US 11,906,957 B1 · Method for assessing short-term voltage stability of power grid based on synchronous spatio-temporal information learning

Abstract

A method for assessing short-term voltage stability of a power grid based on synchronous spatio-temporal information learning includes: performing a time-domain simulation for each transient fault of a power grid under each operating mode, and extracting temporal response trajectories of each monitoring bus and stability status information of the power grid to obtain a transient sample set; constructing a spatial adjacency matrix and a temporal adjacency matrix separately, and integrating the spatial adjacency matrix and the temporal adjacency matrix into a spatio-temporal adjacency matrix; performing synchronous spatio-temporal information learning by using a graph convolutional neural network algorithm, and performing training to obtain a short-term voltage stability assessment model driven by spatio-temporal information synchronization; and inputting a transient temporal responsive trajectory obtained into the short-term voltage stability assessment model, to obtain an assessment result of short-term voltage stability of the power grid.

A method for assessing a short-term voltage stability of a power grid based on a synchronous spatio-temporal information learning, comprising:

step S1:
performing a time-domain simulation for each transient fault of the power grid under each operating mode, and extracting a temporal responsive trajectory of each monitoring bus and status information regarding the short-term voltage stability of the power grid from a time-domain simulation process to obtain a transient sample set;
wherein an operating mode set and a transient fault set of the power grid are obtained from a dispatching platform of the power grid;
N time-domain simulations are performed on each transient fault of the power grid under each operating mode by using an electromechanical transient time-domain simulation method, wherein N=a*b, a represents the number of operating modes, and b represents the number of transient faults;
temporal response trajectories of a voltage V, a current I, an active power P, and a reactive power Q within L=50 time instants are recorded for each monitoring bus after the monitoring bus suffers from the transient fault, and a power grid status Y in each time-domain simulation process is recorded, wherein Y=1 represents a transient voltage stability, and Y=−1 represents a transient voltage instability;
all temporal response trajectories and power grid statuses recorded in one time-domain simulation process form a transient sample;
and all transient samples obtained from the N time-domain simulations are collected to form the transient sample set containing N samples;
step S2:
constructing a spatial adjacency matrix and a temporal adjacency matrix separately by using the network impedance matrix of the power grid and correlations between power grid statuses at different time instants, and integrating the spatial adjacency matrix and the temporal adjacency matrix into a spatio-temporal adjacency matrix to comprehensively characterize a spatio-temporal correlation between monitoring buses within different time instants in a transient process of the power grid;
wherein step S2 comprises:
step S21:
constructing the spatial adjacency matrix AS for measuring an adjacency degree between the monitoring buses of the power grid, as shown in following formulae:
D
ij
=
|
(
Z
ii
-
Z
ij
)
-
(
Z
ji
-
Z
j
⁢
j
)
|
2
(
1
)
D
=
[
D
ij
]
n
*
⁢
n
(
2
)
D
ij
norm
=
D
ij
/
(
max
1
≤
u
,
v
≤
n
D
u
⁢
v
)
(
3
)
A
ij
S
=
1
1
+
D
ij
norm
/
(
max
1
≤
u
,
v
≤
n
1
1
+
D
uv
norm
)
(
4
)
A
S
=
[
A
ij
S
]
n
*
n
(
5
)
wherein Z represents an impedance value between the monitoring buses;
i and j represent different monitoring buses, wherein 1≤i, and j≤n;
n represents the total number of monitoring buses in the power grid;
Dij represents an electrical distance between the monitoring bus i and the monitoring bus j;
D represents an electrical distance matrix;
max1≤u,v≤n Duv represents a maximum value of each element in the electrical distance matrix D, wherein u and v represent different monitoring buses, 1≤u, and v≤n;
Dijnorm represents a normalized electrical distance between the monitoring bus i and the monitoring bus j;
Duvnorm represents a normalized electrical distance between the monitoring bus u and the monitoring bus v;
and AijS represents a metric of a spatial adjacency degree between the monitoring bus i and the monitoring bus j;
step S22:
constructing the temporal adjacency matrix AT representing a temporal correlation between the monitoring buses within two consecutive time instants after the power grid suffers from the transient fault, as shown in following formulae:
A
ij
T
=
⁢
{
1
,
i
=
j
0
,
i
≠
j
(
6
)
A
T
=
[
A
ij
T
]
n
*
n
(
7
)
wherein AijT represents a metric of a temporal adjacency degree between the monitoring bus i and the monitoring bus j;
and
step S23:
constructing the spatio-temporal adjacency matrix AST representing a spatio-temporal correlation between the monitoring buses within three consecutive time instants after the power grid suffers from the transient fault, as shown in a following formula:
A
S
⁢
T
=
[
A
S
A
T
0
A
T
A
S
A
T
0
A
T
A
S
]
;
(
8
)
step S3:
performing the synchronous spatio-temporal information learning on the transient sample set by using a graph convolutional neural network algorithm, and performing a training to obtain a short-term voltage stability assessment model driven by a spatio-temporal information synchronization;
and
step S4:
when the power grid suffers from the transient fault during an online operation, obtaining a transient temporal responsive trajectory of each monitoring bus in real time, inputting the transient temporal responsive trajectory into the short-term voltage stability assessment model, and outputting, by the short-term voltage stability assessment model, an online assessment result of the short-term voltage stability of the power grid in real time;
and
taking an emergency control measure to restore the stable transient voltage for the power grid.

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Method for assessing short-term voltage stability of power grid based on synchronous spatio-temporal information learning

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Method for assessing short-term voltage stability of power grid based on synchronous spatio-temporal information learning

Life of this patent

Abstract

Claims4