TY - JOUR
T1 - Classification of direct load control curves for performance evaluation
AU - Yang, Hong Tzer
AU - Chen, Shih Chieh
AU - Tsai, Win Ni
N1 - Funding Information:
Manuscript received September 26, 2003. This work was supported in part by Tai-power Company under Contract 2819-18 and in part by the National Science Council, R.O.C., under Contract NSC-90-2213-E-033-045. H.-T. Yang and S.-C. Chen are with the Department of Electrical Engineering, Chung Yuan Christian University, Chung-Li 32023, Taiwan, R.O.C. W.-N. Tsai is with MXIC, Hsin-Chu 300, Taiwan, R.O.C. Digital Object Identifier 10.1109/TPWRS.2004.825884
PY - 2004/5
Y1 - 2004/5
N2 - In evaluating the performance of direct load control (DLC) programs, an essential task is to classify the DLC curves into either the one complying with the program or not. This paper presents an efficient approach to clustering the DLC curves through a structure of self-organizing maps (SOM). Aiming at selecting significant features of DLC curves, methods of nonlinear principal component analysis (NLPCA) and periodic analysis are proposed for feature extraction. The dual multilayer neural networks (DMNN) model is employed in the proposed NLPCA method. In the periodic analysis method, the periodic characteristics of the DLC curves are investigated. In the SOM, Davies-Bouldin (DB) indexes and a k-means algorithm decide the best number of clusters to be classified. Through the proposed methods, the DLC curves are thus divided into the two categories by the SOM: DLC complying and DLC noncomplying loads. Results obtained from the comparison of six different approaches show that the clusters obtained from the proposed approach exhibit lowest degrees of misclassification for the practical data on Taiwan Power Company (TPC) DLC programs.
AB - In evaluating the performance of direct load control (DLC) programs, an essential task is to classify the DLC curves into either the one complying with the program or not. This paper presents an efficient approach to clustering the DLC curves through a structure of self-organizing maps (SOM). Aiming at selecting significant features of DLC curves, methods of nonlinear principal component analysis (NLPCA) and periodic analysis are proposed for feature extraction. The dual multilayer neural networks (DMNN) model is employed in the proposed NLPCA method. In the periodic analysis method, the periodic characteristics of the DLC curves are investigated. In the SOM, Davies-Bouldin (DB) indexes and a k-means algorithm decide the best number of clusters to be classified. Through the proposed methods, the DLC curves are thus divided into the two categories by the SOM: DLC complying and DLC noncomplying loads. Results obtained from the comparison of six different approaches show that the clusters obtained from the proposed approach exhibit lowest degrees of misclassification for the practical data on Taiwan Power Company (TPC) DLC programs.
UR - http://www.scopus.com/inward/record.url?scp=2542613720&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=2542613720&partnerID=8YFLogxK
U2 - 10.1109/TPWRS.2004.825884
DO - 10.1109/TPWRS.2004.825884
M3 - Article
AN - SCOPUS:2542613720
VL - 19
SP - 811
EP - 817
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
SN - 0885-8950
IS - 2
ER -