A Data-Based Learning and Control Method for Long-Term Voltage Stability

被引：13

作者：

Cai, Huaxiang ^{[1
]}

Ma, Haomin ^{[1
]}

Hill, David J. ^{[1
,2
]}

机构：

[1] Univ Hong Kong, Dept Elect & Elect Engn, Hong Kong 810101, Peoples R China

[2] Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2020年 / 35卷 / 04期

关键词：

Voltage control; Power system stability; Databases; Control systems; Stability analysis; System dynamics; Principal component analysis; Voltage stability; coordinated control; feature extraction; principle component analysis; artificial neural network;

D O I：

10.1109/TPWRS.2020.2967434

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Based on the knowledge accumulated off-line and feature extractions, a novel data-based learning and control method is proposed for the long-term voltage stability problem in this paper. All the spatial-temporal data is considered and the features of different emergency events are extracted by principle component analysis which can reduce the dimension and reveal the significant internal structure of the data. An artificial neural network is used to build a classifier to reinforce the relationship directly between the system dynamics and optimal control actions. With the prepared control knowledge, it is faster to find an optimal control action online with a good system performance. Simulation results on the 6-bus system, New England 39-bus system and Iceland 189-bus system are given to show the potential of this method for on-line control.

引用

页码：3203 / 3212

页数：10

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