DT-CWT based event feature extraction for high impedance faults detection in distribution system

2014 ◽  
Vol 25 (12) ◽  
pp. 3288-3303 ◽  
Author(s):  
Zahra Moravej ◽  
Seyed Hamid Mortazavi ◽  
Seyed Mohammad Shahrtash
Keyword(s):  
Feature Extraction ◽  
Distribution System ◽  
High Impedance ◽  
High Impedance Faults

Using Positive and Negative Sequence Components of Currents and Voltages for High Impedance Fault Analysis via ANFIS

2012 ◽  
Vol 1 (4) ◽  
pp. 132-157 ◽  
Author(s):  
Mohamed M. Ismail ◽  
M. A. Moustafa Hassan
Keyword(s):  
Distribution System ◽  
Electrical Contact ◽  
Design Procedure ◽  
Fault Analysis ◽  
Foreign Object ◽  
Inference System ◽  
Negative Sequence ◽  
High Impedance ◽  
Sequence Components ◽  
High Impedance Faults

High Impedance Faults are defined as unwanted electrical contact between an energized conductor and a non-conducting foreign object. Non-conducting foreign object present high impedances to current flow due to their material, so a fault of this type will not appear to the classical protection equipment as abnormal conditions. Presented is an approach for detection, classification, and location of high impedance faults in a distribution system using Adaptive Neuro Fuzzy Inference System (ANFIS) based on positive and negative sequence components of voltages and currents. The proposed scheme was trained by data from simulation of a distribution system under different faults conditions and different distances in a short and long transmission lines. Details of the design procedure and the results of performance using the proposed method are discussed in this paper.

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

2012 ◽  
Vol 1 (2) ◽  
pp. 44-59 ◽  
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.

scholarly journals Identifying Faulty Feeder for Single-Phase High Impedance Fault in Resonant Grounding Distribution System

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 598
Author(s):  
Tao Tang ◽  
Chun Huang ◽  
Zhenxing Li ◽  
Xiuguang Yuan
Keyword(s):  
Distribution System ◽  
Single Phase ◽  
Fault Resistance ◽  
High Impedance ◽  
Significant Difference ◽  
High Impedance Fault ◽  
Transition Resistance ◽  
Zero Sequence ◽  
High Impedance Faults ◽  
Zero Sequence Voltage

The identification of faulty feeder for single-phase high impedance faults (HIFs), especially in resonant grounding distribution system (RGDS), has always been a challenge, and existing faulty feeder identification techniques for HIFs suffer from some drawbacks. For this problem, the fault transient characteristic of single-phase HIF is analyzed and a faulty feeder identification method for HIF is proposed. The analysis shows that the transient zero-sequence current of each feeder is seen as a linear relationship between bus transient zero-sequence voltage and bus transient zero-sequence voltage derivative, and the coefficients are the reciprocal of transition resistance and feeder own capacitance, respectively, in both the over-damping state and the under-damping state. In order to estimate transition resistance and capacitance of each feeder, a least squares algorithm is utilized. The estimated transition resistance of a healthy feeder is infinite theoretically, and is a huge value practically. However, the estimated transition resistance of faulty feeder is approximately equal to actual fault resistance value, and it is far less than the set threshold. According to the above significant difference, the faulty feeder can be identified. The efficiency of the proposed method for the single-phase HIF in RGDS is verified by simulation results and experimental results that are based on RTDS.

scholarly journals Comparative Analysis of Fuzzy Inference System (FIS) And Adaptive Neuro-Fuzzy Inference System (ANFIS) Methods in the Classification and Location of High Impedance Faults on Distribution System

2020 ◽  
Vol 5 (8) ◽  
pp. 966-969
Author(s):  
Nseobong I. Okpura ◽  
E. N. C. Okafor ◽  
Kufre M. Udofia
Keyword(s):  
Power Distribution ◽  
Distribution System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Power Distribution System ◽  
High Impedance ◽  
Study Power ◽  
Distribution Line ◽  
High Impedance Faults

Unlike low impedance faults, which involve relatively large magnitude of fault currents and are easily detected by conventional over-current protection devices, high impedance faults pose a serious challenge to protection engineers because they can remain on the system without the protective relays being able to detect them. This paper presents an improved method for detection and location of high impedance fault using ANFIS model. The study was conducted on the 33 kV Uyo-Ikot Ekpene power distribution line. The case study power distribution system was modeled using MATLAB software. HIFs were introduced at various locations along the distribution line. The data obtained from the MATLAB/Simulink simulated fault using discrete wavelet transform (DWT) were used to train the ANFIS for the location of HIF points along the distribution system as well as for prediction of the distance of the fault location to the nearest injection substation. The results show that ANFIS model gives 52.5 percentage reduction in error compared with FIS in the location of fault points on the case study power distribution system.

Sign in / Sign up

Export Citation Format

Share Document