def gcall(self):
if self.n == 0:
return
system.DAE.y = matrix(system.DAE.y)
V = system.DAE.y[self.v]
V2 = mul(V, V)
p = mul(matrix(self.g), V2)
q = mul(matrix(self.b), V2)
# #V = matrix(0, (system.Shunt.n, 1))
# V = [1.0] * system.Shunt.n
# print(V)
# for i in range(system.Shunt.n):
# V[i] = system.DAE.y[self.v[i]]
# print(V)
# V = np.array(V)
# V2 = V * V
# print(V2)
# J = [1] * system.Shunt.n
# print(self.a)
# print(J)
# p = [0] * system.Shunt.n
# q = [0] * system.Shunt.n
# for i in range(system.Shunt.n):
# p[i] = self.g[i] * V2[i]
# q[i] = self.b[i] * V2[i]
for key, value in zip(self.a, p):
system.DAE.g[key] += value
for key, value in zip(self.v, q):
system.DAE.g[key] -= value
def Gycall(self):
if self.n == 0:
return
T1 = self.T1
T2 = self.T2
T3 = self.T3
T4 = self.T4
Kp = self.Kp
Kv = self.Kv
vsmax = self.vsmax
vsmin = self.vsmin
vss = system.DAE.y[self.vss]
z = [0] * self.n
z = matrix(z)
for i in range(self.n):
if vss[i] < vsmax[i] and vss[i] > vsmin[i]:
if self.u[i]:
z[i] = 1
system.DAE.Gy = system.DAE.Gy - spmatrix(1, self.vss, self.vss, (system.DAE.ny, system.DAE.ny))\
+ spmatrix(z, self.vref, self.vss, (system.DAE.ny, system.DAE.ny))
A = div(T1, T2)
C = div(T3, T4)
E = mul(C, A)
system.DAE.Gy = system.DAE.Gy + spmatrix(mul(z, mul(Kv, E)), self.vss, self.v, (system.DAE.ny, system.DAE.ny)) \
+ spmatrix(mul(z, mul(Kp, E)), self.vss, self.p, (system.DAE.ny, system.DAE.ny))
def setx0(self):
if self.n == 0:
return
VSI = [0] * self.n
SIw = [0] * self.n
SIp = [0] * self.n
SIv = [0] * self.n
for i in range(self.n):
if self.ni[i] == 1:
SIw[i] = i
VSI[i] = system.DAE.x[self.omega[SIw[i]]]
elif self.ni[i] == 2:
SIp[i] = i
VSI[i] = system.DAE.y[self.p[SIp[i]]]
self.Kp[i] = self.Kw[i]
self.Kw[i] = 0.0
self.Kv[i] = 0.0
elif self.ni[i] == 3:
SIv[i] = i
VSI[i] = system.DAE.y[self.v[SIv[i]]]
self.Kv[i] = self.Kw[i]
self.Kw[i] = 0.0
self.Kp[i] = 0.0
VSI = matrix(VSI)
# VSI = system.DAE.x[self.omega[SIw]]
Kw = mul(self.u, self.Kw)
Kp = mul(self.u, self.Kp)
Kv = mul(self.u, self.Kv)
Tw = self.Tw
T2 = self.T2
T4 = self.T4
Ta = self.Ta
for i in range(self.n):
if Tw[i] == 0.0:
Tw[i] = 0.01
print(
' Tw cannot be zero. Default value Tw = 0.01 will be used')
if T2[i] == 0.0:
T2[i] = 0.01
print(
' T2 cannot be zero. Default value Tw = 0.01 will be used')
if T4[i] == 0.0:
T4[i] = 0.01
print(
' T2 cannot be zero. Default value Tw = 0.01 will be used')
system.DAE.x[self.v1] = mul(-(Kw + Kp + Kv), VSI)
system.DAE.x[self.v2] = 0
system.DAE.x[self.v3] = 0
for i in range(self.n):
if self.s2[i] == 0.0:
self.s2[i] = -1.0
system.DAE.y[self.vss] = 0.0
def fcall(self):
if self.n == 0:
return
tg1 = system.DAE.x[self.tg1]
tg2 = system.DAE.x[self.tg2]
tg3 = system.DAE.x[self.tg3]
wref = system.DAE.y[self.wref]
gain = div(matrix(1.0, (self.n, 1)), self.R)
a_s = div(matrix(1.0, (self.n, 1)), self.Ts)
ac = div(matrix(1.0, (self.n, 1)), self.Tc)
a5 = div(matrix(1.0, (self.n, 1)), self.T5)
K1 = mul(self.T3, ac)
K2 = 1 - K1
K3 = mul(self.T4, a5)
K4 = 1 - K3
system.Syn6.omega = matrix(system.Syn6.omega)
omega = system.Syn6.omega[self.a]
tin = self.Porder + mul(gain, wref - system.DAE.x[omega])
for i in range(self.n):
tin[i] = max(tin[i], self.Pmin[i])
tin[i] = min(tin[i], self.Pmax[i])
system.DAE.f[self.tg1] = mul(self.u, a_s, -tg1+tin)
system.DAE.f[self.tg2] = mul(self.u, ac, -tg2 + mul(K2, tg1))
system.DAE.f[self.tg3] = mul(self.u, a5, -tg2 + mul(K4, tg2+mul(K1, tg1)))
def Gycall(self):
if self.n == 0:
return
V = mul(2 * self.u, system.DAE.y[self.v])
system.DAE.Gy = system.DAE.Gy + spmatrix(
mul(self.dat[0], V), self.a, self.v,
(system.DAE.ny, system.DAE.ny)) - spmatrix(
mul(self.dat[1], V), self.v, self.v,
(system.DAE.ny, system.DAE.ny))
def Fxcall(self):
if self.n == 0:
return
wref = system.DAE.y[self.wref]
