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.
All Science Journal Classification (ASJC) codes
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering