Abstract

During the operation process of the high voltage circuit breaker, the changes of vibration signals can reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition (EEMD). Firstly, the original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs). Secondly, calculating the envelope of each IMF and separating the envelope by equal-time segment and then forming equal-time segment energy entropy to reflect the change of vibration signal are performed. At last, the energy entropies could serve as input vectors of support vector machine (SVM) to identify the working state and fault pattern of the circuit breaker. Practical examples show that this diagnosis approach can identify effectively fault patterns of HV circuit breaker.

Highlights

  • As an import part of the electric system, a HV circuit breaker is a key device to control and protect the power network

  • This paper presents an extraction method based on ensemble empirical mode decomposition (EEMD)

  • In contrast to the wavelet transform (WT) approach, the empirical mode decomposition (EMD) [8] method adaptively decomposes nonstationary time series into narrow-band components, namely, intrinsic mode functions (IMFs), by empirically identifying the physical time scales intrinsic to the data without assuming any basis functions

Read more

Summary

Introduction

As an import part of the electric system, a HV circuit breaker is a key device to control and protect the power network. Research on diagnosis method of circuit breaker is growing fast, and many new techniques have been used in practice, in which the technique based on the analysis of the vibration signal has gradually become hot [1,2,3]. EEMD is the repeated EMD by adding Gauss white noise in each of the decompositions. Mathematical Problems in Engineering distribution statistical characteristics of Gauss white noise in frequency domain [13]. Through this method, EEMD could decompose signal continuously in different scales. A nonstationary vibration signal is decomposed into a series of intrinsic mode functions (IMFs) by EEMD. The experiment result indicates the method that applied the EEMD-energy entropy and support vector machine is effective and has many potential applications in practice

EEMD Method
EEMD-Energy Entropy
Experiment and Analysis
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call