gain = div(matrix(1.0, (self.n, 1)), self.R)
a_s = div(matrix(1.0, (self.n, 1)), self.Ts)
ac = div(matrix(1.0, (self.n, 1)), self.Tc)
a5 = div(matrix(1.0, (self.n, 1)), self.T5)
K1 = mul(self.T3, ac)
K2 = 1 - K1
K3 = mul(self.T4, a5)
K4 = 1 - K3
system.Syn6.omega = matrix(system.Syn6.omega)
omega = system.Syn6.omega[self.a]
tin = self.Porder + mul(gain, wref - system.DAE.x[omega])
for i in range(self.n):
tin[i] = max(tin[i], self.Pmin[i])
tin[i] = min(tin[i], self.Pmax[i])
u = matrix(1, (self.n, 1))
# windup limit
for i in range(self.n):
if tin[i] < self.Pmax[i] and tin[i] > self.Pmin[i]:
u[i] = 1
else:
u[i] = 0
system.DAE.Fx = system.DAE.Fx \
- spmatrix(mul(u, self.u, a_s, gain), self.tg1, omega, (system.DAE.nx, system.DAE.nx)) \
- spmatrix(a_s, self.tg1, self.tg1, (system.DAE.nx, system.DAE.nx)) \
- spmatrix(ac, self.tg2, self.tg2, (system.DAE.nx, system.DAE.nx)) \
- spmatrix(a5, self.tg3, self.tg3, (system.DAE.nx, system.DAE.nx)) \
+ spmatrix(mul(ac, self.u, K2), self.tg2, self.tg1, (system.DAE.nx, system.DAE.nx)) \
+ spmatrix(mul(a5, self.u, K4), self.tg3, self.tg2, (system.DAE.nx, system.DAE.nx)) \
+ spmatrix(mul(a5, self.u, K1, K4), self.tg3, self.tg2, (system.DAE.nx, system.DAE.nx))
system.DAE.Fy = system.DAE.Fy \
+ spmatrix(mul(u, self.u, a_s, gain), self.tg1, self.wref, (system.DAE.nx, system.DAE.ny))
system.DAE.Gx = system.DAE.Gx \
+ spmatrix(u, self.pm, self.tg3, (system.DAE.ny, system.DAE.nx)) \
+ spmatrix(mul(u, K3), self.pm, self.tg2, (system.DAE.ny, system.DAE.nx)) \
+ spmatrix(mul(u, K1, K3), self.pm, self.tg1, (system.DAE.ny, system.DAE.nx))
def gcall(self):
if self.n == 0:
return
V = system.DAE.y[self.v]
V2 = mul(self.u, V, V)
V2 = matrix(V2)
system.DAE.g = system.DAE.g + spmatrix(
mul(self.dat[0], V2), self.a, [0],
(system.DAE.ny, 1)) - spmatrix(mul(self.dat[1], V2), self.v, [0],
(system.DAE.ny, 1))
def base(self, Sb = 100.0, Vb = None):
for var in self._voltages:
self.__dict__[var] = mul(self.__dict__[var], self.Vn)
self.__dict__[var] = div(self.__dict__[var], Vb)
for var in self._powers:
self.__dict__[var] = mul(self.__dict__[var], self.Sn)
self.__dict__[var] = div(self.__dict__[var], Sb)
for var in self._currents:
self.__dict__[var] = mul(self.__dict__[var], self.Sn)
self.__dict__[var] = div(self.__dict__[var], self.Vn)
self.__dict__[var] = mul(self.__dict__[var], Vb)
self.__dict__[var] /= Sb
if len(self._z) or len(self._y):
Zn = div(self.Vn**2, self.Sn)
#print(Zn)
Zb = div(mul(Vb, Vb), Sb)
#print(Zb)
for var in self._z:
self.__dict__[var] = mul(self.__dict__[var], Zn)
self.__dict__[var] = div(self.__dict__[var], Zb)
for var in self._y:
if self.__dict__[var].typecode == 'd':
self.__dict__[var] = div(self.__dict__[var], Zn)
self.__dict__[var] = mul(self.__dict__[var], Zb)
#print(self.__dict__[var])
if self.__dict__[var].typecode == 'z':
self.__dict__[var] = div(self.__dict__[var], Zn + 0j)
self.__dict__[var] = mul(self.__dict__[var], Zb + 0j)
def Gycall(self):
if self.n == 0:
return
V = 2 * system.DAE.y[self.v]
g = matrix(self.g)
b = matrix(self.b)
m = system.DAE.ny
system.DAE.Gy = system.DAE.Gy + spmatrix(mul(V, g), self.a, self.v,
(m, m)) - spmatrix(
mul(V, b), self.v, self.v,
(m, m))
def setx0(self):
vg = system.DAE.y[self.bv]
vf = mul(self.u, system.Syn6.vf0[self.a])
vrmax = mul(self.u, self.vrmax)
vrmin = mul(self.u, self.vrmin)
# 检查参数
for i in range(self.n):
if self.Te[i] == 0:
self.Te[i] = 1
print('<%i> Te不能小于等于0，设置Te = 1 [s]' % i)
if self.Tr[i] <= 0:
self.Tr[i] = 0.001
print('<%i> Tr不能小于等于0，设置Te = 0.001 [s]' % i)
if self.Ke[i] <= 0:
self.Ke[i] = 1
print('<%i> Ke不能小于等于0，设置Ke = 1 ' % i)
if self.Tf[i] <= 0:
self.Tf[i] = 0.1
print('<%i> T1不能小于等于0，设置T1 = 0.1 [s]' % i)
if self.Ta[i] <= 0:
self.Ta[i] = 0.1
print('<%i> T4不能小于等于0，设置T4 = 0.1 [s]' % i)
