US 12,681,058 B2 · Method for fast and accurately sensing power grid information based on nonlinear robust estimation

Abstract

A method for sensing power grid information based on nonlinear robust estimation, may include: acquiring a phase voltage signal of actual power grid; obtaining a nonlinear state-space distorted power grid model based on the phase voltage signal of actual power grid and a virtual orthogonal signal of phase voltage signal; establishing an H∞ smoothing estimation performance indicator based on the nonlinear state-space distorted power grid model, converting an H∞ smoothing estimation problem into a generalized H2 smoothing estimation problem according to the H∞ smoothing estimation performance indicator, and constructing an H∞ smoothing estimator; and obtaining an initial sensed amplitude value and an initial sensed phase value of power grid voltage signal by using the H∞ smoothing estimator, and performing zero-crossing point detection on the initial sensed amplitude and phase values of the power grid voltage signal to obtain sensed amplitude, frequency and phase values of a distorted voltage.

A method for sensing power grid information based on nonlinear robust estimation, comprising:

acquiring a phase voltage signal of an actual power grid;
obtaining a nonlinear state-space distorted power grid model based on the phase voltage signal of the actual power grid and a virtual orthogonal signal of the phase voltage signal;
establishing an H∞ smoothing estimation performance indicator based on the nonlinear state-space distorted power grid model, converting an H∞ smoothing estimation problem into a generalized H2 smoothing estimation problem according to the H∞ smoothing estimation performance indicator, and constructing an H∞ smoothing estimator;
obtaining an initial sensed amplitude value and an initial sensed phase value of a power grid voltage signal by using the H∞ smoothing estimator, and performing zero-crossing point detection on the initial sensed amplitude value and the initial sensed phase value of the power grid voltage signal to obtain sensed amplitude, frequency and phase values of a distorted voltage;
and
based on the obtained sensed amplitude, frequency and phase values of the distorted voltage, controlling a phase difference between an output current in a current loop control of the grid-connected inverter and a grid-side fundamental voltage to be 0, to implement a grid connection under a unit power factor;
wherein,
the converting the H∞ smoothing estimation problem into the generalized H2 smoothing estimation problem according to the H∞ smoothing estimation performance indicator and constructing the H∞ smoothing estimator specifically comprises:
(1) setting an H∞ indicator upper limit constant γ>0, a sampling moment i=kl=0, a state estimate initial value x̌0=0, and a Riccati equation initial value P0=l, N being a specific time point:
C
2
=
[
C
T
⁢
F
T
⁢
L
T
]
T
Q
2
(
i
)
=
