Earticle

초록

영어

Power quality (PQ) disturbances recognition is the foundation of power quality analysis and improvement. In order to improve the classification accuracy and efficiency, a new classification approach based on modified Fourier neural networks (FNN) and Hyperbolic S-transform (HST) was designed for PQ disturbances classification. HST has better a time-frequency resolution than S-transform. The features extracted from HST results compose the input vectors of classifier. The DFP emendatory Quasi-Newton method is used to improve the learning ability of FNN and avoid local minimum problem. Three modified FNNs were used to construct a classifier with the structure of decision tree. Six types of disturbances with different noise ratio were simulated to test the classification ability of the new approach. Simulation results show that the new classifier has better classification accuracy than other classifiers based on BP neural networks and Fourier neural networks. The new approach is effective.

목차

Abstract
 1. Introduction
 2. The Modified Fourier Neural Network
  2.1. The Theory of Fourier Neural Network
  2.2. The Improved Learning Algorithm based on DFP Emendatory Quasi-Newton Method
 3. The Feature Extraction by HS-Transform
 4. The Structure of New Classifier
 5. Conclusions
 References

키워드

저자

• Lin Lin [ College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China ]
• Xiaohuan Wu [ Municipal Power Supply Company of State Grid Zhejiang Electric Power Company, Hangzhou 310007, China ]
• Jiajin Qi [ Municipal Power Supply Company of State Grid Zhejiang Electric Power Company, Hangzhou 310007, China ]
• Hongxin Ci [ Jilin Petrochemical Co information network data center, Jilin 132022, China ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605