On field experience results related to high-impedance faults in power distribution system

2009 ◽  
Author(s):  
V. Ziolkowski ◽  
I. N. da Silva ◽  
D. M. B. S de Souza ◽  
R. A. Flauzino
Keyword(s):  
Power Distribution ◽  
Distribution System ◽  
Field Experience ◽  
Power Distribution System ◽  
High Impedance ◽  
High Impedance Faults

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.

Detection and Classification of High Impedance Fault in Power Distribution System using Hybrid Technique

2019 ◽  
Vol 29 (08) ◽  
pp. 2050118
Author(s):  
N. Narasimhulu ◽  
D. V. Ashok Kumar ◽  
M. Vijaya Kumar
Keyword(s):  
Power Distribution ◽  
Distribution System ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Reference Signal ◽  
Hybrid Technique ◽  
Hybrid Strategy ◽  
Power Distribution System ◽  
High Impedance ◽  
High Impedance Fault

In this paper, a hybrid strategy is introduced for detecting and classifying the High Impedance Fault in Power Distribution System. For hybridization, Gravitational Search Algorithm is combined with Artificial Neural Network to crease the classification performance. The ANN is utilized to characterize the blame signal from the reference signal and the execution is enhanced in view of the GSA calculation. The yield of the proposed method is recognized and arranged whether it is HIF fault or no-fault. At first, the ordinary practices of the appropriation framework are assessed. After that, the deficiencies are connected and the signals are measured. At that point, these are given to the contribution of the enhanced ANN procedure, which gives the dataset to breaking down the framework exhibitions. Finally, the proposed strategy is implemented in the MATLAB/Simulink model and its execution is assessed and compared with other conventional techniques like DWT-ANFIS, DWT-RBFFN, MWT-ANFIS, and MWT-FLC based GA. From the experimental results, it shows that the proposed method achieved better performance than existing methods.

scholarly journals High impedance fault detection in distribution system

2019 ◽  
Vol 8 (2) ◽  
pp. 95
Author(s):  
Kavaskar Sekar ◽  
Nalin Kant Mohanty
Keyword(s):  
Power Distribution ◽  
Distribution System ◽  
Real Data ◽  
Power Distribution Network ◽  
High Impedance ◽  
Electric Power Distribution ◽  
Voltage Signal ◽  
High Impedance Fault ◽  
High Impedance Faults ◽  
Non Linear Load

<p>High impedance faults (HIFs) present a huge complexity of identification in an electric power distribution network (EPDN) due to their characteristics. Further, the growth of non-linear load adds complexity in HIF detection. One primary challenge of power system engineers is to reliably detect and discriminate HIFs from normal distribution system load and other switching transient disturbances. In this study, a novel HIF detection method is proposed based on the simulation of an accurate model of an actual EPDN study with real data. The proposed method uses current signal alone and does not require voltage signal. Wavelet transform (WT) is used for signal decomposition to extract statistical features and classification of HIF into Non-HIF (NHIF) by Neural Networks (NNs). The simulation study of the proposed method provides good, consistent and powerful protection for HIF.</p>

SFCL for Energy Storage Protection in a Power Distribution System

2016 ◽  
Vol 4 (1) ◽  
pp. 15-24
Author(s):  
V. Mohanbabu ◽  
◽  
Sk. Moulali ◽  
Ju Chan Na ◽  
Peng Cheng ◽  
...  
Keyword(s):  
Energy Storage ◽  
Power Distribution ◽  
Distribution System ◽  
Power Distribution System
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