diag
⁢
{
I
,
-
γ
1
-
2
⁢
I
,
-
γ
2
⁢
I
}
y
˜
2
(
i
,
2
)
=
y
2
(
i
)
-
C
2
⁢
x
ˆ
(
i
,
2
)
y
˜
2
(
i
,
2
)
=
[
y
T
(
i
)
z
⌣
∅
T
⁢
(
i
|
i
)
z
⌣
T
(
i
|
i
+
l
)
]
T
,
and
wherein,
C
=
[
1
0
0
0
0
1
]
,
F represents a constant matrix of the nonlinear portion in the phase voltage model, and L represents a unit matrix;
Q represents a diagonal matrix constructed based on diag{ } function, γ1 represents a Lipschitz constant of a nonlinear portion in a phase voltage model;
{tilde over (y)}2(i, 2) represents an observed estimation error, {circumflex over (x)}(i, 2) is a second-piece initial state value;
y(i) is an observation sequence, žØ(i|i) is an estimation equation of the virtual output žØ;
ž(kl|k) is an estimator of the signal z(k), wherein kl=k−l, when let the sampling moment i=kl=0, it can be written as ž(i|i+l);
i represents a first sampling moment, k represents a second sampling moment, l is a smoothing step number, and the superscript T represents transposed matrix;
calculating:
a second-piece output error variance R2(i, 2):
R
2
(
i
,
2
)
=
C
2
⁢
P
2
(
i
)
⁢
C
2
T
+
Q
2
(
i
)
,
(
13
)
wherein P2(i) represents a second-piece Riccati equation value;
a second-piece gain parameter K2(i):
K
2
(
i
)
=
A
⁢
P
2
(
i
)
⁢
C
2
T
⁢
R
2
-
1
(
i
,
2
)
,
(
14
)
wherein,
A
⁢
=
[
1
0
0
T
⁢
ω
¯
1
0
0
0
1
]
,
T represents a sampling period, and w represents a set initial power grid frequency;
a second-piece Riccati equation P2(i+1):
P
2
(
i
+
1
)
=
A
⁢
P
2
(
i
)
⁢
A
T
+
B
⁢
B
T
+
I
-
K
2
(
i
)
⁢
R
2
(
i
,
2
)
⁢
K
2
T
(
i
)
(
15
-
1
)
P
2
(
0
)
=
P
0
,
(
15
-
2
)
wherein,
B
=
[
-
T
0
0
]
;
and, a second-piece state estimation equation {circumflex over (x)}(i+1,2):
x
ˆ
(
i
+
1
,
2
)
=
A
⁢
x
ˆ
(
i
,
2
)
+
∅
⁡
(
x
⌣
(
i
|
i
)
)
+
K
2
(
i
)
⁢
y
˜
2
(
i
,
2
)
(
16
-
1
)
x
ˆ
(
0
,
2
)
=
x
⌣
0
;
(
16
-
2
)
wherein, Ø represents a nonlinear term;
(2) setting a first-piece initial state value {circumflex over (x)}(i, 1)={circumflex over (x)}(i, 2) and a first-piece Riccati equation initial value P1(i)=P2(i), and calculating:
a zeroth-piece output error variance R0(i):
R
0
(
i
)
=
C
⁢
P
1
(
i
)
⁢
C
T
+
I
,
(
17
)
a zeroth-piece state gain parameter K0(i):
K
0
(
i
)
=
F
⁢
P
1
(
i
)
⁢
C
T
⁢
R
0
-
1
(
i
)
,
(
18
)
and, an estimation error variance RØ(i|i) of a virtual output žØ:
R
∅
(
i
|
i
)
=
F
⁢
P
1
(
i
)
⁢
F
T
-
γ
1
-
2
⁢
I
-
K
0
(
i
)
⁢
R
0
(
i
)
⁢
K
0
T
(
i
)
;
(
19
)
(3) if R0(i)>0 and RØ(i|i)<0 are satisfied, calculating:
an estimation equation žØ(i|i) of the virtual output žØ:
z
⌣
∅
(
i
|
i
)
=
F
⁢
x
ˆ
(
i
,
1
)
+
K
0
(
i
)
⁢
y
˜
0
(
i
)
,
(
20
)
wherein, a zeroth-piece output estimation error {tilde over (y)}0(i) is:
y
~
0
(
i
)
=
y
⁡
(
i
)
-
C
⁢
x
^
(
i
,
1
)
further performing (4);
or:
if R0(i)>0 and RØ(i|i)<0 are not satisfied, resetting the value of γ, and performing (1);
(4) setting an initial value of a smoothing gain parameter to be
R
x
,
k
l
k
l
=
P
1
(
k
l
)
,
wherein P1(kl) represents a Riccati equation obtained in an estimator design process,
