Mohammad Pazoki

Fault location in multi-terminal transmission lines is faced with different challenges such as high computation burden imposed by signal processing tools and different transmission line topologies. In this paper, a new method based on the Gabor transform (GT) for fault section determination and fault location calculation in multi-terminal lines is presented. The paper describes some benefits of the GT for analyzing modal components of synchronized current (or voltage) signals at all terminals. Then, to determine fault section and fault location, the arrival times of the first peak waves generated by the fault is utilized. One of the features of the GT is that it does not need to choose any tuning parameters. Moreover, it has robust performance under noisy conditions. The GT exerts low computation burden and it is based on the Fast Fourier Transform (FFT), making it suitable for practical implementation. In this paper, the proposed method is also compared with a number of different fault location algorithms. The obtained numerical results under various fault conditions confirm the efficacy of the proposed method. The results also show that the proposed fault locator is independent of the power system configuration. © 2018 Elsevier Ltd

This paper presents a novel intelligent fault classification scheme for fixed series capacitor compensated transmission line. The singular value decomposition principle is applied along with the fast discrete orthonormal S-transform (FDOST) and bagged tree ensemble classifier for classification of faults under different scenarios. Classification input features are extracted from 1/2 cycle post-fault current data through the FDOST. The feasibility of the proposed relaying scheme is tested on a modified WSCC 3-machine 9-bus system under different fault conditions using PSCAD/EMTDC, and the results indicate the proposed scheme to be fast and accurate. The robustness of the proposed scheme to noise and current transformer saturation is also established. The obtained results under different fault scenarios confirm the efficacy of the proposed scheme. © 2013 IEEE.

This paper presents a new adaptive method based on the intrinsic time-scale decomposition (ITD) tool for suppressing the decaying dc component effect on phasor estimation. The ITD decomposes a non-stationary fault signal into a proper rotation component (PRC) and a monotonic trend signal. The PRC is the fundamental frequency component, which is used by the discrete Fourier transform (DFT) to estimate the phasor, and the monotonic trend signal is the decaying dc component. The combination of the ITD and the DFT is a simple accurate method for phasor estimation that is applicable to protection schemes with the minimum sampling rate and also to the off-nominal power system frequency. Three types of data, i.e., mathematical, simulated, and real field fault, are examined to assess the performance of the proposed method. The obtained results confirm that the proposed method not only improves the distance relay operation, but it is not also affected by the change in the fault inception angle, fault type, fault location, power system frequency, and network topology. Furthermore, the method works both with a series compensated transmission line and a parallel transmission line. © 1986-2012 IEEE.

As nonstationarity exists in fault signals of transmission lines, their classification and quantification remain a challenging issue. This paper presents a new scheme for feature extraction in an attempt to achieve high fault classification accuracy. The proposed scheme consists of three steps: first, the proper rotation components (PRCs) matrix of current signals captured from one end of the protected line is constructed using the intrinsic time decomposition, a fast time-domain signal processing tool with no need for sensitive tuning parameters. Second, the singular value decomposition and nonnegative matrix factorization are employed to decompose the PRCs into its significant components. Finally, eight new normalized features extracted from the output of the data processing techniques are fed into the probabilistic neural network classifier. The data processing techniques employed for classification substantially improve the overall quality of the input patterns classified and increase the generalization capability of the trained classifiers. The proposed scheme is evaluated through two simulated sample systems in the PSCAD/EMTDC software and field fault data. Moreover, the effects of the current transformer saturation, decaying dc component, and noisy conditions are evaluated. The comparison results and discussion regarding the different aspects of the problem confirm the efficacy of the proposed scheme. © 2005-2012 IEEE.

