期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 33, 期 1, 页码 514-524出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2017.2694048
关键词
Clustering; feature selection; future grids; machine learning; scenario analysis; stability scanning; small-signal stability; time-series analysis; voltage stability
Future grid scenario analysis requires a major departure from conventional power system planning, where only a handful of most critical conditions is typically analyzed. To capture the interseasonal and temporal variations in the renewable generation of a future grid scenario necessitates the use of computationally intensive time-series analysis. In this paper, we propose a framework for fast stability scanning of future grid scenarios using an improved feature selection and self-adaptive PSO-k-means clustering algorithm. To achieve the computational speedup, the stability analysis is performed only on small number of representative cluster centroids instead of on the full set of operating conditions. As a case study, we perform small-signal stability and steady-state voltage stability scanning of a simplified model of the Australian national electricity market with significant penetration of renewable generation. The simulation results show the effectiveness of the proposed approach. Compared to an exhaustive time series scanning, the proposed framework reduced the computational burden up to ten times, with an acceptable level of accuracy.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据