DocumentCode :
676416
Title :
Automatic reactive power and voltage control for regional power grid based on SVM
Author :
Xiangxing Meng ; Guozhong Sun ; Jianxiang Li ; Haibo Liu
Author_Institution :
Sch. of Autom., Harbin Eng. Univ., Harbin, China
fYear :
2013
fDate :
22-25 Oct. 2013
Firstpage :
1
Lastpage :
4
Abstract :
In this paper, the traditional reactive power and voltage control problem is regarded as a multi-class classification problem, and a novel approach based on Support Vector Machine (SVM) classifier is proposed. According to the approach, the power grid operating status is classified according to the power factor and voltage at each substation and the corresponding control strategy is selected to control the capacitors and transformer taps. A naive progressive learning strategy is also presented to make sure the classifier can keep learning in the operation process. The approach is suitable for online operation. The decision results are robust and coordination operation between the substations can be achieved. A simple radial system containing three substations is used for case study. The results illustrate the effectiveness of the proposed approach.
Keywords :
capacitors; control engineering computing; learning (artificial intelligence); pattern classification; power engineering computing; power grids; power system control; reactive power control; substations; support vector machines; transformers; voltage control; SVM classifier; automatic reactive power; capacitors control; control strategy; multiclass classification problem; power factor; power grid operating status; progressive learning strategy; regional power grid; simple radial system; substation; support vector machine; transformer taps control; voltage control; Capacitors; Optimization; Reactive power; Substations; Support vector machine classification; Voltage control; SVM; multi-class classification; power systems; progressive learning; reactive and voltage control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
TENCON 2013 - 2013 IEEE Region 10 Conference (31194)
Conference_Location :
Xi´an
ISSN :
2159-3442
Print_ISBN :
978-1-4799-2825-5
Type :
conf
DOI :
10.1109/TENCON.2013.6718482
Filename :
6718482
Link To Document :
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