The prominence of incorporating the renewable energy resources in a power system via microgrids has increased in the recent years, which impose a caution on conventional protection schemes. Protection schemes proposed earlier use local measurements, but fault classification for selective phase tripping using wide area measurements for microgrid has not been reported so far. This paper presents a wide area monitoring and protection of microgrid with distributed generations (DGs) using modular artificial neural networks (MANNs) for the fault detection and classification without affecting the relays in non-faulty or healthy sections of the microgrid. The distinct characteristics of the microgrid sort the proposed methodology into two stages. In stage 1, ANN 1 is developed to identify the operating mode of microgrid, whether it is operating in grid-connected mode (GCM) or islanded mode (IM). In stage 2, there are two MANNs corresponding to GCM and IM. Each MANNs consists of three separate ANNs for fault detection, classification, and section identification. A standard IEC 61850-7-420 microgrid with DGs (wind and photovoltaic) penetration is modeled in MATLAB/Simulink. The three-phase voltages and currents are measured with time synchronization considering the microphasor measurement units located at each bus. The extensive study includes different simulation scenarios such as shunt faults, high impedance fault, and dynamic situations like connection/disconnection of DGs/distribution lines. The results confirm the efficacy of the proposed methodology. © 2018, The Natural Computing Applications Forum.

This paper presents a new method for locating a fault in distribution systems using the similarity of fault impedance. A four-step approach is proposed to locate the fault in the networks. First, pre- and during-fault voltages from smart feeders along the primary feeders are measured. Second, three-phase impedance to calculate fault current corresponding to voltage deviation from each smart meter is achieved. Third, a relation between fault current and fault impedance for each type of fault is extracted. Finally, based on the similarity of the estimated fault impedance for each bus, an error index is calculated. Moreover, an auxiliary process is utilized to analyze the estimated fault impedance. The proposed method uses both smart meters and phasor measurement units to obtain voltage sag. Distributed generation and loads are modeled as constant impedances and then they are considered in the three-phase impedance matrix. The suggested approach is evaluated in the IEEE 34-bus test distribution network, which is simulated in the PSCAD/EMTDC environment. The obtained numerical results confirm the acceptable accuracy of the proposed methodology for all types of faults with various fault resistances. © TÜBITAK.

In this paper, an effective fault location technique based on transient voltage signals for three terminal lines using digital signal processing (DSP) method is proposed. In the proposed method, to locate the faults, Hilbert transform (HT) and discrete wavelet transform (DWT) have been used. These methods have been applied to the modal components of synchronized voltage signals. Then, by extracting the arrival time of first wave head at all terminals and a comparison between these arrival times, the algorithm of fault section identification and fault location calculation are presented. The simulated signals corresponding to different fault conditions are obtained from PSCAD/EMTDC environment and using frequency-dependent transmission line. MATLAB software is used to analyze the voltage signals and to calculate the fault location. Numerical results shows the accurate performance of the proposed methods under different fault conditions for three-terminal transmission lines. © 2015 IEEE.

This paper proposes a novel fault location algorithm based on the traveling-waves for three-terminal transmission lines. Three signal processing tools are used to show the performance of the proposed fault location methodology. The suggested algorithm is based on the application of the arrival time of first wave head at all terminals. Then, fault location indices are used to determine the fault section and the fault location. Simulation results confirm that the proposed method has high accuracy under different fault conditions for three-terminal transmission lines with arbitrary configuration. © 2015 IEEE.

Recognition of power quality events by analyzing voltage waveform disturbances is a very important task for power system monitoring. This paper presents a hybrid intelligent scheme for the classification of power quality disturbances. The proposed algorithm is realized through three main steps: feature extraction, feature selection and feature classification. The feature vectors are extracted using S-transform (ST) and Wavelet transform (WT) which are very powerful time-frequency analysis tools. In order to avoid large dimension of feature vector, three different approaches are applied for feature selection step, namely Sequential Forward Selection (SFS), Sequential Backward Selection (SBS) and Genetic Algorithm (GA). In the next step, the most meaningful features are applied to Probabilistic Neural Network (PNN) as classifier core. Various transient events, such as voltage sag, swell, interruption, harmonics, transient, sag with harmonics, swell with harmonics, and flicker, are tested. Sensitivity of the proposed algorithm under different noisy conditions is investigated in this article. Results show that the classifier can detect and classify different power quality signals, even under noisy conditions, with high accuracy. © 2015 - IOS Press and the authors. All rights reserved.