Mathematical Morphology-Based Feature-Extraction Technique for Detection and Classification of Faults on Power Transmission Line

Abstract

The permanency of highly-reliable power supply is a core trait of an electric power transmission network. A transmission line is the main part of this network through which power is transmitted to the utility. These lines are often damaged by accidental breakdowns owing to different random origins. Hence, researchers are trying to detect and identify these failures at the earliest to avoid financial losses. This paper offers a new real-time fast mathematical morphology-based fault feature extraction scheme for detection and classification of transmission line faults. The morphological median filter is exploited to wrest unique fault features which are then fed as an input to a decision tree classifier to classify the fault type. The acquired graphical and numerical results of the extracted features affirm the potency of the offered scheme. The proposed scheme is verified for different fault cases simulated on high-voltage transmission line modelled using ATP/EMTP with varying system constraints. The performance of the stated technique is also validated for fault detection and classification on real-field transmission lines. The results state that the proposed method is capable of detecting and classifying the faults with adequate precision and reduced computational intricacy, in less than a quarter of a cycle.

Review of distributed generator integratedACmicrogrid protection: issues, strategies, and future trends

“…This transform requires frequency domain data to detect the fault location. By using FFT (Fast Fourier Transform), the transient voltage signals, current signals, and fault distance can be obtained 172 ; considering the traveling wave frequency has a lower value. This method is having an accuracy of 1% and during the fault, due to the phase angle and resistance, the accuracy remains unaffected to some extent.…”

Fluid Reconstruction and Editing from a Monocular Video based on the SPH Model with External Force Guidance

“…Given a threshold γ on the height field of target particles, all the target particles whose height is above γ are added to FOI (see Figure 4a). We also extend mathematical morphological operators [GB20] to allow the user to interactively and easily refine features of interest selected by the vertex variables. By extracting iso-surface of target particles (see Figure 4b), we can utilize dilatation for connecting and enlarging parts of FOI (see Figure 4c) and erosion for disconnecting, reducing, or removing parts of FOI (see Figure 4d).…”

Fault Detection Based on the Generalized S-Transform With a Variable Factor for Resonant Grounding Distribution Networks

“…Owing to the richness and stability of transient fault signal features, the detection methods employed in RG systems are mainly the passive detection based on transient signals. Primary examples of detection methods employing these features include the wavelet transform (WT) [7]- [10], Hibert-Huang transform (HHT) [11], [12], S-transform (ST) [13]- [18], Prony algorithm [19], [20], mathematical morphology (MM) [21]- [23], fuzzy c-means (FCM) clustering [24], [25], and support vector machine (SVM) [26], [27]. Each of these techniques have pros and cons as listed in Table 1.…”

“…Shu et al [22] detected faulty feeders according to the morphological peak-valley features of the constructed wavelet coefficients under characteristic bands. The morphological median filter is exploited to wrest unique fault features which are then fed as an input to a decision tree classifier to classify the fault type [23].…”