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 Effective Electricity Theft Detection in Power Distribution Grids Using an Adaptive Neuro Fuzzy Inference System

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3110
Author(s):  
Konstantinos V. Blazakis ◽  
Theodoros N. Kapetanakis ◽  
George S. Stavrakakis
Keyword(s):  
Artificial Intelligence ◽  
Electric Power ◽  
Power Distribution ◽  
Fuzzy Inference System ◽  
Distribution System ◽  
Fuzzy Inference ◽  
Electrical Power ◽  
Electrical Power System ◽  
Inference System ◽  
Neuro Fuzzy

Electric power grids are a crucial infrastructure for the proper operation of any country and must be preserved from various threats. Detection of illegal electricity power consumption is a crucial issue for distribution system operators (DSOs). Minimizing non-technical losses is a challenging task for the smooth operation of electrical power system in order to increase electricity provider’s and nation’s revenue and to enhance the reliability of electrical power grid. The widespread popularity of smart meters enables a large volume of electricity consumption data to be collected and new artificial intelligence technologies could be applied to take advantage of these data to solve the problem of power theft more efficiently. In this study, a robust artificial intelligence algorithm adaptive neuro fuzzy inference system (ANFIS)—with many applications in many various areas—is presented in brief and applied to achieve more effective detection of electric power theft. To the best of our knowledge, there are no studies yet that involve the application of ANFIS for the detection of power theft. The proposed technique is shown that if applied properly it could achieve very high success rates in various cases of fraudulent activities originating from unauthorized energy usage.

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.

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.

A new approach to estimate anthropometric measurements by adaptive neuro-fuzzy inference system

2003 ◽  
Vol 32 (2) ◽  
pp. 105-114 ◽  
Author(s):  
M. Dursun Kaya ◽  
A. Samet Hasiloglu ◽  
Mahmut Bayramoglu ◽  
Hakki Yesilyurt ◽  
A. Fahri Ozok
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Anthropometric Measurements ◽  
Inference System ◽  
New Approach ◽  
Neuro Fuzzy
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