
size = A.size
data = np.array(ccs[2]).reshape((-1, ))
indices = np.array(ccs[1]).reshape((-1, ))
indptr = np.array(ccs[0]).reshape((-1, ))
A = csc_matrix((data, indices, indptr), shape=size)
if self.sparselib == 'spsolve':
x = spsolve(A, b)
return matrix(x)
elif self.sparselib == 'cupy':
# delayed import for startup speed
import cupy as cp # NOQA
from cupyx.scipy.sparse import csc_matrix as csc_cu # NOQA
from cupyx.scipy.sparse.linalg.solve import lsqr as cu_lsqr # NOQA
cu_A = csc_cu(A)
cu_b = cp.array(np.array(b).reshape((-1, )))
x = cu_lsqr(cu_A, cu_b)
return matrix(cp.asnumpy(x[0]))
def linsolve(self, A, b):
"""
Solve linear equation set ``Ax = b`` and store the solutions in ``b``.
Parameters
----------
A
Sparse matrix
b
RHS of the equation
Returns
-------
None
"""
if self.sparselib == 'umfpack':
return umfpack.linsolve(A, b)
elif self.sparselib == 'klu':
return klu.linsolve(A, b)
def init1(self, dae):
"""New initialization function"""
self.servcall(dae)
retval = True
mva = self.system.mva
self.p0 = mul(self.p0, self.gammap)
self.q0 = mul(self.q0, self.gammaq)
dae.y[self.vsd] = mul(dae.y[self.v], -sin(dae.y[self.a]))
dae.y[self.vsq] = mul(dae.y[self.v], cos(dae.y[self.a]))
rs = matrix(self.rs)
rr = matrix(self.rr)
xmu = matrix(self.xmu)
x1 = matrix(self.xs) + xmu
x2 = matrix(self.xr) + xmu
Pg = matrix(self.p0)
Qg = matrix(self.q0)
Vc = dae.y[self.v]
vsq = dae.y[self.vsq]
vsd = dae.y[self.vsd]
toSn = div(mva, self.Sn) # to machine base
toSb = self.Sn / mva # to system base
# rotor speed
omega = 1 * (ageb(mva * Pg, self.Sn)) + \
mul(0.5 + 0.5 * mul(Pg, toSn),
aandb(agtb(Pg, 0), altb(mva * Pg, self.Sn))) + \
0.5 * (aleb(mva * Pg, 0))
slip = 1 - omega
theta = mul(self.Kp, mround(1000 * (omega - 1)) / 1000)
theta = mmax(theta, 0)
# prepare for the iterations
irq = mul(-x1, toSb, (2 * omega - 1), div(1, Vc), div(1, xmu),
div(1, omega))
isd = zeros(*irq.size)
isq = zeros(*irq.size)
# obtain ird isd isq
for i in range(self.n):
A = sparse([[-rs[i], vsq[i]], [x1[i], -vsd[i]]])
B = matrix([vsd[i] - xmu[i] * irq[i], Qg[i]])
linsolve(A, B)
isd[i] = B[0]
isq[i] = B[1]
ird = -div(vsq + mul(rs, isq) + mul(x1, isd), xmu)
vrd = -mul(rr, ird) + mul(
slip,
mul(x2, irq) + mul(xmu, isq)) # todo: check x1 or x2
vrq = -mul(rr, irq) - mul(slip, mul(x2, ird) + mul(xmu, isd))
# main iterations
for i in range(self.n):
mis = ones(6, 1)
rows = [0, 0, 0, 1, 1, 1, 2, 2, 3, 3, 4, 4, 5]
cols = [0, 1, 3, 0, 1, 2, 2, 4, 3, 5, 0, 1, 2]
x = matrix([isd[i], isq[i], ird[i], irq[i], vrd[i], vrq[i]])
# vals = [-rs, x1, xmu, -x1, -rs, -xmu, -rr,
# -1, -rr, -1, vsd, vsq, -xmu * Vc / x1]
vals = [
