DocumentCode :
105292
Title :
Active islanding detection method using d-axis disturbance signal injection with intelligent control
Author :
Faa-Jeng Lin ; Yi-Sheng Huang ; Kuang-Hsiung Tan ; Jian-Hsing Chiu ; Yung-Ruei Chang
Author_Institution :
Dept. of Electr. Eng., Nat. Central Univ., Chungli, Taiwan
Volume :
7
Issue :
5
fYear :
2013
fDate :
May-13
Firstpage :
537
Lastpage :
550
Abstract :
An active islanding detection method using d-axis disturbance signal injection with intelligent control is proposed in this study. The proposed active islanding detection method is based on injecting a new disturbance signal into the system through the d-axis current that leads to a frequency deviation at the terminal of the resistance inductance and capacitance (RLC) load when the grid is disconnected. The feasibility of the proposed method is evaluated under the UL1741 anti-islanding test configuration. The proposed d-axis disturbance signal-injection method is intended to achieve a reliable detection with quasi-zero non-detection zone, minimum effects on power quality and easy implementation without additional sensing devices or equipments. Moreover, in order to achieve accurate tracking control of active and reactive power and rapid response of the islanding detection of the distributed generator (DG) system, a wavelet fuzzy neural network (WFNN) intelligent controller is proposed to replace the conventional proportional-integral controller for the control of the DG system. Furthermore, the network structure and the online learning algorithm of the WFNN are introduced in detail. Finally, the feasibility and effectiveness of the proposed d-axis disturbance signal-injection method are verified with experimental results.
Keywords :
distributed power generation; intelligent control; neurocontrollers; power generation control; power supply quality; reactive power control; DG system; RLC load terminal; UL1741 antiislanding test configuration; WFNN intelligent controller; active islanding detection method; active power tracking control; d-axis current; d-axis disturbance signal injection; detection reliability; distributed generator system; frequency deviation; intelligent control; online learning algorithm; power quality; quasizero nondetection zone; reactive power tracking control; wavelet fuzzy neural network intelligent controller;
fLanguage :
English
Journal_Title :
Generation, Transmission & Distribution, IET
Publisher :
iet
ISSN :
1751-8687
Type :
jour
DOI :
10.1049/iet-gtd.2012.0488
Filename :
6531923
Link To Document :
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