#
Ce = mul(self.Ke, vf) + mul(mul(self.Ae, exp(mul(self.Be, abs(vf)))),
vf)
system.DAE.x[self.vm] = mul(self.u, vg)
self.vref0 = div(Ce, self.Ka) + vg
system.DAE.x[self.vr1] = Ce
system.DAE.x[self.vr2] = div(mul(-self.Kf, vf), self.Tf)
system.DAE.x[self.vf] = vf
system.DAE.y[self.vref] = mul(self.u, self.vref0)
for i in range(self.n):
if Ce[i] > self.vrmax[i]:
print('Warn: vr1超出最大值vrmax')
if Ce[i] < self.vrmin[i]:
print('Warn: vr1小于最小值vrmin')
for i in range(self.n):
if self.u[i] == 1:
system.Syn6.vf0[self.a[i]] = 0
def __init__(self):
base_device.__init__(self)
self.u = mul(matrix(self.u), matrix(system.PV.u))
self._data.update({
'bus': None,
'Type': 1,
'vrmax': 3.3,
'vrmin': -2.6,
'K0': 0,
'T1': 0,
'T2': 0,
'T3': 0,
'T4': 0,
'Te': 0,
'Tr': 0,
'Ae': 0,
'Be': 0
})
self._type = 'Avr1'
self._name = 'Avr1'
self._bus = {'bus': ['a', 'v']}
self._algebs = ['vref'] # 后续得修改
self._states = ['vm', 'vr1', 'vr2', 'vf'] # 后续得修改
self._params.extend([
'u', 'vrmax', 'vrmin', 'K0', 'T1', 'T2', 'T3', 'T4', 'Te', 'Tr',
'Ae', 'Be'
])
self._voltages = ['V0'] # 后续得修改
self._powers = ['Pg'] # 后续得修改
self.ba = []
self.bv = []
def Gycall(self):
system.DAE.y = matrix(system.DAE.y)
U = exp(system.DAE.y[system.Bus.a] * 1j)
V = mul(system.DAE.y[system.Bus.v] + 0j, U)
I = self.Y * V
nb = len(system.Bus.a)
diagU = spmatrix(U, system.Bus.a, system.Bus.a, (nb, nb), 'z')
diagV = spmatrix(V, system.Bus.a, system.Bus.a, (nb, nb), 'z')
diagI = spmatrix(I, system.Bus.a, system.Bus.a, (nb, nb), 'z')
dS = self.Y * diagU
dS = diagV * dS.H.T
dS += diagI.H.T * diagU
dR = diagI
dR = dR - self.Y * diagV
dR = diagV.H.T * dR
# system.DAE.Gy = matrix(0.0, (system.DAE.ny, system.DAE.ny))
#
# system.DAE._list2matrix()
# system.DAE.Gy = spmatrix(dR.imag().V, dR.imag().I, dR.imag().J, (system.DAE.ny, system.DAE.ny)) \
# + spmatrix(dR.real().V, dR.real().I, dR.real().J+system.Bus.n, (system.DAE.ny, system.DAE.ny)) \
# + spmatrix(dS.real().V, dS.real().I+system.Bus.n, dS.real().J, (system.DAE.ny, system.DAE.ny)) \
# + spmatrix(dS.imag().V, dS.imag().I+system.Bus.n, dS.imag().J+system.Bus.n, (system.DAE.ny, system.DAE.ny))
Gy = sparse([[dR.imag(), dR.real()], [dS.real(), dS.imag()]])
# system.DAE.Gy = zeros([system.DAE.ny,system.DAE.ny])
system.DAE.Gy = spmatrix(Gy.V, Gy.I, Gy.J,
(system.DAE.ny, system.DAE.ny))
system.DAE.Gy = matrix(system.DAE.Gy)
def gcall(self):
self.vfd = system.Syn6.vf[self.a]
system.DAE.g = system.DAE.g + spmatrix(system.DAE.x[self.vf], self.vfd, [0]*self.n, (system.DAE.ny, 1)) \
+ spmatrix(mul(self.u, self.vref0)-system.DAE.y[self.vref], self.vref, [0]*self.n, (system.DAE.ny, 1))
def gcall(self):
system.DAE.y = matrix(system.DAE.y)
zeros = [0.0] * system.DAE.ny
system.DAE.g = zeros
system.DAE.g = matrix(system.DAE.g)
Vn = exp(system.DAE.y[system.Bus.a] * 1j)
Vc = mul(system.DAE.y[system.Bus.v] + 0j, Vn)
Ic = self.Y * Vc
S = mul(Vc, Ic.H.T)
self.p = S.real()
self.q = S.imag()
for i in range(system.Bus.n):
system.DAE.g[i] = self.p[i]
system.DAE.g[i+system.Bus.n] = self.q[i]
def synsat(self):
# print(type([0.8] * self.n))
# print(matrix(0.8, (self.n, 1)))
b = matrix([[matrix(0.8, (self.n, 1))],
list([1.0 - self.S10]),
list([1.2 * (1.0 - self.S12)])])
# print(matrix([12.5, -25, 12.5]))
c2 = b * matrix([12.5, -25, 12.5])
c1 = b * matrix([-27.5, 50, -22.5])
c0 = b * matrix([15, -24, 10])
output = system.DAE.x[self.e1q]
for i in range(self.n):
if system.DAE.x[self.e1q[i]] > 0.8:
output[i] = mul(c2[i], system.DAE.x[self.e1q[i]]**2) + mul(
c1[i], system.DAE.x[self.e1q[i]]) + c0[i]
return output
def G(u, v, alpha=1.0, beta=0.0, trans='N'):
"""
If trans is 'N':
v := alpha * [ -diag(d), -d, -I; 0, 0, -I ] * u + beta * v.
If trans is 'T':
v := alpha * [ -diag(d), 0; -d', 0; -I, -I ] * u + beta * v.
