Transient stability assessment method of power system based on improved CatBoost

被引：0

作者：

Du Y. ^{[1
]}

Hu Z. ^{[1
]}

Chen W. ^{[1
]}

Wang F. ^{[1
]}

Zhang Y. ^{[2
]}

机构：

[1] School of Electrical Engineering and Automation, Wuhan University, Wuhan

[2] Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning

来源：

Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2021年 / 41卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Artificial intelligence; CatBoost algorithm; Electric power systems; Ensemble learning; Machine learning; Transient stability assessment;

D O I：

10.16081/j.epae.202107026

中图分类号：

学科分类号：

摘要：

In the practical operation process of power grid, the dynamic parameters of power grid collected in real time by phase measurement units usually contain some noise, and sometimes the values are randomly deletion due to communication failures, making great influence on the transient stability assessment models of power system based on artificial intelligence, for which, a transient stability assessment method based on improved CatBoost is proposed. The binning algorithm is used to discretize the input feature data for improving the robustness of model to noise. The weighted focal loss function is used for replacing cross-entropy loss function, which improves the confidence of the model and reduces the misjudgment of model to unstable samples. The samples with part of the measurement data missing are divided into separate nodes for continue modeling, thus the transient information can be fully exploited from incomplete samples. The experimental results of New England 10-generator 39-bus system show that the accuracy rate and recall rate of the proposed method are superior than other machine learning algorithms, and the proposed method performs good robustness to noise and values missing and has fast training speed and prediction speed. © 2021, Electric Power Automation Equipment Press. All right reserved.

引用

页码：115 / 122

页数：7

共 18 条

[1]

YU J J Q, HILL D J, LAM A Y S, Et al., Intelligent time-adaptive transient stability assessment system, IEEE Tran-sactions on Power Systems, 33, 1, pp. 1049-1058, (2018)

[2]

LIANG Zhifeng, GE Rui, DONG Yu, Et al., Analysis of large-scale blackout occurred on July 30 and July 31, 2012 in India and its lessons to China's power grid dispatch and operation, Power System Technology, 37, 7, pp. 1841-1848, (2013)

[3]

JU Ping, ZHOU Xiaoxin, CHEN Weijiang, Et al., Smart Grid Plus" research overview, Electric Power Automation Equipment, 38, 5, pp. 2-11, (2018)

[4]

KARAMI A., Power system transient stability margin estimation using neural networks, International Journal of Electrical Power & Energy Systems, 33, 4, pp. 983-991, (2011)

[5]

YAO Dequan, JIA Hongjie, ZHAO Shuai, Power system transient stability assessment and stability margin prediction based on compound neural network, Automation of Electric Po-wer Systems, 37, 20, pp. 41-46, (2013)

[6]

DAI Yuanhang, CHEN Lei, ZHANG Weiling, Et al., Power system transient stability assessment based on multi-support vector machines, Proceedings of the CSEE, 36, 5, pp. 1173-1180, (2016)

[7]

LIU Li, LI Yong, CAO Yijia, Et al., Transient rotor angle stability prediction method based on SVM and LSTM network, Electric Power Automation Equipment, 40, 2, pp. 129-139, (2020)

[8]

RAHMATIAN M, CHEN Y C, PALIZBAN A, Et al., Transient stability assessment via decision trees and multivariate adaptive regression splines, Electric Power Systems Research, 142, pp. 320-328, (2017)

[9]

SHI Fang, ZHANG Linlin, HU Xiongwei, Et al., Power system transient stability rules extraction based on multi-attribute decision tree, Transactions of China Electrotechnical Society, 34, 11, pp. 2364-2374, (2019)

[10]

ZHANG Chenyu, WANG Huifang, YE Xiaojun, Transient stabi-lity assessment of power system based on XGBoost algorithm, Electric Power Automation Equipment, 39, 3, pp. 77-83, (2019)

← 1 2 →