Transient stability assessment method for an electric power system

Patent metadata

Field	Value
Publication number	US-12100954-B2
Application number	US-202117383764-A
Country	US
Kind code	B2
Filing date	Jul 23, 2021
Priority date	Apr 29, 2021
Publication date	Sep 24, 2024
Grant date	Sep 24, 2024

Abstract

A transient stability assessment method for an electric power system is disclosed. Transient stability tags and steady-state data of the electric power system before a failure occurs are collected from transient stability simulation data. Data sets under different predetermined failures are obtained based on a statistical result of the transient stability tags and a maximum-minimum method. A similarity evaluation index between different predetermined failures is constructed based on a Jaccard distance and a Hausdorff distance. Different predetermined failures are clustered based on a clustering algorithm. A parameters-shared siamese neural network is trained for different predetermined failures in each cluster to obtain a multi-task siamese neural network for the transient stability assessment. Transient stability assessment results of the electric power system under all the predetermined failures are obtained based on the statistical result of the transient stability tags and the multi-task siamese neural network for the transient stability assessment.

First claim

What is claimed is:

1. A transient stability assessment method for an electric power system, comprising:
collecting steady-state data of the electric power system before a failure occurs and transient stability tags from transient stability simulation data;
obtaining data sets under different predetermined failures based on a statistical result of the transient stability tags and a maximum-minimum method;
constructing a similarity evaluation index between different predetermined failures based on a Jaccard distance and a Hausdorff distance;
clustering the different predetermined failures based on a clustering algorithm;
training a parameters-shared siamese neural network for different predetermined failures in each cluster to obtain a multi-task siamese neural network for the transient stability assessment; and
obtaining transient stability assessment results of the electric power system under all the predetermined failures based on the statistical result of the transient stability tags and the multi-task siamese neural network for the transient stability assessment.

2. The method of claim 1 , comprising:
(1) setting so operating conditions based on historical operational aspects and future plannings of the electric power system in consideration of a situation that the electric power system has heavy loads in future, setting ƒ predetermined failures for each operating condition, performing a simulating calculation on transient stabilities of the electric power system under the ƒ predetermined failures occurring in the so operating conditions using a numerical computation method, respectively, collecting generator features and line features before a failure occurs as well as transient stability tags under different predetermined failures, and obtaining a data set O 0 based on a statistical result of the transient stability tags and a maximum-minimum normalization method; wherein the step (1) includes the following:
(1-1) for the electric power system having N generators, setting the so operating conditions based on the historical operational aspects and the future plannings of the electric power system in consideration of the situation that the electric power system has heavy loads in future, setting the ƒ predetermined failures for each operating condition, performing a simulating calculation on the transient stabilities of the electric power system under the ƒ predetermined failures occurring in the so operating conditions using a numerical computation method, respectively, collecting active power P Gi k and a generator voltage V Gi k of each generator, an active power P Lj k and an reactive power Q Lj k of each line before the failure occurs as well as the transient stability tags [y 1 k , y 2 k . . . ,y a k , . . . ,y ƒ k ] in the k-th operating condition to construct a raw data set [P Gi k , V Gi k ,P Lj k , Q Lj k y 1 k , y 2 k , . . . , y a k , . . . ,y ƒ k ], where k represents a numerical order of the operating condition, which indicates the k-th operating condition in the so operating conditions, k=1,2, . . . ,s 0 , i represents the i-th generator, i=1, . . . ,N, j represents the j-th line, j=1, . . . ,M, M represents a total number of lines in the electric power system, y a k represents the transient stability tag of the electric power system under the a-th predetermined failure occurring in the k-th operating condition, a represents the a-th predetermined failure, a=1, . . . ƒ wherein when a transient instability occurs in the electric power system after the a-th predetermined failure occurs in the k-th operating condition, y a k =1; and when the electric power system maintains transient stability after the a-th predetermined failure occurs in the k-th operating condition, y a k =0;
(1-2) normalizing P Gi k , V Gi k , P Lj k and Q Lj k in the raw data set obtained in step (1-1) based on the maximum-minimum normalization method to obtain a normalized active power {tilde over (P)} Gi k and a normalized generator voltage {tilde over (V)} Gi k of each generator, a normalized active power {tilde over (P)} Lj k and a normalized reactive power {tilde over (Q)} Lj k of each line before the failure occurs in the k-th operating condition, wherein a normalization formula is denoted as follows:

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Assignees

• Univ Tsinghua

Inventors

• Sun Hongbin
• Zhou Yanzhen
• Guo Qinglai
• Wang Bin
• Wu Wenchuan
• Wang Zhengcheng
• Lan Jian

Classifications

• H02J2103/30

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

• H02J3/0014 Primary

  for preventing or reducing power oscillations in networks · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/08

  Learning methods · CPC title