"""
v *= beta
if trans is 'N':
v[:N] -= alpha * (base.mul(d, u[:N] + u[N]) + u[-N:])
v[-N:] -= alpha * u[-N:]
else:
v[:N] -= alpha * base.mul(d, u[:N])
v[N] -= alpha * blas.dot(d, u, n=N)
v[-N:] -= alpha * (u[:N] + u[N:])
def Gycall(self):
system.DAE.y = matrix(system.DAE.y)
V = 2 * system.DAE.y[self.v] #shunt bus 的索引？
conductance = self.g #网络中每条母线的电导列向量
conductance = matrix(conductance)
susceptance = self.b #电纳列向量
susceptance = matrix(susceptance)
m = len(system.DAE.y) #代数变量的个数
conducv = mul(conductance, V)
suscepv = mul(susceptance, V)
spconducv = spmatrix(conducv, self.a, self.v, (m, m), 'z')
spcsuscepv = spmatrix(suscepv, self.v, self.v, (m, m), 'z')
system.DAE.Gy = system.DAE.Gy + spmatrix(conducv, self.a, self.v,
(m, m), 'z') - spmatrix(
suscepv, self.v, self.v,
(m, m), 'z')
system.DAE.Gy = sparse(system.DAE.Gy)
system.DAE.Gy = matrix(system.DAE.Gy.real())
def setdata(self):
Wn = 2 * 3.1415926 * system.Settings.freq
# 转矩系数：A+B*slip+C*slip^2
self.A = self.a1 + self.b + self.c
self.B = -self.b - 2 * self.c
self.C = self.c
# 1/2*Hm
self.i2Hm = div(1, 2 * self.Hm)
# x0,x',x''
self.x0 = self.xs + self.xm
self.x1 = self.xs + div(mul(self.xr1, self.xm), self.xr1 + self.xm)
self.x2 = self.xs + div(
mul(self.xr1, self.xm, self.xr2),
mul(self.xr1, self.xm) + mul(self.xr2, self.xm) +
mul(self.xr1, self.xr2))
# T'0,T''0
for i in range(self.n):
if self.rr1[i] == 0:
self.rr1[i] = 1
if self.rr2[i] == 0:
self.rr2[i] = 1
self.T10 = div(self.xr1 + self.xm, mul(self.rr1, Wn))
self.T20 = div(
self.xr2 + div(mul(self.xr1, self.xm), self.xr1 + self.xm),
mul(self.rr2, Wn))
# 1/xm, x's = xs+xr1
for i in range(self.n):
if self.xm[i] == 0:
print('第%i 台感应电机的磁抗为0' % i)
self.xm[i] = 1
if self.tup[i] < 0:
self.tup = 0
self.ixm = div(1, self.xm)
self.x1s = self.xs + self.xr1
if self.sup == 0:
self.tup = 0
def compute_eigs(As):
(mu, N) = eig(matrix(As))
N = matrix(N)
n = len(mu)
idx = range(n)
W = matrix(spmatrix(1.0, idx, idx, (n, n), v.typecode))
gesv(N, W)
pf = mul(abs(W.T), abs(V))
b = matrix(1.0, (1, n))
WN = b * pf
pf = pf.T
for item in idx:
mur = mu[item].real
mui = mu[item].imag
mu[item] = complex(round(mur, 5), round(mui, 5))
pf[item, :] /= WN[item]
def g(x, y, z):
# Solve
#
# [ K d3 ] [ ux_y ]
# [ ] [ ] =
# [ d3' 1'*d3 ] [ ux_b ]
#
# [ bx_y ] [ D ]
# [ ] - [ ] * D3 * (D2 * bx_v + bx_z - bx_w).
# [ bx_b ] [ d' ]
x[:N] -= mul(d, mul(d3, mul(d2, x[-N:]) + z[:N] - z[-N:]))
x[N] -= blas.dot(d, mul(d3, mul(d2, x[-N:]) + z[:N] - z[-N:]))
# Solve dy1 := K^-1 * x[:N]
blas.copy(x, dy1, n=N)
chompack.trsm(L, dy1, trans='N')
chompack.trsm(L, dy1, trans='T')
# Find ux_y = dy1 - ux_b * dy2 s.t
#
# d3' * ( dy1 - ux_b * dy2 + ux_b ) = x[N]
#
# i.e. x[N] := ( x[N] - d3'* dy1 ) / ( d3'* ( 1 - dy2 ) ).
x[N] = ( x[N] - blas.dot(d3, dy1) ) / \
( blas.asum(d3) - blas.dot(d3, dy2) )
x[:N] = dy1 - x[N] * dy2
# ux_v = D4 * ( bx_v - D1^-1 (bz_z + D * (ux_y + ux_b))
# - D2^-1 * bz_w )
x[-N:] = mul(
d4, x[-N:] - div(z[:N] + mul(d, x[:N] + x[N]), d1) -
div(z[N:], d2))
# uz_z = - D1^-1 * ( bx_z - D * ( ux_y + ux_b ) - ux_v )
# uz_w = - D2^-1 * ( bx_w - uz_w )
z[:N] += base.mul(d, x[:N] + x[N]) + x[-N:]
z[-N:] += x[-N:]
blas.scal(-1.0, z)
# Return W['di'] * uz
blas.tbmv(W['di'], z, n=2 * N, k=0, ldA=1)
def gcall(self):
if self.n == 0:
return
a_s = div(matrix(1.0, (self.n, 1)), self.Ts)
ac = div(matrix(1.0, (self.n, 1)), self.Tc)
a5 = div(matrix(1.0, (self.n, 1)), self.T5)
K1 = mul(self.T3, ac)
K2 = 1 - K1
K3 = mul(self.T4, a5)
K4 = 1 - K3
pmech = system.DAE.x[self.tg3] + mul(K3, system.DAE.x[self.tg2]) + mul(K1, system.DAE.x[self.tg1])
system.DAE.g = system.DAE.g \
+ spmatrix(mul(self.u, pmech), self.pm, matrix(0, (self.n, 1)), (system.DAE.ny, 1)) \
+ spmatrix(mul(self.u, self.wref0) - system.DAE.y[self.wref], self.wref, matrix(0, (self.n, 1)), (system.DAE.ny, 1))
def __init__(self):
base_device.__init__(self)
self.u = mul(matrix(self.u), matrix(system.PV.u))
