Candidate bus identification for voltage stability enhancement of wind‐penetrated power system based on spectral learning technique

Abstract Dynamic reactive power sources can efficiently address the voltage stability problem of a wind‐penetrated power system and the possibility of cascading failures, which are caused by the decreasing inertia and the increasing complexity of the system dynamics. However, their applications are limited by the high investment cost. Identifying appropriate buses for the deployment can improve the economy and efficiency of reactive power source configuration and reduce the complexity of deployment models. Here, a new metric is proposed to guide the selection of candidate buses, based on the improved spectral learning technique and the quantitative assessment of the short‐term voltage stability. Specifically, a new short‐term voltage stability metric is developed to assess the dynamic voltage responses of different stages after a contingency. Then, an improved spectral learning algorithm with objective priorities assigned to different buses is used for the bus selection, aiming to identify the most influential buses, in terms of short‐term voltage stability and propagation potentials. A two‐dimensional decision‐making methodology is proposed, considering both the capacity sensitivity and the bus's structural characteristics. The effectiveness of the proposed methodology is validated on a New England 39‐bus system using an electromechanical transient model.

Authors

Institutions

Publication Details

Journal
IET Generation Transmission & Distribution
Published
2024-12-30
DOI
https://doi.org/10.1049/gtd2.13351
Primary Topic
Power System Optimization and Stability
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Candidate bus identification for voltage stability enhancement of wind‐penetrated power system based on spectral learning technique

Yuan Chi, Qianggang Wang, Jinsheng Guo, Xinying Zheng et al.
IET Generation Transmission & Distribution
Power System Optimization and Stability
article

Candidate bus identification for voltage stability enhancement of wind‐penetrated power system based on spectral learning technique

Yuan Chi, Qianggang Wang, Jinsheng Guo, Xinying Zheng, Niancheng Zhou, Yongjie Luo, Jia Ye
article en

Abstract

Abstract Dynamic reactive power sources can efficiently address the voltage stability problem of a wind‐penetrated power system and the possibility of cascading failures, which are caused by the decreasing inertia and the increasing complexity of the system dynamics. However, their applications are limited by the high investment cost. Identifying appropriate buses for the deployment can improve the economy and efficiency of reactive power source configuration and reduce the complexity of deployment models. Here, a new metric is proposed to guide the selection of candidate buses, based on the improved spectral learning technique and the quantitative assessment of the short‐term voltage stability. Specifically, a new short‐term voltage stability metric is developed to assess the dynamic voltage responses of different stages after a contingency. Then, an improved spectral learning algorithm with objective priorities assigned to different buses is used for the bus selection, aiming to identify the most influential buses, in terms of short‐term voltage stability and propagation potentials. A two‐dimensional decision‐making methodology is proposed, considering both the capacity sensitivity and the bus's structural characteristics. The effectiveness of the proposed methodology is validated on a New England 39‐bus system using an electromechanical transient model.

IET Generation Transmission & DistributionVol. 19(1)
Chongqing University (CN)
Centre Scientifique et Technique du Bâtiment
Affordable and clean energy
Openalex Percentile: Top 77%
Power System Optimization and Stability
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Candidate bus identification for voltage stability enhancement of wind‐penetrated power system based on spectral learning technique — Yuan Chi, Qianggang Wang, et al. · IET Generation Transmission & Distribution (2024) | TGRS Research Map | TGRS