Title :
Fast Transient Stability Assessment Based on Data Mining for Large-Scale Power System
Author :
Yu, Zhonghong ; Zhou, Xiaoxin ; Wu, Zhongxi
Author_Institution :
PSASP Lab., CEPRI, Beijing
Abstract :
One of the most challenging problems in real-time operation of power system is the assessment of transient stability. Fast and accurate techniques are imperative to achieve on-line transient stability assessment (TSA). Based on the statistical learning theory, a novel learning-based nonlinear classifier, i.e., the support vector machines (SVMs) for TSA was presented here. In the approach, the feature variables, which describe the system state before and after the occurrence of a fault, were selected for TSA. Abundance of initial data was preprocessed by feature extraction to improve the data quality. By using SVM training, models were built and used to predict the operation state whether is stable or not for given operation data. The validity of the approach was verified by the simulation for the 4933-bus state grid of China system
Keywords :
data mining; feature extraction; pattern classification; power system analysis computing; power system faults; power system transient stability; statistical analysis; support vector machines; 4933-bus state grid; China; SVM; data mining; data preprocessing; feature extraction; large-scale power system; nonlinear classifier; real-time operation; statistical learning theory; support vector machines; transient stability; Data mining; Feature extraction; Large-scale systems; Power system stability; Power system transients; Predictive models; Real time systems; Statistical learning; Support vector machine classification; Support vector machines; Support vector machines; Transient stability Assessment; data mining;
Conference_Titel :
Transmission and Distribution Conference and Exhibition: Asia and Pacific, 2005 IEEE/PES
Conference_Location :
Dalian
Print_ISBN :
0-7803-9114-4
DOI :
10.1109/TDC.2005.1546982
