Machine learning-based fault diagnosis for research nuclear reactor medium voltage power cables in fraction Fourier domain

Abstract

Fault diagnosis of Medium Voltage power Cables (MVCs) research nuclear reactor, incredibly inaccessible/remote ones, has to be carefully identified, located, and fixed within a short time. Therefore, this paper proposes a perfect simultaneous fault diagnosis scheme based on Multiclass Support-Vector Machine (MCSVM) in the fractional Fourier domain. First, the three-phase sending currents are simulated under different conditions then their features are extracted using Discrete Fractional Fourier Transform (DFRFT). Afterward, the features reduction process occurs via the Singular Value Decomposition (SVD) approach. MCSVM scheme is used to diagnose faults (i.e., discover, categorize, and trace) using reduced features outcome from DFRFT and SVD stages. Alternating Transient Program/Electromagnetic Transient Program (ATP/EMTP) simulations have been carried out for 22 kV unreachable MVC. Different kernels of SVM, i.e., linear, quadratic, or polynomial, and diverse factors of DFRFT, i.e., α, are investigated in simulations to obtain the optimum performance (i.e., best α and kernel pair). Hence, performance analysis of the proposed diagnosis method under different conditions (i.e., various fault resistances, locations, and inception angles) concluded two highest accuracy and lowest time settings, which were found at α = 0.5 (for both) quadratic kernel, and linear kernel, respectively. Moreover, the linear kernel achieves 99.8% accuracy rate, the lowest execution time (10 ms), and fault tracing error rate of 0.525789%, which is proper for real-time applications. Besides, our proposed method is more reliable and accurate against variable operating conditions (fault resistances, distances, and inception angles), leading to more reliable power production systems.

Support Vector Machine Parameters Optimization for 500 kV Long OHTL Fault Diagnosis

“…The defective phase or phases in a power transmission network must be removed in order to restore system stability. Researchers have developed numerous computational techniques for fault diagnosis [11], [12].…”

“…The transient patterns of voltage and current are used to determine the fault distance in transient-based fault location methods. Moreover, in comparison to prior impedance-based approaches, a fault location methodology was proposed that uses both the peak time and amplitude of the fault current at the local place with reasonable accuracy and speed [11], [12].…”

“…Even though SVM-based algorithms are very popular for fault analysis as well, noise contamination might cause them to require extensive training [11], [12].…”

Fault detection and location based SVM for three phase transmission lines utilizing positive sequence fault components

“…In order to restore system stability, the problematic phase or phases in a power transmission network must be removed. Numerous computational methods for fault diagnosis have been developed by researchers [28,29]. Impedance-based techniques, traveling wave-based algorithms, and state estimation-based techniques are examples of well-known techniques that have been used to diagnosis problems.…”

Research on Fault Diagnosis Algorithm of Power Cable Based on Deep Learning

“…Literature [6] takes remote sensing images as the research object and proposes a method based on multi-scale gray co-occurrence matrix, which improves the overall recognition rate of images to 81.75%. Literature [7] intends to study an object skeleton recognition method based on multi-level structure to achieve high-probability capture of object contours. In literature [8], multi-scale gray image segmentation algorithm is used to achieve image recognition accuracy of more than 96%.…”