-rs[i], x1[i], xmu[i], -x1[i], -rs[i], -xmu[i], -rr[i], -1,
-rr[i], -1, vsd[i], vsq[i], -xmu[i] * Vc[i] / x1[i]
]
jac0 = spmatrix(vals, rows, cols, (6, 6), 'd')
niter = 0
while max(abs(mis)) > self.system.tds.config.tol:
if niter > 20:
logger.error('Initialization of DFIG <{}> failed.'.format(
self.name[i]))
retval = False
break
mis[0] = -rs[i] * x[0] + x1[i] * x[1] + xmu[i] * x[3] - vsd[i]
mis[1] = -rs[i] * x[1] - x1[i] * x[0] - xmu[i] * x[2] - vsq[i]
mis[2] = -rr[i] * x[2] + slip[i] * (x2[i] * x[3] +
xmu[i] * x[1]) - x[4]
mis[3] = -rr[i] * x[3] - slip[i] * (x2[i] * x[2] +
xmu[i] * x[0]) - x[5]
mis[4] = vsd[i] * x[0] + vsq[i] * x[1] + x[4] * x[2] + \
x[5] * x[3] - Pg[i]
mis[5] = -xmu[i] * Vc[i] * x[2] / x1[i] - \
Vc[i] * Vc[i] / x1[i] - Qg[i]
rows = [2, 2, 3, 3, 4, 4, 4, 4]
cols = [1, 3, 0, 2, 2, 3, 4, 5]
vals = [
slip[i] * xmu[i], slip[i] * x2[i], -slip[i] * xmu[i],
-slip[i] * x2[i], x[4], x[5], x[2], x[3]
]
jac = jac0 + spmatrix(vals, rows, cols, (6, 6), 'd')
linsolve(jac, mis)
x -= mis
niter += 1
isd[i] = x[0]
isq[i] = x[1]
ird[i] = x[2]
irq[i] = x[3]
vrd[i] = x[4]
vrq[i] = x[5]
dae.x[self.ird] = mul(self.u0, ird)
dae.x[self.irq] = mul(self.u0, irq)
dae.y[self.isd] = isd
dae.y[self.isq] = isq
dae.y[self.vrd] = vrd
dae.y[self.vrq] = vrq
dae.x[self.omega_m] = mul(self.u0, omega)
dae.x[self.theta_p] = mul(self.u0, theta)
dae.y[self.pwa] = mmax(mmin(2 * dae.x[self.omega_m] - 1, 1), 0)
self.vref0 = mul(aneb(self.KV, 0),
Vc - div(ird + div(Vc, xmu), self.KV))
dae.y[self.vref] = self.vref0
k = mul(div(x1, Vc, xmu, omega), toSb)
self.irq_off = -mul(k, mmax(mmin(2 * omega - 1, 1), 0)) - irq
# electrical torque in pu
te = mul(
xmu,
mul(dae.x[self.irq], dae.y[self.isd]) -
mul(dae.x[self.ird], dae.y[self.isq]))
for i in range(self.n):
if te[i] < 0:
logger.error(
'Pe < 0 on bus <{}>. Wind speed initialize failed.'.format(
self.bus[i]))
retval = False
# wind power in pu
pw = mul(te, omega)
dae.y[self.pw] = pw
# wind speed initialization loop
R = 4 * pi * self.system.freq * mul(self.R, self.ngb, div(
1, self.npole))
AA = pi * self.R**2
vw = 0.9 * self.Vwn
for i in range(self.n):
mis = 1
niter = 0
while abs(mis) > self.system.tds.config.tol:
if niter > 50:
logger.error(
'Wind <{}> init failed. '
'Try increasing the nominal wind speed.'.format(
self.wind[i]))
retval = False
break
pw_iter, jac = self.windpower(self.ngen[i], self.rho[i], vw[i],
AA[i], R[i], omega[i], theta[i])
mis = pw_iter - pw[i]
inc = -mis / jac[1]
vw[i] += inc
niter += 1
# set wind speed
dae.x[self.vw] = div(vw, self.Vwn)
lamb = div(omega, vw, div(1, R))
ilamb = div(1,
(div(1, lamb + 0.08 * theta) - div(0.035, theta**3 + 1)))
cp = 0.22 * mul(
