A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm

Aiming at the difficulty of unstable pattern recognition after power system fault, a novel identification framework for transient instability mode identification based on knowledge discovery by accuracy maximization (KODAMA) and extreme gradient boosting (XGBoost) algorithm is proposed. In this meth...

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Autores principales:	Neng Zhang, Huimin Qian, Yuchao He, Lirong Li, Chaoyun Sun
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Transient stability KODAMA algorithm XGBoost algorithm machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/0d7ed6350b6f4095991d74927eaf5cd8
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id	oai:doaj.org-article:0d7ed6350b6f4095991d74927eaf5cd8
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spelling	oai:doaj.org-article:0d7ed6350b6f4095991d74927eaf5cd82021-11-24T00:00:35ZA Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm2169-353610.1109/ACCESS.2021.3124051https://doaj.org/article/0d7ed6350b6f4095991d74927eaf5cd82021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9592755/https://doaj.org/toc/2169-3536Aiming at the difficulty of unstable pattern recognition after power system fault, a novel identification framework for transient instability mode identification based on knowledge discovery by accuracy maximization (KODAMA) and extreme gradient boosting (XGBoost) algorithm is proposed. In this method, the transient stability of all typical fault scenarios of power system is obtained firstly by XGBoost. Then, to make full use of the structure of the raw data and mine the contained data information, a novel data mining algorithm KODAMA is introduced to cluster the mode of rotor angle in case of instability, thus to convert pattern-unlabeled case data into pattern-labeled data. Finally, based on this labeled data, to fully reflect the dynamic characteristics, a multiple XGBoost assessment strategy is designed to recognize different instable modes. The proposed technique is tested on the Nordic test system, and the results indicate that the proposed approach can provide fast and accurate recognition of instable mode and has a certain prospect of online application.Neng ZhangHuimin QianYuchao HeLirong LiChaoyun SunIEEEarticleTransient stabilityKODAMA algorithmXGBoost algorithmmachine learningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154172-154182 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Transient stability KODAMA algorithm XGBoost algorithm machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Transient stability KODAMA algorithm XGBoost algorithm machine learning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Neng Zhang Huimin Qian Yuchao He Lirong Li Chaoyun Sun A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm
description	Aiming at the difficulty of unstable pattern recognition after power system fault, a novel identification framework for transient instability mode identification based on knowledge discovery by accuracy maximization (KODAMA) and extreme gradient boosting (XGBoost) algorithm is proposed. In this method, the transient stability of all typical fault scenarios of power system is obtained firstly by XGBoost. Then, to make full use of the structure of the raw data and mine the contained data information, a novel data mining algorithm KODAMA is introduced to cluster the mode of rotor angle in case of instability, thus to convert pattern-unlabeled case data into pattern-labeled data. Finally, based on this labeled data, to fully reflect the dynamic characteristics, a multiple XGBoost assessment strategy is designed to recognize different instable modes. The proposed technique is tested on the Nordic test system, and the results indicate that the proposed approach can provide fast and accurate recognition of instable mode and has a certain prospect of online application.
format	article
author	Neng Zhang Huimin Qian Yuchao He Lirong Li Chaoyun Sun
author_facet	Neng Zhang Huimin Qian Yuchao He Lirong Li Chaoyun Sun
author_sort	Neng Zhang
title	A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm
title_short	A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm
title_full	A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm
title_fullStr	A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm
title_full_unstemmed	A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm
title_sort	data-driven method for power system transient instability mode identification based on knowledge discovery and xgboost algorithm
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/0d7ed6350b6f4095991d74927eaf5cd8
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A Data-Driven Method for Power System Transient Instability Mode Identification Based on Knowledge Discovery and XGBoost Algorithm

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