Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)
- 1998.07c
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- Pages.1030-1032
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- 1998
Automatic Classification of Power Quality Disturbances Using Efficient Feature Vector Extraction and Neural Networks
효율적 특징벡터 추출기법와 신경회로망을 이용한 전력외란 자동 식별
- Ban, Ji-Hoon (Dept. of Electrical Engineering, Hanyang Univ.) ;
- Kim, Hyun-Soo (Dept. of Electrical Engineering, Hanyang Univ.) ;
- Nam, Sang-Won (Dept. of Electrical Engineering, Hanyang Univ.)
- Published : 1998.07.20
Abstract
In this paper, an efficient feature vector extraction method and MLP neural network are utilized to automatically detect and classify power quality disturbances, where the proposed classification procedure consists of the following three parts: i.e., (i) PQ disturbance detection using discrete wavelet transform. (ii) feature vector extraction from the detected disturbance. using several methods, such as FFT, DWT, Fisher's criterion. etc.. and (iii) classification of the corresponding type of each PQ disturbance by recognizing the pattern of the extracted feature vector. To demonstrate the performance and, applicability of the proposed classification algorithm. some test results obtained by analyzing 10-class PQ disturbances are also provided.
Keywords