Title :
The application of artificial immune network in load classification
Author :
Gu Danzhen ; Ai Qian ; Chen, Chen
Author_Institution :
Dept. of Electr. Eng., Shanghai Univ. of Electr. Power, Shanghai
Abstract :
Characteristic clustering of dynamic loads is necessary for load modeling practicization. This paper presents a novel method of characteristic clustering of dynamic loads based on fuzzy artificial immune network (FaiNet). Firstly, artificial immune network learning algorithm reflects the samples in a compact network. By analyzing the nodes of obtained network with minimal spanning tree, the cluster number and related cluster centers are easily gotten. At last, load samples are sorted on the basis of fuzzy rules. Test results show that this method not only finds the clustering structure effectively, but also is independent of the initial prototypes selection and predefined class number. To compare with fuzzy C-means method, the FaiNet method fit more for characteristic clustering of dynamic loads.
Keywords :
artificial immune systems; fuzzy set theory; learning (artificial intelligence); power engineering computing; trees (mathematics); FaiNet method; artificial immune network learning algorithm; characteristic clustering; dynamic loads; fuzzy C-means method; fuzzy artificial immune network; load classification; load modeling practicization; minimal spanning tree; Artificial intelligence; Artificial neural networks; Clustering algorithms; Clustering methods; Load modeling; Pattern recognition; Power system dynamics; Power system modeling; Prototypes; Testing; dynamic characteristic clustering; fuzzy artificial immune network (FaiNet); load classification;
Conference_Titel :
Electric Utility Deregulation and Restructuring and Power Technologies, 2008. DRPT 2008. Third International Conference on
Conference_Location :
Nanjuing
Print_ISBN :
978-7-900714-13-8
Electronic_ISBN :
978-7-900714-13-8
DOI :
10.1109/DRPT.2008.4523624