self._data.update({
'bus': None,
'Type': 2,
'vrmax': 5,
'vrmin': 0,
'Ka': 0,
'Ta': 0,
'Kf': 0,
'Tf': 0,
'Ke': 0,
'Te': 0,
'Tr': 0,
'Ae': 0,
'Be': 0
})
self._type = 'Avr2'
self._name = 'Avr2'
self.n = 0
self._bus = {'bus': ['a', 'v']}
self._algebs = ['vref'] # 后续得修改
self._states = ['vm', 'vr1', 'vr2', 'vf'] # 后续得修改
self._params.extend([
'u', 'vrmax', 'vrmin', 'Ka', 'Ta', 'Kf', 'Tf', 'Ke', 'Te', 'Tr',
'Ae', 'Be'
])
self._voltages = ['V0'] # 后续得修改
self._powers = ['Pg'] # 后续得修改
self.ba = []
self.bv = []
self.properties.update({
'gcall': True,
'Gycall': True,
'fcall': True,
'Fxcall': True
})
def gcall(self):
zeros = [0] * system.Bus.n
Vn = zeros[:]
Vc = zeros[:]
for item1, item2 in zip(system.Bus.a, system.Bus.v):
Vn[item1] = exp(system.DAE.y[item1] * 1j)
Vc[item1] = (system.DAE.y[item2] + 0j) * Vn[item1]
# system.DAE.y = matrix(system.DAE.y)
# Vn = exp(system.DAE.y[system.Bus.a] * 1j)
# Vc = mul(system.DAE.y[system.Bus.v] + 0j, Vn)
Vc = matrix(Vc)
Ic = self.Y * Vc
S = mul(Vc, Ic.H.T)
self.p = S.real()
self.q = S.imag()
for i in range(system.Bus.n):
system.DAE.g[i] = self.p[i]
system.DAE.g[i + system.Bus.n] = self.q[i]
def compute_eig(As):
mu, N = eig(matrix(As))
N = matrix(N)
n = len(mu)
idx = range(n)
W = matrix(spmatrix(1.0, idx, idx, (n, n), N.typecode))
gesv(N, W)
# W = np.linalg.pinv(N)
# W = matrix(W)
pf = mul(abs(W.T), abs(N))
b = matrix(1.0, (1, n))
WN = b * pf
pf = pf.T
for item in idx:
mur = mu[item].real
mui = mu[item].imag
mu[item] = complex(round(mur, 5), round(mui, 5))
pf[item, :] /= WN[item]
# print(pf)
return pf
def setx0(self):
if self.n == 0:
return
system.Syn6._list2matrix()
self.Porder = system.Syn6.pm0[self.a] # 需要改进，若不是Syn6
gain = div(matrix(1.0, (self.n, 1)), self.R)
a_s = div(matrix(1.0, (self.n, 1)), self.Ts)
ac = div(matrix(1.0, (self.n, 1)), self.Tc)
a5 = div(matrix(1.0, (self.n, 1)), self.T5)
K1 = mul(self.T3, ac)
K2 = 1 - K1
K3 = mul(self.T4, a5)
K4 = 1 - K3
system.DAE.x[self.tg1] = mul(self.u, self.Porder)
system.DAE.x[self.tg2] = mul(self.u, K2, self.Porder)
system.DAE.x[self.tg3] = mul(self.u, K4, self.Porder)
system.DAE.f[self.tg1] = 0
system.DAE.f[self.tg2] = 0
system.DAE.f[self.tg3] = 0
system.Syn6.pm0[self.a] = 0
system.DAE.y[self.wref] = mul(self.u, self.wref0)
for i in range(self.n):
if self.Porder[i] > self.Pmax[i]:
print('第%i 台Tg1机械功率超过最大值'% i)
elif self.Porder[i] < self.Pmin[i]:
print('第%i 台Tg1机械功率低于最小值' % i)
else:
print('初始化Tg1完成')
self.pmech = system.DAE.x[self.tg3] + mul(K3, system.DAE.x[self.tg2]) + mul(K1, system.DAE.x[self.tg1])
def Fxcall(self):
if self.n == 0:
return
vg = system.DAE.y[self.bv]
vrmax = mul(self.u, self.vrmax)
vrmin = mul(self.u, self.vrmin)
system.DAE.Gx = system.DAE.Gx + spmatrix(
self.u, self.vfd, self.vf, (system.DAE.ny, system.DAE.nx))
system.DAE.Fx = system.DAE.Fx - spmatrix(
div(matrix(1.0, (self.n, 1)), self.Tr), self.vm, self.vm,
(system.DAE.nx, system.DAE.nx))
system.DAE.Fy = system.DAE.Fy + spmatrix(div(
self.u, self.Tr), self.vm, self.bv, (system.DAE.nx, system.DAE.ny))
vm = system.DAE.x[self.vm]
vr1 = system.DAE.x[self.vr1]
vr2 = system.DAE.x[self.vr2]
vf = system.DAE.x[self.vf]
vref = system.DAE.y[self.vref]
K1 = div(mul(self.K0, self.T2), self.T1)
K2 = self.K0 - K1
K3 = div(self.T4, self.T3)
K4 = matrix(1, (self.n, 1)) - K3
vr = mul(self.K0, vr2) + mul(K3, mul(K1, vref - vm) + vr1)
Se = mul(self.Ae, exp(mul(self.Be, abs(vf))))
Se = Se + mul(mul(Se, self.Ae), vf)
z = matrix(0, (self.n, 1))
for i in range(self.n):
if vr[i] < vrmax[i] and vr[i] > vrmin[i]:
z[i] = 1
system.DAE.Fx = system.DAE.Fx \
- spmatrix(div(K2, self.T1), self.vr1, self.vm, (system.DAE.nx, system.DAE.nx)) \
- spmatrix(div(matrix(1.0, (self.n, 1)), self.T1), self.vr1, self.vr1, (system.DAE.nx, system.DAE.nx)) \
- spmatrix(div(mul(K4, K1), mul(self.K0, self.T3)), self.vr2, self.vm, (system.DAE.nx, system.DAE.nx)) \
+ spmatrix(div(K4, mul(self.T3, self.K0)), self.vr2, self.vr1, (system.DAE.nx, system.DAE.nx)) \
