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

Patent metadata

Field	Value
Publication number	US-11906957-B1
Application number	US-202318223559-A
Country	US
Kind code	B1
Filing date	Jul 19, 2023
Priority date	Mar 9, 2023
Publication date	Feb 20, 2024
Grant date	Feb 20, 2024

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.

First claim

What is claimed is:

1. 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 A S for measuring an adjacency degree between the monitoring buses of the power grid, as shown in following formulae:

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Assignees

• Univ Hunan

Inventors

• Zhu Lipeng
• Shuai Zhikang
• Li Jiayong
• Zhou Bin
• Zhang Cong
• Hu Yuhan
• Huang Weizhi
• Zheng Limengqian

Classifications

• H02J2103/30

  Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks · CPC title

• G05B23/0221 Primary

  Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods · CPC title

• H02J3/001 Primary

  Arrangements for handling faults or abnormalities, e.g. emergencies or contingencies · CPC title

• G05B2223/06

  Remote monitoring · CPC title

• H02J2203/20

  Electricity · mapped topic