Linear and Nonlinear Fault Location in Smart Distribution Network Under Line Parameter Uncertainty

Mirshekali, Hamid; Dashti, Rahman; Shaker, Hamid Reza; Samsami, Reza; Torabi, Amin J.

doi:10.1109/tii.2021.3067007

Cited by 30 publications

(7 citation statements)

References 34 publications

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“…Each typ of fault was simulated with different fault resistances of 1, 5, 10, 15, 20, 25, 30, 35, 40, 45 The SVM classifier was trained for single-phase-to-ground (AG), two-phase-to-ground (ABG), three-phase-to-ground (ABCG), and phase-to-phase (AB) faults. Each type of fault was simulated with different fault resistances of 1,5,10,15,20,25,30,35,40,45, and 50 ohms. There were 10 sections in the test network, which resulted in 10 classes.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Transient and hazardous situations can arise in distribution networks due to various types of uncertainties such as loading circumstances, distributed generations (DGs) performances, and defective scenarios [1]. Some may be identified and repaired easily, while others, such as line faults, can be caused by an external object or equipment failure.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU

Mirshekali

Dashti

Keshavarz

et al. 2022

Sensors

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Faults in distribution networks occur unpredictably, causing a threat to public safety and resulting in power outages. Automated, efficient, and precise detection of faulty sections could be a major element in immediately restoring networks and avoiding further financial losses. Distributed generations (DGs) are used in smart distribution networks and have varied current levels and internal impedances. However, fault characteristics are completely unknown because of their stochastic nature. Therefore, in these circumstances, locating the fault might be difficult. However, as technology advances, micro-phasor measurement units (micro-PMU) are becoming more extensively employed in smart distribution networks, and might be a useful tool for reducing protection uncertainties. In this paper, a new machine learning-based fault location method is proposed for use regardless of fault characteristics and DG performance using recorded data of micro-PMUs during a fault. This method only uses the recorded voltage at the sub-station and DGs. The frequency component of the voltage signals is selected as a feature vector. The neighborhood component feature selection (NCFS) algorithm is utilized to extract more informative features and lower the feature vector dimension. A support vector machine (SVM) classifier is then applied to the decreased dimension training data. The simulations of various fault types are performed on the 11-node IEEE standard feeder equipped with three DGs. Results reveal that the accuracy of the proposed fault section identification algorithm is notable.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU

Mirshekali

Dashti

Keshavarz

et al. 2022

Sensors

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“…For instance, in Figure 3, only three smart feeder meters are needed on nodes 2, 3, and 8 to determine the actual fault point. If a fault happens in section (2)(3)(4)(5)(6), due to the nature of the FL algorithm and the network topology, the algorithm may determine the section (2-6), (3-7), (3)(4), (8)(9), and (8-10), as well as the faulty sections. The substation relay sends a pulse to all SFMs for receiving the recorded post-fault active power.…”

Section: Real Faulty Section Detectionmentioning

confidence: 99%

“…and unfavorable weather conditions such as lightning, falling trees, or external objects on the network lines, cause faults and consequently lead to inevitable power outages [1]. Therefore, faults may occur in the distribution network with different resistances and types (single-, two-, three-phase to ground fault) at different locations of the network, based on the fault circumstances and conditions [2]. On the other hand, power outages cause customer dissatisfaction, financial losses, and reduced reliability.…”

Section: Introductionmentioning

confidence: 99%

Real Fault Location in a Distribution Network Using Smart Feeder Meter Data

Mirshekali

Dashti

Handrup³

et al. 2021

Energies

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Distribution networks transmit electrical energy from an upstream network to customers. Undesirable circumstances such as faults in the distribution networks can cause hazardous conditions, equipment failure, and power outages. Therefore, to avoid financial loss, to maintain customer satisfaction, and network reliability, it is vital to restore the network as fast as possible. In this paper, a new fault location (FL) algorithm that uses the recorded data of smart meters (SMs) and smart feeder meters (SFMs) to locate the actual point of fault, is introduced. The method does not require high-resolution measurements, which is among the main advantages of the method. An impedance-based technique is utilized to detect all possible FL candidates in the distribution network. After the fault occurrence, the protection relay sends a signal to all SFMs, to collect the recorded active power of all connected lines after the fault. The higher value of active power represents the real faulty section due to the high-fault current. The effectiveness of the proposed method was investigated on an IEEE 11-node test feeder in MATLAB SIMULINK 2020b, under several situations, such as different fault resistances, distances, inception angles, and types. In some cases, the algorithm found two or three candidates for FL. In these cases, the section estimation helped to identify the real fault among all candidates. Section estimation method performs well for all simulated cases. The results showed that the proposed method was accurate and was able to precisely detect the real faulty section. To experimentally evaluate the proposed method’s powerfulness, a laboratory test and its simulation were carried out. The algorithm was precisely able to distinguish the real faulty section among all candidates in the experiment. The results revealed the robustness and effectiveness of the proposed method.

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“…Distribution networks, which are responsible for delivering electricity to consumers, are essential parts of the electrical logistics chain. A resilient and stable distribution network should transfer electrical energy to local light manufacturers, residential and commercial customers with minor fluctuation and interruption [1]. However, unwanted events such as equipment malfunction, unauthorized involvements (e.g., human disruptions), and extreme weather conditions may lead the system to experience a fault and, as a result, a shutdown.…”

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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Linear and Nonlinear Fault Location in Smart Distribution Network Under Line Parameter Uncertainty

Cited by 30 publications

References 34 publications

Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU

Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU

Real Fault Location in a Distribution Network Using Smart Feeder Meter Data

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

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