- spmatrix(div(matrix(1.0, (self.n, 1)), self.T3), self.vr2, self.vr2, (system.DAE.nx, system.DAE.nx)) \
- spmatrix(div(1+Se, self.Te), self.vf, self.vf, (system.DAE.nx, system.DAE.nx)) \
+ spmatrix(div(mul(z, K3), self.Te), self.vf, self.vr1, (system.DAE.nx, system.DAE.nx)) \
- spmatrix(div(mul(mul(z, K3), K1), self.Te), self.vf, self.vm, (system.DAE.nx, system.DAE.nx)) \
+ spmatrix(div(mul(mul(z, self.u), self.K0), self.Te), self.vf, self.vr2, (system.DAE.nx, system.DAE.nx))
system.DAE.Fy = system.DAE.Fy \
+ spmatrix(div(K2, self.T1), self.vr1, self.vref, (system.DAE.nx, system.DAE.ny)) \
+ spmatrix(div(mul(K4, K1), mul(self.K0, self.T3)), self.vr2, self.vref, (system.DAE.nx, system.DAE.ny)) \
+ spmatrix(div(mul(mul(z, K3), K1), self.Te), self.vf, self.vref, (system.DAE.nx, system.DAE.ny))
def fcall(self):
if self.n == 0:
return
vg = system.DAE.y[self.bv]
vrmax = mul(self.u, self.vrmax)
vrmin = mul(self.u, self.vrmin)
vm = system.DAE.x[self.vm]
vr1 = system.DAE.x[self.vr1]
vr2 = system.DAE.x[self.vr2]
vf = system.DAE.x[self.vf]
vref = system.DAE.y[self.vref]
system.DAE.f[self.vm] = div(mul(self.u, vg - system.DAE.x[self.vm]),
self.Tr)
K1 = div(mul(self.K0, self.T2), self.T1)
K2 = self.K0 - K1
K3 = div(self.T4, self.T3)
K4 = matrix(1, (self.n, 1)) - K3
vr = mul(self.K0, vr2) + mul(K3, mul(K1, vref - vm) + vr1)
system.DAE.f[self.vr1] = div(mul(self.u,
mul(K2, vref - vm) - vr1), self.T1)
system.DAE.f[self.vr2] = div(
mul(self.u,
mul(K4, vr1 + mul(K1, vref - vm)) - mul(self.K0, vr2)),
mul(self.T3, self.K0))
# hard limit
for i in range(self.n):
vr[i] = min(vr[i], vrmax[i])
vr[i] = max(vr[i], vrmin[i])
Se = mul(self.Ae, exp(mul(self.Be, abs(vf))))
system.DAE.f[self.vf] = div(mul(self.u, -vf + vr - mul(Se, vf)),
self.Te)
def fcall(self, x):
fvec = mul(self.A, sin(self.omega*x + self.phi))
return sum(fvec)
def Fxcall(self):
vg = system.DAE.y[self.bv]
vrmax = mul(self.u, self.vrmax)
vrmin = mul(self.u, self.vrmin)
system.DAE.Gx = system.DAE.Gx + spmatrix(
self.u, self.vfd, self.vf, (system.DAE.ny, system.DAE.nx))
system.DAE.Fx = system.DAE.Fx - spmatrix(
div(matrix(1.0, (self.n, 1)), self.Tr), self.vm, self.vm,
(system.DAE.nx, system.DAE.nx))
system.DAE.Fy = system.DAE.Fy + spmatrix(div(
self.u, self.Tr), self.vm, self.bv, (system.DAE.nx, system.DAE.ny))
vr1 = system.DAE.x[self.vr1]
vf = system.DAE.x[self.vf]
Ka = mul(self.u, self.Ka)
Kf = mul(self.u, self.Kf)
K5 = div(Kf, self.Tf)
Se = mul(self.Ae, exp(mul(self.Be, abs(vf))))
Se = Se + mul(mul(Se, self.Be), vf)
z = matrix(0, (self.n, 1))
for i in range(self.n):
if vr1[i] < vrmax[i] and vr1[i] > vrmin[i]:
z[i] = 1
system.DAE.Fx = system.DAE.Fx \
- spmatrix(div(matrix(1.0, (self.n, 1)), self.Tf), self.vr2, self.vr2, (system.DAE.nx, system.DAE.nx)) \
- spmatrix(div(K5, self.Tf), self.vr2, self.vf, (system.DAE.nx, system.DAE.nx)) \
- spmatrix(div(self.Ke+Se, self.Te), self.vf, self.vf, (system.DAE.nx, system.DAE.nx)) \
+ spmatrix(div(z, self.Te), self.vf, self.vr1, (system.DAE.nx, system.DAE.nx)) \
- spmatrix(div(mul(z, Ka), self.Ta), self.vr1, self.vm, (system.DAE.nx, system.DAE.nx)) \
- spmatrix(div(matrix(1.0, (self.n, 1)), self.Ta), self.vr1, self.vr1, (system.DAE.nx, system.DAE.nx)) \
- spmatrix(div(mul(z, Ka), self.Ta), self.vr1, self.vr2, (system.DAE.nx, system.DAE.nx)) \
- spmatrix(div(mul(mul(z, K5), Ka), self.Ta), self.vr1, self.vf, (system.DAE.nx, system.DAE.nx))
system.DAE.Fy = system.DAE.Fy \
+ spmatrix(div(mul(z, Ka), self.Ta), self.vr1, self.vref, (system.DAE.nx, system.DAE.ny))
def dfcall(self, x):
dfvec = mul(mul(self.A, self.omega),
cos(self.omega*x + self.phi))
return sum(dfvec)
def fcall(self):
vg = system.DAE.y[self.bv]
vrmax = mul(self.u, self.vrmax)
vrmin = mul(self.u, self.vrmin)
vm = system.DAE.x[self.vm]
vr1 = system.DAE.x[self.vr1]
vr2 = system.DAE.x[self.vr2]
vf = system.DAE.x[self.vf]
vref = system.DAE.y[self.vref]
