Earticle

초록

영어

In recent years, many countries have actively studied wind power generation as a means of realizing low-carbon green growth through a new renewable energy source. The most efficient method of securing the stable operation of wind turbines and reduce maintenance costs is monitoring and analyzing their operational status in realtime through a remote monitoring system. Remote monitoring systems employ various sensor technologies and the Wireless Sensor Network to collect and transmit data on the status of individual parts in realtime, and they diagnose faults through a signal analysis system. Application of the fault analysis method can reduce fault resolution times and minimize losses. In this study, signals collected from wind turbines were analyzed, and their characteristics were extracted through empirical mode decomposition (EMD). In the experiment, EMD learning was carried out using the following fault signals as examples: The back-propagation (BP) neural network algorithm with generator vibration, an unbalanced rotor, and a bearing misalignment fault. This article proposes a method of diagnosing faults through signal analysis and recognition, and it demonstrates the validity of the method through a simulation.

목차

Abstract
 1. Introduction
 2. Paper Method of Analyzing Fault Signals of Wind Turbines
  2.1. Hilbert–Huang Transform
  2.2. Transform Improvement Suggestion for Enveloping
  2.3. Neural Network Algorithms
 3. Experiment and Results
  3.1. System Block Diagram
  3.2. Noise Reduction
  3.3. Signal Characteristics Analysis
  3.4. Determination of Generator Faults
 4. Conclusion
 Acknowledgment
 References

키워드

저자

• Ming-ShouAn [ Dong-A University, Dept. of Electronics Engineering, 37 Nakdong-daero 550 beon-gilSaha-gu, Busan, Korea ]
• Dae-Seong Kang [ Dong-A University, Dept. of Electronics Engineering, 37 Nakdong-daero 550 beon-gilSaha-gu, Busan, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605