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In this work, a new fault classification and phase selection methodology for half-wavelength (HWL) transmission lines is developed. Initially, the basic principles of phase currents transformations are presented. Then, an innovative faulted phase selection methodology based on the variation of those currents is developed. The behavior of the electrical quantities related to short circuits in such long lines is very different when compared to conventional lines. The faulted phase selection method employed in this work is built on the fact that those decoupled currents are expected to not vary or vary less than the others, depending on how many phases are in short circuit. The robustness of the method was analyzed considering different load conditions and fault resistances. Furthermore, different sampling rates were considered for the phase currents. Extensive simulation studies showed that the developed method can accurately identify the fault types and the faulted phase with a relatively low sample rate. Moreover, the simulation results showed that the proposed method is not affected by the presence of inter-harmonics and responds precisely with high-resistance faults. The proposed algorithm presented errors smaller than 0.04%. • A new method for ground and phase fault classification based on decoupled currents by means of modal transformation was developed in this study. • The decoupled currents are obtained by means of a modal transformation. • The developed method is robust to faults with high resistance. • The developed method is not affected by the presence of interharmonics. [ABSTRACT FROM AUTHOR]

Several methods for the location and classification of faults in power transmission lines using computational intelligence and digital signal processing techniques have been described in literature. Artificial neural networks (ANNs) and wavelet transform (WT) have drawn significant attention lately, but they present some drawbacks when dealing with power systems faults where data are often contaminated by noise. This paper proposes an approach by combining independent component analysis (ICA) with travelling wave (TW) theory and support vector machine (SVM). The approach is adequate to locate and recognize faults in high-voltage (HV) transmission lines, while the acquired signals are noisy. Experiments performed for distinct types and locations of faults in a real transmission line model have shown that the proposed combined methods are able to provide excellent performance in fault location. The obtained errors are lower than 1% and accuracy is 100% for the classification of fault signals with noise. It can be stated that this method presents better performance than those regarding the main conventional techniques such as wavelets and neural networks in the presence of noise. [ABSTRACT FROM AUTHOR]

The conventional distance relaying algorithms are unable to detect the inter-circuit faults, cross-country faults, high resistance faults which may occur in a double circuit line. This paper presents combined Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) based directional relaying and fault classification scheme including inter-circuit faults, cross-country faults and high resistance faults. SVM modules are designed for forward or reverse fault identification and fault classification using single terminal data. The 3rd level approximate discrete wavelet transform coefficients of three phase current signals only have been used. Proposed method is tested with variations in fault type, fault location, fault inception angle, fault resistance, inter-circuit faults, and cross-country faults. The proposed method based on SVM does not need any threshold to operate which is an exceptional attribute for a protective function. As SVMs are not based on comparing with some threshold, rather initially the SVMs are trained with the wide variety of fault patterns which is an offline process and then the trained SVMs are tested online to detect and classify the fault within short time. The test results show that all types of shunt faults can be identified within half cycle time. The proposed scheme offers both primary protection to 95% of the line section and also backup protection to 95% of the adjacent reverse and forward line section also. [ABSTRACT FROM AUTHOR]

• Novel fault classification algorithm (FCA) for GUPFC-compensated transmission lines. • FCA is based on the loci of active and reactive powers calculated using PMU signals. • Presenting faulty area and faulty circuit detection algorithms using new indices. • The proposed method is robust and independent of the operating modes of the GUPFC. • Easily applicable in regular fault locators available in the protection industry. The paper presents a fault area detection and classification algorithm in the phasor domain for double-circuit lines compensated by a generalized unified power flow controller (GUPFC). In the first step, by defining and using a voltage index, the algorithm determines the side of the GUPFC in which the fault has occurred. In the second step, by defining and employing a current index, the algorithm will be able to identify the circuit of the line on which the fault has happened. Finally, the fault is classified. In this step, the algorithm detects the faulty phase by analyzing the loci of three-phase active and reactive powers at the line terminal. The algorithm uses the current and voltage phasors of the line terminals measured by the phasor measurement units (PMUs) in one cycle before and during the fault. The GUPFC and the test system have been modeled in MATLAB/Simulink. The presented algorithm is tested and evaluated under different conditions including different fault types and fault resistances for all normal series and shunt fault types. The results obtained from simulations verify the accurate performance of the algorithm for various fault conditions. [ABSTRACT FROM AUTHOR]