C
1
=
[
C
T
F
T
]
T
Q
1
(
k
l
)
=
diag
⁢
{
I
,
-
γ
1
-
2
⁢
I
}
,
calculating:
a first-piece output error variance R1(kl, 1):
R
1
(
k
l
,
1
)
=
C
1
⁢
P
1
(
k
l
)
⁢
C
1
T
+
Q
1
(
k
l
)
,
(
21
)
and, a filtering estimation error variance Rz(kl|kl) of a signal to be estimated:
R
z
(
k
l
❘
k
l
)
=
LP
1
(
k
l
)
⁢
L
T
-
γ
2
⁢
I
-
K
z
(
k
l
)
⁢
R
1
(
k
l
,
1
)
⁢
K
z
T
(
k
l
)
,
(
22
)
wherein,
K
z
(
k
l
)
=
LP
1
(
k
l
)
⁢
C
1
T
⁢
R
1
-
1
(
k
l
,
1
)
if Rz(kl|kl)<0, calculating an H∞ filtering estimate ž(kl|kl):
z
⌣
(
k
l
❘
k
l
)
=
L
⁢
x
^
(
k
l
,
1
)
+
LR
x
,
k
l
k
l
⁢
C
1
T
⁢
R
1
-
1
(
k
l
,
1
)
⁢
y
~
1
(
k
l
,
1
)
,
(
23
)
wherein, a first-piece output estimation error {tilde over (y)}1(kl, 1) and an output signal y1(kl) are as follows:
y
~
1
(
k
l
,
1
)
=
y
1
(
k
l
)
-
C
1
⁢
x
^
(
k
l
,
1
)
⁢
y
1
(
k
l
)
=
[
y
T
(
k
l
)
z
⌣
∅
T
(
k
l
❘
k
l
)
]
T
further performing (5);
or:
if Rz(kl|kl)<0 is not satisfied, resetting the value of γ, and performing (1);
(5) setting i=i+1, calculating R1(i, 1) according to (21), calculating:
a first-piece state gain parameter K1(i):
K
1
(
i
)
=
AP
1
(
i
)
⁢
C
1
T
⁢
R
1
-
1
(
i
,
1
)
,
(
24
)
a first-piece Riccati equation P1(i+1):
P
1
(
i
+
1
)
=
AP
1
(
i
)
⁢
A
T
+
BB
T
+
I
-
K
1
(
i
)
⁢
R
1
(
i
,
1
)
⁢
K
1
T
(
i
)
⁢
P
1
(
k
l
)
=
P
2
(
k
l
)
,
and
(
25
)
and, a first-piece state estimation equation {circumflex over (x)}(i+1,1):
x
^
(
i
+
1
,
1
)
=
A
⁢
x
^
(
i
,
1
)
+
∅
⁡
(
x
⌣
(
i
❘
i
)
)
+
K
1
(
i
)
⁢
y
~
1
(
i
,
1
)
⁢
x
^
(
k
l
,
1
)
=
x
^
(
k
l
,
2
)
,
(
26
)
calculating R0(i), K0(i), and RØ(i|i) according to formulas (17) to (19), and
if R0(i)>0 and RØ(i|i)<0, calculating žØ(i|i) according to formula (20), and performing (6);
or
if R0(i)>0 and RØ(i|i)<0 are not satisfied, resetting the value of γ, and performing (1);
(6) calculating the smoothing gain parameter
R
x
,
k
l
i
:
{
R
x
,
k
l
i
=
R
x
,
k
l
i
-
1
(
A
-
K
1
(
i
-
1
)
⁢
C
1
)
T
R
x
,
k
l
k
l
=
P
1
(
k
l
)
,
(
27
)
and, a smoothing estimation error variance Rz(kl|i):
R
z
(
k
l
❘
i
)
=
LP
1
(
k
l
)
⁢
L
T
-
γ
2
⁢
I
-
K
z
(
k
l
)
⁢
R
1
(
k
l
,
1
)
⁢
K
z
T
(
k
l
)
-
∑
j
=
k
l
+
1
i
⁢
R
k
l
j
⁢
R
1
(
j
,
1
)
⁢
(
R
k
l
j
)
T
,
(
28
)
wherein,
R
k
l
j
=
LR
x
,
k
l
j
⁢
C
1
T
⁢
R
1
-
1
(
j
,
1
)
,
if Rz(kl|i)<0, calculating an H∞ smoothing estimate ž(kl|i):
z
⌣
(
k
l
❘
i
)
=
z
^
(
k
l
❘
i
-
1
)
+
LR
x
,
k
l
i
⁢
C
1
T
⁢
R
1
-
1
(
i
,
1
)
⁢
y
~
1
(
i
,
1
)
;
(
29
)
or,
if Rz(kl|i)<0 is not satisfied, resetting the value of γ, and performing (1);
(7) repeating (5) to (6) until i=k;
and
(8) setting k=k+1, and repeating (1) to (7) until k=N, wherein N represents a termination moment of measurement or prediction.

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Method for fast and accurately sensing power grid information based on nonlinear robust estimation

Life of this patent

Method for fast and accurately sensing power grid information based on nonlinear robust estimation

Life of this patent

Abstract

Claims8