Applying wavelet entropy principle in fault classification

Abstract

The ability to detect and classify the type of fault plays a great role in the protection of power system. This procedure is required to be precise with no time consumption. In this paper detection of fault type has been implemented using wavelet analysis together with wavelet entropy principle. The simulation of power system is carried out using PSCAD/EMTDC. Different types of faults were studied obtaining various current waveforms. These current waveforms were decomposed using wavelet analysis into different approximation and details. The wavelet entropy of such decompositions is analyzed reaching a successful methodology for fault classification. The suggested approach is tested using different fault types and proven successful identification for the type of fault.

Application of Shannon Wavelet Entropy and Shannon Wavelet Packet Entropy in Analysis of Power System Transient Signals

“…Comparing with other methods such as the energy-based method and the similarity-comparing method, the proposed wavelet-entropy-based method is more effective in PD signal de-noising. In reference [17], detection of fault type has been implemented by using Shannon wavelet entropy. Different types of faults are studied obtaining various current waveforms.…”

Wavelet Entropy and Neural Network Based Fault Detection on A Non Radial Power System Network

“…The wavelet transform is useful in detecting and extracting faulty features of various types transmission line faults both in radial and non radial network because it is sensitive to signal irregularities but insensitive to the regular-like signal behavior. The wavelet transform can be interpreted as the inner product of the complex conjugate of wavelet function h*a,τ(t) and the input signal s(t) WT(a,τ)=∫h*a,τ(t).s(t)dt (1) Where the wavelet function h* a,τ(t) is defined as h*a,τ(t)=aˉ¹/²h(t-τ/a) (2) The variable a is the scale parameter of the wavelet function and is proportional to the reciprocal of the frequency, τ is the translation parameter and h is called the mother wavelet. Wavelet analysis deals with unsteady signal while entropy expresses information of the signal.…”

“…This can be done by detecting, localizing and classifying different fault types and identifying faulty lines. Safty and Zonkoly [1] presented a paper where EMTDC/PSCAD software has been used to detect fault types and wavelet entropy principle is implemented to analyze the current signals. Routray et al [2] proposed a real time wavelet-fuzzy combined approach for digital relaying.…”

Operational Reliability Assessment of Compressor Gearboxes with Normalized Lifting Wavelet Entropy from Condition Monitoring Information

“…For example, Bafroui proposed using wavelet energy and Shannon entropy for feature extraction in gearbox fault detection under varying speed conditions [16]. Safty et al adopted wavelet information entropy with a neural-fuzzy inference system to identify transmission line faults and determine the phases involved in power system faults [17]. Lin et al detected motor shaft misalignments using multiscale entropy with wavelet denoising [18].…”