div(116, ilamb) - 0.4 * theta - 5, exp(div(-12.5, ilamb)))
dae.y[self.lamb] = lamb
dae.y[self.ilamb] = ilamb
dae.y[self.cp] = cp
self.system.rmgen(self.gen)
if not retval:
logger.error('DFIG initialization failed')
return retval
def init1(self, dae):
self.servcall(dae)
mva = self.system.mva
self.p0 = mul(self.p0, 1)
self.v120 = self.v12
self.toMb = div(mva, self.Sn) # to machine base
self.toSb = self.Sn / mva # to system base
rs = matrix(self.rs)
xd = matrix(self.xd)
xq = matrix(self.xq)
psip = matrix(self.psip)
Pg = matrix(self.p0)
# rotor speed
omega = 1 * (ageb(mva * Pg, self.Sn)) + \
mul(0.5 + 0.5 * mul(Pg, self.toMb),
aandb(agtb(Pg, 0), altb(mva * Pg, self.Sn))) + \
0.5 * (aleb(mva * Pg, 0))
theta = mul(self.Kp, mround(1000 * (omega - 1)) / 1000)
theta = mmax(theta, 0)
# variables to initialize iteratively: vsd, vsq, isd, isq
vsd = matrix(0.8, (self.n, 1))
vsq = matrix(0.6, (self.n, 1))
isd = matrix(self.p0 / 2)
isq = matrix(self.p0 / 2)
for i in range(self.n):
# vsd = 0.5
# vsq = self.psip[i]
# isd = Pg / 2
# isq = Pg / 2
x = matrix([vsd[i], vsq[i], isd[i], isq[i]])
mis = ones(4, 1)
jac = sparse(matrix(0, (4, 4), 'd'))
iter = 0
while (max(abs(mis))) > self.system.tds.config.tol:
if iter > 40:
logger.error(
'Initialization of WTG4DC <{}> failed.'.format(
self.name[i]))
break
mis[0] = x[0] * x[2] + x[1] * x[3] - Pg[i]
# mis[1] = omega[i]*x[3] * (psip[i] + (xq[i] - xd[i]) * x[2])\
# - Pg[i]
mis[1] = omega[i] * x[3] * (psip[i] - xd[i] * x[2]) - Pg[i]
mis[2] = -x[0] - rs[i] * x[2] + omega[i] * xq[i] * x[3]
mis[3] = x[1] + rs[i] * x[3] + omega[i] * xd[i] * x[2] - \
omega[i] * psip[i]
jac[0, 0] = x[2]
jac[0, 1] = x[3]
jac[0, 2] = x[0]
jac[0, 3] = x[1]
jac[1, 2] = omega[i] * x[3] * (-xd[i])
jac[1, 3] = omega[i] * (psip[i] + (-xd[i]) * x[2])
jac[2, 0] = -1
jac[2, 2] = -rs[i]
jac[2, 3] = omega[i] * xq[i]
jac[3, 1] = 1
jac[3, 2] = omega[i] * xd[i]
jac[3, 3] = rs[i]
linsolve(jac, mis)
x -= mis
iter += 1
vsd[i] = x[0]
vsq[i] = x[1]
isd[i] = x[2]
isq[i] = x[3]
dae.y[self.isd] = isd
dae.y[self.vsd] = vsd
dae.y[self.vsq] = vsq
dae.x[self.isq] = isq
dae.x[self.omega_m] = mul(self.u0, omega)
dae.x[self.theta_p] = mul(self.u0, theta)
dae.y[self.pwa] = mmax(mmin(2 * dae.x[self.omega_m] - 1, 1), 0)
self.ps0 = mul(vsd, isd) + mul(vsq, isq)
self.qs0 = mul(vsq, isd) - mul(vsd, isq)
self.te0 = mul(isq, psip + mul(xq - xd, isd))
dae.y[self.te] = self.te0
dae.y[self.ps] = self.ps0
MPPT.init1(self, dae)
Turbine.init1(self, dae)
self.system.rmgen(self.dcgen)
def state_matrix(system):
"""Return state matrix"""
Gyx = matrix(system.DAE.Gx)
linsolve(system.DAE.Gy, Gyx)
return matrix(system.DAE.Fx - system.DAE.Fy*Gyx)
