FSQV and artificial neural networks to voltage stability assessment

被引：0

作者：

Andrade, Antonio C. ^{[1
]}

Barbosa, F. P. Maciel ^{[1
]}

Khodr, H. M. ^{[2
]}

机构：

[1] Inst Engn Polytech Porto, Dept Elect Engn, Rua Dr Antonio Bernardino de Almeida, P-4200072 Oporto, Portugal

[2] Univ Simon Bolivar, INESC Porto, Energy Convers & Delivery Dept, Caracas, Venezuela

来源：

2006 IEEE/PES TRANSMISSION & DISTRIBUTION CONFERENCE & EXPOSITION: LATIN AMERICA, VOLS 1-3 | 2006年

关键词：

voltage stability; voltage collapse; Jacobian singularity; continuation power flow; artificial neural networks;

D O I：

暂无

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：

This paper presents a study of the application of artificial neural network (ANN) to the evaluation of the distance to the voltage collapse point. Voltage stability has been of the major concern in power system operation. To prevent these problems, technical staff evaluates frequently the distance of the operation state to the voltage collapse point. This distance normally is calculated with power flow equations. This classic technique is very slow for electric power systems with large dimension. In abnormal exploration situations it may introduce serious limitation in the voltage stability analysis process. So, the application of a fast and reliable evaluation technique is very important to diminish the evaluation time. This paper also presents the method FSQV (Full sum dQ/dV) for the detection of the collapse point.

引用

页码：532 / +

页数：3

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