system.DAE.f[self.vm] = div(mul(self.u, vg - system.DAE.x[self.vm]),
self.Tr) #
K5 = div(self.Kf, self.Tf)
system.DAE.f[self.vr1] = div(
mul(self.u,
mul(self.Ka, vref - vm - vr2 - mul(K5, vf)) - vr1), self.Ta)
system.DAE.f[self.vr2] = div(mul(-self.u, mul(K5, vf) + vr2), self.Tf)
# non-windup limit
for i in range(self.n):
if vr1[i] >= vrmax[i] and system.DAE.f[self.vr1[i]] > 0:
system.DAE.f[self.vr1[i]] = 0
if vr1[i] <= vrmin[i] and system.DAE.f[self.vr1[i]] < 0:
system.DAE.f[self.vr1[i]] = 0
for i in range(self.n):
vr1[i] = min(vr1[i], vrmax[i])
vr1[i] = max(vr1[i], vrmin[i])
system.DAE.x[self.vr1] = mul(self.u, vr1)
Se = mul(self.Ae, exp(mul(self.Be, abs(vf))))
system.DAE.f[self.vf] = div(
mul(self.u, vr1 - mul(self.Ke, vf) - mul(Se, vf)), self.Te)
p, q = d, a
else:
p, q = a, d
x0 = y[:]
S = matrix(0.0, (q,1))
# First, compute two initial points on the tradeoff curve
lamT = [50.0, 0.02]
nrmT = [0.0, 0.0]
errT = [0.0, 0.0]
for i in xrange(len(lamT)):
Cd = matrix(2.0*lamT[i], (n,1))
C = base.spdiag(Cd)
d = -base.mul(Cd, x0)
sol = nucnrm.nrmapp(A, B, C = C, d = d)
x = sol['x']
lapack.gesvd(matrix(A*x, (p,q)) + B, S)
nrmT[i] = sum(S)
errT[i] = blas.dot(x-x0, x-x0)
# plot the tradeoff curve upper/lower bounds with the initial 2 points
pylab.figure(0)
N = 200
slope = -matrix(lamT)
errM = matrix(errT)
nrmM = matrix(nrmT)
xx = matrix(range(N))*((errM[-1]-errM[0])/(N-1))+errM[0]
def setx0(self):
if self.n == 0:
return
vg = system.DAE.y[self.bv]
vf = mul(self.u, system.Syn6.vf0[self.a])
vrmax = mul(self.u, self.vrmax)
vrmin = mul(self.u, self.vrmin)
# 检查参数
for i in range(self.n):
if self.Te[i] <= 0:
self.Te[i] = 1
print('<%i> Te不能小于等于0，设置Te = 1 [s]' % i)
if self.Tr[i] <= 0:
self.Tr[i] = 0.001
print('<%i> Tr不能小于等于0，设置Te = 0.001 [s]' % i)
if self.K0[i] <= 0:
self.K0[i] = 400
print('<%i> K0不能小于等于0，设置K0 = 400 ' % i)
if self.T1[i] <= 0:
self.T1[i] = 0.1
print('<%i> T1不能小于等于0，设置T1 = 0.1 [s]' % i)
if self.T4[i] <= 0:
self.T4[i] = 0.01
print('<%i> T4不能小于等于0，设置T4 = 0.01 [s]' % i)
#
Ce = -vf - mul(mul(self.Ae, exp(mul(self.Be, vf))), vf)
K1 = mul(self.K0, div(self.T2, self.T1))
K2 = self.K0 - K1
K3 = div(self.T3, self.T4)
K4 = 1 - K3
system.DAE.x[self.vm] = mul(self.u, vg)
self.vref0 = vg - div(Ce, self.K0)
system.DAE.x[self.vr1] = mul(self.u, mul(K2, self.vref0 - vg))
system.DAE.x[self.vr2] = mul(self.u, mul(K4, self.vref0 - vg))
system.DAE.x[self.vf] = vf
system.DAE.y[self.vref] = mul(self.u, self.vref0)
vr = mul(self.u, mul(self.K0, system.DAE.x[self.vr2])) + mul(
K3,
mul(K1, self.vref0 - vg) + system.DAE.x[self.vr1])
for i in range(self.n):
if vr[i] > self.vrmax[i]:
print('Warn: vr1超出最大值vrmax')
if vr[i] < self.vrmin[i]:
print('Warn: vr1小于最小值vrmin')
system.Syn6.vf0[self.a] = 0
def sysid(y, u, vsig, svth = None):
"""
System identification using the subspace method and nuclear norm
optimization. Estimate a linear time-invariant state-space model
given inputs and outputs. The algorithm is described in [1].
INPUT
y 'd' matrix of size (p, N). y are the measured outputs, p is
the number of outputs, and N is the number of data points
measured.
u 'd' matrix of size (m, N). u are the inputs, m is the number
of inputs, and N is the number of data points.
vsig a weighting parameter in the nuclear norm optimization, its
value is approximately the 1-sigma output noise level
svth an optional parameter, if specified, the model order is
determined as the number of singular values greater than svth
times the maximum singular value. The default value is 1E-3
OUTPUT
sol a dictionary with the following words
-- 'A', 'B', 'C', 'D' are the state-space matrices
-- 'svN', the original singular values of the Hankel matrix
-- 'sv', the optimized singular values of the Hankel matrix
-- 'x0', the initial state x(0)
-- 'n', the model order
[1] Zhang Liu and Lieven Vandenberghe. "Interior-point method for
nuclear norm approximation with application to system
identification."
