Traveling Wave Fault Location Using Layer Peeling

Many fault-location algorithms rely on a simulation model incorporating network parameters which closely represent the real network. Estimations of the line parameters are usually based on limited geometrical information which do not reflect the complexity of a real network. In practice, obtaining an accurate model of the network is difficult without comprehensive field measurements of each constituent part of the network in question. Layer-peeling algorithms offer a solution to this problem by providing a fast “mapping” of the network based only on the response of a probing impulse. Starting with the classical “Schur” layer-peeling algorithm, this paper develops a new approach to map the reflection coefficients of an electrical network, then use this information post-fault to determine accurately and robustly the location of either permanent or incipient faults on overhead networks. The robustness of the method is derived from the similarity between the post-fault energy reaching the observation point and the predicted energy, which is based on real network observations rather than a simulation model. The method is shown to perform well for different noise levels and fault inception angles on the IEEE 13-bus network, indicating that the method is well suited to radial distribution networks.

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An Artificial Intelligence Based Approach for High Impedance Faults Analysis in Distribution Networks

International Journal of System Dynamics Applications ◽

10.4018/ijsda.2012040104 ◽

2012 ◽

Vol 1 (2) ◽

pp. 44-59 ◽

Cited By ~ 8

Author(s):

M. S. Abdel Aziz ◽

M. A. Moustafa Hassan ◽

E. A. El-Zahab

Keyword(s):

Artificial Intelligence ◽

Distribution System ◽

Fault Location ◽

Fuzzy Inference ◽

Distribution Networks ◽

Inference System ◽

New Approach ◽

High Impedance ◽

Neuro Fuzzy ◽

High Impedance Faults

This paper presents a new approach for high impedance faults analysis (detection, classification and location) in distribution networks using Adaptive Neuro Fuzzy Inference System. The proposed scheme was trained by data from simulation of a distribution system under various faults conditions and tested for different system conditions. Details of the design process and the results of performance using the proposed method are discussed. The results show the proposed technique effectiveness in detecting, classifying, and locating high impedance faults. The 3rd harmonics, magnitude and angle, for the 3 phase currents give superior results for fault detection as well as for fault location in High Impedance faults. The fundamental components magnitude and angle for the 3 phase currents give superior results for classification phase of High Impedance faults over other types of data inputs.

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

Energies ◽

10.3390/en14113242 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3242

Author(s):

Hamid Mirshekali ◽

Rahman Dashti ◽

Karsten Handrup ◽

Hamid Reza Shaker

Keyword(s):

Fault Location ◽

Network Reliability ◽

Distribution Network ◽

Estimation Method ◽

Electrical Energy ◽

Distribution Networks ◽

Active Power ◽

Financial Loss ◽

Smart Meters ◽

The Real

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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