Title :
Fake Measurement Detection in Automatic Voltage Control using Support Vector Machine
Author :
Chen Bo ; Liu Yuanyuan ; Jing Zhaoxia ; Zhang Yao
Author_Institution :
Sch. of Electr. Power, South China Univ. of Technol., Guangzhou, China
Abstract :
As a novel data mining method featured with excellent pattern recognition capability, Support vector machine (SVM) is utilized in this paper to detect erroneous remote measurement, and therefore, prevent mis-operation of automatic voltage control (AVC) systems. A SVM nonlinear regression algorithm is first used to predict remote measurement. The remote measurement deviate obviously from the predicted value is fed to another SVM to identify the erroneous remote measurement, and thereafter, prevent mis-operation of AVC due to erroneous remote measurement. Numerical simulation shows effectiveness of the proposed approach.
Keywords :
control engineering computing; data mining; nonlinear equations; pattern recognition; power engineering computing; regression analysis; support vector machines; voltage control; SVM nonlinear regression algorithm; automatic voltage control; erroneous remote measurement; fake measurement detection; numerical simulation; pattern recognition; support vector machine; Artificial neural networks; Automatic voltage control; Control systems; Pollution measurement; Power generation; Power grids; Power measurement; Support vector machine classification; Support vector machines; Voltage control;
Conference_Titel :
Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-4812-8
Electronic_ISBN :
978-1-4244-4813-5
DOI :
10.1109/APPEEC.2010.5448729