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웨이블릿 기반의 뉴럴네트웍을 이용한 전원의 왜란분류 시스템
김홍균(Hongkyun Kim),이진목(Jinmok Lee),최재호(Jeaho Choi) 전력전자학회 2005 전력전자학술대회 논문집 Vol.- No.-
This paper presents a wavelet-based neural network technology for the detection and classification of the short durations type of power quality disturbances. Transients happen during very short durations to the nano- and microsecond. Thus, a method for detecting and classifying transient signals at the same time and in an automatic combines the properties of the wavelet transform and the advantages of neural networks. Especially, the additional feature extraction to improve the recognition rate is considered. The configuration of the hardware of TMS320C6711 DSP based with 16 channel 20Mhz sampling rate A/D(Analog to Digital) converter and some case studies are described.
웨이블릿 기반의 RBF 신경망을 이용한 전력품질 진단시스템
김홍균(Hong kyun Kim),이진목(Jinmok Lee),최재호(Jeaho Choi),이상훈(Sanghoon Lee),김재식(Jaesig Kim) 전력전자학회 2004 전력전자학술대회 논문집 Vol.- No.-
This paper presents a wavelet-based neural network technology for the detection and classification of the various types of power quality disturbances. Power quality phenomena are short-time problems and of many varieties. Particularly, the transients happen during very short durations to the nano- and microsecond. Thus, a method for detecting and classifying transient signals at the same time and in an automatic combines the properties of the wavelet transform and the advantages of neural networks. Especially, the additional feature extraction to improve the recognition rate is considered. The configuration of the hardware of WN (PQ-DAS) and some case studies are described.