A general approach for optimal allocation of FACTS devices using equivalent impedance models of VSCs

被引:4
|
作者
Mohammadalizadeh-Shabestary, Masoud [1 ]
Hashemi-Dezaki, Hamed [1 ]
Mortazavian, Shahed [1 ]
Askarian-Abyaneh, Hossein [1 ]
Gharehpetian, Gevork [1 ]
机构
[1] Amirkabir Univ Technol, Dept Elect Engn, Tehran 15914, Iran
关键词
static synchronous series compensators (SSSC); static synchronous compensators (STATCOM); unified power flow controllers (UPFC); optimized placement; genetic algorithm (GA); transmission capability; transient stability; sensitivity analysis; flexible AC transmission systems (FACTS); POWER-FLOW; SYSTEM; STATCOM; PLACEMENT;
D O I
10.1002/etep.1896
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
Flexible AC transmission system (FACTS) devices such as static synchronous series compensators, static synchronous compensators, and unified power flow controllers which use voltage source convertors (VSCs) can noticeably improve different characteristics of power systems. Therefore, finding their optimal allocation is a vital issue. To find the optimized placement of different FACTS devices, it is necessary to calculate the effects of such devices in the whole system. Sensitivity analysis has been recently proposed as a fast and reliable method to find the effects of voltage source convertors on system variables. In this paper, a novel approach is introduced to optimize the allocation of different FACTS devices based on the equivalent impedance model obtained from sensitivity analysis. This method requires neither the exact modeling of voltage source convertors nor more than one load flow, so it is much faster than conventional methods. Due to its simplicity and fastness, it can cover all possible locations, continuous sizing, type, and number of compensators. Therefore, its performance and accuracy are considerably high. The proposed method based on a novel objective function, which includes voltage profile, transmission capability, and stability, is applied to a typical 6-bus and the 30-bus IEEE test systems. Furthermore, genetic algorithm is implemented to solve the optimization problem. The test results illustrate the effectiveness of the introduced method. Copyright (c) 2014 John Wiley & Sons, Ltd.
引用
收藏
页码:1187 / 1203
页数:17
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