A Novel Method for PD Feature Extraction of Power Cable with Renyi Entropy

Abstract:In this paper, a novel method with cross-wavelet singular entropy (XWSE)-based feature extractor and support vector machine (SVM) is proposed for analog circuit fault diagnosis. Primarily, cross-wavelet transform (XWT), which possesses a good capability to restrain the environment noise, is applied to transform the fault signal into time-frequency spectra (TFS). Then, a simple segmentation method is utilized to decompose the TFS into several blocks. We employ the singular value decomposition (SVD) to analysis the blocks, then Tsallis entropy of each block is obtained to construct the original features. Subsequently, the features are imported into parametric t-distributed stochastic neighbor embedding (t-SNE) for dimension reduction to yield the discriminative and concise fault characteristics. Finally, the fault characteristics are entered into SVM classifier to locate circuits' defects that the free parameters of SVM are determined by quantum-behaved particle swarm optimization (QPSO). Simulation results show the proposed approach is with superior diagnostic performance than other existing methods.

Section: Discussionmentioning

confidence: 99%

Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE

Wei

et al. 2018

“…To tackle this problem, we introduce four types of classical entropies into ADTP as the features for the enhancement of recognition performance and impairment tolerance. It has been successfully proven that the value of entropy indicates information-related properties for an accurate representation of a given image [18][19][20]. Usually, the entropy E must be an additive cost function, such that Ep0q " 0.…”

Section: Multidimensional Asynchronous Delay-tap Plot Entropymentioning

confidence: 99%

Using Multidimensional ADTPE and SVM for Optical Modulation Real-Time Recognition

Wei

Huang

et al. 2016

Based on the feature extraction of multidimensional asynchronous delay-tap plot entropy (ADTPE) and multiclass classification of support vector machine (SVM), we propose a method for recognition of multiple optical modulation formats and various data rates. We firstly present the algorithm of multidimensional ADTPE, which is extracted from asynchronous delay sampling pairs of modulated optical signal. Then, a multiclass SVM is utilized for fast and accurate classification of several widely-used optical modulation formats. In addition, a simple real-time recognition scheme is designed to reduce the computation time. Compared to the existing method based on asynchronous delay-tap plot (ADTP), the theoretical analysis and simulation results show that our recognition method can effectively enhance the tolerance of transmission impairments, obtaining relatively high accuracy. Finally, it is further demonstrated that the proposed method can be integrated in an optical transport network (OTN) with flexible expansion. Through simply adding the corresponding sub-SVM module in the digital signal processer (DSP), arbitrary new modulation formats can be recognized with high recognition accuracy in a short response time.

“…Therefore, high frequency current transformer (HFCT) (band width between 0.1 and 100MHz) is connected with a cross connection box or ground wire, pulse current signal can be collected based on the principle of electromagnetic coupling, and stored in acquisition equipment through coaxial cable. The PD detection process is shown in Figure 3 [33]. As shown in Figure 4, the HFCT is installed on the three-phase ground wire of the cross connection box, and is connected to the acquisition terminal APD-120D consisting of A/D module and data storage through the coaxial cable.…”

Section: Comparison In Accuracy Of Feature Extraction For Swe and Swpementioning

confidence: 99%

“…So, feature extraction accuracy of SWPE is significantly greater than SWE. SWSE and Shannon entropy wavelet packet singular entropy (SWPSE) are cited as examples to compare their accuracy of feature extraction, and derivative process is as follows [33].…”

Section: Comparison In Accuracy Of Feature Extraction For Swe and Swpementioning

confidence: 99%

See 1 more Smart Citation

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

Chen

Dou

et al. 2016

Self Cite

Abstract:In a power system, the analysis of transient signals is the theoretical basis of fault diagnosis and transient protection theory. Shannon wavelet entropy (SWE) and Shannon wavelet packet entropy (SWPE) are powerful mathematics tools for transient signal analysis. Combined with the recent achievements regarding SWE and SWPE, their applications are summarized in feature extraction of transient signals and transient fault recognition. For wavelet aliasing at adjacent scale of wavelet decomposition, the impact of wavelet aliasing is analyzed for feature extraction accuracy of SWE and SWPE, and their differences are compared. Meanwhile, the analyses mentioned are verified by partial discharge (PD) feature extraction of power cable. Finally, some new ideas and further researches are proposed in the wavelet entropy mechanism, operation speed and how to overcome wavelet aliasing.