Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 811-817 |
| Number of pages | 7 |
| Journal | IEEE Transactions on Power Systems |
| Volume | 19 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2004 May 1 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
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Classification of direct load control curves for performance evaluation. / Yang, Hong-Tzer; Chen, Shih Chieh; Tsai, Win Ni.
In: IEEE Transactions on Power Systems, Vol. 19, No. 2, 01.05.2004, p. 811-817.Research output: Contribution to journal › Article
TY - JOUR
T1 - Classification of direct load control curves for performance evaluation
AU - Yang, Hong-Tzer
AU - Chen, Shih Chieh
AU - Tsai, Win Ni
PY - 2004/5/1
Y1 - 2004/5/1
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
VL - 19
SP - 811
EP - 817
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
SN - 0885-8950
IS - 2
ER -