Real Fault Section Estimation in Electrical Distribution Networks Based on the Fault Frequency Component Analysis

Gord, Ehsan; Dashti, Rahman; Najafi, Mohammad; Shaker, Hamid Reza

doi:10.3390/en12061145

Cited by 17 publications

(6 citation statements)

References 29 publications

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“…However, this approach is not applicable in various conditions and modes. In [20], a novel method of impedance-based was proposed to identify the precise fault location, in terms of fault point and distance, in the distribution networks of electric energy. One of the main drawbacks of this methodology is the necessity of categorizing the faults before being able to define their locations.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

et al. 2022

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Effective fault detection and classification play essential roles in reducing the hazards such as electric shocks and fire in photovoltaic (PV) systems. However, the issues of interest in fault detection and classification for PV systems remain an open-ended challenge due to manual and time-consuming processes that require the relevant domain knowledge and experience of fault diagnoses. This paper proposes a hybrid deep-learning (DL) model-based combined architectures as the novel DL approaches to achieve the real-time automatic fault detection and classification of a PV system. This research employed the wavelet packet transform (WPT) as a data preprocessing technique to handle the PV voltage signal collected and feeding them as the inputs for combined DL architectures that consist of the equilibrium optimizer algorithm (EOA) and long short-term memory (LSTM-SAE) approaches. The combined DL architectures are able to extract the fault features automatically from the preprocessed data without requiring any previous knowledge, therefore can override the traditional shortages of manual feature extraction and manual selection of optimal features from the extracted fault features. These desirable features are anticipated to speed up the fault detection and classification capability of the proposed DL model with higher accuracy. In order to determine the performance of the proposed fault model, we carried out a comprehensive evaluation study on a 250-kW grid-connected PV system. In this paper, symmetrical and asymmetrical faults have been studied involving all the phases and ground faults such as single phase to ground, phases to phase, phase to phase to ground, and three-phase to ground. The simulation results validate the efficacy of the proposed model in terms of computation time, accuracy of fault detection, and noise robustness. Comprehensive comparisons between the simulation results and previous studies demonstrate the multidisciplinary applications of the present study.INDEX TERMS Deep distributed energy, equilibrium optimizer algorithm (EOA), fault detection and classification, grid-connected photovoltaic systems, optimal feature selection, wavelet packet transform (WPT).

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Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

et al. 2022

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“…The authors of Ref. [13] resolve the multi-response issue associated with impedance-based techniques by using the frequency component analysis methodology. An innovative impedance-based prevention technique for active distribution networks operating both in islanded and grid-connected modes is suggested in [14].…”

Section: Introductionmentioning

confidence: 99%

A Parameter-Free Approach for Fault Section Detection on Distribution Networks Employing Gated Recurrent Unit

et al. 2021

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Faults in distribution networks can result in severe transients, equipment failure, and power outages. The quick and accurate detection of the faulty section enables the operator to avoid prolonged power outages and economic losses by quickly retrieving the network. However, the occurrence of diverse fault types with various resistances and locations and the highly non-linear nature of distribution networks make fault section detection challenging for numerous conventional techniques. This study presents a cutting-edge deep learning-based algorithm to distinguish fault sections in distribution networks to address these issues. The proposed gated recurrent unit model utilizes only two samples of the angle between the voltage and current on either side of the feeders, which record by smart feeder meters, to detect faulty sections in real time. When a network fault occurs, the protection relays trigger the trip command for the breakers. Immediately, the angle data are obtained from all smart feeder meters of the network, which comprises a pre-fault sample and a post-fault sample. The data are then employed as an input to the pre-trained gated recurrent unit model to determine the faulted line. The performance of this novel algorithm was validated through simulations of various fault types in the IEEE-33 bus system. The model recognizes the faulty section with competitive performance in terms of accuracy.

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“…The use of the state estimation method for fault location in distribution networks has been the focus of recent studies [4][5][6][7][8][9]. These techniques can be classified according to location, quantity, and technology of the available monitoring infrastructure [10].…”

Section: Introductionmentioning

confidence: 99%

“…These techniques can be classified according to location, quantity, and technology of the available monitoring infrastructure [10]. The method proposed by Gord et al [9] estimates the fault location based on a comparison between the stored current and voltages measured at the beginning of the feeder with the corresponding values obtained by simulating short circuits at different points of a distribution system. Moreover, the work determines the faulted section by calculating an index developed by analyzing the frequency components.…”

Section: Introductionmentioning

confidence: 99%

A New Linear State Estimator for Fault Location in Distribution Systems Based on Backward-Forward Currents Sweep

et al. 2020

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This paper presents a new linear state estimation model based on a current summation load flow method for three-phase distribution systems. The developed estimator may be applied to both the supervision of distribution systems under normal operating conditions and the fault location in cases of low and high impedance faults. Our studies were conducted using a real distribution feeder. For fault location analysis, the system was modeled using the ATP (alternative transient program) in order to emulate measurements of voltages and currents at the substation, and voltage magnitudes registered by other meters during the fault. We used the MATLAB™ software to process the algorithms. The main contributions that arose after integrating the current method into system supervision in case of network failures under normal operating conditions and the fault location are as follows: (i) estimation of system losses; (ii) modeling of loads in real time to consider their contributions in the fault location process; and (iii) low influence of fault resistance in the location algorithm. The results show that the proposed method has good precision, low computational processing time, and is promising for distribution system supervision with a reduced number of meters.

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Real Fault Section Estimation in Electrical Distribution Networks Based on the Fault Frequency Component Analysis

Cited by 17 publications

References 29 publications

Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

A Parameter-Free Approach for Fault Section Detection on Distribution Networks Employing Gated Recurrent Unit

A New Linear State Estimator for Fault Location in Distribution Systems Based on Backward-Forward Currents Sweep

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