"""
m, N, p = u.size[0], u.size[1], y.size[0]
if y.size[1] != N:
raise ValueError, "y and u must have the same length"
# Y = G*X + H*U + V, Y has size a x b, U has size c x b, Un has b x d
r = min(int(30/p),int((N+1.0)/(p+m+1)+1.0))
a = r*p
c = r*m
b = N-r+1
d = b-c
# construct Hankel matrix Y
Y = Hankel(y,r,b,p=p,q=1)
# construct Hankel matrix U
U = Hankel(u,r,b,p=m,q=1)
# compute Un = null(U) and YUn = Y*Un
Vt = matrix(0.0,(b,b))
Stemp = matrix(0.0,(c,1))
Un = matrix(0.0,(b,d))
YUn = matrix(0.0,(a,d))
lapack.gesvd(U,Stemp,jobvt='A',Vt=Vt)
Un[:,:] = Vt.T[:,c:]
blas.gemm(Y,Un,YUn)
# compute original singular values
svN = matrix(0.0,(min(a,d),1))
lapack.gesvd(YUn,svN)
# variable, [y(1);...;y(N)]
# form the coefficient matrices for the nuclear norm optimization
# minimize | Yh * Un |_* + alpha * | y - yh |_F
AA = Hankel_basis(r,b,p=p,q=1)
A = matrix(0.0,(a*d,p*N))
temp = spmatrix([],[],[],(a,b),'d')
temp2 = matrix(0.0,(a,d))
for ii in xrange(p*N):
temp[:] = AA[:,ii]
base.gemm(temp,Un,temp2)
A[:,ii] = temp2[:]
B = matrix(0.0,(a,d))
# flip the matrix if columns is more than rows
if a < d:
Itrans = [i+j*a for i in xrange(a) for j in xrange(d)]
B[:] = B[Itrans]
B.size = (d,a)
for ii in xrange(p*N):
A[:,ii] = A[Itrans,ii]
# regularized term
x0 = y[:]
Qd = matrix(2.0*svN[0]/p/N/(vsig**2),(p*N,1))
# solve the nuclear norm optimization
sol = nrmapp(A, B, C = base.spdiag(Qd), d = -base.mul(x0, Qd))
status = sol['status']
x = sol['x']
# construct YhUn and take the svd
YhUn = matrix(B)
blas.gemv(A,x,YhUn,beta=1.0)
if a < d:
YhUn = YhUn.T
Uh = matrix(0.0,(a,d))
sv = matrix(0.0,(d,1))
lapack.gesvd(YhUn,sv,jobu='S',U=Uh)
# determine model order
if svth is None:
svth = 1E-3
svthn = sv[0]*svth
n=1
while sv[n] >= svthn and n < 10:
n=n+1
# estimate A, C
Uhn = Uh[:,:n]
for ii in xrange(n):
blas.scal(sv[ii],Uhn,n=a,offset=ii*a)
syseC = Uhn[:p,:]
Als = Uhn[:-p,:]
Bls = Uhn[p:,:]
lapack.gels(Als,Bls)
syseA = Bls[:n,:]
Als[:,:] = Uhn[:-p,:]
Bls[:,:] = Uhn[p:,:]
blas.gemm(Als,syseA,Bls,beta=-1.0)
Aerr = blas.nrm2(Bls)
# stabilize A
Sc = matrix(0.0,(n,n),'z')
w = matrix(0.0, (n,1), 'z')
Vs = matrix(0.0, (n,n), 'z')
def F(w):
return (abs(w) < 1.0)
Sc[:,:] = syseA
ns = lapack.gees(Sc, w, Vs, select = F)
while ns < n:
#print "stabilize matrix A"
w[ns:] = w[ns:]**-1
Sc[::n+1] = w
Sc = Vs*Sc*Vs.H
syseA[:,:] = Sc.real()
Sc[:,:] = syseA
ns = lapack.gees(Sc, w, Vs, select = F)
# estimate B,D,x0 stored in vector [x0; vec(D); vec(B)]
F1 = matrix(0.0,(p*N,n))
F1[:p,:] = syseC
for ii in xrange(1,N):
F1[ii*p:(ii+1)*p,:] = F1[(ii-1)*p:ii*p,:]*syseA
F2 = matrix(0.0,(p*N,p*m))
ut = u.T
for ii in xrange(p):
F2[ii::p,ii::p] = ut
F3 = matrix(0.0,(p*N,n*m))
F3t = matrix(0.0,(p*(N-1),n*m))
for ii in xrange(1,N):
for jj in xrange(p):
for kk in xrange(n):
F3t[jj:jj+(N-ii)*p:p,kk::n] = ut[:N-ii,:]*F1[(ii-1)*p+jj,kk]
F3[ii*p:,:] = F3[ii*p:,:] + F3t[:(N-ii)*p,:]
F = matrix([[F1],[F2],[F3]])
yls = y[:]
Sls = matrix(0.0,(F.size[1],1))
Uls = matrix(0.0,(F.size[0],F.size[1]))
Vtls = matrix(0.0,(F.size[1],F.size[1]))
lapack.gesvd(F, Sls, jobu='S', jobvt='S', U=Uls, Vt=Vtls)
Frank=len([ii for ii in xrange(Sls.size[0]) if Sls[ii] >= 1E-6])
#print 'Rank deficiency = ', F.size[1] - Frank
xx = matrix(0.0,(F.size[1],1))
xx[:Frank] = Uls.T[:Frank,:] * yls
xx[:Frank] = base.mul(xx[:Frank],Sls[:Frank]**-1)
xx[:] = Vtls.T[:,:Frank]*xx[:Frank]
blas.gemv(F,xx,yls,beta=-1.0)
xxerr = blas.nrm2(yls)
x0 = xx[:n]
syseD = xx[n:n+p*m]
syseD.size = (p,m)
syseB = xx[n+p*m:]
syseB.size = (n,m)
return {'A': syseA, 'B': syseB, 'C': syseC, 'D': syseD, 'svN': svN, 'sv': \
sv, 'x0': x0, 'n': n, 'Aerr': Aerr, 'xxerr': xxerr}
